Life evaluation, life satisfaction, and happiness: assessing inter-relations and 15 childhood and demographic factors across 22 Countries in the Global Flourishing Study

Life evaluation, life satisfaction, and happiness: assessing inter-relations and 15 childhood and demographic factors across 22 Countries in the Global Flourishing Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Life evaluation, life satisfaction, and happiness: assessing inter-relations and 15 childhood and demographic factors across 22 Countries in the Global Flourishing Study Tim Lomas, Hayami Koga, R. Padgett, James Pawelski, Eric Kim, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6420806/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Despite a vast literature on subjective wellbeing (SWB), issues remain, including (a) debates around which concepts best represent it, (b) disjointed understanding of relevant factors, and (c) limited appreciation of cross-national variation regarding (a) and (b). We address these using data from the Global Flourishing Study on three constructs pertaining to evaluative SWB (life evaluation, life satisfaction, and, more ambiguously, happiness), examining associations with 15 childhood and demographic factors in 202,898 participants from 22 countries. Key findings include, for (a), life satisfaction being the best performing construct (in correlations with overall flourishing), (b) all factors being significantly associated with all constructs (with the largest variation for employment status among demographic factors and self-reported health among childhood factors), and (c) patterns varying substantively across countries (suggesting the general trends are not universal but differ according to local socio-cultural dynamics). The findings advance the methodological, socio-demographic, and cross-national understanding of evaluative SWB. Biological sciences/Psychology Health sciences/Health care life evaluation life satisfaction happiness subjective wellbeing cross-cultural Introduction Over recent decades prominent intergovernmental organisations (e.g., UN, WHO, OECD) have advocated for including subjective wellbeing (SWB) indicators alongside traditional economic metrics like GDP when assessing societies and making policy. 1 , 2 , 3 , 4 , 5 SWB scholarship has three interrelated issues however which limit our understanding and application of this topic. First, there remains uncertainty about how SWB should best be assessed, including regarding its evaluative dimension (E-SWB), with life evaluation (LE) and life satisfaction (LS) the dominant candidates for indexing E-SWB, but with happiness (H) also a possible option (albeit more ambiguously, since in many classifications this is considered a hedonic/affective construct). 6 Second, there is a relatively disjointed understanding of factors associated with E-SWB: while one can find analyses of almost every conceivable factor, few studies include a comprehensive array that would allow meaningful comparison among them. 7 Third, while E-SWB has been extensively assessed internationally, there remains a relatively impoverished understanding of granular national-level variation. Many studies address one or two of these issues, but rarely all three together, hence the value of this paper, which examines wave 1 data on LE, LS, and H in the Global Flourishing Study (GFS), an intended five-year panel study investigating predictors of flourishing involving 202,898 participants from 22 countries. Before reviewing the three issues, we briefly contextualize the research by situating SWB within the GFS and the broader concept of flourishing, though our focus in this paper will be on E-SWB itself. Flourishing and SWB Over recent years “flourishing” has emerged as an overarching construct encompassing the various aspects/forms of wellbeing across different dimensions of life. VanderWeele for example, 8 co-PI of the GFS, defines it as “the relative attainment of a state in which all aspects of a person’s life are good, including the contexts in which that person lives,” and identifies five key domains: happiness and life satisfaction; mental and physical health; meaning and purpose; character and virtue; and close social relationships. To these, a sixth domain, financial and material stability, is added as a key means for “secure flourishing” over time. While not exhaustive of flourishing, VanderWeele argues all six are “arguably at least a part of what we mean by flourishing.” Of particular interest here is the first domain, which closely relates to certain understandings of SWB. Regarding SWB itself, there are various definitions in the literature, some broader, some narrower. 9 One of the most influential conceptualizations is that of Diener and colleagues, which comprises two dimensions: cognitive/evaluative (e.g., LS), and affective (positive and negative affect). 10 , 11 Given the myriad conceptualizations of both flourishing and SWB, their relationship inevitably depends on the definitions used. In VanderWeele’s framework, LS and H constitute one domain of flourishing. But in some definitions SWB can reflect/encompass subjective assessments of all other dimensions (and some scholars even view LS and H as doing this as well). Consider the widely used definition of LS by Shin and Johnson (who call it “avowed happiness”) as “a global assessment of a person's quality of life according to his chosen criteria,” 12 which potentially offers a summary of flourishing overall. It is beyond our scope to resolve the broader question of the relationship between SWB and flourishing under all definitions of these terms. Our main focus is SWB itself, and specifically, as described below, evaluative SWB (E-SWB). Despite a vast literature on E-SWB, the scholarship has three main issues, and although many studies address one or two, by addressing all three together, our paper is able to shed new light on these issues. Contribution 1: Comparing Evaluative SWB Items The construct of SWB is well-established, although definitions vary: the National Research Council for example define it quite broadly as including not only evaluative and affective but also eudaimonic aspects. However, many scholars understand SWB through the prism of Diener’s slightly narrower framework. Yet even within the parameters of that framework, there is ongoing debate about how SWB should best be assessed . Appraising the scene in 2013, the National Research Council concluded that concepts related to SWB “have often been ambiguously applied, which has muddled discussion and possibly slowed progress in the field,” 13 and the picture has not improved much since, 14 with efforts towards greater clarity continuing. The OECD for example is currently updating/revising their SWB guidelines, 15 , 16 with the lead author here being on their informal working group which has been extensively discussing this very issue. The debate around assessment includes of course the evaluative aspects of SWB (E-SWB). Single item assessments play prominently here, and are not only deployed for practical reasons of simplicity/concision, but sometimes also because of a view that they constitute adequate global judgements. 9 , 17 , 18 Two main candidates for single-item assessments of E-SWB are LS and LE. The OECD’s current SWB guidelines, 19 for instance, recommend LS, whereas the World Happiness Report (WHR) 20 harnesses a Gallup World Poll (GWP) item, known as Cantril’s “ladder,” 21 that is usually interpreted as indexing LE (with respondents envisaging where they stand on a ladder whose base and top reflect the worst and best life imaginable). Part of the issue is there has been relatively little empirical enquiry comparing LE and LS: given space constraints in many surveys, assessments tend to pick just one. Relatedly, there has been surprisingly little investigation into the basis on which people appraise LE or LS. New natural language analysis though suggests the ladder metaphor encourages people to think about power, achievement, and success, and to evaluate life in those terms, 22 an interpretation which aligns with observations that national Cantril averages strongly correlate with GDP. 23 However, the question of whether LE or LS best represents E-SWB remains understudied and unresolved. In addition, some scholars also suggest H might pertain to E-SWB. Although in most classifications H is considered a hedonic/affective construct, Diener himself seemed open to, and indeed to favour, an evaluative interpretation. His 1984 paper in Psychological Bulletin reviewed definitions of H and grouped these into three types: normative (invoking “external criteria such as virtue”); evaluative (e.g., Shin and Johnson’s operationalization); and affective (involving “pleasant emotional experience”). 24 Given this conceptual breadth, in a 2009 paper with Oishi and Lucas, Diener wrote “we use the term happiness interchangeably with subjective wellbeing or the subjective evaluation of one’s life.” 25 Moreover, they clarify they prefer to interpret/use H as an evaluative concept: while they “conceptualize happiness as being hierarchically organized to emphasize complexity of the concept,” the “highest level of the abstraction is… a summary judgment of one’s life. That is, we do not use the term happiness to refer to the momentary feeling state of happiness. Rather, we use this term to refer to a relatively stable feeling of happiness one has towards his or her life” (p.347). Thus, although most assessments use happiness to capture affective aspects of SWB (a “momentary feeling state”), as recommended by the OECD for instance, 19 it can also be interpreted as indexing E-SWB, even if such evaluations still have some affective dimension (“a relatively stable feeling”). The question about which concept best represents E-SWB thus potentially extends from just LE versus LS to also include H. To be clear, we are not saying H is an E-SWB concept, merely that it is inherently ambiguous (despite the dominant tendency to view it as hedonic/affective), so is worth comparing alongside LE and LS in seeking to better understand E-SWB. Contribution 2: A Comprehensive Coverage of Factors Our paper’s second contribution is an unusually comprehensive assessment of factors associated with E-SWB. This is helpful in relation to the first issue (differentiating LE/LS/H), but is also valuable in its own right. There is a vast literature on factors associated with E-SWB, including systemic factors such as national economics, 26 governance quality, 27 political/economic freedom, 28 community infrastructure, 29 and social capital, 30 demographic factors like income, 31 age, 32 sex, 33 sexuality, 34 and race/ethnicity, 35 and childhood factors such as Adverse Childhood Experiences. 36 , 37 One issue however with many studies is a relatively limited coverage of factors, with most publications tending to focus on just a handful, and often just one key factor (together with relevant confounders). An example of the latter is an analysis of age (SWB “across the lifespan”) that controls for five socio-demographic characteristics (gender, employment, marital status, education, and health). 38 The literature overall is consequently somewhat disjointed. There are analyses of almost any conceivable factor, from childhood health 39 and socioeconomic status 40 to adult employment 41 and immigration 42 status, so many “puzzle pieces” (analyses of factors) are on the table. But with most papers only including a few “pieces,” it is hard to gauge the relative strength of their associations and hence ascertain the overall picture, which requires comparing factors in the same analysis with the same population. Hence another contribution here is we analyse 15 factors selected for the GFS as potentially relevant to all aspects of flourishing (analysed across a series of papers in relation to all flourishing outcomes in the GFS questionnaire 43 ). Contribution 3: Granular Cross-National Details A third contribution is the analysis permits granular exploration of national variation, both for the three outcomes and the 15 factors. There is already an extensive literature exploring international variation on SWB, harnessing datasets like the World Values Survey, European Social Survey, and Gallup World Poll. A search on Google Scholar in March 2025 for the combined phrases “Gallup World Poll” and “subjective well-being,” for example, returned 5,700 results. Thus, although the bias towards relatively “WEIRD” populations highlighted by Henrich and colleagues 44 in 2010 arguably still remains an issue 45 (albeit one that is gradually improving 46 , 47 ), research into SWB may be among the notable exceptions. That the GFS covers 22 countries in itself is therefore unremarkable. However, its analytic approach is quite unusual, in that it effectively constitutes 22 separate country-specific studies, the results of which are meta-analysed to identify overall patterns. Consequently, we can access and provide detailed data for each country (including seven tables each in the Supplemental files), facilitating granular understanding of cross-national variation. Moreover, the combination of all three contributions makes our study particularly valuable, with each contribution magnifying the impact of the others. Few studies with granular international coverage for example also assess three E-SWB concepts and/or 15 factors, and vice versa. This means we are not only able to compare the three concepts (contribution 1) and 15 factors (contribution 2), but also explore national variation across both of these. Results Table 1 provides the distribution of descriptive statistics (weighted counts and proportions). The GFS assessed 15 socio-demographic factors: four demographic, eight childhood, and three pertaining to both (age/birth cohort, gender, and immigration status, analysed/presented below both as demographic and childhood factors). Most participants were: married (52%), attained 9-15 years of education (57%), born in their country of residence (94%), and employed (39%). Counts and proportions for demographic characteristics weighted to be representative of each country’s population are reported in supplemental Tables S2a-S23a. [Table 1 here] Country Level LE/LS/H Country averages on LE/LS/H are reported in Table 2. The mean score is reported with associated 95% confidence intervals, country-level standard deviation, and Gini coefficient of inequality. The approximate intraclass correlation coefficients are 9.9% (LE), 7.7% (LS), and 5.6% (H), indicating that greater than 90% of the variability in scores cannot be explained by mean differences in countries, leading to nuanced differences within countries. [Table 2 here] Variation in LE/LS/H among Demographic Groups Meta-analytic estimates based on subgroups of demographic characteristics are presented in Tables 3 (LE), 4 (LS), and 5 (H). On average across countries, SWB is highest in older age groups, married individuals, retirees, those with more education, and those attending religious services more than once weekly, and with women slightly higher. However, for all categories and outcomes, country-level averages varied by at least 0.14 points (gender “other” for LE) and up to 1.12 (“none/other” for employment status for LS), where variability was evaluated with tau (standard deviation country of means). The global p-value was significant (< .001) for LE/LS/H across all demographic characteristics, indicating that in at least one country every demographic characteristic had mean differences on these outcomes among categories. However, levels were similar across demographic categories within at least one country for all characteristics, as shown in the Supplementary country-specific tables. [Table 3 here] [Table 4 here] [Table 5 here] Childhood Experiences Predicting LE/LS/H Meta-analytic estimates of how childhood experiences predict SWB are shown in Tables 6 (LE), 7 (LS), and 8 (H). Childhood factors associated with increased SWB in adulthood include: better than good health; regular religious service attendance; a very/somewhat good relationship with one’s mother and/or father; one’s family subjectively comfortably meeting its financial needs; not experiencing abuse; and not feeling like an outsider. Note: these Tables feature the three factors interpretable as either demographic or childhood factors, which were also analysed/presented as demographic factors above. Regarding age, in a childhood predictor context this is framed as birth cohort (the time-period people were born/raised in). Some effects were differentially associated with SWB depending on the country. The effect of divorced parents was more likely negative (an estimated 41% of effects below -0.10), for example, but could be positive on average in some countries (an estimated 27% of effects above 0.10), conditional on relationship with parents and other variables. Additional country specific effects are reported in Tables S2c-S23c, and more information on the heterogeneity of effects are reported in the Supplemental forest plots (Figures S34). [Table 6 here] [Table 7 here] [Table 8 here] Sensitivity of Childhood Predictors to Unmeasured Confounding An important consideration in the childhood results is the sensitivity of estimates to unmeasured confounding, reported in Tables 9 (LE), 10 (LS), and 11 (H). Some were moderately robust. For example, to explain away the association between excellent (versus good) self-rated health in childhood with adult H, an unmeasured confounder associated with both excellent health and higher H with risk ratios of 1.73 each, above and beyond measured covariates, could suffice, but weaker joint confounder associations could not; to shift the 95% confidence interval to include the null, an unmeasured confounder associated with both excellent health and higher H with risk ratios of 1.55 each, above and beyond measured covariates, could suffice, but weaker joint confounder associations could not. However, other associations were less robust. Sensitivity of associations for country specific analyses are reported in Tables S2d-S23d. [Table 9 here] [Table 10 here] [Table 11 here] Discussion This paper makes three useful contributions to the already-extensive literature on SWB: (1) addressing uncertainty about whether LE, LS or even H best represents E-SWB; (2) providing expansive coverage of 15 factors associated with E-SWB; and (3) allowing granular exploration of cross-national variation. Taken in isolation each contribution may not be remarkable (though (1) is quite rare), but the combination of all three is arguably unique. Moreover, each contribution magnifies the value of the others. The research thus augments existing scholarship and advances understanding of E-SWB in key ways. The GWP for instance has excelled in assessing around 150 countries annually for nearly 20 years on some SWB measures. However, these have mostly been limited to LE and positive/negative affect, with H occasionally included, while its socio-demographic data have not generally included childhood details. The many studies drawing on SWB data in the GWP, such as the WHR, therefore certainly contribute along the lines of (3). However, rarely do they also allow comprehensive exploration of (1) and (2). Regarding the factors, for instance, our findings across the dataset as a whole are mostly consistent with prior literature. But the additional lenses of (1) and (3) show considerable country-level variation in these general patterns, indicating that such trends are not universal but contingent on socio-cultural dynamics. Here we delve into each contribution in turn, but first note some important observations regarding cross-country comparisons. Cross-Cultural Considerations and Caveats Methodologically, comparing and ranking countries can be problematic for various reasons, 48 perhaps above all the complexities of language. Translation is difficult, and it can be hard to find exact equivalents for terms across languages. 49 In empirical cross-country comparisons, cultural and linguistic nuances may thus influence results. While Gallup employs a well-established TRAPD (translation, review, adjudication, pretesting, and documentation) model 50 to ensure accuracy, and the translation process for the GFS involved experts in relevant languages, 43 perfect equivalence cannot be guaranteed. Subtle differences may mean terms assess slightly different outcomes across languages, a limitation requiring further qualitative investigation to fully ascertain. Given this and other factors noted below, the primary goal of the GFS was not cross-cultural comparison but separate within-country analyses of 22 closely related cohort studies, followed by meta-analyses across countries. This approach does not assume items are interpreted identically across countries but are relatively closely related (just as a meta-analysis of similar interventions may differ in specific administration, dose, etc.). While cross-country comparisons are possible, they should be interpreted with caution. Other factors also influence assessment across countries, 51 including cultural norms, item interpretation, response scales, sample characteristics, and seasonal variations from different data collection times. For instance, comparing our LE data (gathered mostly in 2023) with the 2024 WHR, 20 which aggregates GWP data from 2021–2023 (see Table S25a), we observe some consistency in certain countries (e.g., Germany scored 6.74 in our data and 6.72 in the WHR). Notable discrepancies are seen elsewhere however, with differences greater than 1.00 in several (e.g., Hong Kong scored 6.85 in our data, but 5.32 in the WHR). These discrepancies may result from parameters including sampling timeframes, participant selection, and modes of interviews (e.g., web vs. phone). Regarding data collection windows, for example, the GFS is broader (up to 18 months in Australia), while the GWP is generally only 3–4 months. Seasonal patterns or local socio-political events could thus differentially affect GFS and GWP data. The 2023 GWP data for Israel, for example, was collected after the October 7th attacks (10.17.23–12.2.23), whereas most GFS data was before (11.7.22–11.23.23), resulting in less influence from the event. Even without such impactful events, within-country LE can fluctuate significantly. While the GWP is an annual snapshot, Gallup also collects LE data monthly in the US, with considerable variation sometimes within a single year. 52 Another factor is participant selection. The GWP is a one-off survey, whereas the GFS is a five-year longitudinal study, and conceivably those willing to commit to a long-term survey may have higher LE than one-off participants. This explanation is supported by LE scores in the GFS being generally higher than those in the GWP (with a few exceptions). Using 2023 GWP data, the LE mean across all 142 GWP countries is 5.60, whereas the GWP mean across the 22 GFS countries is 5.91, while the GFS overall mean is 6.34. There is thus a substantial standardized mean difference between the GFS and GWP (Table S25b), calculated as either 0.29 (if including all GWP countries) or 0.17 (if only including GFS countries in the GWP). GFS countries may also have higher LE due to being on average more prosperous, since unlike the GWP the GFS has no low-income countries (only lower-middle-, upper-middle-, and high-income ones). For all these reasons, comparing across datasets can be problematic, meaning one cannot definitively determine the E-SWB of a given nation. But comparing within a dataset is certainly meaningful, as we do here, firstly by comparing three E-SWB candidates. Contribution 1: Comparing Evaluative SWB Items Our first contribution is comparing three options for assessing E-SWB. We should reiterate that the suitability of H in that respect is ambiguous, especially since it is more commonly used/interpreted as an affective concept. However, not only can it also have evaluative aspects or connotations, and indeed Diener himself appeared to favour this usage, 25 our data here also support this interpretation. As discussed below regarding age, for instance, E-SWB is widely regarded as relatively U-shaped while positive affect tends to decline with age; 31 significantly, here H followed the same roughly U-shaped pattern as LE and LS. We thus tentatively regard H as a viable candidate for assessing E-SWB, including even our item specifically, which asks how happy participants “usually feel” (as opposed to a more explicit cognitive framing like “are you happy with your life?”). Indeed, the qualifier “usually” arguably takes the item into the conceptual territory described by Diener and colleagues as evaluative (“a relatively stable feeling of happiness one has towards his or her life”), whereas more immediate qualifiers like “right now” or “yesterday” invite an affective interpretation (“the momentary feeling state of happiness”), and perhaps this very temporal framing is what shifts the balance between evaluative and affective understandings of H. Alternatively though, the phrase “usually feels” could conceivably prompt participants to think about their general genetically-influenced temperamental “baseline” of positive affect. 53 , 54 As such, we recognize some people may object to interpreting H as evaluative, in which case they could just use our data to decide between LE and LS. Whether a two-way comparison (LE vs LS) or a three way one, one way to ascertain which best represents E-SWB, if understood as a global subjective evaluative judgement of all aspects of life, is to consider the empirical association with numerous specific aspects of flourishing more broadly. Although the conceptual relationship between SWB and flourishing is ambiguous, since it depends on the definitions used, and beyond the scope to resolve here, we can still use data on various flourishing assessments to potentially gauge the merits of specific single item assessments of our central E-SWB constructs, drawing on a separate analysis focused entirely on exploring this relationship in depth. 55 The GFS questionnaire includes VanderWeele’s 12-item Secure Flourish Index, with two items per domain. Table S26 shows the correlations between our three items and all the flourishing items. Strikingly, both LS and H had much stronger average meta-analytic mean correlation coefficients with all other items (both .45) compared to LE (0.38). Likewise, when separated by domain (excluding the first domain, since this comprises LS and H themselves), LS and H had stronger correlations for all other main domains (with H marginally higher than LS on five, and vice versa on two, with one equal). Relatedly, LS-H were more tightly correlated (.69) than LE-LS or LE-H (both .57). With the additional sixth domain, LS also had the strongest correlations, closely followed this time by LE then H. Similar patterns are revealed by considering the relative performance of countries on the items. Given issues around cross-country comparisons noted above, we do not wish to make much of rankings per se . But within-country analyses of how each place fares relative to itself are certainly informative. While the data are complex, the main pattern observed is that, as with the correlations above, LS and H often seem closely related (e.g., scores for countries often track together), while LE captures something different. There are exceptions, (e.g., Egypt), but this pattern holds across many countries. It also applies when grouping countries by region (Table S27), especially those categorized as relatively non-WEIRD – even while we dislike this binary 56 and had difficulty grouping the countries, as footnoted in the Table – which collectively have low average LE (5.81) and higher LS (7.02) and H (6.74), while relatively WEIRD nations have less obvious clustering (6.60/7.03/7.16). Moreover, these patterns are evident when our data is compared with GDP-per-capita (Tables S28a-b), with a .58 correlation with LE (corroborating prior research 23 ) but near zero for LS (.05) and H (-.08). Notably though, despite LS having near-zero correlation with GDP-per-capita, LS still had the strongest correlation with personal financial/material security in the GFS, suggesting such security is only weakly related to GDP per se. Further nuance regarding economics is provided by correlations with Gini, where LE and LS were closer together (-.11 and − .20), while H was somewhat distinct (.07). Given higher Gini scores mean greater inequality, this implies more equal places are liable to higher LE and LS, but perhaps counterintuitively slightly lower H, though these correlations are very weak. Overall then, both LS and H are more strongly related to the main flourishing domains, while LE fares better when it comes to financial/material stability (where it “catches up” with LS and H). An interesting comparison which illustrates these differences is Sweden versus Indonesia, in which the relative performance of the constructs is reversed. Sweden does very well on LE (2nd in our data and the latest WHR 20 ), but is only mid-ranked for LS (9th ) and H (11th ). By contrast, although Indonesia excels on all three, it does particularly well on H and LS (1st for both, with similar scores of 8.04 and 7.99), with rather lower LE (5th, at 6.97). Given that LE seems to particularly tap into financial/material stability, and likewise strongly correlates with GDP-per-capita, it is relevant then that Sweden fares very well on the latter and Gini (Table S28a), ranked 3rd and 2nd respectively, and Indonesia less so (17th and 11th ). Further context to this comparison between Sweden and Indonesia, and by extension, LE versus LS/H, is provided by analysis of GWP data comparing 145 countries (for most items) on 38 metrics pertaining to wellbeing. 57 Overall, Sweden did better on variables relating to standards of living, like feeling stable and secure (ranked 1st, vs. Indonesia at 66), having money for shelter (1–93), and satisfaction with standard of living (2–53). Indonesia by contrast excelled on items pertaining to the other flourishing domains, including: physical and mental health (e.g., well-rested: Indonesia 4th, Sweden 64th ); character and virtue (e.g., volunteering: 1-109); and social relationships (e.g., opportunities to make friends: 5–23). Given the correlations with the flourishing domains, it becomes understandable why Indonesia would fare so well on LS and H whereas Sweden does better on LE. Contribution 2: A Comprehensive Coverage of Factors Our second key contribution is comprehensive coverage of E-SWB-related factors. Although our findings overall mostly corroborate well-established trends, the next section highlights significant national variation, indicating the patterns are not universal but contingent on socio-cultural factors. But even just in terms of general trends, our analysis is useful in its expansive coverage of 15 factors, allowing comparison of their relative strength of association with E-SWB. Here we summarize, in turn, the four demographic, eight childhood, and three factors that pertain to both categories. Behind the headline summaries, each factor has a substantial literature which our findings support and/or refine in various ways. It is beyond our scope to consider this scholarship for all factors, so in each category we delve briefly into the factor with the greatest variation to illustrate the contribution of our findings. Of the purely demographic factors, higher E-SWB is associated with: being retired (LE = 6.53/LS = 7.14/H = 7.19), especially relative to those unemployed and job-seeking (5.59/5.97/6.30); being married (6.54/7.12/7.23), especially relative to those separated (5.89/6.31/6.56); attending religious services, especially at least once weekly (6.80/7.40/7.54) relative to never attending (6.12/6.55/6.71); and more education, especially over 16 years (6.64/7.00/7.16) compared to less than eight (6.20/6.84/6.95). Of the factor with the greatest variation, employment status, our findings align with an extensive literature showing the impact of working patterns on wellbeing, with numerous reviews showing employment generally has a positive impact on E-SWB. 41 , 58 Notably though, the highest E-SWB was among retirees, who are technically “out of work,” which corroborates other studies finding a potential SWB boon to retirement, even if the association is complex. 59 There are of course interactions with age, given E-SWB generally increases into older age, as discussed below. Nevertheless, our data on employment suggests it is not necessarily lacking employment per se that is detrimental to E-SWB, but needing work yet failing to find it, with E-SWB relatively unaffected if people are materially secure and not wanting/needing work. 60 , 61 Of the childhood factors, higher E-SWB is associated with: “excellent” self-rated health (RRs = 0.40/0.46/0.50) relative to “good,” especially in contrast to “poor” (-0.40/-0.46/-0.41); subjective financial status of “living comfortably” (0.29/0.25/0.23) relative those who “got by,” especially contrasted with those who “found it very difficult” (-0.42/-0.31/-0.31); not experiencing abuse (relative to those who did: -0.25/-0.39/-0.33); not feeling like an outsider (relative to those who did: -0.16/-0.29/-0.28); attending religious services, especially once weekly (0.22/0.21/0.27), relative to those who never did; a good relationship with one’s mother (0.17/0.21/0.25) and father (0.18/0.19/0.13); and parents being married, especially relative to parents being single and never married (-0.14/-0.13/-0.13). Briefly considering the predictor with the strongest association, self-rated health, one must note our findings rely on retrospective assessments, which are subject to recall bias (one analysis found nearly half their sample revised this during a 10-year period 62 ). Moreover, while we adjusted associations for other potential childhood predictors, residual confounding may be present. However, we reported E-values 63 to assess the robustness of our findings to unmeasured confounding, and the high values (e.g., 1.73 for “excellent health” and H) suggest the observed associations are relatively robust. Moreover, numerous longitudinal studies have assessed this association prospectively and show childhood health is associated with multiple aspects of adult life, 64 including SWB. 65 Despite the limitations of retrospective assessments, the observed patterns regarding self-reported health therefore likely reflect true associations. Finally, three factors could be interpreted either as childhood or demographic factors: age/birth cohort, gender, and immigration status. Of these, higher E-SWB is associated with: older age, though levels are fairly high at 18–24 (6.35/6.78/6.96), falling to their lowest at 40–49 (6.18/6.70/6.86), then peaking at 80+ (6.83/7.17/7.39); being female (6.38/6.89/7.03) rather than male (6.31/6.82/6.98), especially relative to the small percentage reporting their gender as “other” (5.98/5.91/5.95); and living in one’s country of birth (6.34/6.85/7.01) compared to those born elsewhere (6.36/6.81/6.87), although only marginally, and not for LE. Regarding the most prominent factor, age, our results broadly support the well-established view that E-SWB is somewhat “U-shaped,” declining into middle-age before rising again. 32 The finding has generated lively debate though. 66 One must be careful of selection bias for example: a longitudinal analysis in the US suggested LS actually declined after 65 (due to health issues and widowing), and that apparent higher LS in older age is due to those with high LS being more likely to participate in surveys. The pattern is also subject to cultural variation, 67 as shown below. The SWB metric also matters: one analysis of GWP data found that although LE showed a slight U-shaped pattern, positive affect (measured dichotomously based on items on H, enjoyment, and smiling/laughing on the previous day, then averaged) decreased with age globally and across regions. 31 In our data though H aligned with LE and LS in being higher in older age, suggesting our H item may not index positive affect but rather E -SWB, as argued above. Moreover, rather than U-shaped, our trends are more “J-shaped,” with the 80 + group having scores 0.48 (LE), 0.39 (LS), and 0.43 (H) points higher than those 18–24. Even if selection bias applies to the over-80s or even over-70s, 18–24 year olds also fared worse than those 60–70, and even than those 50–59 on LS and H (albeit very slightly). One wonders whether this is a cohort effect, echoing recent research suggesting the left-hand side of the U is flattening lately with younger people faring worse compared to people of similar age in earlier eras. 66 , 68 Contribution 3: Granular Cross-National Details A third contribution is the study’s cross-national coverage. In itself this is not remarkable, with an extensive literature exploring international variation in SWB using datasets like the GWP. But its particular value here is in amplifying the first two contributions, permitting granular exploration of national variation both of the three outcomes and the 15 factors. In that regard, trends for the factors across the countries collectively, elucidated above, are not uniform but have striking national nuances and exceptions, detailed in the Supplementary tables, suggesting the trends are contingent on socio-cultural factors. Thus even if one is tempted to dismiss some of our overall findings as not especially surprising or as replicating past work, the cross-national variation revealed here is a valuable contribution. The data firstly highlight the liabilities of generalizing from WEIRD to non-WEIRD places. While it was beyond our scope to compare these categories across all variables (especially being wary of reifying this binary dichotomy 56 ), a basic comparison of countries that could be classified as WEIRD or non-WEIRD (Table S27) shows the former score much higher on LE (6.80 vs. 5.81), though are closer for H (7.16–6.74) and essentially equal for LS (7.03–7.02). Moreover, part of our reservations with the WEIRD-non-WEIRD dichotomy is our data show considerable heterogeneity among countries commonly classed in either category, illustrated by countries usually described as non-WEIRD being ranked top (Indonesia) and bottom (Turkey) for both LS and H. This heterogeneity is also shown by the differences between continents (Table S27): Africa and Asia might both usually be classed as non-WEIRD, for example, but Asia fares better on LE (5.87 vs. 5.46) and LS (7.01–6.54), while Africa does better on H (6.82–6.69). Such internal diversity highlights the limits of these very labels, which obscure the complexity of international variation. While space limits prevent us exploring all factors, we revisit the three discussed above to illustrate the nuances. Regarding employment status, retirees were not the most prosperous everywhere, faring less well than people employed for an employer in Argentina (on LE and H), Egypt (LE/LS), Germany (LE/LS), Hong Kong (LE/LS/H), Indonesia (LS/H), Israel (LE/LS/H), Kenya (LE), Poland (LS/H), South Africa (LS/H), Spain (LS/H), and Tanzania (LE/LS/H), and even worse than the unemployed in Kenya (H) and Tanzania (LE/H). Conversely, while the unemployed fared worst in most countries, exceptions included the employed in India (H), self-employed people in Egypt (LE/H) and Philippines (LE), homemakers in Tanzania (H) and South Africa (LE), and “other” in Argentina (LE/LS/H), Australia (LS), Hong Kong (LE/LS/H), Kenya (LE/H), Nigeria (H), Philippines (H), Poland (H), Tanzania (LE/LS), and UK (LE/LS/H). Other variation concerns the range of values. Comparing the unemployed, employed, and retirees, some countries had only narrow differences between these, especially Kenya (LE = 0.33/LS = 0.53/H = 0.17), Egypt (0.18/0.37/0.26), and Philippines (0.08/0.49/0.31). By contrast, other countries had far larger ranges, notably Australia (1.82/2.47/1.90), Japan (2.25/2.38/2.07), Sweden (2.46/2.93/2.41), and the US (2.33/2.51/2.36), indicating stronger links between employment status and E-SWB. There is also potential interaction with age in the association between employment status (particularly retirement) and E-SWB. While older age is generally associated with E-SWB, noted above, regional variations make this relationship complex. No significant associations with age were observed for LE/LS/H in Mexico and Spain, and only for some constructs for Argentina, Indonesia, Nigeria, Philippines, Turkey, and South Africa. Further, some countries deviated from the J-shaped pattern, with linear decreases with age in Israel (LE/LS/H), Poland (LS/H), and Tanzania (LE). There was also variation in the range of scores within countries, further suggesting the relationship with age is conditioned by location. Differences were smallest in Mexico (0.18/0.56/0.22), and largest in Australia (1.58/2.03/1.94, with those 18–24 scoring lowest and 80 + the highest). Returning to the question of retirement, these findings highlight the complexity of the employment-SWB relationship, aligning with research indicating the associations are inconsistent, 69 and that factors such as economic resources 70 and social relationships 71 affect retirees’ SWB. 83 The childhood predictor with the strongest association with E-SWB, self-rated health, also showed considerable variation. First, relative to the difference in outcomes comparing poor and excellent childhood health in the pooled meta-analysis (0.80/0.88/0.91), there was real variation in range, with Egypt the narrowest (0.37/0.14/0.25) and Hong Kong the largest (2.81/2.64/3.11), indicating greater association between childhood health and adult E-SWB in the latter. Further research is needed to explain such regional variation, but it may involve factors like economic conditions, healthcare provisions, and levels of inequality. Childhood health might plausibly have smaller impact on adult SWB in wealthier countries because they can invest more in healthcare to mitigate effects of poor childhood health. 72 GDP considerations alone though may be insufficient to explain regional variation, given that childhood health generates more variation in Hong Kong than Egypt (since the former is wealthier). It is likely therefore that factors like socio-economic equality also contribute, and greater inequality may amplify the negative effects of poor childhood health, especially in countries without good universal healthcare coverage. 73 , 74 All such dynamics warrant further exploration to better understand the interplay between childhood health and adult SWB. There were also intriguing patterns that are harder to explain and require further investigation too, above all that in some countries the effect estimates seemed “out of order.” Overall, relative to people with “good” childhood health, people with worse health had lower E-SWB while people with very good or excellent health had higher. Individually though only 13 countries conformed to this linear rising trend (Argentina, Australia, Brazil, Hong Kong, Indonesia, Japan, Kenya, Nigeria, Philippines, Sweden, Tanzania, UK, US). In the remaining countries, this pattern was subverted in various ways. For example, people with poor health fared better than those with good health in Germany (LE/LS/H), Israel (LE/LS/H), Mexico (LE), Poland (LE/H), and Spain (LS/H). The reasons behind these findings are unclear. One possible explanation is poor childhood health may encourage individuals to develop qualities that might contribute to E-SWB in adulthood, including psychological (e.g., resilience) 75 and social resources (e.g., supportive childhood friends). 76 However, why this association is found in certain countries and not others remains an open question. Limitations This study has various limitations. First, as discussed above, caution is needed interpreting cross-national differences, which may be influenced by factors including cultural/linguistic variation, local/national/international events, modes of data collection, and seasonal differences from varying data collection windows. Second, while we constructed a synthetic longitudinal study by retrospectively assessing childhood experiences, its cross-sectional design partially limits definitive conclusions about causality. Such synthetic longitudinal designs may also be subject to recall bias. However, for recall bias to completely explain away the observed associations would require the effect of current LE/LS/H on biasing retrospective assessments of childhood predictors to be at least as strong as the observed associations themselves, 77 and some were quite substantial. Moreover, although we adjusted associations for these childhood predictors for other potential childhood predictors, residual confounding may still be present. We reported E-values for the childhood predictors analysis to assess the robustness of findings to unmeasured confounding for each predictor and some of these associations were at least moderately robust. Moreover, some predictors may lie on the causal pathway linking others to adult E-SWB. Adjusting for such mediators may lead to conservative estimates of the associations. Penultimately, LE/LS/H were assessed using one-item measures, which may not fully capture their complexity. Future studies could use multi-item measures for higher validity and reliability. There are always trade-offs though in survey research between depth and breadth. Using multi-item scales limits the number of constructs assessed, and the GFS team decided that, overall, the benefits of including more constructs outweighed the limitations of single-item measures. 55 Finally, the three outcomes are situated close together in the annual GFS questionnaire as the first (LE), third (H), and fourth (LS) items (with the second item being anticipated LE in five years). Such proximity potentially inflates the differences between the items, given that asking similar questions in sequence can make participants think they are supposed to give different answers. 78 That said, there was tighter correlation between the consecutive items (H-LS: .69) than those separated by an item (LE-H: .57), which perhaps argues against that critique. But even if the critique has merit, the answers are still informative, even if one must interpret them with caution, since if someone does feel compelled to give a different answer, the direction they do so is meaningful. To return to Indonesia versus Sweden, for instance, having first given their LE score, people in the former tend to give higher scores for LS and H, whereas people in the latter give lower scores. Even if people in Indonesia may not have LE levels “1” lower than LS/H, and people in Sweden “1” higher – whatever increment “1” might mean – it seems reasonable to conclude people in Indonesia do genuinely have lower LE than LS/H, and vice versa for Sweden. Conclusion The value of measuring SWB is increasingly recognized, including by governmental organizations that recommend its utility in guiding policy. The literature is constrained by various issues however, to which this study makes three key interrelated contributions, as summarized here through the lens of potential practical implications. The first contribution is directly comparing three candidates for indexing E-SWB. Most striking were their associations with flourishing, with LS and H having stronger correlations across all domains of VanderWeele’s framework (excluding the first domain, as this is constituted by LS and H themselves), while LE only attained relative parity on the additional domain of financial/material stability. Given that stakeholders (e.g., policy makers) may look to articles like this for guidance on which measures to use and how to interpret them, here are some tentative recommendations. Overall, if recommending just one item, we suggest LS, given its strong correlations with the flourishing domains. This is preferred to H, despite the latter having similarly strong correlations, mainly because of the ambiguity still attached to H. Although our data suggests it can indeed function as an evaluative item (per Diener’s perspective), it does nevertheless have a double meaning, and some respondents may still interpret it affective terms, so we cannot be certain it indexes E-SWB (despite seeming to here). Then, if space for another item, we recommend LE, mainly because H tracks LS closely, whereas LE seemingly captures different experiential terrain, particularly around financial/material stability, so is a useful complement/counterpart to LS. A second contribution is expansive coverage of 15 socio-demographic factors, and while all were significant predictors of E-SWB, considering them together shows their relative strength of association. Regarding practical implications, our analysis highlights people who might be particularly at risk of low E-SWB, indicating where policy/intervention efforts to improve SWB may most effectively be targeted. Demographically our data suggests E-SWB is liable to be lowest in people who are: unemployed and looking for a job; separated; not attendees of religious services; of less than eight years education; 40–49; male (or especially “other” gender); and an immigrant. It would also likely be lower for people whose childhood was characterized by: poor health; very difficult financial situations; abuse; feeling like an outsider; never attending religious services; poor relationships with one’s mother and/or father; and parents who were single and never married. Moreover, while each factor itself is meaningful, from an intersectional perspective, 79 the more categories someone belongs to, the lower their E-SWB is likely to be. The findings thus suggest that targeted interventions, such as employment support, could enhance E-SWB, especially for people facing multiple vulnerabilities. While not all factors are amenable to intervention/policy, focusing on those that are may provide meaningful improvements, particularly for people most in need. Most analyses of SWB-related factors show steeper improvement trajectories for those worse off, as for example with income satiation, 31 where the impact of income gains on SWB diminishes the richer people are. While one would want to improve SWB for all, it may be more effective, and more morally worthy, to prioritize helping those with the lowest SWB. This must be done sensitively though, avoiding both stigmatizing populations with the lowest SWB and making everyone else feel they are being treated unfairly. Finally, the third contribution is our cross-national coverage, which although not novel itself accentuates the first two contributions, permitting granular exploration of national variation both of the three outcomes and the 15 factors, which is relevant to the conclusions above. Regarding implications of our analysis of the factors, for instance, policy interventions require accounting for local dynamics (e.g., which people particularly need help). In that regard, while it is customary for papers to plead for more research, this concern is especially justified regarding the cross-cultural implications of this study. There is particular need, for instance, for utilizing other methodologies, especially qualitative techniques, to delve into the first and third issues (the meaning of the items and cross-cultural variation in these meanings). Above we cited for example analysis of Cantril’s ladder which suggests the metaphor encourages people to think of power, achievement, and success. 22 That study was only in English and on just one measure though. It would therefore be instructive to conduct comparable analyses in other languages and on other E-SWB constructs, thereby helping us further understand this vitally important topic. Methods This Methods section has been adapted from VanderWeele and colleagues, 80 with further detail available elsewhere. 43 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 The methods of the current article are intended to align with these works to provide methodological consistency for comparability of results across papers. The GFS involves a 109-item questionnaire 43 involving: (a) questions covering the six domains of VanderWeele’s flourishing framework, 8 which in addition to SWB include health, meaning, character, relationships, and financial stability; and (b) other demographic, social, economic, political, religious, personality, childhood, community, health, and wellbeing variables. Among the (b) items, questions pertaining to 15 childhood and demographic factors have been selected to be analysed across an extensive series of papers, each focused on different flourishing outcomes in the GFS. The present paper focuses on three SWB outcomes specifically, reporting on demographic variation 65 and childhood predictor 64 analyses of LE, LS, and H, data in Wave 1 of the GFS, allowing for comparison of results reported elsewhere (see VanderWeele and colleagues 58 for an example of comparing across papers). Data The GFS is a study of 202,898 participants (in this first wave) from 22 geographically and culturally diverse countries, with nationally representative sampling within each country. Wave 1 included the following countries and territories: Argentina, Australia, Brazil, Egypt, Germany, Hong Kong, India, Indonesia, Israel, Japan, Kenya, Mexico, Nigeria, the Philippines, Poland, South Africa, Spain, Sweden, Tanzania, Turkey, UK, and US. The countries were selected to: (a) maximize coverage of the world's population, (b) ensure geographic, cultural, and religious diversity, and (c) prioritize feasibility and existing data collection infrastructure. Data collection was carried out by Gallup. Data for Wave 1 were collected principally during 2023, with some countries beginning data collection in 2022, and exact dates varying by country, 87 as detailed in Supplementary Table S24. Four additional waves of panel data on these participants will be collected annually from 2024–2027. The precise sampling design to ensure nationally representative samples varied by country and further details are available ( https://osf.io/k2s7u) . 87 The data are publicly available through the Center for Open Science ( https://www.cos.io/gfs ). During the translation process, Gallup adhered to the TRAPD model (translation, review, adjudication, pretesting, and documentation) for cross-cultural survey research; for additional details, see the GFS Translation document. 89 Measures Outcome Variables The GFS includes three separate items on our main topics: (1) LE, assessed with Cantril’s ladder – “Please imagine a ladder with steps numbered from zero at the bottom to ten at the top. The top of the ladder represents the best possible life for you, and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” [0 = Worst possible, to 10 = Best possible]; (2) LS – “Overall, how satisfied are you with life as a whole these days?” [from 0 = Not satisfied with your life at all, to 10 = Completely satisfied with your life]; and (3) H – “In general, how happy or unhappy do you usually feel?” [from 0 = extremely unhappy to 10 = extremely happy]. Our report investigates mean and variability differences in these outcomes across demographic characteristics and how retrospective recall of childhood experiences predicts these items. Wave 1 of the GFS involves participants first completing an intake questionnaire featuring 43 items (mainly gathering demographic information), followed by second questionnaire, to also be completed annually, involving a further 66 items (covering all different aspects of flourishing). The three concepts in the present paper are the first (LE), third (H), and fourth (LS) items of the second questionnaire. 326 Variables for Demographic Variation Analyses. The demographic factors are standard factors one can find in most empirical surveys of this nature. Continuous age was classified as 18–24, 25–29, 30–39, 40–49, 50–59, 60–69, 70–79, and 80 or older. Gender was assessed as male, female, or other. Marital status was assessed as single/never married, married, separated, divorced, widowed, and domestic partner. Employment was assessed as employed, self-employed, retired, student, homemaker, unemployed and searching, and other. Education was assessed as up to 8 years, 9–15 years, and 16 + years. Religious service attendance was assessed as more than once/week, once/week, one-to-three times/month, a few times/year, or never. Immigration status was dichotomously assessed with: “Were you born in this country, or not?” Religious tradition/affiliation with categories of Christianity, Islam, Hinduism, Buddhism, Judaism, Sikhism, Baha’i, Jainism, Shinto, Taoism, Confucianism, Primal/Animist/Folk religion, Spiritism, African-Derived, some other religion, or no religion/atheist/agnostic; precise response categories varied by country. 88 Racial/ethnic identity was assessed in some, but not all, countries, with response categories varying by country. For additional details on the assessments see the COS GFS codebook 89 or Crabtree et al. 81 Variables for Childhood Predictor Analyses The childhood predictor questions were selected based on prior literature concerning longitudinal associations of childhood factors with subsequent health and well-being. These factors include factors that past literature has indicated have beneficial associations with subsequent well-being (e.g. good relationship with parents, religious service attendance, financial security) along with questions that cover the two major domains of adverse childhood experiences: threat (the abuse questions) and neglect (the feeling like an outsider question). Relationship with mother during childhood was assessed with the question: “Please think about your relationship with your mother when you were growing up. In general, would you say that relationship was very good, somewhat good, somewhat bad, or very bad?” Responses were dichotomized to very/somewhat good versus very/somewhat bad. An analogous variable was used for relationship with father. “Does not apply” was treated as a dichotomous control variable for respondents who did not have a mother or father due to death or absence. Parental marital status during childhood was assessed with responses of married, divorced, never married, and one or both had died. Financial status was measured with: “Which one of these phrases comes closest to your own feelings about your family's household income when you were growing up, such as when you were around 12 years old?” Responses were lived comfortably, got by, found it difficult, and found it very difficult. Abuse was assessed with yes/no responses to “Were you ever physically or sexually abused when you were growing up?” Participants were separately asked: “When you were growing up, did you feel like an outsider in your family?” Childhood health was assessed by: “In general, how was your health when you were growing up? Was it excellent, very good, good, fair, or poor?” Immigration status was assessed with: “Were you born in this country, or not?” Religious service attendance during childhood was assessed with: “How often did you attend religious services or worship at a temple, mosque, shrine, church, or other religious building when you were around 12 years old?” with responses of at least once/week, one-to-three times/month, less than once/month, or never. Childhood religious tradition/affiliation had response categories of Christianity, Islam, Hinduism, Buddhism, Judaism, Sikhism, Baha’i, Jainism, Shinto, Taoism, Confucianism, Primal/Animist/Folk religion, Spiritism, African-Derived, some other religion, or no religion/atheist/agnostic; response categories varied by country. 88 When the category no religion/atheist/agnostic had more than 5% of the within country sample size, this was used as the reference category; otherwise, the most prominent religious group was used. Additionally, all religious categories endorsed by less than 3% of the within country sample size were collapsed into a single religious category. For inclusion in the childhood predictor regression analyses, race/ethnic identity was collapsed as a binary variable of whether an individual was in the most prominent group versus a minority group (race plurality). Statistical Analysis Descriptive statistics for the full sample, weighted to be nationally representative within each country, were estimated for each of the demographic and childhood experience variables. Nationally representative means for LE, LS, and H were estimated separately for each country and ordered from highest to lowest along with 95% confidence intervals, standard deviations, and Gini coefficients. Variation in means in LE, LS, and H scores across categories of demographic variables (see Variables for Demographic Variation Analyses section) were estimated. 81 A weighted linear regression model with complex survey adjusted standard errors was fit by regressing each outcome on all the aforementioned childhood predictor variables (see Variables for Childhood Predictor Analyses section) simultaneously. 85 All analyses were initially conducted by country (see Supplementary Tables). Primary results pooled across country-specific estimates using a random effects meta-analyses 90 , 91 along with 95% confidence intervals, standard errors, upper and lower limits of a prediction interval across countries, estimate proportions of effects across countries with effect sizes larger than 0.1 and smaller than − 0.1, heterogeneity (τ), and I 2 where appropriate for a given outcome/analysis for evidence concerning variation within a particular estimate across countries. 92 Discussion of the rationale underpinning the choice of a meta-analytic approach (over multilevel modelling) can be found in Padgett et al. 87 – 88 Forest plots of estimates are available in the Supplementary files. All meta-analyses were conducted in R 93 using the metafor package. 94 Within each country, a global test of variation (or association) of outcome across levels of each particular demographic variable was conducted, and a pooled p-value 95 across countries reported concerning evidence for variation within any country. Bonferroni corrected p-value thresholds are provided based on the number of demographic variables or number of childhood predictors. 96 , 97 For each childhood predictor, we calculated E-values to evaluate the sensitivity of results to unmeasured confounding. An E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association. 63 Religious affiliation and racial/ethnic identity were not included in the meta-analyses because these variables were not measured consistently across all 22 countries. As a supplementary analysis, population weighted meta-analyses were also conducted. All analyses were pre-registered with Center for Open Science prior to data access with separate registrations by construct (albeit with LE and LS combined) and focal analysis: LE and LS (childhood: https://osf.io/8nvw6 ; demographic: https://osf.io/b8z59 ); and H (childhood: https://osf.io/shnp6 ; demographics: https://osf.io/46vr3 ). An unplanned analysis to estimate the intraclass correlation based on the results from ordered means analysis was also conducted. The ICC was approximated from the results presented in Table 2 by estimating (a) the sample variance in the means and (b) the average within country variance, then the ICC was approximated by ICC = a/(a + b). The ICC is interpreted as the proportion of the variable in an outcome that is explained by mean differences on that outcome on a grouping variable. Missing Data and Multiple Imputation All missing variables are imputed using multivariate imputation by chained equations, with five imputed datasets generated. 98 , 99 The imputation model incorporated the criterion/outcome variable, all demographic or childhood experience characteristics, including race/ethnicity and religious affiliation when available, and sampling weights. The sampling weights were included as a variable in the imputation models to allow for specific variable missingness to be related to probability of study inclusion. The Gallup-provided sampling weights incorporate nonresponse and poststratification adjustments which helps to account for missingness being related to nonresponse of specific subgroups. To account for variations in the assessment of certain variables across countries (e.g., race/ethnicity and religious affiliation), we conducted the imputation process separately for each country. The within-country imputation approach ensured that the imputation model accurately reflects country-specific contexts and assessment methods. The percent of missing data for all variables is reported in our Supplementary Tables by country. Supplemental Post-Hoc Analyses. Complete-case analyses were conducted to replicate all primary analyses (country-specific and meta-analytic); these are reported on in our online supplement, first in terms of providing versions of the main tables based on complete case analysis (Tables S1a-g), and then for each country individually (Tables e-g for each country). The meta-analytic pooled correlations among LE, LS, and H and these variables with indicators of flourishing were computed. The means of each outcome were additionally computed by world region (WEIRD, Non-WEIRD, and by continent). Accounting for Complex Sampling Design The GFS used different sampling schemes across countries based on availability of existing panels and recruitment needs. 87 All analyses accounted for the complex survey design components by including weights, primary sampling units, and strata. Additional methodological detail, including accounting for the complex sampling design is provided elsewhere. 83 Declarations Data Availability Data that support the findings of this article are openly available on the Open Science Framework (Wave 1 non-sensitive Global data: https://osf.io/sm4cd/), and are available from February 2024 - March 2026 via preregistration and publicly from then onwards. Subsequent waves of the GFS will similarly be made available. Please see https://www.cos.io/gfs-access-data for more information about data access. Code Availability Code in multiple software is openly available in an online repository 84 for the demographic variation and childhood predictor analyses (https://doi.org/10.17605/osf.io/vbype). Acknowledgements N/A Author Contributions T.J.V. and B.R.J. led the overall study of which this paper reports a subset of results. H.K. and T.L. conceptualized, designed, and planned the paper, in collaboration with all authors. T.L. managed the development of the paper and the coordination of author input. R.N.P. led the analyses and prepared all the tables and figures. T.L. and H.K. wrote the first draft and subsequent revisions. All authors provided feedback of the various drafts of the manuscript, helped edit and refine the text, and reviewed the final version. Competing Interests Statement Tyler J. VanderWeele reports partial ownership and licensing fees from Gloo, Inc. The remaining authors have no competing interests to declare. References Stiglitz, J. E. 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Demographic characteristics of the GFS sample (wave 1) Characteristic N=202,898 Demographic Characteristics Age group 18-24 27,007 (13%) 25-29 20,700 (10%) 30-39 40,256 (20%) 40-49 34,464 (17%) 50-59 31,793 (16%) 60-69 27,763 (14%) 70-79 16,776 (8.3%) 80 or older 4,119 (2.0%) (Missing) 20 (<0.1%) Gender Male 98,411 (49%) Female 103,488 (51%) Other 602 (0.3%) (Missing) 397 (0.2%) Current Marital status Married 107,354 (53%) Separated 5,195 (2.6%) Divorced 11,654 (5.7%) Widowed 9,823 (4.8%) Single, never married 52,115 (26%) Domestic Partner 14,931 (7.4%) (Missing) 1,826 (0.9%) Employment status Employed for an employer 78,815 (39%) Self-employed 36,362 (18%) Retired 29,303 (14%) Student 10,726 (5.3%) Homemaker 21,677 (11%) Unemployed and looking for a job 16,790 (8.3%) None of these/Other 8,431 (4.2%) (Missing) 793 (0.4%) Current Religious service attendance More than 1/week 26,537 (13%) 1/week 39,157 (19%) 1-3/month 19,749 (9.7%) A few times a year 41,436 (20%) Never 75,297 (37%) (Missing) 722 (0.4%) Education Up to 8 years 45,078 (22%) 9-15 years 115,097 (57%) 16+ years 42,578 (21%) (Missing) 146 (<0.1%) Immigration status Born in this country 190,998 (94%) Born in another country 9,791 (4.8%) (Missing) 2,110 (1.0%) Childhood Experiences and Characteristics Relationship with mother growing up Very good 127,836 (63%) Somewhat good 52,439 (26%) Somewhat bad 11,060 (5.5%) Very bad 4,642 (2.3%) Does not apply 5,965 (2.9%) (Missing) 956 (0.5%) Relationship with father growing up Very good 107,742 (53%) Somewhat good 55,714 (27%) Somewhat bad 15,807 (7.8%) Very bad 8,278 (4.1%) Does not apply 13,985 (6.9%) (Missing) 1,372 (0.7%) Parent marital status at age 12 Parents married 152,001 (75%) Divorced 17,726 (8.7%) Parents were never married 15,534 (7.7%) One or both parents had died 7,794 (3.8%) (Missing) 9,843 (4.9%) Subjective financial status of family growing up Lived comfortably 70,861 (35%) Got by 82,905 (41%) Found it difficult 35,852 (18%) Found it very difficult 12,606 (6.2%) (Missing) 674 (0.3%) Abuse Yes 29,139 (14%) No 167,279 (82%) (Missing) 6,479 (3.2%) Outsider growing up Yes 28,732 (14%) No 170,577 (84%) (Missing) 3,589 (1.8%) Self-rated health growing up Excellent 67,121 (33%) Very good 63,086 (31%) Good 47,378 (23%) Fair 19,877 (9.8%) Poor 4,906 (2.4%) (Missing) 530 (0.3%) Age 12 religious service attendance At least 1/week 83,237 (41%) 1-3/month 33,308 (16%) <1/month 36,928 (18%) Never 47,445 (23%) (Missing) 1,980 (1.0%) Country Argentina 6,724 (3.3%) Australia 3,844 (1.9%) Brazil 13,204 (6.5%) Egypt 4,729 (2.3%) Germany 9,506 (4.7%) Hong Kong (S.A.R. of China) 3,012 (1.5%) India 12,765 (6.3%) Indonesia 6,992 (3.4%) Israel 3,669 (1.8%) Japan 20,543 (10%) Kenya 11,389 (5.6%) Mexico 5,776 (2.8%) Nigeria 6,827 (3.4%) Philippines 5,292 (2.6%) Poland 10,389 (5.1%) South Africa 2,651 (1.3%) Spain 6,290 (3.1%) Sweden 15,068 (7.4%) Tanzania 9,075 (4.5%) Turkey 1,473 (0.7%) United Kingdom 5,368 (2.6%) United States 38,312 (19%) Note. S.A.R. = Special Administrative Region. Table 2. Countries ordered by means on LE/LS/H Life Evaluation Life Satisfaction Happiness Rank Country Mean 95% CI SD Gini Country Mean 95% CI SD Gini Country Mean 95% CI SD Gini 1. Israel 7.33 (7.18, 7.48) 1.81 0.13 Indonesia 7.99 (7.91, 8.07) 2.29 0.15 Indonesia 8.04 (7.97, 8.11) 2.19 0.14 2. Sweden 7.20 (7.16, 7.23) 1.76 0.13 Mexico 7.85 (7.78, 7.92) 2.14 0.14 Mexico 7.79 (7.72, 7.85) 2.03 0.14 3. Poland 7.12 (7.04, 7.21) 1.62 0.13 Egypt 7.69 (7.58, 7.79) 2.83 0.19 Israel 7.76 (7.64, 7.88) 1.65 0.12 4. Mexico 7.10 (7.03, 7.17) 2.11 0.16 Poland 7.52 (7.43, 7.62) 1.73 0.12 Poland 7.55 (7.46, 7.65) 1.60 0.11 5. Indonesia 6.97 (6.87, 7.06) 2.52 0.20 Philippines 7.50 (7.42, 7.59) 2.43 0.17 Argentina 7.36 (7.29, 7.44) 2.14 0.16 6. United States 6.94 (6.89, 6.99) 1.85 0.15 Israel 7.47 (7.31, 7.64) 2.00 0.14 Brazil 7.33 (7.28, 7.39) 2.30 0.17 7. Hong Kong 6.85 (6.75, 6.94) 2.02 0.16 Argentina 7.22 (7.13, 7.30) 2.38 0.18 Philippines 7.33 (7.24, 7.41) 2.33 0.17 8. Australia 6.79 (6.71, 6.86) 1.77 0.14 Brazil 7.15 (7.10, 7.21) 2.48 0.19 Kenya 7.27 (7.19, 7.36) 2.96 0.22 9. Argentina 6.75 (6.67, 6.83) 2.18 0.18 Sweden 7.09 (7.05, 7.13) 2.07 0.16 Hong Kong 7.16 (7.07, 7.26) 2.00 0.15 10. Germany 6.74 (6.69, 6.78) 1.82 0.15 Hong Kong 7.03 (6.93, 7.13) 2.08 0.16 Nigeria 7.06 (6.95, 7.17) 2.59 0.20 11. Spain 6.67 (6.60, 6.73) 1.93 0.16 India 7.00 (6.92, 7.09) 3.47 0.26 Sweden 7.03 (6.99, 7.07) 1.96 0.15 12. Brazil 6.59 (6.54, 6.64) 2.28 0.19 Germany 6.93 (6.88, 6.98) 2.08 0.16 United States 7.01 (6.96, 7.06) 1.92 0.15 13. United Kingdom 6.56 (6.48, 6.63) 2.01 0.17 United States 6.86 (6.80, 6.91) 2.14 0.17 South Africa 6.95 (6.80, 7.11) 2.65 0.21 14. Philippines 6.38 (6.30, 6.46) 2.40 0.21 Spain 6.79 (6.72, 6.86) 2.18 0.17 Spain 6.92 (6.86, 6.99) 2.00 0.16 15. South Africa 6.11 (5.92, 6.29) 2.84 0.26 Australia 6.72 (6.63, 6.81) 2.10 0.17 Germany 6.90 (6.85, 6.95) 1.91 0.15 16. Japan 5.90 (5.86, 5.93) 2.15 0.20 Nigeria 6.51 (6.38, 6.64) 2.86 0.24 Australia 6.88 (6.81, 6.96) 1.82 0.14 17. Nigeria 5.73 (5.60, 5.85) 2.79 0.28 United Kingdom 6.49 (6.40, 6.58) 2.31 0.20 United Kingdom 6.71 (6.63, 6.79) 2.08 0.17 18. India 5.63 (5.53, 5.72) 3.58 0.36 South Africa 6.36 (6.20, 6.52) 2.81 0.25 Tanzania 6.58 (6.46, 6.70) 3.26 0.27 19. Kenya 5.51 (5.41, 5.61) 3.28 0.34 Japan 6.03 (5.99, 6.07) 2.32 0.21 India 6.48 (6.39, 6.57) 3.56 0.30 20. Turkey 5.18 (5.02, 5.35) 2.56 0.28 Kenya 5.97 (5.87, 6.07) 3.51 0.33 Japan 6.22 (6.18, 6.25) 2.13 0.19 21. Egypt 5.04 (4.93, 5.15) 2.87 0.32 Tanzania 5.33 (5.17, 5.50) 3.77 0.40 Egypt 6.18 (6.05, 6.31) 2.94 0.27 22. Tanzania 4.40 (4.24, 4.56) 3.30 0.42 Turkey 5.19 (4.98, 5.40) 3.24 0.36 Turkey 5.54 (5.35, 5.73) 2.93 0.30 Table 3. LE meta-analysis of means by demographic category Prediction Interval Variable Category Est 95% CI SE LL UL Heterogeneity (τ) Global p-value Age group <.001** .. 18-24 6.35 (6.07,6.62) 0.14 4.99 7.69 0.65 98.1 25-29 6.31 (6.00,6.62) 0.16 4.75 7.55 0.73 98.2 30-39 6.22 (5.86,6.57) 0.18 4.32 7.43 0.85 99.3 40-49 6.18 (5.81,6.54) 0.19 4.15 7.31 0.87 99.2 50-59 6.32 (5.96,6.68) 0.18 3.97 7.26 0.85 99.2 60-69 6.43 (6.04,6.82) 0.20 3.88 7.59 0.92 99.3 70-79 6.51 (6.09,6.94) 0.22 3.45 7.88 0.97 99.0 80 or older 6.83 (6.42,7.23) 0.21 4.73 7.98 0.80 92.9 Gender <.001** Male 6.31 (5.95,6.67) 0.18 4.29 7.46 0.85 99.7 Female 6.38 (6.07,6.69) 0.16 4.52 7.21 0.74 99.6 Other 5.98 (5.70,6.26) 0.14 5.68 6.21 0.14 8.6 Marital status <.001** Married 6.54 (6.15,6.93) 0.20 4.35 7.73 0.94 99.8 Separated 5.89 (5.54,6.24) 0.18 3.97 6.85 0.77 93.3 Divorced 5.93 (5.51,6.36) 0.22 3.62 7.01 0.97 98.2 Widowed 6.33 (5.91,6.75) 0.21 3.85 7.53 0.98 97.4 Domestic partner 6.35 (6.04,6.65) 0.15 4.80 7.15 0.64 97.8 Single, never married 6.15 (5.88,6.42) 0.14 4.79 7.44 0.65 99.0 Employment status <.001** Employed for an employer 6.41 (6.07,6.74) 0.17 4.80 7.45 0.80 99.6 Self-employed 6.40 (6.02,6.78) 0.19 4.37 7.61 0.90 99.1 Retired 6.53 (6.11,6.94) 0.21 3.69 7.72 0.96 99.3 Student 6.41 (6.16,6.66) 0.13 5.37 7.84 0.58 95.8 Homemaker 6.26 (5.95,6.57) 0.16 4.36 7.12 0.72 97.6 Unemployed and looking for a job 5.59 (5.31,5.88) 0.14 4.31 6.56 0.64 95.5 None of these/other 5.94 (5.58,6.31) 0.19 3.74 7.61 0.81 95.3 Education <.001** Up to 8 years 6.20 (5.83,6.56) 0.19 4.22 7.37 0.86 98.4 9-15 years 6.33 (6.04,6.63) 0.15 4.95 7.37 0.70 99.6 16+ years 6.64 (6.36,6.93) 0.14 5.22 7.39 0.67 99.4 Religious service attendance 1/week 6.80 (6.33,7.27) 0.24 4.36 9.02 1.12 99.1 1/week 6.60 (6.22,6.97) 0.19 4.41 7.53 0.90 99.1 1-3/month 6.41 (6.06,6.77) 0.18 4.63 7.27 0.83 98.2 A few times a year 6.33 (5.99,6.67) 0.18 4.37 7.46 0.82 99.4 Never 6.12 (5.78,6.46) 0.17 4.40 7.08 0.80 99.6 Immigration status <.001** Born in this country 6.34 (6.01,6.68) 0.17 4.41 7.43 0.80 99.8 Born in another country 6.36 (6.05,6.68) 0.16 4.75 7.14 0.67 95.3 Note. N = 202,898. *p < .05; **p < .007 (Bonferroni corrected threshold); ǂ Group is very small (<0.1% of the observed sample) within several countries leading to large uncertainty in this estimate—be cautious about interpreting this estimate; LL=lower limits of prediction interval; UL=upper limit of prediction interval; prediction interval is the range of likely values of the estimate for a randomly selected country; τ is the standard deviation of the distribution of means across countries, which is an indicator of cross-national heterogeneity; I 2 is an estimate of the variability in means due to heterogeneity across countries vs. sampling variability which is not uncommonly nearly 100% when there is nice precision in estimated mean within country; and the Global p -value corresponds to a test of the null hypothesis that there are no differences between the groups for that socio-demographic characteristic in all of the 22 countries. Table 4. LS meta-analysis of means by demographic category Prediction Interval Variable Category Est 95% CI SE LL UL Heterogeneity (τ) Global p-value Age group <.001** .. 18-24 6.78 (6.43,7.12) 0.18 5.04 8.00 0.82 98.5 25-29 6.74 (6.37,7.10) 0.19 4.94 8.16 0.87 98.5 30-39 6.72 (6.37,7.07) 0.18 5.06 8.10 0.83 99.1 40-49 6.70 (6.35,7.06) 0.18 4.83 7.98 0.85 99.0 50-59 6.83 (6.51,7.16) 0.17 4.74 7.97 0.76 98.8 60-69 7.02 (6.71,7.33) 0.16 5.33 8.02 0.72 98.6 70-79 7.15 (6.83,7.46) 0.16 5.53 8.20 0.69 98.0 80 or older 7.17 (6.77,7.57) 0.20 4.78 8.09 0.82 92.6 Gender <.001** Male 6.82 (6.49,7.16) 0.17 4.94 7.91 0.80 99.6 Female 6.89 (6.59,7.18) 0.15 5.35 8.09 0.70 99.5 Other 5.91 (5.45,6.36) 0.23 5.07 6.90 0.55 53.9 Marital status <.001** Married 7.12 (6.79,7.44) 0.17 5.35 8.16 0.78 99.6 Separated 6.31 (5.95,6.67) 0.18 4.24 7.33 0.79 92.7 Divorced 6.42 (6.07,6.77) 0.18 4.62 7.47 0.76 96.4 Widowed 6.97 (6.66,7.28) 0.16 5.34 7.94 0.71 94.6 Domestic partner 6.70 (6.37,7.03) 0.17 5.14 7.96 0.71 97.7 Single, never married 6.54 (6.18,6.90) 0.18 4.86 7.77 0.85 99.3 Employment status <.001** Employed for an employer 6.89 (6.60,7.19) 0.15 5.27 7.85 0.69 99.4 Self-employed 6.93 (6.59,7.26) 0.17 5.17 8.09 0.78 98.7 Retired 7.14 (6.82,7.45) 0.16 5.50 8.39 0.73 98.6 Student 6.81 (6.47,7.15) 0.17 4.92 8.17 0.80 97.1 Homemaker 6.94 (6.65,7.23) 0.15 5.64 8.16 0.67 97.1 Unemployed and looking for a job 5.97 (5.57,6.37) 0.20 4.32 7.44 0.94 97.5 None of these/other 6.31 (5.82,6.81) 0.25 4.03 8.40 1.13 97.0 Education <.001** Up to 8 years 6.84 (6.49,7.18) 0.18 5.20 8.05 0.80 98.2 9-15 years 6.83 (6.51,7.15) 0.16 5.04 7.93 0.76 99.6 16+ years 7.00 (6.71,7.30) 0.15 5.52 7.97 0.69 99.3 Religious service attendance 1/week 7.40 (7.02,7.78) 0.19 5.39 8.84 0.90 98.5 1/week 7.16 (6.86,7.46) 0.15 5.38 8.02 0.71 98.5 1-3/month 6.92 (6.61,7.23) 0.16 5.36 7.96 0.73 97.5 A few times a year 6.76 (6.41,7.12) 0.18 4.55 7.86 0.84 99.2 Never 6.55 (6.23,6.86) 0.16 4.60 7.93 0.74 99.4 Immigration status <.001** Born in this country 6.85 (6.54,7.17) 0.16 5.20 7.99 0.75 99.7 Born in another country 6.81 (6.61,7.01) 0.10 6.02 7.30 0.38 84.0 Note. N = 202,898. *p < .05; **p < .007 (Bonferroni corrected threshold); ǂ Group is very small (<0.1% of the observed sample) within several countries leading to large uncertainty in this estimate—be cautious about interpreting this estimate; LL=lower limits of prediction interval; UL=upper limit of prediction interval; prediction interval is the range of likely values of the estimate for a randomly selected country; τ is the standard deviation of the distribution of means across countries, which is an indicator of cross-national heterogeneity; I 2 is an estimate of the variability in means due to heterogeneity across countries vs. sampling variability which is not uncommonly nearly 100% when there is nice precision in estimated mean within country; and the Global p -value corresponds to a test of the null hypothesis that there are no differences between the groups for that socio-demographic characteristic in all of the 22 countries. Table 5. H meta-analysis of means by demographic category Prediction Interval Variable Category Est 95% CI SE LL UL Heterogeneity (τ) Global p-value Age group <.001** .. 18-24 6.96 (6.66,7.26) 0.15 5.50 8.07 0.71 98.4 25-29 6.94 (6.65,7.22) 0.14 5.50 8.16 0.66 97.8 30-39 6.89 (6.61,7.18) 0.15 5.31 8.11 0.68 98.9 40-49 6.86 (6.59,7.14) 0.14 5.42 8.05 0.66 98.6 50-59 6.97 (6.71,7.22) 0.13 5.87 7.88 0.60 98.4 60-69 7.09 (6.84,7.34) 0.13 5.97 7.94 0.57 98.2 70-79 7.26 (7.01,7.50) 0.13 5.96 7.96 0.53 97.1 80 or older 7.40 (7.13,7.67) 0.14 5.87 8.06 0.50 85.6 Gender <.001** Male 6.98 (6.73,7.24) 0.13 5.45 7.93 0.62 99.4 Female 7.03 (6.80,7.26) 0.12 5.71 8.16 0.54 99.2 Other 5.95 (5.45,6.44) 0.25 4.95 6.98 0.64 62.7 Marital status <.001** Married 7.23 (6.98,7.49) 0.13 5.68 8.14 0.61 99.5 Separated 6.56 (6.28,6.84) 0.14 5.39 7.34 0.60 89.4 Divorced 6.58 (6.26,6.91) 0.17 4.84 7.61 0.72 96.8 Widowed 6.92 (6.61,7.22) 0.15 5.15 8.00 0.69 95.2 Domestic partner 7.01 (6.75,7.27) 0.13 6.15 8.01 0.55 96.8 Single, never married 6.73 (6.43,7.03) 0.15 5.30 7.88 0.72 99.2 Employment status <.001** Employed for an employer 7.04 (6.79,7.29) 0.13 5.72 7.91 0.60 99.3 Self-employed 7.09 (6.84,7.34) 0.13 5.73 8.08 0.60 98.1 Retired 7.19 (6.94,7.43) 0.13 5.93 8.04 0.56 98.0 Student 6.94 (6.64,7.24) 0.15 5.46 8.21 0.70 97.0 Homemaker 7.00 (6.75,7.25) 0.13 5.67 8.27 0.57 96.6 Unemployed and looking for a job 6.30 (5.94,6.66) 0.18 4.56 7.49 0.84 97.5 None of these/other 6.54 (6.18,6.89) 0.18 5.14 8.17 0.78 95.0 Education <.001** Up to 8 years 6.95 (6.67,7.24) 0.15 5.61 8.11 0.66 97.7 9-15 years 7.01 (6.76,7.25) 0.13 5.43 7.99 0.58 99.5 16+ years 7.16 (6.95,7.36) 0.10 5.78 7.92 0.48 98.7 Religious service attendance 1/week 7.54 (7.21,7.87) 0.17 6.10 9.22 0.77 98.3 1/week 7.29 (7.06,7.51) 0.12 5.95 8.04 0.53 97.7 1-3/month 7.10 (6.86,7.33) 0.12 5.83 7.95 0.54 96.4 A few times a year 6.92 (6.65,7.18) 0.14 5.03 7.84 0.63 98.9 Never 6.71 (6.45,6.96) 0.13 4.92 7.88 0.59 99.2 Immigration status <.001** Born in this country 7.01 (6.76,7.25) 0.13 5.56 8.04 0.59 99.6 Born in another country 6.87 (6.61,7.14) 0.13 5.61 7.64 0.55 93.2 Note. N = 202,898. *p < .05; **p < .007 (Bonferroni corrected threshold); ǂ Group is very small (<0.1% of the observed sample) within several countries leading to large uncertainty in this estimate—be cautious about interpreting this estimate; LL=lower limits of prediction interval; UL=upper limit of prediction interval; prediction interval is the range of likely values of the estimate for a randomly selected country; τ is the standard deviation of the distribution of means across countries, which is an indicator of cross-national heterogeneity; I 2 is an estimate of the variability in means due to heterogeneity across countries vs. sampling variability which is not uncommonly nearly 100% when there is nice precision in estimated mean within country; and the Global p -value corresponds to a test of the null hypothesis that there are no differences between the groups for that socio-demographic characteristic in all of the 22 countries. Table 6. Random effects meta-analysis of regressing LE on childhood predictors Estimated Proportion of Effects by Threshold Variable Category Est 95% CI SE 0.10 Heterogeneity (τ) Global p-value Relationship with mother (Ref: Very bad/somewhat bad) <.001** Very good/somewhat good 0.17 (0.10,0.23) 0.04 0.00 0.82 0.07 23.1 Relationship with father (Ref: Very bad/somewhat bad) <.001** Very good/somewhat good 0.18 (0.11,0.24) 0.03 0.00 0.77 0.09 39.4 Parent marital status (Ref: Parents married) <.001** Divorced -0.02 (-0.08,0.04) 0.03 0.05 0.00 0.06 20.0 Single, never married -0.14 (-0.27,-0.01) 0.07 0.55 0.18 0.24 75.8 One or both parents had died -0.18 (-0.28,-0.09) 0.05 0.77 0.00 0.11 25.9 Subjective financial status of family growing up (Ref: Got by) <.001** Lived comfortably 0.29 (0.20,0.38) 0.05 0.05 0.82 0.20 88.8 Found it difficult -0.17 (-0.23,-0.11) 0.03 0.73 0.00 0.11 54.5 Found it very difficult -0.42 (-0.55,-0.29) 0.07 0.95 0.00 0.24 64.4 Abuse (Ref: No) <.001** Yes -0.25 (-0.34,-0.16) 0.04 0.90 0.05 0.16 70.8 Outsider growing up (Ref: No) <.001** Yes -0.16 (-0.25,-0.07) 0.05 0.59 0.05 0.18 72.4 Self-rated health growing up (Ref: Good) <.001** Excellent 0.40 (0.26,0.55) 0.07 0.05 0.91 0.33 92.1 Very good 0.24 (0.15,0.33) 0.05 0.00 0.73 0.18 81.7 Fair -0.25 (-0.33,-0.17) 0.04 0.91 0.00 0.12 47.3 Poor -0.40 (-0.59,-0.22) 0.09 0.82 0.09 0.31 58.4 Immigration status (Ref: Born in this country) <.001** Born in another country 0.09 (-0.02,0.20) 0.05 0.18 0.36 0.15 48.5 Age 12 religious service attendance (Ref: Never) <.001** At least 1/week 0.22 (0.11,0.33) 0.05 0.09 0.82 0.20 74.2 1-3/month 0.23 (0.12,0.34) 0.06 0.05 0.73 0.21 75.1 < 1/month 0.12 (0.06,0.17) 0.03 0.00 0.55 0.06 31.2 Year of birth (Ref: 1998-2005; age 18-24) <.001** 1993-1998; age 25-29 -0.00 (-0.08,0.07) 0.04 0.23 0.23 0.12 45.9 1983-1993; age 30-39 -0.06 (-0.19,0.07) 0.07 0.41 0.27 0.29 87.2 1973-1983; age 40-49 -0.11 (-0.26,0.04) 0.08 0.55 0.23 0.34 89.1 1963-1973; age 50-59 0.03 (-0.14,0.19) 0.08 0.32 0.45 0.36 89.1 1953-1963; age 60-69 0.14 (-0.08,0.36) 0.11 0.27 0.50 0.49 92.2 1943-1953; age 70-79 0.20 (-0.10,0.50) 0.15 0.27 0.50 0.65 93.5 1943 or earlier; age 80+ 0.27 (-0.10,0.65) 0.19 0.27 0.64 0.75 89.3 Gender (Ref: Male) <.001** Female 0.12 (0.03,0.20) 0.04 0.09 0.55 0.19 90.7 Other 0.01 (-0.56,0.58) 0.29 0.56 0.39 1.08 90.7 Note. N = 202,898. *p < .05; **p < .004 (Bonferroni corrected threshold); ǂ Group is very small (<0.1% of the observed sample) within several countries leading large uncertainty in this estimate—be cautious about interpreting this estimate; CI = confidence interval; the estimated proportion of effects is the estimated proportion of effects above (or below) a threshold based on the calibrated effect sizes (Mathur & VanderWeele, 2020); I 2 is an estimate of the variability in means due to heterogeneity across countries vs. sampling variability; the Global p -value corresponds to the joint test of the null hypothesis that the country-specific joint parameter Wald tests (all parameters within variable groups are zero) are all null all 22 countries; and additional details of heterogeneity of effects are available in the forest plots of our online supplemental material. Table 7. Random effects meta-analysis of regressing LS on childhood predictors Estimated Proportion of Effects by Threshold Variable Category Est 95% CI SE 0.10 Heterogeneity (τ) Global p-value Relationship with mother (Ref: Very bad/somewhat bad) 0.009* Very good/somewhat good 0.21 (0.12,0.29) 0.04 0.00 0.86 0.10 29.9 Relationship with father (Ref: Very bad/somewhat bad) <.001** Very good/somewhat good 0.19 (0.10,0.27) 0.04 0.00 0.73 0.13 54.2 Parent marital status (Ref: Parents married) <.001** Divorced -0.04 (-0.17,0.09) 0.06 0.36 0.18 0.25 77.3 Single, never married -0.13 (-0.25,-0.01) 0.06 0.45 0.18 0.22 69.7 One or both parents had died -0.08 (-0.24,0.08) 0.08 0.45 0.23 0.30 71.1 Subjective financial status of family growing up (Ref: Got by) <.001** Lived comfortably 0.25 (0.17,0.33) 0.04 0.00 0.82 0.16 81.6 Found it difficult -0.14 (-0.22,-0.07) 0.04 0.68 0.09 0.14 65.3 Found it very difficult -0.31 (-0.46,-0.16) 0.08 0.77 0.00 0.27 66.7 Abuse (Ref: No) <.001** Yes -0.39 (-0.48,-0.30) 0.04 0.95 0.00 0.15 64.7 Outsider growing up (Ref: No) <.001** Yes -0.29 (-0.39,-0.20) 0.05 0.82 0.00 0.18 68.8 Self-rated health growing up (Ref: Good) <.001** Excellent 0.46 (0.29,0.62) 0.08 0.00 0.82 0.37 93.0 Very good 0.25 (0.15,0.35) 0.05 0.05 0.68 0.21 83.1 Fair -0.33 (-0.41,-0.26) 0.04 1.00 0.00 0.10 32.4 Poor -0.46 (-0.70,-0.22) 0.12 0.86 0.09 0.47 73.5 Immigration status (Ref: Born in this country) <.001** Born in another country 0.09 (-0.08,0.26) 0.09 0.18 0.50 0.30 75.2 Age 12 religious service attendance (Ref: Never) <.001** At least 1/week 0.21 (0.03,0.38) 0.09 0.18 0.77 0.37 88.2 1-3/month 0.16 (-0.01,0.32) 0.08 0.23 0.64 0.34 86.7 < 1/month 0.05 (-0.09,0.19) 0.07 0.18 0.41 0.28 87.5 Year of birth (Ref: 1998-2005; age 18-24) <.001** 1993-1998; age 25-29 -0.00 (-0.10,0.09) 0.05 0.32 0.27 0.17 60.9 1983-1993; age 30-39 -0.00 (-0.15,0.14) 0.08 0.27 0.41 0.33 87.9 1973-1983; age 40-49 -0.03 (-0.22,0.16) 0.10 0.36 0.45 0.43 91.6 1963-1973; age 50-59 0.08 (-0.15,0.30) 0.11 0.32 0.59 0.50 93.0 1953-1963; age 60-69 0.24 (0.01,0.46) 0.11 0.23 0.64 0.49 91.1 1943-1953; age 70-79 0.33 (0.02,0.65) 0.16 0.27 0.59 0.69 93.2 1943 or earlier; age 80+ 0.44 (0.01,0.88) 0.22 0.27 0.68 0.94 91.3 Gender (Ref: Male) <.001** Female 0.12 (0.03,0.21) 0.05 0.09 0.50 0.20 90.5 Other -0.29 (-0.63,0.05) 0.17 0.61 0.17 0.53 63.0 Note. N = 202,898. *p < .05; **p < .004 (Bonferroni corrected threshold); ǂ Group is very small (<0.1% of the observed sample) within several countries leading large uncertainty in this estimate—be cautious about interpreting this estimate; CI = confidence interval; the estimated proportion of effects is the estimated proportion of effects above (or below) a threshold based on the calibrated effect sizes (Mathur & VanderWeele, 2020); I 2 is an estimate of the variability in means due to heterogeneity across countries vs. sampling variability; the Global p -value corresponds to the joint test of the null hypothesis that the country-specific joint parameter Wald tests (all parameters within variable groups are zero) are all null all 22 countries; and additional details of heterogeneity of effects are available in the forest plots of our online supplemental material. Table 8. Random effects meta-analysis of regressing H on childhood predictors Estimated Proportion of Effects by Threshold Variable Category Est 95% CI SE 0.10 Heterogeneity (τ) Global p-value Relationship with mother (Ref: Very bad/somewhat bad) <.001** Very good/somewhat good 0.25 (0.15,0.36) 0.05 0.05 0.86 0.17 62.4 Relationship with father (Ref: Very bad/somewhat bad) <.001** Very good/somewhat good 0.13 (0.03,0.22) 0.05 0.09 0.55 0.16 69.7 Parent marital status (Ref: Parents married) <.001** Divorced -0.04 (-0.15,0.08) 0.06 0.45 0.27 0.23 77.4 Single, never married -0.13 (-0.22,-0.04) 0.05 0.55 0.05 0.15 52.4 One or both parents had died -0.10 (-0.23,0.02) 0.06 0.50 0.14 0.22 57.8 Subjective financial status of family growing up (Ref: Got by) <.001** Lived comfortably 0.23 (0.16,0.30) 0.04 0.00 0.77 0.15 80.6 Found it difficult -0.12 (-0.18,-0.05) 0.03 0.59 0.05 0.12 60.9 Found it very difficult -0.31 (-0.42,-0.21) 0.05 0.86 0.00 0.17 45.9 Abuse (Ref: No) <.001** Yes -0.33 (-0.42,-0.24) 0.04 0.95 0.05 0.16 69.5 Outsider growing up (Ref: No) <.001** Yes -0.28 (-0.37,-0.20) 0.04 0.91 0.00 0.15 65.0 Self-rated health growing up (Ref: Good) <.001** Excellent 0.50 (0.34,0.66) 0.08 0.00 0.86 0.37 93.8 Very good 0.27 (0.17,0.36) 0.05 0.00 0.77 0.20 83.8 Fair -0.28 (-0.36,-0.20) 0.04 0.95 0.00 0.13 50.2 Poor -0.41 (-0.62,-0.20) 0.11 0.82 0.14 0.38 67.8 Immigration status (Ref: Born in this country) <.001** Born in another country 0.01 (-0.14,0.16) 0.08 0.41 0.32 0.26 73.5 Age 12 religious service attendance (Ref: Never) <.001** At least 1/week 0.27 (0.14,0.40) 0.07 0.09 0.82 0.26 80.9 1-3/month 0.23 (0.09,0.36) 0.07 0.14 0.64 0.27 82.7 < 1/month 0.12 (0.06,0.18) 0.03 0.00 0.55 0.09 45.9 Year of birth (Ref: 1998-2005; age 18-24) <.001** 1993-1998; age 25-29 -0.01 (-0.11,0.09) 0.05 0.23 0.36 0.20 71.0 1983-1993; age 30-39 -0.01 (-0.14,0.11) 0.07 0.27 0.32 0.28 86.9 1973-1983; age 40-49 -0.05 (-0.22,0.12) 0.09 0.32 0.50 0.39 91.5 1963-1973; age 50-59 0.03 (-0.19,0.25) 0.11 0.23 0.55 0.50 94.1 1953-1963; age 60-69 0.14 (-0.10,0.38) 0.12 0.27 0.55 0.54 93.7 1943-1953; age 70-79 0.27 (-0.04,0.58) 0.16 0.32 0.59 0.69 94.3 1943 or earlier; age 80+ ǂ 0.46 (0.10,0.81) 0.18 0.32 0.64 0.72 88.5 Gender (Ref: Male) <.001** Female 0.10 (0.03,0.17) 0.03 0.05 0.55 0.14 84.0 Other ǂ -0.69 (-0.98,-0.40) 0.15 0.94 0.00 0.39 51.4 Note. N = 202,898. *p < .05; **p < .004 (Bonferroni corrected threshold); ǂ Group is very small (<0.1% of the observed sample) within several countries leading large uncertainty in this estimate—be cautious about interpreting this estimate; CI = confidence interval; the estimated proportion of effects is the estimated proportion of effects above (or below) a threshold based on the calibrated effect sizes (Mathur & VanderWeele, 2020); I 2 is an estimate of the variability in means due to heterogeneity across countries vs. sampling variability; the Global p -value corresponds to the joint test of the null hypothesis that the country-specific joint parameter Wald tests (all parameters within variable groups are zero) are all null all 22 countries; and additional details of heterogeneity of effects are available in the forest plots of our online supplemental material. Table 9. Sensitivity of meta-analyzed childhood predictors of LE to unmeasured confounding Variable Category E-value for Estimate E-value for 95% CI Relationship with mother (Ref: Very bad/somewhat bad) Very good/somewhat good 1.33 1.24 Relationship with father (Ref: Very bad/somewhat bad) Very good/somewhat good 1.35 1.26 Parent marital status (Ref: Parents married) Divorced 1.09 1.00 Single, never married 1.30 1.07 One or both parents had died 1.35 1.22 Subjective financial status of family (Ref: Got by) growing up Lived comfortably 1.49 1.38 Found it difficult 1.34 1.25 Found it very difficult 1.63 1.48 Abuse (Ref: No) Yes 1.44 1.33 Outsider growing up (Ref: No) Yes 1.32 1.19 Self-rated health growing up (Ref: Good) Excellent 1.62 1.45 Very good 1.43 1.32 Fair 1.44 1.34 Poor 1.62 1.40 Immigration status (Ref: Born in this country) Born in another country 1.23 1.00 Age 12 religious service attendance (Ref: Never) At least 1/week 1.40 1.26 1-3/month 1.41 1.27 < 1/month 1.27 1.18 Year of birth (Ref: 1998-2005; age 18-24) 1993-1998; age 25-29 1.03 1.00 1983-1993; age 30-39 1.18 1.00 1973-1983; age 40-49 1.26 1.00 1963-1973; age 50-59 1.11 1.00 1953-1963; age 60-69 1.30 1.00 1943-1953; age 70-79 1.37 1.00 1943 or earlier; age 80+ ǂ 1.47 1.00 Gender (Ref: Male) Female 1.27 1.12 Other ǂ 1.08 1.00 Note. N = 202,898; the E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association (VanderWeele & Ding, 2017, p. 269-270); and ǂ Group is very small (<0.1% of the observed sample) within several countries potentially large uncertainty in this estimate—be cautious about interpreting this estimate. Table 10. Sensitivity of meta-analyzed childhood predictors of LS to unmeasured confounding Variable Category E-value for Estimate E-value for 95% CI Relationship with mother (Ref: Very bad/somewhat bad) Very good/somewhat good 1.37 1.26 Relationship with father (Ref: Very bad/somewhat bad) Very good/somewhat good 1.34 1.24 Parent marital status (Ref: Parents married) Divorced 1.14 1.00 Single, never married 1.27 1.05 One or both parents had died 1.21 1.00 Subjective financial status of family (Ref: Got by) growing up Lived comfortably 1.42 1.33 Found it difficult 1.29 1.18 Found it very difficult 1.49 1.32 Abuse (Ref: No) Yes 1.57 1.48 Outsider growing up (Ref: No) Yes 1.46 1.35 Self-rated health growing up (Ref: Good) Excellent 1.64 1.46 Very good 1.42 1.30 Fair 1.51 1.43 Poor 1.65 1.38 Immigration status (Ref: Born in this country) Born in another country 1.21 1.00 Age 12 religious service attendance (Ref: Never) At least 1/week 1.37 1.12 1-3/month 1.31 1.00 < 1/month 1.16 1.00 Year of birth (Ref: 1998-2005; age 18-24) 1993-1998; age 25-29 1.03 1.00 1983-1993; age 30-39 1.04 1.00 1973-1983; age 40-49 1.12 1.00 1963-1973; age 50-59 1.20 1.00 1953-1963; age 60-69 1.40 1.08 1943-1953; age 70-79 1.51 1.09 1943 or earlier; age 80+ ǂ 1.63 1.05 Gender (Ref: Male) Female 1.26 1.11 Other ǂ 1.46 1.00 Note. N = 202,898; the E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association (VanderWeele & Ding, 2017, p. 269-270); and ǂ Group is very small (<0.1% of the observed sample) within several countries potentially large uncertainty in this estimate—be cautious about interpreting this estimate. Table 11. Sensitivity of meta-analyzed childhood predictors of H to unmeasured confounding Variable Category E-value for Estimate E-value for 95% CI Relationship with mother (Ref: Very bad/somewhat bad) Very good/somewhat good 1.45 1.31 Relationship with father (Ref: Very bad/somewhat bad) Very good/somewhat good 1.28 1.13 Parent marital status (Ref: Parents married) Divorced 1.13 1.00 Single, never married 1.28 1.13 One or both parents had died 1.25 1.00 Subjective financial status of family (Ref: Got by) growing up Lived comfortably 1.41 1.32 Found it difficult 1.27 1.17 Found it very difficult 1.52 1.39 Abuse (Ref: No) Yes 1.54 1.43 Outsider growing up (Ref: No) Yes 1.48 1.38 Self-rated health growing up (Ref: Good) Excellent 1.73 1.55 Very good 1.46 1.35 Fair 1.48 1.38 Poor 1.63 1.38 Immigration status (Ref: Born in this country) Born in another country 1.05 1.00 Age 12 religious service attendance (Ref: Never) At least 1/week 1.47 1.31 1-3/month 1.42 1.24 < 1/month 1.27 1.17 Year of birth (Ref: 1998-2005; age 18-24) 1993-1998; age 25-29 1.06 1.00 1983-1993; age 30-39 1.08 1.00 1973-1983; age 40-49 1.16 1.00 1963-1973; age 50-59 1.12 1.00 1953-1963; age 60-69 1.30 1.00 1943-1953; age 70-79 1.47 1.00 1943 or earlier; age 80+ ǂ 1.68 1.25 Gender (Ref: Male) Female 1.24 1.13 Other ǂ 1.95 1.62 Note. N = 202,898; the E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association (VanderWeele & Ding, 2017, p. 269-270); and ǂ Group is very small (<0.1% of the observed sample) within several countries potentially large uncertainty in this estimate—be cautious about interpreting this estimate. Additional Declarations Competing interest reported. Tyler J. VanderWeele reports partial ownership and licensing fees from Gloo, Inc. The remaining authors have no competing interests. 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Padgett","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"R.","middleName":"","lastName":"Padgett","suffix":""},{"id":453873774,"identity":"f575a84e-a744-4455-8ffa-2ba5c44ba44d","order_by":3,"name":"James Pawelski","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Pawelski","suffix":""},{"id":453873775,"identity":"58a3ca59-eca0-425f-b64d-fdf18b943784","order_by":4,"name":"Eric Kim","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Kim","suffix":""},{"id":453873776,"identity":"90224a5d-5da3-4a77-a383-209f59d4c96f","order_by":5,"name":"Christos Makridis","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Christos","middleName":"","lastName":"Makridis","suffix":""},{"id":453873777,"identity":"39806c41-2a62-4bc9-95c7-e359ce5b35bc","order_by":6,"name":"Craig Gundersen","email":"","orcid":"","institution":"Baylor University","correspondingAuthor":false,"prefix":"","firstName":"Craig","middleName":"","lastName":"Gundersen","suffix":""},{"id":453873778,"identity":"627c0a7d-b00d-4121-849a-02d87fd93c0d","order_by":7,"name":"Matt Bradshaw","email":"","orcid":"","institution":"Baylor University","correspondingAuthor":false,"prefix":"","firstName":"Matt","middleName":"","lastName":"Bradshaw","suffix":""},{"id":453873779,"identity":"240b0bb8-5a01-4c3a-8a87-8ff64435c3ab","order_by":8,"name":"Koichiro Shiba","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Koichiro","middleName":"","lastName":"Shiba","suffix":""},{"id":453873781,"identity":"280ae894-55cb-46a3-883e-386f8e2b518f","order_by":9,"name":"Noemie Le Pertel","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Noemie","middleName":"Le","lastName":"Pertel","suffix":""},{"id":453873783,"identity":"9c8e2894-5c01-40f2-b2e8-96f20e57e9b4","order_by":10,"name":"Chris Felton","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"","lastName":"Felton","suffix":""},{"id":453873785,"identity":"62b8c439-902b-42eb-81fa-2114fa82b3b5","order_by":11,"name":"Byron Johnson","email":"","orcid":"","institution":"Baylor University","correspondingAuthor":false,"prefix":"","firstName":"Byron","middleName":"","lastName":"Johnson","suffix":""},{"id":453873787,"identity":"6831cf07-4a51-4c3c-a2e4-89b079e3e421","order_by":12,"name":"Tyler VanderWeele","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Tyler","middleName":"","lastName":"VanderWeele","suffix":""}],"badges":[],"createdAt":"2025-04-10 13:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6420806/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6420806/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-35777-y","type":"published","date":"2026-02-10T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102785118,"identity":"501366f5-efeb-40fe-a0c8-6817c0cf6ab2","added_by":"auto","created_at":"2026-02-16 15:59:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3162671,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6420806/v1/cce0bd3b-7d53-45b8-becf-646ffdb13d3d.pdf"},{"id":82592841,"identity":"ae79f2df-6590-49a1-b70d-645f8a50e101","added_by":"auto","created_at":"2025-05-13 08:22:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":59976830,"visible":true,"origin":"","legend":"","description":"","filename":"GFSSWBsupplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-6420806/v1/e4ea8ceb478650ca1543dcb1.docx"}],"financialInterests":"Competing interest reported. Tyler J. VanderWeele reports partial ownership and licensing fees from Gloo, Inc. The remaining authors have no competing interests.","formattedTitle":"Life evaluation, life satisfaction, and happiness: assessing inter-relations and 15 childhood and demographic factors across 22 Countries in the Global Flourishing Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver recent decades prominent intergovernmental organisations (e.g., UN, WHO, OECD) have advocated for including subjective wellbeing (SWB) indicators alongside traditional economic metrics like GDP when assessing societies and making policy.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e SWB scholarship has three interrelated issues however which limit our understanding and application of this topic. First, there remains uncertainty about how SWB should best be assessed, including regarding its evaluative dimension (E-SWB), with life evaluation (LE) and life satisfaction (LS) the dominant candidates for indexing E-SWB, but with happiness (H) also a possible option (albeit more ambiguously, since in many classifications this is considered a hedonic/affective construct).\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Second, there is a relatively disjointed understanding of factors associated with E-SWB: while one can find analyses of almost every conceivable factor, few studies include a comprehensive array that would allow meaningful comparison among them.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Third, while E-SWB has been extensively assessed internationally, there remains a relatively impoverished understanding of granular national-level variation. Many studies address one or two of these issues, but rarely all three together, hence the value of this paper, which examines wave 1 data on LE, LS, and H in the Global Flourishing Study (GFS), an intended five-year panel study investigating predictors of flourishing involving 202,898 participants from 22 countries. Before reviewing the three issues, we briefly contextualize the research by situating SWB within the GFS and the broader concept of flourishing, though our focus in this paper will be on E-SWB itself.\u003c/p\u003e\n\u003ch3\u003eFlourishing and SWB\u003c/h3\u003e\n\u003cp\u003eOver recent years \u0026ldquo;flourishing\u0026rdquo; has emerged as an overarching construct encompassing the various aspects/forms of wellbeing across different dimensions of life. VanderWeele for example,\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e co-PI of the GFS, defines it as \u0026ldquo;the relative attainment of a state in which all aspects of a person\u0026rsquo;s life are good, including the contexts in which that person lives,\u0026rdquo; and identifies five key domains: happiness and life satisfaction; mental and physical health; meaning and purpose; character and virtue; and close social relationships. To these, a sixth domain, financial and material stability, is added as a key means for \u0026ldquo;secure flourishing\u0026rdquo; over time. While not exhaustive of flourishing, VanderWeele argues all six are \u0026ldquo;arguably at least a part of what we mean by flourishing.\u0026rdquo; Of particular interest here is the first domain, which closely relates to certain understandings of SWB. Regarding SWB itself, there are various definitions in the literature, some broader, some narrower.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e One of the most influential conceptualizations is that of Diener and colleagues, which comprises two dimensions: cognitive/evaluative (e.g., LS), and affective (positive and negative affect).\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Given the myriad conceptualizations of both flourishing and SWB, their relationship inevitably depends on the definitions used. In VanderWeele\u0026rsquo;s framework, LS and H constitute one domain of flourishing. But in some definitions SWB can reflect/encompass subjective assessments of all other dimensions (and some scholars even view LS and H as doing this as well). Consider the widely used definition of LS by Shin and Johnson (who call it \u0026ldquo;avowed happiness\u0026rdquo;) as \u0026ldquo;a global assessment of a person's quality of life according to his chosen criteria,\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e which potentially offers a summary of flourishing overall. It is beyond our scope to resolve the broader question of the relationship between SWB and flourishing under all definitions of these terms. Our main focus is SWB itself, and specifically, as described below, evaluative SWB (E-SWB). Despite a vast literature on E-SWB, the scholarship has three main issues, and although many studies address one or two, by addressing all three together, our paper is able to shed new light on these issues.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eContribution 1: Comparing Evaluative SWB Items\u003c/h2\u003e \u003cp\u003eThe construct of SWB is well-established, although definitions vary: the National Research Council for example define it quite broadly as including not only evaluative and affective but also eudaimonic aspects. However, many scholars understand SWB through the prism of Diener\u0026rsquo;s slightly narrower framework. Yet even within the parameters of that framework, there is ongoing debate about how SWB should best be \u003cem\u003eassessed\u003c/em\u003e. Appraising the scene in 2013, the National Research Council concluded that concepts related to SWB \u0026ldquo;have often been ambiguously applied, which has muddled discussion and possibly slowed progress in the field,\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and the picture has not improved much since,\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e with efforts towards greater clarity continuing. The OECD for example is currently updating/revising their SWB guidelines,\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e with the lead author here being on their informal working group which has been extensively discussing this very issue.\u003c/p\u003e \u003cp\u003eThe debate around assessment includes of course the evaluative aspects of SWB (E-SWB). Single item assessments play prominently here, and are not only deployed for practical reasons of simplicity/concision, but sometimes also because of a view that they constitute adequate global judgements.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Two main candidates for single-item assessments of E-SWB are LS and LE. The OECD\u0026rsquo;s current SWB guidelines,\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e for instance, recommend LS, whereas the World Happiness Report (WHR)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e harnesses a Gallup World Poll (GWP) item, known as Cantril\u0026rsquo;s \u0026ldquo;ladder,\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e that is usually interpreted as indexing LE (with respondents envisaging where they stand on a ladder whose base and top reflect the worst and best life imaginable). Part of the issue is there has been relatively little empirical enquiry comparing LE and LS: given space constraints in many surveys, assessments tend to pick just one. Relatedly, there has been surprisingly little investigation into the basis on which people appraise LE or LS. New natural language analysis though suggests the ladder metaphor encourages people to think about power, achievement, and success, and to evaluate life in those terms,\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e an interpretation which aligns with observations that national Cantril averages strongly correlate with GDP.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e However, the question of whether LE or LS best represents E-SWB remains understudied and unresolved.\u003c/p\u003e \u003cp\u003eIn addition, some scholars also suggest H might pertain to E-SWB. Although in most classifications H is considered a hedonic/affective construct, Diener himself seemed open to, and indeed to favour, an evaluative interpretation. His 1984 paper in Psychological Bulletin reviewed definitions of H and grouped these into three types: normative (invoking \u0026ldquo;external criteria such as virtue\u0026rdquo;); evaluative (e.g., Shin and Johnson\u0026rsquo;s operationalization); and affective (involving \u0026ldquo;pleasant emotional experience\u0026rdquo;).\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Given this conceptual breadth, in a 2009 paper with Oishi and Lucas, Diener wrote \u0026ldquo;we use the term happiness interchangeably with subjective wellbeing or the subjective evaluation of one\u0026rsquo;s life.\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Moreover, they clarify they prefer to interpret/use H as an \u003cem\u003eevaluative\u003c/em\u003e concept: while they \u0026ldquo;conceptualize happiness as being hierarchically organized to emphasize complexity of the concept,\u0026rdquo; the \u0026ldquo;highest level of the abstraction is\u0026hellip; a summary judgment of one\u0026rsquo;s life. That is, we do not use the term happiness to refer to the momentary feeling state of happiness. Rather, we use this term to refer to a relatively stable feeling of happiness one has towards his or her life\u0026rdquo; (p.347). Thus, although most assessments use happiness to capture affective aspects of SWB (a \u0026ldquo;momentary feeling state\u0026rdquo;), as recommended by the OECD for instance,\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e it can \u003cem\u003ealso\u003c/em\u003e be interpreted as indexing E-SWB, even if such evaluations still have some affective dimension (\u0026ldquo;a relatively stable feeling\u0026rdquo;). The question about which concept best represents E-SWB thus potentially extends from just LE versus LS to also include H. To be clear, we are not saying H \u003cem\u003eis\u003c/em\u003e an E-SWB concept, merely that it is inherently ambiguous (despite the dominant tendency to view it as hedonic/affective), so is worth comparing alongside LE and LS in seeking to better understand E-SWB.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eContribution 2: A Comprehensive Coverage of Factors\u003c/h3\u003e\n\u003cp\u003eOur paper\u0026rsquo;s second contribution is an unusually comprehensive assessment of factors associated with E-SWB. This is helpful in relation to the first issue (differentiating LE/LS/H), but is also valuable in its own right. There is a vast literature on factors associated with E-SWB, including systemic factors such as national economics, \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e governance quality,\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e political/economic freedom,\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e community infrastructure,\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and social capital,\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e demographic factors like income,\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e age,\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e sex,\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e sexuality,\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and race/ethnicity,\u003csup\u003e35\u003c/sup\u003e and childhood factors such as Adverse Childhood Experiences.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e One issue however with many studies is a relatively limited coverage of factors, with most publications tending to focus on just a handful, and often just one key factor (together with relevant confounders). An example of the latter is an analysis of age (SWB \u0026ldquo;across the lifespan\u0026rdquo;) that controls for five socio-demographic characteristics (gender, employment, marital status, education, and health).\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e The literature overall is consequently somewhat disjointed. There are analyses of almost any conceivable factor, from childhood health\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and socioeconomic status\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e to adult employment\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and immigration\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e status, so many \u0026ldquo;puzzle pieces\u0026rdquo; (analyses of factors) are on the table. But with most papers only including a few \u0026ldquo;pieces,\u0026rdquo; it is hard to gauge the relative strength of their associations and hence ascertain the overall picture, which requires comparing factors in the same analysis with the same population. Hence another contribution here is we analyse 15 factors selected for the GFS as potentially relevant to all aspects of flourishing (analysed across a series of papers in relation to all flourishing outcomes in the GFS questionnaire\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e\n\u003ch3\u003eContribution 3: Granular Cross-National Details\u003c/h3\u003e\n\u003cp\u003eA third contribution is the analysis permits granular exploration of national variation, both for the three outcomes and the 15 factors. There is already an extensive literature exploring international variation on SWB, harnessing datasets like the World Values Survey, European Social Survey, and Gallup World Poll. A search on Google Scholar in March 2025 for the combined phrases \u0026ldquo;Gallup World Poll\u0026rdquo; and \u0026ldquo;subjective well-being,\u0026rdquo; for example, returned 5,700 results. Thus, although the bias towards relatively \u0026ldquo;WEIRD\u0026rdquo; populations highlighted by Henrich and colleagues\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e in 2010 arguably still remains an issue\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (albeit one that is gradually improving\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e), research into SWB may be among the notable exceptions. That the GFS covers 22 countries in itself is therefore unremarkable. However, its analytic approach \u003cem\u003eis\u003c/em\u003e quite unusual, in that it effectively constitutes 22 separate country-specific studies, the results of which are meta-analysed to identify overall patterns. Consequently, we can access and provide detailed data for each country (including seven tables each in the Supplemental files), facilitating granular understanding of cross-national variation. Moreover, the combination of all three contributions makes our study particularly valuable, with each contribution magnifying the impact of the others. Few studies with granular international coverage for example also assess three E-SWB concepts and/or 15 factors, and vice versa. This means we are not only able to compare the three concepts (contribution 1) and 15 factors (contribution 2), but also explore national variation across both of these.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1 provides the distribution of descriptive statistics (weighted counts and proportions). The GFS assessed 15 socio-demographic factors: four demographic, eight childhood, and three pertaining to both (age/birth cohort, gender, and immigration status, analysed/presented below both as demographic and childhood factors). Most participants were: married (52%), attained 9-15 years of education (57%), born in their country of residence (94%), and employed (39%). Counts and proportions for demographic characteristics weighted to be representative of each country’s population are reported in supplemental Tables S2a-S23a.\u003c/p\u003e\n\u003cp\u003e[Table 1 here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCountry Level LE/LS/H\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCountry averages on LE/LS/H are reported in Table 2. The mean score is reported with associated 95% confidence intervals, country-level standard deviation, and Gini coefficient of inequality. The approximate intraclass correlation coefficients are 9.9% (LE), 7.7% (LS), and 5.6% (H), indicating that greater than 90% of the variability in scores cannot be explained by mean differences in countries, leading to nuanced differences within countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 2 here]\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eVariation in LE/LS/H among Demographic Groups\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMeta-analytic estimates based on subgroups of demographic characteristics are presented in Tables 3 (LE), 4 (LS), and 5 (H). On average across countries, SWB is highest in older age groups, married individuals, retirees, those with more education, and those attending religious services more than once weekly, and with women slightly higher. However, for all categories and outcomes, country-level averages varied by at least 0.14 points (gender “other” for LE) and up to 1.12 (“none/other” for employment status for LS), where variability was evaluated with tau (standard deviation country of means). The global p-value was significant (\u0026lt; .001) for LE/LS/H across all demographic characteristics, indicating that in at least one country every demographic characteristic had mean differences on these outcomes among categories. However, levels were similar across demographic categories within at least one country for all characteristics, as shown in the Supplementary country-specific tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 3 here]\u003c/p\u003e\n\u003cp\u003e[Table 4 here]\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 5 here]\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eChildhood Experiences Predicting LE/LS/H\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMeta-analytic estimates of how childhood experiences predict SWB are shown in Tables 6 (LE), 7 (LS), and 8 (H). Childhood factors associated with increased SWB in adulthood include: better than good health; regular religious service attendance; a very/somewhat good relationship with one’s mother and/or father; one’s family subjectively comfortably meeting its financial needs; not experiencing abuse; and not feeling like an outsider. Note: these Tables feature the three factors interpretable as either demographic or childhood factors, which were also analysed/presented as demographic factors above. Regarding age, in a childhood predictor context this is framed as birth cohort (the time-period people were born/raised in). Some effects were differentially associated with SWB depending on the country. The effect of divorced parents was more likely negative (an estimated 41% of effects below -0.10), for example, but could be positive on average in some countries (an estimated 27% of effects above 0.10), conditional on relationship with parents and other variables. Additional country specific effects are reported in Tables S2c-S23c, and more information on the heterogeneity of effects are reported in the Supplemental forest plots (Figures S34).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 6 here]\u003c/p\u003e\n\u003cp\u003e[Table 7 here]\u003c/p\u003e\n\u003cp\u003e[Table 8 here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity of Childhood Predictors to Unmeasured Confounding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn important consideration in the childhood results is the sensitivity of estimates to unmeasured confounding, reported in Tables 9 (LE), 10 (LS), and 11 (H). Some were moderately robust. For example, to explain away the association between excellent (versus good) self-rated health in childhood with adult H, an unmeasured confounder associated with both excellent health and higher H with risk ratios of 1.73 each, above and beyond measured covariates, could suffice, but weaker joint confounder associations could not; to shift the 95% confidence interval to include the null, an unmeasured confounder associated with both excellent health and higher H with risk ratios of 1.55 each, above and beyond measured covariates, could suffice, but weaker joint confounder associations could not. However, other associations were less robust. Sensitivity of associations for country specific analyses are reported in Tables S2d-S23d.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 9 here]\u003c/p\u003e\n\u003cp\u003e[Table 10 here]\u003c/p\u003e\n\u003cp\u003e[Table 11 here]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper makes three useful contributions to the already-extensive literature on SWB: (1) addressing uncertainty about whether LE, LS or even H best represents E-SWB; (2) providing expansive coverage of 15 factors associated with E-SWB; and (3) allowing granular exploration of cross-national variation. Taken in isolation each contribution may not be remarkable (though (1) \u003cem\u003eis\u003c/em\u003e quite rare), but the combination of all three is arguably unique. Moreover, each contribution magnifies the value of the others. The research thus augments existing scholarship and advances understanding of E-SWB in key ways. The GWP for instance has excelled in assessing around 150 countries annually for nearly 20 years on some SWB measures. However, these have mostly been limited to LE and positive/negative affect, with H occasionally included, while its socio-demographic data have not generally included childhood details. The many studies drawing on SWB data in the GWP, such as the WHR, therefore certainly contribute along the lines of (3). However, rarely do they also allow comprehensive exploration of (1) and (2). Regarding the factors, for instance, our findings across the dataset as a whole are mostly consistent with prior literature. But the additional lenses of (1) and (3) show considerable country-level variation in these general patterns, indicating that such trends are not universal but contingent on socio-cultural dynamics. Here we delve into each contribution in turn, but first note some important observations regarding cross-country comparisons.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCross-Cultural Considerations and Caveats\u003c/h2\u003e \u003cp\u003eMethodologically, comparing and ranking countries can be problematic for various reasons,\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e perhaps above all the complexities of language. Translation is difficult, and it can be hard to find exact equivalents for terms across languages.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e In empirical cross-country comparisons, cultural and linguistic nuances may thus influence results. While Gallup employs a well-established TRAPD (translation, review, adjudication, pretesting, and documentation) model\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e to ensure accuracy, and the translation process for the GFS involved experts in relevant languages,\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e perfect equivalence cannot be guaranteed. Subtle differences may mean terms assess slightly different outcomes across languages, a limitation requiring further qualitative investigation to fully ascertain. Given this and other factors noted below, the primary goal of the GFS was not cross-cultural comparison but separate within-country analyses of 22 closely related cohort studies, followed by meta-analyses across countries. This approach does not assume items are interpreted identically across countries but are relatively closely related (just as a meta-analysis of similar interventions may differ in specific administration, dose, etc.). While cross-country comparisons are possible, they should be interpreted with caution.\u003c/p\u003e \u003cp\u003eOther factors also influence assessment across countries,\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e including cultural norms, item interpretation, response scales, sample characteristics, and seasonal variations from different data collection times. For instance, comparing our LE data (gathered mostly in 2023) with the 2024 WHR,\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e which aggregates GWP data from 2021\u0026ndash;2023 (see Table S25a), we observe some consistency in certain countries (e.g., Germany scored 6.74 in our data and 6.72 in the WHR). Notable discrepancies are seen elsewhere however, with differences greater than 1.00 in several (e.g., Hong Kong scored 6.85 in our data, but 5.32 in the WHR). These discrepancies may result from parameters including sampling timeframes, participant selection, and modes of interviews (e.g., web vs. phone). Regarding data collection windows, for example, the GFS is broader (up to 18 months in Australia), while the GWP is generally only 3\u0026ndash;4 months. Seasonal patterns or local socio-political events could thus differentially affect GFS and GWP data. The 2023 GWP data for Israel, for example, was collected after the October 7th attacks (10.17.23\u0026ndash;12.2.23), whereas most GFS data was before (11.7.22\u0026ndash;11.23.23), resulting in less influence from the event. Even without such impactful events, within-country LE can fluctuate significantly. While the GWP is an annual snapshot, Gallup also collects LE data monthly in the US, with considerable variation sometimes within a single year.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e Another factor is participant selection. The GWP is a one-off survey, whereas the GFS is a five-year longitudinal study, and conceivably those willing to commit to a long-term survey may have higher LE than one-off participants. This explanation is supported by LE scores in the GFS being generally higher than those in the GWP (with a few exceptions). Using 2023 GWP data, the LE mean across all 142 GWP countries is 5.60, whereas the GWP mean across the 22 GFS countries is 5.91, while the GFS overall mean is 6.34. There is thus a substantial standardized mean difference between the GFS and GWP (Table S25b), calculated as either 0.29 (if including all GWP countries) or 0.17 (if only including GFS countries in the GWP). GFS countries may also have higher LE due to being on average more prosperous, since unlike the GWP the GFS has no low-income countries (only lower-middle-, upper-middle-, and high-income ones). For all these reasons, comparing \u003cem\u003eacross\u003c/em\u003e datasets can be problematic, meaning one cannot definitively determine \u003cem\u003ethe\u003c/em\u003e E-SWB of a given nation. But comparing \u003cem\u003ewithin\u003c/em\u003e a dataset is certainly meaningful, as we do here, firstly by comparing three E-SWB candidates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eContribution 1: Comparing Evaluative SWB Items\u003c/h2\u003e \u003cp\u003eOur first contribution is comparing three options for assessing E-SWB. We should reiterate that the suitability of H in that respect is ambiguous, especially since it is more commonly used/interpreted as an affective concept. However, not only can it also have evaluative aspects or connotations, and indeed Diener himself appeared to favour this usage,\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e our data here also support this interpretation. As discussed below regarding age, for instance, E-SWB is widely regarded as relatively U-shaped while positive affect tends to decline with age;\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e significantly, here H followed the same roughly U-shaped pattern as LE and LS. We thus tentatively regard H as a viable candidate for assessing E-SWB, including even \u003cem\u003eour\u003c/em\u003e item specifically, which asks how happy participants \u0026ldquo;usually feel\u0026rdquo; (as opposed to a more explicit cognitive framing like \u0026ldquo;are you happy with your life?\u0026rdquo;). Indeed, the qualifier \u0026ldquo;usually\u0026rdquo; arguably takes the item into the conceptual territory described by Diener and colleagues as evaluative (\u0026ldquo;a relatively stable feeling of happiness one has towards his or her life\u0026rdquo;), whereas more immediate qualifiers like \u0026ldquo;right now\u0026rdquo; or \u0026ldquo;yesterday\u0026rdquo; invite an affective interpretation (\u0026ldquo;the momentary feeling state of happiness\u0026rdquo;), and perhaps this very temporal framing is what shifts the balance between evaluative and affective understandings of H. Alternatively though, the phrase \u0026ldquo;usually feels\u0026rdquo; could conceivably prompt participants to think about their general genetically-influenced temperamental \u0026ldquo;baseline\u0026rdquo; of positive affect.\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e As such, we recognize some people may object to interpreting H as evaluative, in which case they could just use our data to decide between LE and LS.\u003c/p\u003e \u003cp\u003eWhether a two-way comparison (LE vs LS) or a three way one, one way to ascertain which best represents E-SWB, if understood as a global subjective evaluative judgement of all aspects of life, is to consider the empirical association with numerous specific aspects of flourishing more broadly. Although the conceptual relationship between SWB and flourishing is ambiguous, since it depends on the definitions used, and beyond the scope to resolve here, we can still use data on various flourishing assessments to potentially gauge the merits of specific single item assessments of our central E-SWB constructs, drawing on a separate analysis focused entirely on exploring this relationship in depth.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e The GFS questionnaire includes VanderWeele\u0026rsquo;s 12-item Secure Flourish Index, with two items per domain. Table S26 shows the correlations between our three items and all the flourishing items. Strikingly, both LS and H had much stronger average meta-analytic mean correlation coefficients with all other items (both .45) compared to LE (0.38). Likewise, when separated by domain (excluding the first domain, since this comprises LS and H themselves), LS and H had stronger correlations for all other main domains (with H marginally higher than LS on five, and vice versa on two, with one equal). Relatedly, LS-H were more tightly correlated (.69) than LE-LS or LE-H (both .57). With the additional sixth domain, LS also had the strongest correlations, closely followed this time by LE then H.\u003c/p\u003e \u003cp\u003eSimilar patterns are revealed by considering the relative performance of countries on the items. Given issues around cross-country comparisons noted above, we do not wish to make much of rankings \u003cem\u003eper se\u003c/em\u003e. But within-country analyses of how each place fares relative to itself are certainly informative. While the data are complex, the main pattern observed is that, as with the correlations above, LS and H often seem closely related (e.g., scores for countries often track together), while LE captures something different. There are exceptions, (e.g., Egypt), but this pattern holds across many countries. It also applies when grouping countries by region (Table S27), especially those categorized as relatively non-WEIRD \u0026ndash; even while we dislike this binary\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e and had difficulty grouping the countries, as footnoted in the Table \u0026ndash; which collectively have low average LE (5.81) and higher LS (7.02) and H (6.74), while relatively WEIRD nations have less obvious clustering (6.60/7.03/7.16). Moreover, these patterns are evident when our data is compared with GDP-per-capita (Tables S28a-b), with a .58 correlation with LE (corroborating prior research\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e) but near zero for LS (.05) and H (-.08). Notably though, despite LS having near-zero correlation with GDP-per-capita, LS still had the strongest correlation with personal financial/material security in the GFS, suggesting such security is only weakly related to GDP per se. Further nuance regarding economics is provided by correlations with Gini, where LE and LS were closer together (-.11 and \u0026minus;\u0026thinsp;.20), while H was somewhat distinct (.07). Given higher Gini scores mean greater inequality, this implies more equal places are liable to higher LE and LS, but perhaps counterintuitively slightly lower H, though these correlations are very weak. Overall then, both LS and H are more strongly related to the main flourishing domains, while LE fares better when it comes to financial/material stability (where it \u0026ldquo;catches up\u0026rdquo; with LS and H).\u003c/p\u003e \u003cp\u003eAn interesting comparison which illustrates these differences is Sweden versus Indonesia, in which the relative performance of the constructs is reversed. Sweden does very well on LE (2nd in our data and the latest WHR\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e), but is only mid-ranked for LS (9th ) and H (11th ). By contrast, although Indonesia excels on all three, it does particularly well on H and LS (1st for both, with similar scores of 8.04 and 7.99), with rather lower LE (5th, at 6.97). Given that LE seems to particularly tap into financial/material stability, and likewise strongly correlates with GDP-per-capita, it is relevant then that Sweden fares very well on the latter and Gini (Table S28a), ranked 3rd and 2nd respectively, and Indonesia less so (17th and 11th ). Further context to this comparison between Sweden and Indonesia, and by extension, LE versus LS/H, is provided by analysis of GWP data comparing 145 countries (for most items) on 38 metrics pertaining to wellbeing.\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e Overall, Sweden did better on variables relating to standards of living, like feeling stable and secure (ranked 1st, vs. Indonesia at 66), having money for shelter (1\u0026ndash;93), and satisfaction with standard of living (2\u0026ndash;53). Indonesia by contrast excelled on items pertaining to the other flourishing domains, including: physical and mental health (e.g., well-rested: Indonesia 4th, Sweden 64th ); character and virtue (e.g., volunteering: 1-109); and social relationships (e.g., opportunities to make friends: 5\u0026ndash;23). Given the correlations with the flourishing domains, it becomes understandable why Indonesia would fare so well on LS and H whereas Sweden does better on LE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eContribution 2: A Comprehensive Coverage of Factors\u003c/h2\u003e \u003cp\u003eOur second key contribution is comprehensive coverage of E-SWB-related factors. Although our findings overall mostly corroborate well-established trends, the next section highlights significant national variation, indicating the patterns are not universal but contingent on socio-cultural factors. But even just in terms of general trends, our analysis is useful in its expansive coverage of 15 factors, allowing comparison of their relative strength of association with E-SWB. Here we summarize, in turn, the four demographic, eight childhood, and three factors that pertain to both categories. Behind the headline summaries, each factor has a substantial literature which our findings support and/or refine in various ways. It is beyond our scope to consider this scholarship for all factors, so in each category we delve briefly into the factor with the greatest variation to illustrate the contribution of our findings.\u003c/p\u003e \u003cp\u003eOf the purely demographic factors, higher E-SWB is associated with: being retired (LE\u0026thinsp;=\u0026thinsp;6.53/LS\u0026thinsp;=\u0026thinsp;7.14/H\u0026thinsp;=\u0026thinsp;7.19), especially relative to those unemployed and job-seeking (5.59/5.97/6.30); being married (6.54/7.12/7.23), especially relative to those separated (5.89/6.31/6.56); attending religious services, especially at least once weekly (6.80/7.40/7.54) relative to never attending (6.12/6.55/6.71); and more education, especially over 16 years (6.64/7.00/7.16) compared to less than eight (6.20/6.84/6.95). Of the factor with the greatest variation, employment status, our findings align with an extensive literature showing the impact of working patterns on wellbeing, with numerous reviews showing employment generally has a positive impact on E-SWB.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e Notably though, the highest E-SWB was among retirees, who are technically \u0026ldquo;out of work,\u0026rdquo; which corroborates other studies finding a potential SWB boon to retirement, even if the association is complex.\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e There are of course interactions with age, given E-SWB generally increases into older age, as discussed below. Nevertheless, our data on employment suggests it is not necessarily lacking employment \u003cem\u003eper se\u003c/em\u003e that is detrimental to E-SWB, but needing work yet failing to find it, with E-SWB relatively unaffected if people are materially secure and not wanting/needing work.\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOf the childhood factors, higher E-SWB is associated with: \u0026ldquo;excellent\u0026rdquo; self-rated health (RRs\u0026thinsp;=\u0026thinsp;0.40/0.46/0.50) relative to \u0026ldquo;good,\u0026rdquo; especially in contrast to \u0026ldquo;poor\u0026rdquo; (-0.40/-0.46/-0.41); subjective financial status of \u0026ldquo;living comfortably\u0026rdquo; (0.29/0.25/0.23) relative those who \u0026ldquo;got by,\u0026rdquo; especially contrasted with those who \u0026ldquo;found it very difficult\u0026rdquo; (-0.42/-0.31/-0.31); not experiencing abuse (relative to those who did: -0.25/-0.39/-0.33); not feeling like an outsider (relative to those who did: -0.16/-0.29/-0.28); attending religious services, especially once weekly (0.22/0.21/0.27), relative to those who never did; a good relationship with one\u0026rsquo;s mother (0.17/0.21/0.25) and father (0.18/0.19/0.13); and parents being married, especially relative to parents being single and never married (-0.14/-0.13/-0.13). Briefly considering the predictor with the strongest association, self-rated health, one must note our findings rely on retrospective assessments, which are subject to recall bias (one analysis found nearly half their sample revised this during a 10-year period\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e). Moreover, while we adjusted associations for other potential childhood predictors, residual confounding may be present. However, we reported E-values\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e to assess the robustness of our findings to unmeasured confounding, and the high values (e.g., 1.73 for \u0026ldquo;excellent health\u0026rdquo; and H) suggest the observed associations \u003cem\u003eare\u003c/em\u003e relatively robust. Moreover, numerous longitudinal studies have assessed this association prospectively and show childhood health is associated with multiple aspects of adult life,\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e including SWB.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e Despite the limitations of retrospective assessments, the observed patterns regarding self-reported health therefore likely reflect true associations.\u003c/p\u003e \u003cp\u003eFinally, three factors could be interpreted either as childhood or demographic factors: age/birth cohort, gender, and immigration status. Of these, higher E-SWB is associated with: older age, though levels are fairly high at 18\u0026ndash;24 (6.35/6.78/6.96), falling to their lowest at 40\u0026ndash;49 (6.18/6.70/6.86), then peaking at 80+ (6.83/7.17/7.39); being female (6.38/6.89/7.03) rather than male (6.31/6.82/6.98), especially relative to the small percentage reporting their gender as \u0026ldquo;other\u0026rdquo; (5.98/5.91/5.95); and living in one\u0026rsquo;s country of birth (6.34/6.85/7.01) compared to those born elsewhere (6.36/6.81/6.87), although only marginally, and not for LE. Regarding the most prominent factor, age, our results broadly support the well-established view that E-SWB is somewhat \u0026ldquo;U-shaped,\u0026rdquo; declining into middle-age before rising again.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e The finding has generated lively debate though.\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e One must be careful of selection bias for example: a longitudinal analysis in the US suggested LS actually declined after 65 (due to health issues and widowing), and that apparent higher LS in older age is due to those with high LS being more likely to participate in surveys. The pattern is also subject to cultural variation,\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e as shown below. The SWB metric also matters: one analysis of GWP data found that although LE showed a slight U-shaped pattern, positive affect (measured dichotomously based on items on H, enjoyment, and smiling/laughing on the previous day, then averaged) decreased with age globally and across regions.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e In our data though H aligned with LE and LS in being higher in older age, suggesting our H item may not index positive affect but rather \u003cem\u003eE\u003c/em\u003e-SWB, as argued above. Moreover, rather than U-shaped, our trends are more \u0026ldquo;J-shaped,\u0026rdquo; with the 80\u0026thinsp;+\u0026thinsp;group having scores 0.48 (LE), 0.39 (LS), and 0.43 (H) points higher than those 18\u0026ndash;24. Even if selection bias applies to the over-80s or even over-70s, 18\u0026ndash;24 year olds also fared worse than those 60\u0026ndash;70, and even than those 50\u0026ndash;59 on LS and H (albeit very slightly). One wonders whether this is a cohort effect, echoing recent research suggesting the left-hand side of the U is flattening lately with younger people faring worse compared to people of similar age in earlier eras.\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eContribution 3: Granular Cross-National Details\u003c/h2\u003e \u003cp\u003eA third contribution is the study\u0026rsquo;s cross-national coverage. In itself this is not remarkable, with an extensive literature exploring international variation in SWB using datasets like the GWP. But its particular value here is in amplifying the first two contributions, permitting granular exploration of national variation both of the three outcomes and the 15 factors. In that regard, trends for the factors across the countries collectively, elucidated above, are not uniform but have striking national nuances and exceptions, detailed in the Supplementary tables, suggesting the trends are contingent on socio-cultural factors. Thus even if one is tempted to dismiss some of our overall findings as not especially surprising or as replicating past work, the cross-national variation revealed here is a valuable contribution.\u003c/p\u003e \u003cp\u003eThe data firstly highlight the liabilities of generalizing from WEIRD to non-WEIRD places. While it was beyond our scope to compare these categories across all variables (especially being wary of reifying this binary dichotomy\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e), a basic comparison of countries that could be classified as WEIRD or non-WEIRD (Table S27) shows the former score much higher on LE (6.80 vs. 5.81), though are closer for H (7.16\u0026ndash;6.74) and essentially equal for LS (7.03\u0026ndash;7.02). Moreover, part of our reservations with the WEIRD-non-WEIRD dichotomy is our data show considerable heterogeneity among countries commonly classed in either category, illustrated by countries usually described as non-WEIRD being ranked top (Indonesia) and bottom (Turkey) for both LS and H. This heterogeneity is also shown by the differences between continents (Table S27): Africa and Asia might both usually be classed as non-WEIRD, for example, but Asia fares better on LE (5.87 vs. 5.46) and LS (7.01\u0026ndash;6.54), while Africa does better on H (6.82\u0026ndash;6.69). Such internal diversity highlights the limits of these very labels, which obscure the complexity of international variation. While space limits prevent us exploring all factors, we revisit the three discussed above to illustrate the nuances.\u003c/p\u003e \u003cp\u003eRegarding employment status, retirees were not the most prosperous everywhere, faring less well than people employed for an employer in Argentina (on LE and H), Egypt (LE/LS), Germany (LE/LS), Hong Kong (LE/LS/H), Indonesia (LS/H), Israel (LE/LS/H), Kenya (LE), Poland (LS/H), South Africa (LS/H), Spain (LS/H), and Tanzania (LE/LS/H), and even worse than the unemployed in Kenya (H) and Tanzania (LE/H). Conversely, while the unemployed fared worst in most countries, exceptions included the employed in India (H), self-employed people in Egypt (LE/H) and Philippines (LE), homemakers in Tanzania (H) and South Africa (LE), and \u0026ldquo;other\u0026rdquo; in Argentina (LE/LS/H), Australia (LS), Hong Kong (LE/LS/H), Kenya (LE/H), Nigeria (H), Philippines (H), Poland (H), Tanzania (LE/LS), and UK (LE/LS/H). Other variation concerns the range of values. Comparing the unemployed, employed, and retirees, some countries had only narrow differences between these, especially Kenya (LE\u0026thinsp;=\u0026thinsp;0.33/LS\u0026thinsp;=\u0026thinsp;0.53/H\u0026thinsp;=\u0026thinsp;0.17), Egypt (0.18/0.37/0.26), and Philippines (0.08/0.49/0.31). By contrast, other countries had far larger ranges, notably Australia (1.82/2.47/1.90), Japan (2.25/2.38/2.07), Sweden (2.46/2.93/2.41), and the US (2.33/2.51/2.36), indicating stronger links between employment status and E-SWB.\u003c/p\u003e \u003cp\u003eThere is also potential interaction with age in the association between employment status (particularly retirement) and E-SWB. While older age is generally associated with E-SWB, noted above, regional variations make this relationship complex. No significant associations with age were observed for LE/LS/H in Mexico and Spain, and only for some constructs for Argentina, Indonesia, Nigeria, Philippines, Turkey, and South Africa. Further, some countries deviated from the J-shaped pattern, with linear decreases with age in Israel (LE/LS/H), Poland (LS/H), and Tanzania (LE). There was also variation in the range of scores within countries, further suggesting the relationship with age is conditioned by location. Differences were smallest in Mexico (0.18/0.56/0.22), and largest in Australia (1.58/2.03/1.94, with those 18\u0026ndash;24 scoring lowest and 80\u0026thinsp;+\u0026thinsp;the highest). Returning to the question of retirement, these findings highlight the complexity of the employment-SWB relationship, aligning with research indicating the associations are inconsistent,\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and that factors such as economic resources\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e and social relationships\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e affect retirees\u0026rsquo; SWB.\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe childhood predictor with the strongest association with E-SWB, self-rated health, also showed considerable variation. First, relative to the difference in outcomes comparing poor and excellent childhood health in the pooled meta-analysis (0.80/0.88/0.91), there was real variation in range, with Egypt the narrowest (0.37/0.14/0.25) and Hong Kong the largest (2.81/2.64/3.11), indicating greater association between childhood health and adult E-SWB in the latter. Further research is needed to explain such regional variation, but it may involve factors like economic conditions, healthcare provisions, and levels of inequality. Childhood health might plausibly have smaller impact on adult SWB in wealthier countries because they can invest more in healthcare to mitigate effects of poor childhood health.\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e GDP considerations alone though may be insufficient to explain regional variation, given that childhood health generates more variation in Hong Kong than Egypt (since the former is wealthier). It is likely therefore that factors like socio-economic equality also contribute, and greater inequality may amplify the negative effects of poor childhood health, especially in countries without good universal healthcare coverage.\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e All such dynamics warrant further exploration to better understand the interplay between childhood health and adult SWB.\u003c/p\u003e \u003cp\u003eThere were also intriguing patterns that are harder to explain and require further investigation too, above all that in some countries the effect estimates seemed \u0026ldquo;out of order.\u0026rdquo; Overall, relative to people with \u0026ldquo;good\u0026rdquo; childhood health, people with worse health had lower E-SWB while people with very good or excellent health had higher. Individually though only 13 countries conformed to this linear rising trend (Argentina, Australia, Brazil, Hong Kong, Indonesia, Japan, Kenya, Nigeria, Philippines, Sweden, Tanzania, UK, US). In the remaining countries, this pattern was subverted in various ways. For example, people with poor health fared better than those with good health in Germany (LE/LS/H), Israel (LE/LS/H), Mexico (LE), Poland (LE/H), and Spain (LS/H). The reasons behind these findings are unclear. One possible explanation is poor childhood health may encourage individuals to develop qualities that might contribute to E-SWB in adulthood, including psychological (e.g., resilience)\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e and social resources (e.g., supportive childhood friends).\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e However, why this association is found in certain countries and not others remains an open question.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has various limitations. First, as discussed above, caution is needed interpreting cross-national differences, which may be influenced by factors including cultural/linguistic variation, local/national/international events, modes of data collection, and seasonal differences from varying data collection windows. Second, while we constructed a synthetic longitudinal study by retrospectively assessing childhood experiences, its cross-sectional design partially limits definitive conclusions about causality. Such synthetic longitudinal designs may also be subject to recall bias. However, for recall bias to completely explain away the observed associations would require the effect of current LE/LS/H on biasing retrospective assessments of childhood predictors to be at least as strong as the observed associations themselves,\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e and some were quite substantial. Moreover, although we adjusted associations for these childhood predictors for other potential childhood predictors, residual confounding may still be present. We reported E-values for the childhood predictors analysis to assess the robustness of findings to unmeasured confounding for each predictor and some of these associations were at least moderately robust. Moreover, some predictors may lie on the causal pathway linking others to adult E-SWB. Adjusting for such mediators may lead to conservative estimates of the associations. Penultimately, LE/LS/H were assessed using one-item measures, which may not fully capture their complexity. Future studies could use multi-item measures for higher validity and reliability. There are always trade-offs though in survey research between depth and breadth. Using multi-item scales limits the number of constructs assessed, and the GFS team decided that, overall, the benefits of including more constructs outweighed the limitations of single-item measures.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFinally, the three outcomes are situated close together in the annual GFS questionnaire as the first (LE), third (H), and fourth (LS) items (with the second item being anticipated LE in five years). Such proximity potentially inflates the differences between the items, given that asking similar questions in sequence can make participants think they are supposed to give different answers.\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e That said, there was tighter correlation between the consecutive items (H-LS: .69) than those separated by an item (LE-H: .57), which perhaps argues against that critique. But even if the critique has merit, the answers are still informative, even if one must interpret them with caution, since if someone \u003cem\u003edoes\u003c/em\u003e feel compelled to give a different answer, the \u003cem\u003edirection\u003c/em\u003e they do so is meaningful. To return to Indonesia versus Sweden, for instance, having first given their LE score, people in the former tend to give higher scores for LS and H, whereas people in the latter give lower scores. Even if people in Indonesia may not have LE levels \u0026ldquo;1\u0026rdquo; lower than LS/H, and people in Sweden \u0026ldquo;1\u0026rdquo; higher \u0026ndash; whatever increment \u0026ldquo;1\u0026rdquo; might mean \u0026ndash; it seems reasonable to conclude people in Indonesia do genuinely have lower LE than LS/H, and vice versa for Sweden.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe value of measuring SWB is increasingly recognized, including by governmental organizations that recommend its utility in guiding policy. The literature is constrained by various issues however, to which this study makes three key interrelated contributions, as summarized here through the lens of potential practical implications.\u003c/p\u003e \u003cp\u003eThe first contribution is directly comparing three candidates for indexing E-SWB. Most striking were their associations with flourishing, with LS and H having stronger correlations across all domains of VanderWeele’s framework (excluding the first domain, as this is constituted by LS and H themselves), while LE only attained relative parity on the additional domain of financial/material stability. Given that stakeholders (e.g., policy makers) may look to articles like this for guidance on which measures to use and how to interpret them, here are some tentative recommendations. Overall, if recommending just one item, we suggest LS, given its strong correlations with the flourishing domains. This is preferred to H, despite the latter having similarly strong correlations, mainly because of the ambiguity still attached to H. Although our data suggests it can indeed function as an evaluative item (per Diener’s perspective), it does nevertheless have a double meaning, and some respondents may still interpret it affective terms, so we cannot be \u003cem\u003ecertain\u003c/em\u003e it indexes E-SWB (despite seeming to here). Then, if space for another item, we recommend LE, mainly because H tracks LS closely, whereas LE seemingly captures different experiential terrain, particularly around financial/material stability, so is a useful complement/counterpart to LS.\u003c/p\u003e \u003cp\u003eA second contribution is expansive coverage of 15 socio-demographic factors, and while all were significant predictors of E-SWB, considering them together shows their relative strength of association. Regarding practical implications, our analysis highlights people who might be particularly at risk of low E-SWB, indicating where policy/intervention efforts to improve SWB may most effectively be targeted. Demographically our data suggests E-SWB is liable to be lowest in people who are: unemployed and looking for a job; separated; not attendees of religious services; of less than eight years education; 40–49; male (or especially “other” gender); and an immigrant. It would also likely be lower for people whose childhood was characterized by: poor health; very difficult financial situations; abuse; feeling like an outsider; never attending religious services; poor relationships with one’s mother and/or father; and parents who were single and never married. Moreover, while each factor itself is meaningful, from an intersectional perspective,\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e the more categories someone belongs to, the lower their E-SWB is likely to be. The findings thus suggest that targeted interventions, such as employment support, could enhance E-SWB, especially for people facing multiple vulnerabilities. While not all factors are amenable to intervention/policy, focusing on those that are may provide meaningful improvements, particularly for people most in need. Most analyses of SWB-related factors show steeper improvement trajectories for those worse off, as for example with income satiation,\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e where the impact of income gains on SWB diminishes the richer people are. While one would want to improve SWB for all, it may be more effective, and more morally worthy, to prioritize helping those with the lowest SWB. This must be done sensitively though, avoiding both stigmatizing populations with the lowest SWB and making everyone else feel they are being treated unfairly.\u003c/p\u003e \u003cp\u003eFinally, the third contribution is our cross-national coverage, which although not novel itself accentuates the first two contributions, permitting granular exploration of national variation both of the three outcomes and the 15 factors, which is relevant to the conclusions above. Regarding implications of our analysis of the factors, for instance, policy interventions require accounting for local dynamics (e.g., which people particularly need help). In that regard, while it is customary for papers to plead for more research, this concern is especially justified regarding the cross-cultural implications of this study. There is particular need, for instance, for utilizing other methodologies, especially qualitative techniques, to delve into the first and third issues (the meaning of the items and cross-cultural variation in these meanings). Above we cited for example analysis of Cantril’s ladder which suggests the metaphor encourages people to think of power, achievement, and success.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e That study was only in English and on just one measure though. It would therefore be instructive to conduct comparable analyses in other languages and on other E-SWB constructs, thereby helping us further understand this vitally important topic.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis Methods section has been adapted from VanderWeele and colleagues,\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e with further detail available elsewhere.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e The methods of the current article are intended to align with these works to provide methodological consistency for comparability of results across papers. The GFS involves a 109-item questionnaire\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e involving: (a) questions covering the six domains of VanderWeele’s flourishing framework,\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e which in addition to SWB include health, meaning, character, relationships, and financial stability; and (b) other demographic, social, economic, political, religious, personality, childhood, community, health, and wellbeing variables. Among the (b) items, questions pertaining to 15 childhood and demographic factors have been selected to be analysed across an extensive series of papers, each focused on different flourishing outcomes in the GFS. The present paper focuses on three SWB outcomes specifically, reporting on demographic variation\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e and childhood predictor\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e analyses of LE, LS, and H, data in Wave 1 of the GFS, allowing for comparison of results reported elsewhere (see VanderWeele and colleagues\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e for an example of comparing across papers).\u003c/p\u003e\u003ch2\u003eData\u003c/h2\u003e\u003cp\u003eThe GFS is a study of 202,898 participants (in this first wave) from 22 geographically and culturally diverse countries, with nationally representative sampling within each country. Wave 1 included the following countries and territories: Argentina, Australia, Brazil, Egypt, Germany, Hong Kong, India, Indonesia, Israel, Japan, Kenya, Mexico, Nigeria, the Philippines, Poland, South Africa, Spain, Sweden, Tanzania, Turkey, UK, and US. The countries were selected to: (a) maximize coverage of the world's population, (b) ensure geographic, cultural, and religious diversity, and (c) prioritize feasibility and existing data collection infrastructure. Data collection was carried out by Gallup. Data for Wave 1 were collected principally during 2023, with some countries beginning data collection in 2022, and exact dates varying by country,\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e as detailed in Supplementary Table S24. Four additional waves of panel data on these participants will be collected annually from 2024–2027. The precise sampling design to ensure nationally representative samples varied by country and further details are available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/k2s7u)\u003c/span\u003e\u003cspan address=\"https://osf.io/k2s7u)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003csup\u003e87\u003c/sup\u003e The data are publicly available through the Center for Open Science (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cos.io/gfs\u003c/span\u003e\u003cspan address=\"https://www.cos.io/gfs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). During the translation process, Gallup adhered to the TRAPD model (translation, review, adjudication, pretesting, and documentation) for cross-cultural survey research; for additional details, see the GFS Translation document.\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003ch2\u003eOutcome Variables\u003c/h2\u003e\u003cp\u003eThe GFS includes three separate items on our main topics: (1) LE, assessed with Cantril’s ladder – “Please imagine a ladder with steps numbered from zero at the bottom to ten at the top. The top of the ladder represents the best possible life for you, and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” [0 = Worst possible, to 10 = Best possible]; (2) LS – “Overall, how satisfied are you with life as a whole these days?” [from 0 = Not satisfied with your life at all, to 10 = Completely satisfied with your life]; and (3) H – “In general, how happy or unhappy do you usually feel?” [from 0 = extremely unhappy to 10 = extremely happy]. Our report investigates mean and variability differences in these outcomes across demographic characteristics and how retrospective recall of childhood experiences predicts these items. Wave 1 of the GFS involves participants first completing an intake questionnaire featuring 43 items (mainly gathering demographic information), followed by second questionnaire, to also be completed annually, involving a further 66 items (covering all different aspects of flourishing). The three concepts in the present paper are the first (LE), third (H), and fourth (LS) items of the second questionnaire.\u003c/p\u003e\u003cp\u003e326\u003c/p\u003e\u003cp\u003e \u003cem\u003eVariables for Demographic Variation Analyses.\u003c/em\u003e \u003c/p\u003e\u003cp\u003eThe demographic factors are standard factors one can find in most empirical surveys of this nature. Continuous age was classified as 18–24, 25–29, 30–39, 40–49, 50–59, 60–69, 70–79, and 80 or older. Gender was assessed as male, female, or other. Marital status was assessed as single/never married, married, separated, divorced, widowed, and domestic partner. Employment was assessed as employed, self-employed, retired, student, homemaker, unemployed and searching, and other. Education was assessed as up to 8 years, 9–15 years, and 16 + years. Religious service attendance was assessed as more than once/week, once/week, one-to-three times/month, a few times/year, or never. Immigration status was dichotomously assessed with: “Were you born in this country, or not?” Religious tradition/affiliation with categories of Christianity, Islam, Hinduism, Buddhism, Judaism, Sikhism, Baha’i, Jainism, Shinto, Taoism, Confucianism, Primal/Animist/Folk religion, Spiritism, African-Derived, some other religion, or no religion/atheist/agnostic; precise response categories varied by country.\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e Racial/ethnic identity was assessed in some, but not all, countries, with response categories varying by country. For additional details on the assessments see the COS GFS codebook\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e or Crabtree et al.\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003ch2\u003eVariables for Childhood Predictor Analyses\u003c/h2\u003e\u003cp\u003eThe childhood predictor questions were selected based on prior literature concerning longitudinal associations of childhood factors with subsequent health and well-being. These factors include factors that past literature has indicated have beneficial associations with subsequent well-being (e.g. good relationship with parents, religious service attendance, financial security) along with questions that cover the two major domains of adverse childhood experiences: threat (the abuse questions) and neglect (the feeling like an outsider question). Relationship with mother during childhood was assessed with the question: “Please think about your relationship with your mother when you were growing up. In general, would you say that relationship was very good, somewhat good, somewhat bad, or very bad?” Responses were dichotomized to very/somewhat good versus very/somewhat bad. An analogous variable was used for relationship with father. “Does not apply” was treated as a dichotomous control variable for respondents who did not have a mother or father due to death or absence. Parental marital status during childhood was assessed with responses of married, divorced, never married, and one or both had died. Financial status was measured with: “Which one of these phrases comes closest to your own feelings about your family's household income when you were growing up, such as when you were around 12 years old?” Responses were lived comfortably, got by, found it difficult, and found it very difficult. Abuse was assessed with yes/no responses to “Were you ever physically or sexually abused when you were growing up?” Participants were separately asked: “When you were growing up, did you feel like an outsider in your family?” Childhood health was assessed by: “In general, how was your health when you were growing up? Was it excellent, very good, good, fair, or poor?” Immigration status was assessed with: “Were you born in this country, or not?” Religious service attendance during childhood was assessed with: “How often did you attend religious services or worship at a temple, mosque, shrine, church, or other religious building when you were around 12 years old?” with responses of at least once/week, one-to-three times/month, less than once/month, or never. Childhood religious tradition/affiliation had response categories of Christianity, Islam, Hinduism, Buddhism, Judaism, Sikhism, Baha’i, Jainism, Shinto, Taoism, Confucianism, Primal/Animist/Folk religion, Spiritism, African-Derived, some other religion, or no religion/atheist/agnostic; response categories varied by country.\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e When the category no religion/atheist/agnostic had more than 5% of the within country sample size, this was used as the reference category; otherwise, the most prominent religious group was used. Additionally, all religious categories endorsed by less than 3% of the within country sample size were collapsed into a single religious category. For inclusion in the childhood predictor regression analyses, race/ethnic identity was collapsed as a binary variable of whether an individual was in the most prominent group versus a minority group (race plurality).\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics for the full sample, weighted to be nationally representative within each country, were estimated for each of the demographic and childhood experience variables. Nationally representative means for LE, LS, and H were estimated separately for each country and ordered from highest to lowest along with 95% confidence intervals, standard deviations, and Gini coefficients. Variation in means in LE, LS, and H scores across categories of demographic variables (see \u003cem\u003eVariables for Demographic Variation Analyses\u003c/em\u003e section) were estimated.\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e A weighted linear regression model with complex survey adjusted standard errors was fit by regressing each outcome on all the aforementioned childhood predictor variables (see \u003cem\u003eVariables for Childhood Predictor Analyses\u003c/em\u003e section) simultaneously.\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e All analyses were initially conducted by country (see Supplementary Tables). Primary results pooled across country-specific estimates using a random effects meta-analyses\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e along with 95% confidence intervals, standard errors, upper and lower limits of a prediction interval across countries, estimate proportions of effects across countries with effect sizes larger than 0.1 and smaller than − 0.1, heterogeneity (τ), and I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e where appropriate for a given outcome/analysis for evidence concerning variation within a particular estimate across countries.\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e Discussion of the rationale underpinning the choice of a meta-analytic approach (over multilevel modelling) can be found in Padgett et al.\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e–\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e Forest plots of estimates are available in the Supplementary files. All meta-analyses were conducted in R\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e using the metafor package.\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e Within each country, a global test of variation (or association) of outcome across levels of each particular demographic variable was conducted, and a pooled p-value\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e across countries reported concerning evidence for variation within any country. Bonferroni corrected p-value thresholds are provided based on the number of demographic variables or number of childhood predictors.\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e,\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e For each childhood predictor, we calculated E-values to evaluate the sensitivity of results to unmeasured confounding. An E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association.\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e Religious affiliation and racial/ethnic identity were not included in the meta-analyses because these variables were not measured consistently across all 22 countries. As a supplementary analysis, population weighted meta-analyses were also conducted. All analyses were pre-registered with Center for Open Science prior to data access with separate registrations by construct (albeit with LE and LS combined) and focal analysis: LE and LS (childhood: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/8nvw6\u003c/span\u003e\u003cspan address=\"https://osf.io/8nvw6\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; demographic: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/b8z59\u003c/span\u003e\u003cspan address=\"https://osf.io/b8z59\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); and H (childhood: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/shnp6\u003c/span\u003e\u003cspan address=\"https://osf.io/shnp6\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; demographics: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/46vr3\u003c/span\u003e\u003cspan address=\"https://osf.io/46vr3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). An unplanned analysis to estimate the intraclass correlation based on the results from ordered means analysis was also conducted. The ICC was approximated from the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e by estimating (a) the sample variance in the means and (b) the average within country variance, then the ICC was approximated by ICC = a/(a + b). The ICC is interpreted as the proportion of the variable in an outcome that is explained by mean differences on that outcome on a grouping variable.\u003c/p\u003e\u003ch2\u003eMissing Data and Multiple Imputation\u003c/h2\u003e\u003cp\u003eAll missing variables are imputed using multivariate imputation by chained equations, with five imputed datasets generated.\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e The imputation model incorporated the criterion/outcome variable, all demographic or childhood experience characteristics, including race/ethnicity and religious affiliation when available, and sampling weights. The sampling weights were included as a variable in the imputation models to allow for specific variable missingness to be related to probability of study inclusion. The Gallup-provided sampling weights incorporate nonresponse and poststratification adjustments which helps to account for missingness being related to nonresponse of specific subgroups. To account for variations in the assessment of certain variables across countries (e.g., race/ethnicity and religious affiliation), we conducted the imputation process separately for each country. The within-country imputation approach ensured that the imputation model accurately reflects country-specific contexts and assessment methods. The percent of missing data for all variables is reported in our Supplementary Tables by country.\u003c/p\u003e\u003cp\u003e \u003cem\u003eSupplemental Post-Hoc Analyses.\u003c/em\u003e \u003c/p\u003e\u003cp\u003eComplete-case analyses were conducted to replicate all primary analyses (country-specific and meta-analytic); these are reported on in our online supplement, first in terms of providing versions of the main tables based on complete case analysis (Tables S1a-g), and then for each country individually (Tables e-g for each country). The meta-analytic pooled correlations among LE, LS, and H and these variables with indicators of flourishing were computed. The means of each outcome were additionally computed by world region (WEIRD, Non-WEIRD, and by continent).\u003c/p\u003e\u003ch2\u003eAccounting for Complex Sampling Design\u003c/h2\u003e\u003cp\u003eThe GFS used different sampling schemes across countries based on availability of existing panels and recruitment needs.\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e All analyses accounted for the complex survey design components by including weights, primary sampling units, and strata. Additional methodological detail, including accounting for the complex sampling design is provided elsewhere.\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cem\u003eData Availability\u0026nbsp;\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eData that support the findings of this article are openly available on the Open Science Framework (Wave 1 non-sensitive Global data: https://osf.io/sm4cd/), and are available from February 2024 - March 2026 via preregistration and publicly from then onwards. Subsequent waves of the GFS will similarly be made available. Please see https://www.cos.io/gfs-access-data for more information about data access.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003eCode Availability\u0026nbsp;\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eCode in multiple software is openly available in an online repository\u003csup\u003e84\u003c/sup\u003e for the demographic variation and childhood predictor analyses (https://doi.org/10.17605/osf.io/vbype).\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eT.J.V. and B.R.J. led the overall study of which this paper reports a subset of results. H.K. and T.L. conceptualized, designed, and planned the paper, in collaboration with all authors. T.L. managed the development of the paper and the coordination of author input. R.N.P. led the analyses and prepared all the tables and figures. T.L. and H.K. wrote the first draft and subsequent revisions. All authors provided feedback of the various drafts of the manuscript, helped edit and refine the text, and reviewed the final version.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003eCompeting Interests Statement\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eTyler J. VanderWeele reports partial ownership and licensing fees from Gloo, Inc. The remaining authors have no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStiglitz, J. E. \u003cem\u003eMeasuring What Counts: The Global Movement for Well-Being\u003c/em\u003e. (The New Press, 2019).\u003c/li\u003e\n\u003cli\u003eStiglitz, J. E., Fitoussi, J. P. \u0026amp; Durand, M. \u003cem\u003eBeyond GDP: Measuring What Counts for Economic and Social Performance\u003c/em\u003e. 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R. \u003cem\u003eet al.\u003c/em\u003e The Global Flourishing Study. \u003cem\u003ePreprint available at: https://doi.org/10.17605/OSF.IO/3JTZ8\u003c/em\u003e (2024).\u003c/li\u003e\n\u003cli\u003eMarkham, L. \u003cem\u003eet al.\u003c/em\u003e Global Flourishing Study: Wave 1 Codebook. \u003cem\u003ePreprint available at: https://osf.io/7uj6y/\u003c/em\u003e (2024).\u003c/li\u003e\n\u003cli\u003eBorenstein, M., Hedges, L. V., Higgins, J. P. T. \u0026amp; Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. \u003cem\u003eRes Synth Methods\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 97\u0026ndash;111 (2010).\u003c/li\u003e\n\u003cli\u003eHunter, J. E. \u0026amp; Schmidt, F. L. Fixed effects vs. random effects meta‐analysis models: Implications for cumulative research knowledge. \u003cem\u003eInternational Journal of Selection and Assessment\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 275\u0026ndash;292 (2000).\u003c/li\u003e\n\u003cli\u003eMathur, M. B. \u0026amp; VanderWeele, T. J. Robust metrics and sensitivity analyses for meta-analyses of heterogeneous effects. \u003cem\u003eEpidemiology\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 356\u0026ndash;358 (2020).\u003c/li\u003e\n\u003cli\u003eR Core Team. \u003cem\u003eR: A Language and Environment for Statistical Computing\u003c/em\u003e. (R Foundation for Statistical Computing: https://www.R-project.org/\u0026gt;, 2024).\u003c/li\u003e\n\u003cli\u003eViechtbauer, W. Conducting meta-analyses in R with the metafor package. \u003cem\u003eJ Stat Softw\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, (2010).\u003c/li\u003e\n\u003cli\u003eWilson, D. J. The harmonic mean p-value for combining dependent tests. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 1195\u0026ndash;1200 (2019).\u003c/li\u003e\n\u003cli\u003eAbdi, H. Bonferroni and \u0026Scaron;id\u0026aacute;k corrections for multiple comparisons. in \u003cem\u003eEncyclopedia of Measurement and Statistics\u003c/em\u003e (ed. Salkind, N.) (Sage, 2007).\u003c/li\u003e\n\u003cli\u003eVanderWeele, T. J. \u0026amp; Mathur, M. B. Some desirable properties of the Bonferroni correction: Is the Bonferroni correction really so bad? \u003cem\u003eAm J Epidemiol\u003c/em\u003e \u003cstrong\u003e188\u003c/strong\u003e, 617\u0026ndash;618 (2019).\u003c/li\u003e\n\u003cli\u003eSterne, J. A. C. \u003cem\u003eet al.\u003c/em\u003e Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. \u003cem\u003eBMJ\u003c/em\u003e \u003cstrong\u003e338\u003c/strong\u003e, b2393\u0026ndash;b2393 (2009).\u003c/li\u003e\n\u003cli\u003evan Buuren, S. \u003cem\u003eFlexible Imputation of Missing Data\u003c/em\u003e. (https://stefvanbuuren.name/fimd/, 2023).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"572\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 1. Demographic characteristics of the GFS sample (wave 1)\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u003cstrong\u003eN=202,898\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 115.562%;\"\u003e\u003cstrong\u003eDemographic Characteristics\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eAge group\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e18-24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e27,007 (13%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e20,700 (10%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e40,256 (20%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e34,464 (17%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e31,793 (16%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e27,763 (14%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e16,776 (8.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e80 or older\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e4,119 (2.0%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e20 (\u0026lt;0.1%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e98,411 (49%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e103,488 (51%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e602 (0.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e397 (0.2%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eCurrent Marital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eMarried\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e107,354 (53%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSeparated\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e5,195 (2.6%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e11,654 (5.7%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eWidowed\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e9,823 (4.8%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e52,115 (26%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eDomestic Partner\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e14,931 (7.4%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e1,826 (0.9%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eEmployment status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eEmployed for an employer\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e78,815 (39%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSelf-employed\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e36,362 (18%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eRetired\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e29,303 (14%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eStudent\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e10,726 (5.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eHomemaker\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e21,677 (11%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eUnemployed and looking for a job\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e16,790 (8.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eNone of these/Other\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e8,431 (4.2%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e793 (0.4%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eCurrent Religious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eMore than 1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e26,537 (13%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e39,157 (19%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e19,749 (9.7%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eA few times a year\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e41,436 (20%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eNever\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e75,297 (37%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e722 (0.4%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eEducation\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eUp to 8 years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e45,078 (22%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e9-15 years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e115,097 (57%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e16+ years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e42,578 (21%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e146 (\u0026lt;0.1%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eBorn in this country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e190,998 (94%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e9,791 (4.8%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e2,110 (1.0%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 115.562%;\"\u003e\u003cstrong\u003eChildhood Experiences and Characteristics\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eRelationship with mother growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eVery good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e127,836 (63%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSomewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e52,439 (26%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSomewhat bad\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e11,060 (5.5%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eVery bad\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e4,642 (2.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eDoes not apply\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e5,965 (2.9%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e956 (0.5%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eRelationship with father growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eVery good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e107,742 (53%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSomewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e55,714 (27%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSomewhat bad\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e15,807 (7.8%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eVery bad\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e8,278 (4.1%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eDoes not apply\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e13,985 (6.9%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e1,372 (0.7%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eParent marital status at age 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eParents married\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e152,001 (75%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e17,726 (8.7%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eParents were never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e15,534 (7.7%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eOne or both parents had died\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e7,794 (3.8%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e9,843 (4.9%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eSubjective financial status of family growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eLived comfortably\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e70,861 (35%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eGot by\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e82,905 (41%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eFound it difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e35,852 (18%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eFound it very difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e12,606 (6.2%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e674 (0.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 59.0967%;\"\u003eAbuse\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e29,139 (14%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e167,279 (82%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e6,479 (3.2%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eOutsider growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e28,732 (14%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e170,577 (84%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e3,589 (1.8%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eSelf-rated health growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eExcellent\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e67,121 (33%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eVery good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e63,086 (31%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eGood\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e47,378 (23%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eFair\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e19,877 (9.8%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003ePoor\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e4,906 (2.4%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e530 (0.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 59.0967%;\"\u003eAge 12 religious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eAt least 1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e83,237 (41%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e33,308 (16%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e\u0026lt;1/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e36,928 (18%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eNever\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e47,445 (23%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003e(Missing)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e1,980 (1.0%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 115.562%;\"\u003eCountry\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eArgentina\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e6,724 (3.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eAustralia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e3,844 (1.9%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eBrazil\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e13,204 (6.5%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e4,729 (2.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eGermany\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e9,506 (4.7%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eHong Kong (S.A.R. of China)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e3,012 (1.5%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eIndia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e12,765 (6.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eIndonesia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e6,992 (3.4%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eIsrael\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e3,669 (1.8%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eJapan\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e20,543 (10%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eKenya\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e11,389 (5.6%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eMexico\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e5,776 (2.8%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eNigeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e6,827 (3.4%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003ePhilippines\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e5,292 (2.6%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003ePoland\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e10,389 (5.1%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSouth Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e2,651 (1.3%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSpain\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e6,290 (3.1%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eSweden\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e15,068 (7.4%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eTanzania\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e9,075 (4.5%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eTurkey\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e1,473 (0.7%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eUnited Kingdom\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e5,368 (2.6%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.6787%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.6203%;\"\u003eUnited States\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.2624%;\"\u003e38,312 (19%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 115.562%;\"\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eS.A.R. = Special Administrative Region.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"18\" valign=\"top\" style=\"width: 624px;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 2. Countries ordered by means on LE/LS/H\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 177px;\"\u003eLife Evaluation\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 195px;\"\u003eLife Satisfaction\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 195px;\"\u003eHappiness\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003eRank\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eCountry\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003eMean\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003eSD\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003eGini\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eCountry\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003eMean\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003eSD\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003eGini\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eCountry\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003eMean\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003eSD\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003eGini\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e1. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eIsrael\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e7.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(7.18, 7.48)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e1.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eIndonesia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.91, 8.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eIndonesia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e8.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.97, 8.11)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e2.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eSweden\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e7.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(7.16, 7.23)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e1.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eMexico\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.78, 7.92)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eMexico\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.72, 7.85)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e3.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003ePoland\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e7.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(7.04, 7.21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e1.62\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.58, 7.79)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eIsrael\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.64, 7.88)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e1.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e4.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eMexico\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e7.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(7.03, 7.17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003ePoland\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.43, 7.62)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e1.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003ePoland\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.46, 7.65)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e1.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e5.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eIndonesia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.87, 7.06)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003ePhilippines\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.42, 7.59)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eArgentina\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.29, 7.44)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e6.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eUnited States\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.89, 6.99)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e1.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eIsrael\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.31, 7.64)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eBrazil\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.28, 7.39)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e7.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eHong Kong\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.75, 6.94)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eArgentina\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.13, 7.30)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.38\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003ePhilippines\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.24, 7.41)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e8.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eAustralia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.71, 6.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e1.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eBrazil\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.10, 7.21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.48\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eKenya\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.19, 7.36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.22\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e9.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eArgentina\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.67, 6.83)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eSweden\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.05, 7.13)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eHong Kong\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(7.07, 7.26)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e10.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eGermany\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.69, 6.78)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e1.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eHong Kong\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.93, 7.13)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eNigeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.95, 7.17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e11.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eSpain\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.60, 6.73)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e1.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eIndia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.92, 7.09)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e3.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eSweden\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.99, 7.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e1.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e12.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eBrazil\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.54, 6.64)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eGermany\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.88, 6.98)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eUnited States\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e7.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.96, 7.06)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e1.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e13.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eUnited Kingdom\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.48, 6.63)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eUnited States\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.80, 6.91)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eSouth Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.80, 7.11)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e14.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003ePhilippines\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.38\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(6.30, 6.46)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eSpain\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.72, 6.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eSpain\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.86, 6.99)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e15.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eSouth Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e6.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(5.92, 6.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eAustralia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.63, 6.81)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eGermany\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.85, 6.95)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e1.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e16.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eJapan\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e5.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(5.86, 5.93)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eNigeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.38, 6.64)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eAustralia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.88\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.81, 6.96)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e1.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e17.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eNigeria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e5.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(5.60, 5.85)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eUnited Kingdom\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.40, 6.58)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eUnited Kingdom\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.63, 6.79)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e18.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eIndia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e5.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(5.53, 5.72)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e3.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eSouth Africa\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.20, 6.52)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eTanzania\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.46, 6.70)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e3.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e19.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eKenya\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e5.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(5.41, 5.61)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e3.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eJapan\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(5.99, 6.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eIndia\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.48\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.39, 6.57)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e3.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.30\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e20.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eTurkey\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e5.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(5.02, 5.35)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eKenya\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e5.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(5.87, 6.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e3.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eJapan\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.18, 6.25)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e21.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e5.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(4.93, 5.15)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e2.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eTanzania\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e5.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(5.17, 5.50)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e3.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eEgypt\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e6.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(6.05, 6.31)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e22.\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003eTanzania\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e4.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e(4.24, 4.56)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e3.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e0.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eTurkey\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e5.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(4.98, 5.40)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e3.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003eTurkey\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e5.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e(5.35, 5.73)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e2.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e0.30\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"bottom\" style=\"width: 592px;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 3. LE meta-analysis of means by demographic category\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 269px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 100px;\"\u003ePrediction Interval\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 223px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 134px;\"\u003eVariable \u0026nbsp; Category\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003eEst\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003eSE\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003eLL\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003eUL\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003eHeterogeneity (\u0026tau;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003eGlobal p-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 134px;\"\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 15px;\"\u003e..\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e18-24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.07,6.62)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e98.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.00,6.62)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e98.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.86,6.57)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.81,6.54)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.96,6.68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e3.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.04,6.82)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e3.88\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.09,6.94)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e3.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.88\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e80 or older\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.42,7.23)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e92.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 134px;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.95,6.67)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.38\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.07,6.69)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e5.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.70,6.26)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e5.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e6.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e8.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 134px;\"\u003eMarital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eMarried\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.15,6.93)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eSeparated\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e5.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.54,6.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e3.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e6.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e93.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e5.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.51,6.36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e3.62\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e98.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eWidowed\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.91,6.75)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e3.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e97.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eDomestic partner\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.04,6.65)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e97.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.88,6.42)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.44\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 134px;\"\u003eEmployment status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eEmployed for an employer\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.07,6.74)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eSelf-employed\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.02,6.78)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eRetired\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.11,6.94)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e3.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eStudent\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.16,6.66)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e5.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e95.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eHomemaker\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.95,6.57)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e97.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eUnemployed and looking for a job\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e5.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.31,5.88)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e6.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e95.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eNone of these/other\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e5.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.58,6.31)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e3.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e95.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 134px;\"\u003eEducation\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eUp to 8 years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.83,6.56)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e98.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003e9-15 years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.04,6.63)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.70\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003e16+ years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.36,6.93)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e5.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 165px;\"\u003eReligious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003e\u0026gt;1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.33,7.27)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e9.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e1.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003e1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.22,6.97)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.06,6.77)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e98.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eA few times a year\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.99,6.67)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eNever\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(5.78,6.46)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 134px;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eBorn in this country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.01,6.68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e99.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 122px;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e6.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e(6.05,6.68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e4.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e7.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e0.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e95.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" style=\"width: 592px;\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 202,898.\u003cem\u003e\u0026nbsp;\u003c/em\u003e*p \u0026lt; .05; **p \u0026lt; .007 (Bonferroni corrected threshold); \u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;0.1% of the observed sample) within several countries leading to large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate; LL=lower limits of prediction interval; UL=upper limit of prediction interval; prediction interval is the range of likely values of the estimate for a randomly selected country; \u0026tau;\u003cem\u003e\u0026nbsp;\u003c/em\u003eis\u003cem\u003e\u0026nbsp;\u003c/em\u003ethe standard deviation of the distribution of means across countries, which is an indicator of cross-national heterogeneity;\u003cem\u003e\u0026nbsp;I\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eis an estimate of the variability in means due to heterogeneity across countries vs. sampling variability which is not uncommonly nearly 100% when there is nice precision in estimated mean within country; and the Global\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value corresponds to a test of the null hypothesis that there are no differences between the groups for that socio-demographic characteristic in all of the 22 countries.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" style=\"width: 591px;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 4. LS meta-analysis of means by demographic category\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 283px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 96px;\"\u003ePrediction Interval\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 212px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eVariable \u0026nbsp; Category\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003eEst\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003eSE\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003eLL\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003eUL\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003eHeterogeneity\u003cbr\u003e(\u0026tau;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003eGlobal p-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eAge group\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e..\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e18-24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.43,7.12)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.37,7.10)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.37,7.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.70\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.35,7.06)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.51,7.16)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.71,7.33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.83,7.46)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e80 or older\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.77,7.57)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e92.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.49,7.16)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.59,7.18)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.70\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e5.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(5.45,6.36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e6.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e53.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eMarital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eMarried\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.79,7.44)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eSeparated\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(5.95,6.67)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e92.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.07,6.77)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.62\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e96.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eWidowed\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.66,7.28)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e94.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eDomestic partner\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.70\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.37,7.03)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.18,6.90)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eEmployment status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eEmployed for an employer\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.60,7.19)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eSelf-employed\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.59,7.26)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eRetired\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.82,7.45)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eStudent\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.47,7.15)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eHomemaker\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.65,7.23)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eUnemployed and looking for a job\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e5.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(5.57,6.37)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.44\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eNone of these/other\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(5.82,6.81)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e1.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eEducation\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eUp to 8 years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.49,7.18)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e9-15 years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.51,7.15)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e16+ years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.71,7.30)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 175px;\"\u003eReligious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\u0026gt;1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(7.02,7.78)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.86,7.46)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.38\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.61,7.23)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eA few times a year\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.41,7.12)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eNever\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.23,6.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eBorn in this country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.54,7.17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.61,7.01)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e6.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.38\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e84.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" style=\"width: 591px;\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 202,898.\u003cem\u003e\u0026nbsp;\u003c/em\u003e*p \u0026lt; .05; **p \u0026lt; .007 (Bonferroni corrected threshold); \u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;0.1% of the observed sample) within several countries leading to large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate; LL=lower limits of prediction interval; UL=upper limit of prediction interval; prediction interval is the range of likely values of the estimate for a randomly selected country; \u0026tau;\u003cem\u003e\u0026nbsp;\u003c/em\u003eis\u003cem\u003e\u0026nbsp;\u003c/em\u003ethe standard deviation of the distribution of means across countries, which is an indicator of cross-national heterogeneity;\u003cem\u003e\u0026nbsp;I\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eis an estimate of the variability in means due to heterogeneity across countries vs. sampling variability which is not uncommonly nearly 100% when there is nice precision in estimated mean within country; and the Global\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value corresponds to a test of the null hypothesis that there are no differences between the groups for that socio-demographic characteristic in all of the 22 countries.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" style=\"width: 591px;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 5. H meta-analysis of means by demographic category\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 283px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 96px;\"\u003ePrediction Interval\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 212px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eVariable \u0026nbsp; Category\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003eEst\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003eSE\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003eLL\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003eUL\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003eHeterogeneity\u003cbr\u003e(\u0026tau;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003eGlobal p-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eAge group\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e..\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e18-24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.66,7.26)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.65,7.22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.66\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.61,7.18)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.59,7.14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.66\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.71,7.22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.88\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.84,7.34)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(7.01,7.50)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e80 or older\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(7.13,7.67)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e85.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.73,7.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.62\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.80,7.26)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e5.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(5.45,6.44)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e6.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e62.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eMarital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eMarried\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.98,7.49)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eSeparated\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.28,6.84)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e89.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.26,6.91)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e96.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eWidowed\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.61,7.22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e95.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eDomestic partner\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.75,7.27)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e6.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e96.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.43,7.03)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.88\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eEmployment status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eEmployed for an employer\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.79,7.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eSelf-employed\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.84,7.34)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eRetired\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.94,7.43)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eStudent\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.64,7.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.70\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eHomemaker\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.75,7.25)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e96.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eUnemployed and looking for a job\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(5.94,6.66)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eNone of these/other\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.18,6.89)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e95.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eEducation\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eUp to 8 years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.67,7.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.66\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e9-15 years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.76,7.25)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e16+ years\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.95,7.36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.48\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 175px;\"\u003eReligious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\u0026gt;1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(7.21,7.87)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e6.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e9.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(7.06,7.51)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e97.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.86,7.33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e96.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eA few times a year\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.65,7.18)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e98.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eNever\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.45,6.96)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e4.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.88\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 143px;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eBorn in this country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e7.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.76,7.25)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e8.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e99.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e6.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e(6.61,7.14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e5.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e7.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e93.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" style=\"width: 591px;\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 202,898.\u003cem\u003e\u0026nbsp;\u003c/em\u003e*p \u0026lt; .05; **p \u0026lt; .007 (Bonferroni corrected threshold); \u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;0.1% of the observed sample) within several countries leading to large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate; LL=lower limits of prediction interval; UL=upper limit of prediction interval; prediction interval is the range of likely values of the estimate for a randomly selected country; \u0026tau;\u003cem\u003e\u0026nbsp;\u003c/em\u003eis\u003cem\u003e\u0026nbsp;\u003c/em\u003ethe standard deviation of the distribution of means across countries, which is an indicator of cross-national heterogeneity;\u003cem\u003e\u0026nbsp;I\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eis an estimate of the variability in means due to heterogeneity across countries vs. sampling variability which is not uncommonly nearly 100% when there is nice precision in estimated mean within country; and the Global\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value corresponds to a test of the null hypothesis that there are no differences between the groups for that socio-demographic characteristic in all of the 22 countries.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"28\" valign=\"bottom\" style=\"width: 24.07%;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 6. Random effects meta-analysis of regressing LE on childhood predictors\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" valign=\"bottom\" style=\"width: 13.8133%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 2.9777%;\"\u003eEstimated Proportion of Effects by Threshold\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"9\" valign=\"bottom\" style=\"width: 7.3202%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eVariable\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eCategory\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003eEst\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003eSE\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026lt; -0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026gt; 0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003eHeterogeneity\u003cbr\u003e(\u0026tau;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003eGlobal p-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.3913%;\"\u003eRelationship with mother\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 4.2184%;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(0.10,0.23)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e23.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eRelationship with father\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(0.11,0.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e39.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eParent marital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: Parents married)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.08,0.04)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e20.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.27,-0.01)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e75.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eOne or both parents had died\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.28,-0.09)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e25.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eSubjective financial status of family growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: Got by)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eLived comfortably\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(0.20,0.38)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e88.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eFound it difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.23,-0.11)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e54.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eFound it very difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.55,-0.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e64.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eAbuse\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.34,-0.16)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e70.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eOutsider growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.25,-0.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e72.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eSelf-rated health growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: Good)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eExcellent\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(0.26,0.55)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e92.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eVery good\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(0.15,0.33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e81.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eFair\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.33,-0.17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e47.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003ePoor\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.59,-0.22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e58.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: Born in this country)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.02,0.20)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e48.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eAge 12 religious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: Never)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eAt least 1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(0.11,0.33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e74.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(0.12,0.34)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e75.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e\u0026lt; 1/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(0.06,0.17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e31.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eYear of birth\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: 1998-2005; age 18-24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e1993-1998; age 25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.08,0.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e45.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e1983-1993; age 30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.19,0.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e87.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e1973-1983; age 40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e-0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.26,0.04)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e89.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e1963-1973; age 50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.14,0.19)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e89.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e1953-1963; age 60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.08,0.36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e92.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e1943-1953; age 70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.10,0.50)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e93.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e1943 or earlier; age 80+\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.10,0.65)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e89.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003e(Ref: Male)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(0.03,0.20)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e90.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.3913%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 4.2184%;\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 1.4889%;\"\u003e0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 3.3913%;\"\u003e(-0.56,0.58)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e0.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.5716%;\"\u003e0.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 1.4061%;\"\u003e0.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 3.8049%;\"\u003e1.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 1.2821%;\"\u003e90.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 2.2333%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"28\" valign=\"bottom\" style=\"width: 24.07%;\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 202,898.\u003cem\u003e\u0026nbsp;\u003c/em\u003e*p \u0026lt; .05; **p \u0026lt; .004 (Bonferroni corrected threshold); \u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;0.1% of the observed sample) within several countries leading large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate; CI = confidence interval; the estimated proportion of effects is the estimated proportion of effects above (or below) a threshold based on the calibrated effect sizes (Mathur \u0026amp; VanderWeele, 2020); \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eis an estimate of the variability in means due to heterogeneity across countries vs. sampling variability; the Global\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value corresponds to the joint test of the null hypothesis that the country-specific joint parameter Wald tests (all parameters within variable groups are zero) are all null all 22 countries; and additional details of heterogeneity of effects are available in the forest plots of our online supplemental material.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"29\" valign=\"bottom\" style=\"width: 592px;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 7. Random effects meta-analysis of regressing LS on childhood predictors\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" valign=\"bottom\" style=\"width: 322px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003eEstimated Proportion of Effects by Threshold\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"9\" valign=\"bottom\" style=\"width: 170px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eVariable\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eCategory\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003eEst\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003eSE\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026lt; -0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026gt; 0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003eHeterogeneity\u003cbr\u003e(\u0026tau;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003eGlobal p-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003eRelationship with mother\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 117px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 55px;\"\u003e0.009*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.12,0.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e29.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eRelationship with father\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.10,0.27)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e54.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eParent marital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: Parents married)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.17,0.09)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e77.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.25,-0.01)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e69.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eOne or both parents had died\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.24,0.08)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e71.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eSubjective financial status of family growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: Got by)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eLived comfortably\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.17,0.33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e81.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eFound it difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.22,-0.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e65.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eFound it very difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.46,-0.16)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e66.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eAbuse\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.48,-0.30)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e64.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eOutsider growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.39,-0.20)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e68.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eSelf-rated health growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: Good)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eExcellent\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.29,0.62)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e93.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eVery good\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.15,0.35)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e83.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eFair\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.41,-0.26)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e32.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003ePoor\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.70,-0.22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e73.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: Born in this country)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.08,0.26)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e75.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eAge 12 religious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: Never)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eAt least 1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.03,0.38)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e88.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.01,0.32)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e86.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e\u0026lt; 1/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.09,0.19)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e87.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eYear of birth\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: 1998-2005; age 18-24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e1993-1998; age 25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.10,0.09)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e60.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e1983-1993; age 30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.15,0.14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e87.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e1973-1983; age 40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.22,0.16)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e91.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e1963-1973; age 50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.15,0.30)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e93.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e1953-1963; age 60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.01,0.46)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e91.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e1943-1953; age 70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.02,0.65)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e93.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e1943 or earlier; age 80+\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.44\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.01,0.88)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e91.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003e(Ref: Male)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(0.03,0.21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e90.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 117px;\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33px;\"\u003e-0.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 68px;\"\u003e(-0.63,0.05)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 29px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 48px;\"\u003e0.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 52px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 89px;\"\u003e0.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 26px;\"\u003e63.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"29\" valign=\"bottom\" style=\"width: 592px;\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 202,898.\u003cem\u003e\u0026nbsp;\u003c/em\u003e*p \u0026lt; .05; **p \u0026lt; .004 (Bonferroni corrected threshold); \u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;0.1% of the observed sample) within several countries leading large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate; CI = confidence interval; the estimated proportion of effects is the estimated proportion of effects above (or below) a threshold based on the calibrated effect sizes (Mathur \u0026amp; VanderWeele, 2020); \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eis an estimate of the variability in means due to heterogeneity across countries vs. sampling variability; the Global \u003cem\u003ep\u003c/em\u003e-value corresponds to the joint test of the null hypothesis that the country-specific joint parameter Wald tests (all parameters within variable groups are zero) are all null all 22 countries; and additional details of heterogeneity of effects are available in the forest plots of our online supplemental material.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"29\" valign=\"bottom\" style=\"width: 592px;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 8. Random effects meta-analysis of regressing H on childhood predictors\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\" valign=\"bottom\" style=\"width: 347px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 87px;\"\u003eEstimated Proportion of Effects by Threshold\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"8\" valign=\"bottom\" style=\"width: 158px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eVariable\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eCategory\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003eEst\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003eSE\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026lt; -0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026gt; 0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003eHeterogeneity\u003cbr\u003e(\u0026tau;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003eGlobal p-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eRelationship with mother\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.15,0.36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e62.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eRelationship with father\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.03,0.22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e69.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eParent marital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: Parents married)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.15,0.08)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e77.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.22,-0.04)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e52.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eOne or both parents had died\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.23,0.02)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e57.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eSubjective financial status of family growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: Got by)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eLived comfortably\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.16,0.30)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e80.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eFound it difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.18,-0.05)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e60.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eFound it very difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.42,-0.21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e45.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eAbuse\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.42,-0.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e69.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eOutsider growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.37,-0.20)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e65.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eSelf-rated health growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: Good)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eExcellent\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.34,0.66)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e93.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eVery good\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.17,0.36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e83.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eFair\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.36,-0.20)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e50.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003ePoor\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.62,-0.20)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.38\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e67.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: Born in this country)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.14,0.16)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e73.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eAge 12 religious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: Never)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eAt least 1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.14,0.40)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e80.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.09,0.36)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e82.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e\u0026lt; 1/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.06,0.18)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e45.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eYear of birth\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: 1998-2005; age 18-24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e1993-1998; age 25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.11,0.09)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e71.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e1983-1993; age 30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.14,0.11)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e86.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e1973-1983; age 40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.22,0.12)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e91.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e1963-1973; age 50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.19,0.25)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e94.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e1953-1963; age 60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.10,0.38)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e93.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e1943-1953; age 70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.04,0.58)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e94.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e1943 or earlier; age 80+\u003csup\u003eǂ\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.10,0.81)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e88.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003e(Ref: Male)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026lt;.001**\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(0.03,0.17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e84.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 114px;\"\u003eOther\u003csup\u003eǂ\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e-0.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 69px;\"\u003e(-0.98,-0.40)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 26px;\"\u003e0.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 46px;\"\u003e0.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 41px;\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 80px;\"\u003e0.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 27px;\"\u003e51.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 51px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 202,898.\u003cem\u003e\u0026nbsp;\u003c/em\u003e*p \u0026lt; .05; **p \u0026lt; .004 (Bonferroni corrected threshold); \u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;0.1% of the observed sample) within several countries leading large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate; CI = confidence interval; the estimated proportion of effects is the estimated proportion of effects above (or below) a threshold based on the calibrated effect sizes (Mathur \u0026amp; VanderWeele, 2020); \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eis an estimate of the variability in means due to heterogeneity across countries vs. sampling variability; the Global \u003cem\u003ep\u003c/em\u003e-value corresponds to the joint test of the null hypothesis that the country-specific joint parameter Wald tests (all parameters within variable groups are zero) are all null all 22 countries; and additional details of heterogeneity of effects are available in the forest plots of our online supplemental material.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 592px;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 9. Sensitivity of meta-analyzed childhood predictors of LE to unmeasured confounding\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eVariable\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eCategory\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003eE-value for Estimate\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003eE-value for 95% CI\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eRelationship with mother\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.24\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eRelationship with father\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.26\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eParent marital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Parents married)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.07\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eOne or both parents had died\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.22\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eSubjective financial status of family\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Got by)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp; growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eLived comfortably\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.38\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFound it difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.25\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFound it very difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.48\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eAbuse\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.44\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.33\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eOutsider growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.19\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eSelf-rated health growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Good)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eExcellent\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.62\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.45\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eVery good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.32\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFair\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.44\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.34\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003ePoor\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.62\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.40\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Born in this country)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eAge 12 religious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Never)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eAt least 1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.26\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.27\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\u0026lt; 1/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.18\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eYear of birth\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: 1998-2005; age 18-24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1993-1998; age 25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1983-1993; age 30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1973-1983; age 40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1963-1973; age 50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1953-1963; age 60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1943-1953; age 70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1943 or earlier; age 80+\u003csup\u003eǂ\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Male)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eOther\u003csup\u003eǂ\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 592px;\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 202,898; the E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association (VanderWeele \u0026amp; Ding, 2017, p. 269-270); and\u0026nbsp;\u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;0.1% of the observed sample) within several countries potentially large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 592px;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 10. Sensitivity of meta-analyzed childhood predictors of LS to unmeasured confounding\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eVariable\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eCategory\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003eE-value for Estimate\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003eE-value for 95% CI\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eRelationship with mother\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.26\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eRelationship with father\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.24\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eParent marital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Parents married)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.05\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eOne or both parents had died\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eSubjective financial status of family\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Got by)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp; \u0026nbsp;growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eLived comfortably\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.33\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFound it difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.18\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFound it very difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.32\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eAbuse\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.48\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eOutsider growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.35\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eSelf-rated health growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Good)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eExcellent\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.46\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eVery good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.30\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFair\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.43\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003ePoor\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.38\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Born in this country)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eAge 12 religious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Never)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eAt least 1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\u0026lt; 1/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eYear of birth\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: 1998-2005; age 18-24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1993-1998; age 25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1983-1993; age 30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1973-1983; age 40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1963-1973; age 50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1953-1963; age 60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.08\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1943-1953; age 70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.09\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1943 or earlier; age 80+\u003csup\u003eǂ\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.05\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Male)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.11\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eOther\u003csup\u003eǂ\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 592px;\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 202,898; the E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association (VanderWeele \u0026amp; Ding, 2017, p. 269-270); and\u0026nbsp;\u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;0.1% of the observed sample) within several countries potentially large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 592px;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 11. Sensitivity of meta-analyzed childhood predictors of H to unmeasured confounding\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eVariable\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eCategory\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003eE-value for Estimate\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003eE-value for 95% CI\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eRelationship with mother\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.31\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eRelationship with father\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Very bad/somewhat bad)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eVery good/somewhat good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.13\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eParent marital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Parents married)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eDivorced\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eSingle, never married\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.13\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eOne or both parents had died\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eSubjective financial status of family\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Got by)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp; \u0026nbsp;growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eLived comfortably\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.32\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFound it difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.17\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFound it very difficult\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.39\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eAbuse\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.43\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eOutsider growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: No)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.48\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.38\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eSelf-rated health growing up\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Good)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eExcellent\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.73\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.55\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eVery good\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.35\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFair\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.48\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.38\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003ePoor\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.38\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eImmigration status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Born in this country)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eBorn in another country\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eAge 12 religious service attendance\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Never)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eAt least 1/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.31\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1-3/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.24\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e\u0026lt; 1/month\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.17\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eYear of birth\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: 1998-2005; age 18-24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1993-1998; age 25-29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1983-1993; age 30-39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1973-1983; age 40-49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1963-1973; age 50-59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1953-1963; age 60-69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1943-1953; age 70-79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e1943 or earlier; age 80+\u003csup\u003eǂ\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.25\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003eGender\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003e(Ref: Male)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.13\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 177px;\"\u003eOther\u003csup\u003eǂ\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e1.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e1.62\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 592px;\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 202,898; the E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association (VanderWeele \u0026amp; Ding, 2017, p. 269-270); and\u0026nbsp;\u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;0.1% of the observed sample) within several countries potentially large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"life evaluation, life satisfaction, happiness, subjective wellbeing, cross-cultural","lastPublishedDoi":"10.21203/rs.3.rs-6420806/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6420806/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite a vast literature on subjective wellbeing (SWB), issues remain, including (a) debates around which concepts best represent it, (b) disjointed understanding of relevant factors, and (c) limited appreciation of cross-national variation regarding (a) and (b). We address these using data from the Global Flourishing Study on three constructs pertaining to evaluative SWB (life evaluation, life satisfaction, and, more ambiguously, happiness), examining associations with 15 childhood and demographic factors in 202,898 participants from 22 countries. Key findings include, for (a), life satisfaction being the best performing construct (in correlations with overall flourishing), (b) all factors being significantly associated with all constructs (with the largest variation for employment status among demographic factors and self-reported health among childhood factors), and (c) patterns varying substantively across countries (suggesting the general trends are not universal but differ according to local socio-cultural dynamics). The findings advance the methodological, socio-demographic, and cross-national understanding of evaluative SWB.\u003c/p\u003e","manuscriptTitle":"Life evaluation, life satisfaction, and happiness: assessing inter-relations and 15 childhood and demographic factors across 22 Countries in the Global Flourishing Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 08:22:04","doi":"10.21203/rs.3.rs-6420806/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T06:31:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-21T11:46:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T11:06:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150811785868721514981723018198162466473","date":"2025-11-06T18:08:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182892573051069859846736842487432435277","date":"2025-10-19T10:25:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162020419664913280590310293993552799852","date":"2025-08-28T23:28:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122660866935522526805745255227884018452","date":"2025-05-08T14:58:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T10:58:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-15T15:40:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-15T09:48:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-15T09:47:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-10T13:46:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed0c1193-2d3c-4a06-9a19-2fdbc5998d89","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48266249,"name":"Biological sciences/Psychology"},{"id":48266250,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2026-02-16T15:59:41+00:00","versionOfRecord":{"articleIdentity":"rs-6420806","link":"https://doi.org/10.1038/s41598-026-35777-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-10 15:56:57","publishedOnDateReadable":"February 10th, 2026"},"versionCreatedAt":"2025-05-13 08:22:04","video":"","vorDoi":"10.1038/s41598-026-35777-y","vorDoiUrl":"https://doi.org/10.1038/s41598-026-35777-y","workflowStages":[]},"version":"v1","identity":"rs-6420806","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6420806","identity":"rs-6420806","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}