Childhood predictors of inner peace: A cross-national analysis of the Global Flourishing Study

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Noah Padgett, James L. Ritchie-Dunham, Matthew T. Lee, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4602277/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Great efforts have been expended studying how people’s childhood affects outcomes later in life. Although attention has mostly focused on ‘negative’ outcomes, such as mental illness, paradigms like positive psychology have encouraged interest in desirable phenomena too. Yet amidst this ‘positive turn’ some desiderata have still received scant engagement, including inner peace. This lacuna perhaps reflects the Western-centric nature of academia, with low arousal positive emotions being relatively undervalued in the West. But aligning with broader efforts to redress this Western-centricity is an emergent literature on this topic. This report adds to this by presenting the most ambitious study to date of inner peace, namely as an item – “In general, how often do you feel you are at peace with your thoughts and feelings?” – in the Global Flourishing Study, an intended five-year study investigating the predictors of human flourishing involving (in this first year) 202,898 participants from 22 countries. This paper looks at the childhood predictors of peace, using random effects meta-analysis to aggregate all findings, focusing on three research questions. First, how do recalled aspects of a child's upbringing predict peace in adulthood, for which the most impactful factor on average was self-rated health growing up, with Risk Ratios spanning, relative to “good”, 0.93 for “poor” (95% CI [0.88,0.99]) to 1.07 for “excellent” (95% CI [1.04,1.11]). Second, do associations vary by country, with the effect of poor self-rated health spanning 0.37 in Türkiye (95% CI [0.18,0.77]) to 1.19 in Nigeria (95% CI [1.08,1.31]). Third, are relationships robust to potential unmeasured confounding, as assessed by E-values, for which the effect of poor health growing up is robust up to unmeasured confounder association risk ratios of 1.36 with inner peace. These results shed new valuable light on the long-term causal dynamics of this overlooked topic. Biological sciences/Psychology Health sciences/Risk factors peace wellbeing flourishing global cross-cultural Global Flourishing Study Introduction The Roots of Flourishing For decades, indeed centuries, scholars have been intrigued by how a person’s childhood affects outcomes later in life. Answering this question ideally involves, (a) longitudinal studies that, (b) begin tracking participants during childhood, including (c) with attention to numerous contextual factors (e.g., details of their family), and (d) follow participants into adulthood, with (e) relevant outcomes of interest (e.g., variables relating to wellbeing). For some outcomes, a wealth of studies meet these criteria. On the whole though, these tend to focus on ‘negative’ (i.e., undesirable) outcomes, such as mental health problems. Such is the depth of longitudinal research on environmental factors associated with their onset during childhood/adolescence, for example, that sufficient meta-analyses exist to allow an umbrella review of them 1 . Indeed, this focus on negative phenomena has characterised academia for most of the past century. However, this is gradually changing, with increasing attention to more ‘positive’ (i.e., desirable) outcomes. This interest is not new per se; humanistic scholars like Maslow advocated such an approach over 80 years ago 2 . However, not until the emergence of positive psychology in the late 1990s did this work begin to receive more widespread hearing, with Martin Seligman using his ascension to the presidency of the American Psychological Association to encourage greater attention to positive phenomena as serious and legitimate topics of scientific enquiry. As a result, outcomes like happiness – which we briefly discuss here as an exemplar of the wider literature on flourishing, with the latter being a more comprehensive term that includes states like happiness as facets – have now received extensive attention. This includes the kind of longitudinal work mentioned above that allows scholars to explore the childhood roots of such outcomes. One study for example used data from the UK National Child Development Study to study a cohort of 4,400 children born in 1958 who had been repeatedly surveyed for 50 years, including a question on life satisfaction (“how satisfied or dissatisfied you are with the way life has turned out so far”) at four points in adulthood (ages 33, 42, 46, and 50) 3 . Notably, demographic and socio-economic factors in early childhood had a relatively negligible effect, predicting just 1.2% of the variance in adult life satisfaction. By contrast, more individual characteristics – especially childhood behavioural-emotional problems and social maladjustment – were “powerful predictors” of adult satisfaction. Similar findings were obtained in an analysis of 5,124 young participants (aged 11–15) from the British Household Panel Survey 4 . The study began by assessing the determinants of youth happiness – on a scale of 1 (completely happy) to 7 (completely unhappy) – and found contextual factors like family structure had a significant impact. Living with both natural parents, for example, was linked to greater happiness, both relative to a step family (for boys and girls) and living with a single parent (evidence for boys). Most relevantly here, a subset of participants (1,825) went on to participate in the adult panel (i.e., 18+), which features a question on life satisfaction. While satisfaction was influenced by demographic and socio-economic conditions in childhood, their impact was much smaller than a “youthful personality trait for happiness” (i.e., an “individual effect” in the analysis of youth happiness that captures individual heterogeneity). Specifically, youth happiness predicted from socio-economic factors had a coefficient of 0.15 and 0.14 for adult satisfaction for women and men respectively, whereas the coefficient for the youth happiness trait was 0.27 (for both men and women). Moreover, such work is not limited to happiness; research has also indicated that perceived childhood experiences of, for example, parental warmth subsequently predict a range of domains of flourishing, including more positive relationships and a variety of forms of social wellbeing, including social contribution and integration 5 . Through such research we are gaining a better understanding of topics like happiness and other domains of flourishing. However, other desiderata have received relatively little attention, in general, and certainly in any comparable longitudinal way to the happiness studies cited above. These neglected topics include “low arousal positive states” (LAPS) such as inner peace. The theoretical context for understanding these is Russell’s influential circumplex model of affective states 6 , which construes them through the intersection of two parameters: valence (experientially pleasant and approach-inducing, versus unpleasant and withdrawal-inducing), and arousal (high versus low, or active versus passive). Thus, affective states are understood as being generated – physiologically, mentally, experientially, etc. – by their interaction 7 . Juxtaposing the parameters, Russell created a two-dimensional state space with four quadrants: ( 1 ) low arousal and negative valence (e.g., depression); ( 2 ) high arousal and negative valence (e.g., anxiety); ( 3 ) low arousal and positive valence (e.g., calmness); and ( 4 ) high arousal and positive valence (e.g., elation). Most relevantly here, when it comes to research on positively-valenced states, the majority of attention has focused on high arousal forms, like enjoyment. For example, among the most influential constructs in this arena is subjective wellbeing (SWB) 8 , comprising a cognitive component (usually understood and measured using constructs of life satisfaction or evaluation) and an affective component (viewed through the prism of positive affect). However, most research on the latter has concentrated on high arousal forms. For instance, since 2005 the Gallup World Poll (GWP) has included items pertaining to both components of SWB; most relevantly here, until very recently, positive affect had just been assessed through high arousal notions like enjoyment and laughter. Significantly though, since 2020 these items have been augmented by items pertaining to LAPS, as we explore below. First though, our next section considers why LAPS have historically been overlooked. Neglecting LAPS In attempting to understand the tendency in wellbeing research to focus on high rather than low arousal states, two plausible interlinked explanations are, (a) the Western-centric nature of psychology, and (b) greater importance being placed on HAPS than LAPS in Western cultures, and hence psychology. Let’s briefly consider each in turn. To begin with, it is increasingly recognized that psychology has historically been Western-centric, as argued influentially by Henrich and colleagues 9 , who observed that most work has been conducted by and on people in societies that are relatively “WEIRD” (Western, Educated, Industrialised, Rich, and Democratic). While one ought not to simplistically classify societies in a binary way as WEIRD versus non-WEIRD 10 – since each element of the acronym is a spectrum upon which countries may be variously situated – most of the world is certainly not as WEIRD as places like the USA, where most scholarship in top journals takes place. As a result, critics have questioned the extent to which such research is generalizable and universally valid. Although some scholars might retort that people are relatively similar across cultures and share a common human nature, and hence findings from Western contexts can be extrapolated to other locales, many academics would likely agree that the conditions of life can vary dramatically among cultures. As a result, it can be problematic to draw conclusions about human experience based only on comparatively WEIRD contexts. Moreover, the issue is not only about participants, but scholars themselves, who are likewise shaped by their context, which will thus influence all aspects of their work, from their choice of topics and methodologies to subsequent analyses and interpretation of data. Moreover, the Western-centric bias of academia has been implicated by Tsai and colleagues in the relative neglect of LAPS, who suggest the preference for HAPS is a Western-centric concern, whereas by contrast, Eastern cultures place greater value on LAPS. Tsai described such preferences as “ideal affect” 11 – “the affective states that people strive for or ideally want to feel” (p.243) – and has observed these across an extensive series of studies, mostly involving college students in America and China 12,13,14,15 , with similar patterns observed by others 16,17,18,19 . That said, new items on LAPS in the GWP – mentioned above and discussed further below – indicate such emotions are more universally valued and experienced than these East-West generalisations imply 20 . Even so, notwithstanding such findings, one can still argue that LAPS have historically received greater valorization and attention in Eastern cultures, for which various explanations have been mooted. One prominent interpretation invokes another distinction often noted vis-à-vis East-West differences, namely between individualism and collectivism. Generalisations along these lines have been aired for centuries, often by Western scholars seeking to disparage the East, as notably charted by Said in his critical text Orientalism 21 . However, in the modern era, this binary has been harnessed in a relatively neutral way by Hofstede 22 , who deployed it to differentiate cultural contexts, and then Markus and Kitayama 23 , who shifted the emphasis to self-construal (i.e., how people view themselves). Subsequently, this distinction has been explored in hundreds of studies, with numerous meta-analyses, not only of the distinction per se, but specific facets, such as its link to subjective wellbeing 24 . Most relevantly here, it has been suggested that Eastern cultures tend to appraise HAPS as relatively self-aggrandizing and hence disruptive of social harmony, whereas LAPS are more conducive to such harmony 16,25 . Scholars have also pointed towards other cultural trends, such as the rich history of contemplative practices in Eastern cultures, that may also contribute to their greater valorisation of LAPS 26 . Whatever the explanation, there has been a relative inattention to LAPS in academia, with research tending to focus on HAPS 27 . However, the situation may be changing amidst a broader concern with redressing the Western-centricity of psychology: a review of positive psychology interventions found that although 78.2% were in Western countries, there was “a strong and steady increase in publications from non-Western countries since 2012,” indicating an encouraging “trend towards globalization” of happiness research 28 . These dynamics also mean LAPS are beginning to receive more attention, as exemplified by the Global Wellbeing Initiative. Global Wellbeing Initiative and the Gallup World Poll Since 2005 the GWP has collected data annually on wellbeing (and many aspects of life) worldwide. However, it has still been subject to the Western-centricism that characterises wellbeing research more broadly, with its main metrics being Cantril’s “ladder” 29 item on life evaluation and several pertaining to HAPS. To redress these issues, the Global Wellbeing Initiative (GWI), a partnership between Gallup and the Japan-based Wellbeing for Planet Earth foundation, was launched in 2019, focusing on developing items related to Eastern cultures (given the Japanese location of the foundation). The first iteration was in the 2020 GWP, as analysed in a chapter for the 2022 World Happiness Report 30 . Subsequently, the module has been through two substantive iterations 31 , and by the 2022 GWP it was centred exclusively on balance/harmony and LAPS, collectively described as “harmonic principles of wellbeing” 32 . Most notably, this has included an item on inner peace: “Did you feel at peace most of the day yesterday, or not?” (2020); “In general, how often do you feel you are at peace with your thoughts and feelings?” (2021); and “In general, how often can you find inner peace during difficult times?” (2022, 2023, 2024). In a forthcoming analysis (under review), numerous notable patterns have emerged. Of particular relevance here are the contextual factors associated with peace, as revealed by a regression analysis. In that regard, there were intriguing differences between the three peace items, leading to an interpretation that while the latter two are more directly about “inner peace,” the 2020 item is more ambiguous, straddling inner and “outer peace” (i.e., the peacefulness of one’s societal context). Consider for example that poverty only had a significant impact on the 2020 item, both in terms of lacking money for food (B = -0.37) and shelter (B = -0.49). One might suggest that people lacking money for these necessities are indeed likely to experience a lack of outer peace, living in situations that are challenging, which perhaps lies behind these variables having a significant impact on being “at peace most of the day.” By contrast, and perhaps counter expectations, poverty appeared to have no discernible impact on the two items that tapped more directly into inner peace. Similarly, the 2020 item was more strongly associated with life factors such as finding it “very difficult to get by” on present income (B = -1.81, versus − 0.30 for 2021 and − 0.37 for 2022), and having other people to count on (B = 0.58, versus 0.07 for 2021 and 0.15 for 2022). Conversely, the more “inner-oriented” factor of negative emotions had a stronger association with the 2021 (B = -0.44) and 2022/2023/2024 (B = -0.45) items than 2020 (B = -0.18). There were also interesting demographic patterns, but most saliently these had an overall larger impact on the 2020 item. While these kinds of contextual analyses are interesting and relevant, here we are especially interested in the childhood predictors of peace. However, the GWP data is not conducive to that kind of assessment, since it neither has respondents who are children nor asks adults about childhood. Indeed, although academia has paid increasing attention to the childhood predictors of certain flourishing outcomes, such as happiness, this has so far not extended into the domain of LAPS. That is, among the kinds of rigorous longitudinal work that allows assessment of the childhood predictors of positive outcomes, metrics pertaining to LAPS have mostly been absent from such research, reflecting their general omission from scholarship on flourishing more generally. That said, there has been some relevant work: it has been suggested, for example, that childhood trauma may lead to a diminished sense that attainment of inner peace is possible 33 ; similarly, maladaptive stress coping mechanisms, along with disrupted patterns of homeostasis, may contribute to neuropsychiatric disorders that are inimical to inner peace 34 . Conversely, it has been argued that “Positive early interpersonal experience lays the groundwork for a more peaceful individual life,” drawing on a synthesis of relevant studies to conclude that a “cycle of positive early experience” fosters secure relationships that “promote cognitive and social skills, in turn leading to more peaceful relationships within families and beyond” 35 . Likewise, inner peace is also part of a set of spiritual factors and experiences that partially mediate the effect of Adverse Childhood Experiences on quality of life in adulthood 36 , which suggests relationships between inner peace and a variety of social factors may be reciprocal over the life course. Such research, while relatively sparse, is sufficient to suggest that conditions and experiences in childhood may well have a bearing on inner peace later in life. However, more work is needed – especially rigorous longitudinal designs – to explore this neglected area of inquiry more fully. To that end, the present paper reports on an assessment of inner peace that has been included in the Global Flourishing Study (GFS), an ambitious intended five-year longitudinal study investigating the predictors of human flourishing across over 200,000 participants from 22 geographically and culturally diverse countries. The particular distinguishing feature of this study is its longitudinal nature. While there are various laudable endeavours researching flourishing in a global context – such as the GWP – these are mostly cross-sectional, so cannot provide much insight into causal dynamics. Hence the value of the GFS, which includes a comprehensive battery of items relating to all aspects of flourishing, which will be assessed longitudinally. Furthermore, even the first-wave demographic intake form included retrospective enquires into participants’ childhood experiences, allowing for a synthetic longitudinal study of sorts based on the first year of data alone, which is the focus of the present paper. Our dependent variable of interest is an item, as an adult outcome, on inner peace, adapted from the 2021 GWI item: “In general, how often do you feel you are at peace with your thoughts and feelings?" (always, often, rarely, never). The analysis here is guided by three research questions: ( 1 ) how do different aspects of a child's recalled upbringing predict inner peace in adulthood; ( 2 ) do these associations vary by country; and ( 3 ) are the observed relationships robust to potential unmeasured confounding, as assessed by E-values? Specifically, we look at 13 different childhood predictors: ( 1 ) age (year of birth); ( 2 ) gender; ( 3 ) marital status / family structure; ( 4 ) age 12 religious service attendance; ( 5 ) religious affiliation at age 12; ( 6 ) relationship with mother; ( 7 ) relationship with father; ( 8 ) outsider growing up; ( 9 ) abuse; ( 10 ) self-rated health growing up; ( 11 ) immigration status; ( 12 ) subjective financial status of family growing up; and ( 13 ) race/ethnicity (when available). We have three main hypotheses: ( 1 ) among the 13 childhood predictors, certain ones will show meaningful associations with an individual’s inner peace in adulthood; ( 2 ) the strength of associations between the predictors and inner peace in adulthood will vary by country, reflecting the influence of diverse sociocultural, economic, and health contexts that characterize each nation; and ( 3 ) the observed associations between the predictors and inner peace in adulthood will be robust against potential unmeasured confounding (as assessed through E-values, in some cases suggesting that the observed associations would require strong confounding effects by unmeasured variables to explain away, thus enhancing the credibility of our findings). Methods The description of the methods below has been adapted from VanderWeele and colleagues 37 . Further methodological detail is available elsewhere 38,39,40,41,42,43,44,45 . Data The GFS is a study of 202,898 participants (in this first year) from 22 geographically and culturally diverse countries, with nationally representative sampling within each country, concerning the distribution of determinants of well-being. Wave 1 of the data included the following countries and territories: Argentina, Australia, Brazil, Hong Kong [S.A.R of China, with mainland China also included from 2024 onwards], Egypt, Germany, India, Indonesia, Israel, Japan, Kenya, Mexico, Nigeria, Philippines, Poland, South Africa, Spain, Sweden, Tanzania, Turkey, United Kingdom, and United States. (Note: Data from Hong Kong (S.A.R. of China) is available in the first wave of data collection. Data from mainland China were not included in the first data release due to fieldwork delays. The first wave of fieldwork in mainland China began in February 2024, and a second wave is expected to occur in November-December 2024. All wave 1 and 2 data from mainland China will be part of the second dataset release in March 2025.) 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 44 . Four additional waves of panel data on the 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 44 . Survey items included aspects of flourishing such as happiness, health, meaning, character, relationships, and financial stability 46 , plus other demographic, social, economic, political, religious, personality, childhood, community, health, and wellbeing variables. 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 47 . Measures Childhood Antecedents. 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 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. Gender was assessed as male, female, or other. Continuous age (year of birth) was classified as 18–24, 25–29, 30–39, 40–49, 50–59, 60–69, 70–79, and 80 or older. Childhood religious tradition/affiliation was 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; precise response categories varied by country (Johnson et al., 2023). Racial/ethnic identity were assessed in some, but not all, countries, and response categories were unique to each country. For additional details on the assessments see the COS GFS codebook 47 or Crabtree et al. 38 Outcome variable. Inner peace is assessed with one question: "In general, how often do you feel you are at peace with your thoughts and feelings?" The response categories are: always, often, rarely, never. In our analyses, we dichotomized inner peace as always/often [1] vs rarely/never [0]. Analysis Descriptive statistics for the observed sample, weighted to be nationally representative within country, were estimated for each childhood demographic category. A weighted modified Poisson regression model with complex survey adjusted standard errors was fit within each country of inner peace on all of the aforementioned childhood predictor variables simultaneously. In the primary analyses, random effects meta-analyses of the regression coefficients 48,49 along with confidence intervals, estimate proportions of effects across countries with effect sizes (risk-ratios) larger than 1.1 and smaller than 0.9, and \(\:{I}^{2}\) for evidence concerning variation within a given demographic category across countries 50 . Forest plots of estimates are available in the online supplement. Religious affiliation/tradition and race/ethnicity were used within country as control variables, when available, but these coefficients themselves were not included in the meta-analyses since categories/responses varied by country. All meta-analyses were conducted in R 51 using the metafor package 52 . Within each country, a global test of association of each childhood predictor variable group with outcome was conducted, and a pooled p-value 53 across countries reported concerning evidence for association within any country. Bonferroni corrected p-value thresholds are provided based on the number of childhood demographic variables 54,55 . 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 56 . As a supplementary analysis, population weighted meta-analyses of the regression coefficients were estimated. All analyses were pre-registered with COS prior to data access, with only slight subsequent modification in the regression analyses due to multicollinearity ( https://doi.org/10.17605/OSF.IO/ZTM7R ); all code to reproduce analyses are openly available in an online repository 41 . Missing Data Missing data on all variables was imputed using multivariate imputation by chained equations, and five imputed datasets were used 57,58 . To account for variation in the assessment of certain variables across countries (e.g., religious affiliation/tradition and race/ethnicity), the imputation process was conducted separately in each country. This within-country imputation approach ensured that the imputation models accurately reflected country-specific contexts and assessment methods. Sampling weights were included in the imputation model to account for missingness to be related to probability of inclusion. Accounting for Complex Sampling Design The GFS used different sampling schemes across countries based on availability of existing panels and recruitment needs 44 . 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 40 . Data Availability The study design was pre-registered with the Open Science Framework on November 18th, 2023 (see https://osf.io/5yr62/ ). The datasets generated and/or analysed during the current study are available in the Open Science Framework repository upon submission of pre-registration ( https://www.cos.io/gfs-access-data ), as is the methodology for the analyses ( https://osf.io/pv93c ), and all code to reproduce the analyses ( https://osf.io/9egpr ). Results Descriptive Statistics Table 1 provides the distribution of descriptive statistics (weighted counts and proportions). Participant ages ranged the entire adult lifespan (18–80+). The gender distribution was nearly balanced with 51% female, 48% male, along with a small representation from other gender identities (0.3%). Most participants reported either having a somewhat good or very good relationship with either parent while growing up. The distribution of individuals reporting attending religious services growing up shows that 41% of participants report attending at least once a week and 23% of participants reporting never attending. Counts and proportions for demographic characteristics weighted to be representative of each country’s population are reported on in supplemental Tables S1a-S22a. Table 1 Nationally representative descriptive statistics of the observed sample Characteristic N = 202,898 1 Relationship with mother 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 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 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%) Immigration status Born in this country 190,998 (94%) Born in another country 9,791 (4.8%) (Missing) 2,110 (1.0%) 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%) Year of birth 1998–2005; age 18–24 27,007 (13%) 1993–1998; age 25–29 20,700 (10%) 1983–1993; age 30–39 40,256 (20%) 1973–1983; age 40–49 34,464 (17%) 1963–1973; age 50–59 31,793 (16%) 1953–1963; age 60–69 27,763 (14%) 1943–1953; age 70–79 16,776 (8.3%) 1943 or earlier; age 80+ 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%) 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 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%) Türkiye 1,473 (0.7%) United Kingdom 5,368 (2.6%) United States 38,312 (19%) 1 n (%); History of abuse was not collected in Israel. Childhood Experiences Predicting Inner Peace The meta-analytic estimates of how childhood experiences predict inner peace are reported in Table 2 . These results show an association between 10 of the 11 childhood candidate predictors and responses about inner peace (race and religious affiliation categories varied by country and so no meta-analysis is given but these are available by country in the Online Supplement). Childhood experiences associated with endorsing a sense of inner peace more often included having a good relationship with parents, having a sense of a comfortable financial status growing up, being in good or excellent health growing up, and more frequent attendance at religious services. These factors were, on average across countries, associated with a higher frequency of inner peace as an adult. However, these positive associations were not universal across all countries. In India, for example, the effect of having a very/somewhat good relationship with one’s mother was slightly negative to null (RR = 0.89, 95% CI [0.79,1.00]). All country-specific results and variation are given in the Online Supplement, and we comment on the variation across countries further in the Discussion below. For most of these effects, though on average were positive, there was commonly no evidence for the effect being statistically different than zero within the country-specific analyses. The forest plots provided in our Online Supplement provide additional evidence for the heterogeneity of these effects across countries (see Figures S1 -S27). Table 2 Random effects meta-analysis of regressing inner peace on childhood predictors. Estimated Proportion of Effects by Threshold Variable Category Risk Ratio 95% CI 1.10 \(\:{I}^{2}\) Global p-value Relationship with mother (Ref: Very bad/somewhat bad) < .001** Very good/somewhat good 1.06 (1.03,1.09) 0.00 0.32 58.9 Relationship with father (Ref: Very bad/somewhat bad) < .001** Very good/somewhat good 1.03 (1.01,1.06) 0.00 0.09 63.3 Parent marital status (Ref: Parents married) < .001** Divorced 0.97 (0.94,1.00) 0.09 0.09 74.0 Single, never married 0.95 (0.92,0.99) 0.27 0.00 74.0 One or both parents had died 0.95 (0.91,0.99) 0.23 0.05 74.8 Subjective financial status of family growing up (Ref: Got by) < .001** Lived comfortably 1.03 (1.02,1.05) 0.00 0.00 70.5 Found it difficult 0.98 (0.96,0.99) 0.00 0.00 42.9 Found it very difficult 0.96 (0.93,0.99) 0.09 0.00 44.9 Abuse (Ref: No) < .001** Yes 0.94 (0.92,0.96) 0.05 0.00 36.2 Outsider growing up (Ref: No) < .001** Yes 0.94 (0.91,0.97) 0.32 0.00 78.0 Self-rated health growing up (Ref: Good) < .001** Excellent 1.07 (1.04,1.11) 0.00 0.41 88.5 Very good 1.04 (1.02,1.06) 0.00 0.09 77.4 Fair 0.94 (0.91,0.98) 0.27 0.00 72.7 Poor 0.93 (0.88,0.99) 0.36 0.05 69.0 Immigration status (Ref: Born in this country) 0.254 Born in another country 1.01 (0.98,1.03) 0.00 0.00 25.8 Age 12 religious service attendance (Ref: Never) < .001** At least 1/week 1.06 (1.04,1.09) 0.00 0.09 61.3 1–3/month 1.05 (1.02,1.08) 0.00 0.18 70.7 < 1/month 1.03 (1.02,1.05) 0.00 0.00 < 0.1ǂ Year of birth (Ref: 1998–2005; age 18–24) < .001** 1993–1998; age 25–29 1.01 (0.99,1.03) 0.00 0.00 45.2 1983–1993; age 30–39 1.03 (1.00,1.07) 0.00 0.18 82.4 1973–1983; age 40–49 1.03 (0.99,1.08) 0.05 0.23 87.7 1963–1973; age 50–59 1.07 (1.02,1.13) 0.05 0.36 91.8 1953–1963; age 60–69 1.10 (1.04,1.16) 0.05 0.59 90.2 1943–1953; age 70–79 1.14 (1.07,1.20) 0.05 0.64 84.8 1943 or earlier; age 80+ ǂ 1.19 (1.12,1.27) 0.05 0.68 80.1 Gender (Ref: Male) < .001** Female 0.98 (0.96,1.00) 0.00 0.00 88.4 Other ǂ 0.44 (0.13,1.50) 0.61 0.22 99.9 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 or even complete separation—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. Sensitivity of Effects to Unmeasured Confounding Sensitivity to unmeasured confounding was assessed using E-values suggested that some of the observed associations were moderately robust to unmeasured confounding (Table 3 ). Thus, for example, to explain away the estimate for good/somewhat good relationship with mother, an unmeasured confounder associated with both higher inner peace and a good/somewhat good relationship with mother with risk ratios of 1.31 each, above and beyond the measured covariates, could suffice, but weaker joint confounder associations could not. To shift the confidence interval to include the null, an unmeasured confounder associated with both higher inner peace and a good/somewhat good relationship with mother with risk ratios of 1.23 each, above and beyond the measured covariates, could suffice, but weaker joint confounder associations could not. Further, country-specific sensitivity analyses are reported on in the Online Supplement (see Tables S1c-S23c). Table 3 Sensitivity of meta-analyzed childhood predictors 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 1.31 1.20 Relationship with father (Ref: Very bad/somewhat bad) Very good/somewhat good 1.22 1.10 Parent marital status (Ref: Parents married) Divorced 1.22 1.00 Single, never married 1.27 1.10 One or both parents had died 1.29 1.08 Subjective financial status of family growing up (Ref: Got by) Lived comfortably 1.22 1.16 Found it difficult 1.17 1.08 Found it very difficult 1.25 1.12 Abuse (Ref: No) Yes 1.32 1.26 Outsider growing up (Ref: No) Yes 1.34 1.23 Self-rated health growing up (Ref: Good) Excellent 1.35 1.23 Very good 1.24 1.14 Fair 1.32 1.19 Poor 1.36 1.11 Immigration status (Ref: Born in this country) Born in another country 1.09 1.00 Age 12 religious service attendance (Ref: Never) At least 1/week 1.32 1.23 1–3/month 1.29 1.18 < 1/month 1.22 1.18 Year of birth (Ref: 1998–2005; age 18–24) 1993–1998; age 25–29 1.12 1.00 1983–1993; age 30–39 1.21 1.00 1973–1983; age 40–49 1.22 1.00 1963–1973; age 50–59 1.35 1.15 1953–1963; age 60–69 1.42 1.24 1943–1953; age 70–79 1.53 1.36 1943 or earlier; age 80+ ǂ 1.67 1.48 Gender (Ref: Male) Female 1.15 1.00 Other ǂ 4.00 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 (inner peace) 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 leading to complete separation and large uncertainty in this estimate—be cautious about interpreting this estimate. Discussion The analysis has shed unique light on the childhood predictors of inner peace. As indicated above, this outcome has received relatively little attention per se , with low arousal emotions in generally being understudied and underappreciated in research on flourishing and its various aspects. It is thus unsurprising that there has been barely any research into its childhood predictors, hence the value of our study. In summary, all three of our main hypotheses were supported, often strikingly so. As a reminder, our first was that among the 13 childhood predictors, certain ones will show meaningful associations with inner peace in adulthood. Indeed, every predictor – with the sole but striking exception of immigration status – had a significant association with inner peace when meta-analyzed over the 22 countries. Second, the strength of associations between the predictors and inner peace in adulthood will vary by country, reflecting the influence of diverse sociocultural, economic, and health contexts that characterize each nation. Third, some of the observed associations between the predictors and an individual's inner peace in adulthood will be robust against potential unmeasured confounding (as assessed through E-values). Here we shall touch in turn on the predictors, beginning with the one with the strongest impact, namely self-rated health growing up. We discuss this factor in some detail as a way of illustrating the nature and nuances of the data. We then consider the other factors more briefly, referencing and extrapolating the points made in relation to health. Overall, the most impactful factor on average was self-rated health growing up, as assessed on a five-point scale: poor; fair; good; very good; and excellent. Relative to the middle category of “good,” the Risk Ratios (RRs) range from 0.93 for poor (95% CI [0.88, 0.99]) and 0.94 for fair (95% CI [0.91, 0.98]), to 1.04 for very good (95% CI [1.02, 1.06]) to 1.07 for excellent (95% CI [1.04, 1.11]), with all results significant at the p < 0.001 level. An RR can be interpreted as the relative percentage in each category, which in the present paper is calculated in relation to the proportion of people reporting experiencing inner peace. In that respect, although peace was assessed on a four-point scale – never, rarely, often, or always at peace – in our analysis and interpretation we aggregate this into two binary categories, whereby people either have inner peace (endorsing either “often” or “always” on the peace item) or do not have it (endorsing either “rarely” or “never”). Thus, taking the RR of 1.07 (95% CI [1.04, 1.11]) for excellent health as an example, this means that, compared to people who reported that they “only” had good health growing up, the proportion of people with excellent health who have inner peace is 1.07 times greater than those who do not have inner peace. Put another way, there is a 7% increase in having inner peace for those who reported excellent health in childhood relative to those who reported good health (conditional on all other variables in the model). Although this effect size is modest, at the population level it is quite meaningful, and moreover, in some countries the effect size was considerably higher. One can also examine the robustness of these associations through their E-value 56 , which pertains to our third main hypothesis. An E-value measures the strength that an “unmeasured confounder” – a variable not included in the analyses – would need to be to “explain away” the observed relationship. In the case of excellent health, the E-value for the estimate was 1.36. This would mean an unmeasured confounder would need to be both, (a) related to peace with a RR of 1.36 (meaning that being one unit higher on the confounder is associated with a 36% increase in having peace), and (b) simultaneously related to childhood health with a RR of 1.36 (where a one unit increase on the confounder is associated with a 36% increase in being in the excellent health category over the good category). It is this simultaneous association of the unmeasured confounder with our outcome (peace) and our predictor (health) that makes the unknown variable a “confounder.” The usefulness of the E-value is that it serves as a benchmark for thinking – in light of existing knowledge and theory – about whether a confounder could exist that does indeed fulfil this criterion of simultaneous association with responses to the health and peace items. Essentially, the closer the E-value to 1, the more likely it is that such a confounder could indeed exist. Conversely, the higher the E-value, the less likely that it does. In the present case, an E-value of 1.36 is judged as high: especially given observed associations, it may be difficult to envisage an unmeasured variable that could plausibly have an RR of 1.36 with both inner peace and childhood health. That is, this RR would need to both be, (a) much higher than the one observed between excellent childhood health and inner peace (i.e., 1.07), and (b) apply separately to both childhood health and inner peace. So, in terms of our third hypothesis, we have some evidence that the observed RR between inner peace and childhood health is robust to potential unmeasured confounding, so is a “real” effect (i.e., rather than a statistical artefact produced by us not including enough relevant variables in our analysis). This finding that childhood health is associated with inner peace in adulthood is unique: we could find no previous study that has explored such a connection, so simply observing it here is a notable addition to the literature. However, it is worth emphasizing up front a caveat, namely that we did not actually assess people’s health in childhood itself, but rather their retrospective recollections about their childhood. Crucially, there are indications that people sometimes change their ratings of childhood health over time; one analysis found nearly one half of their sample revised this during a 10-year observation period. Older adults who were relatively advantaged (e.g., with socioeconomic resources and better memory) were less likely to revise it, whereas those with multiple childhood health problems were more likely to (either positively or negatively) 59 . As such, we must be somewhat cautious in interpreting our data, given we did not measure health in childhood per se, and recall bias might be present. However, for recall bias to completely explain the observed associations of the childhood predictors with adult inner peace, the effect of adult inner peace on the retrospective assessments of the childhood predictors would have to be at least as strong as the observed associations themselves 60 . Moroever, numerous longitudinal studies have actually measured health in childhood then traced its impact on later outcomes, with a substantial literature showing it does have a substantive effect on myriad aspects of adult life. Given that context, it is reasonable to think – based on our data – that inner peace is indeed one of the variables affected by it. Much of this existing longitudinal work focuses either on physical health or socio-economic status, with poor childhood health having a long-term detrimental health on these outcomes 61,62 . However, there is some work with more direct relevance to inner peace, with various studies connecting poor childhood health to mental health issues specifically in later life, particularly depression. A study involving a nationally representative sample of late midlife adults in the US, for example, found childhood disability was significantly associated with higher levels of depressive symptoms, suggesting such people may accumulate more physical impairment over the life course, thus suffering worse mental health in late midlife 63 . Similarly, another study found people with childhood disability exhibited more depressive symptoms at age 50 compared to those who did not, although there was no difference in the progression of depressive symptoms over time between the two groups, suggesting an initial inequality which was then maintained over the life course 64 . Let us now turn to our second hypothesis, namely that we would observe national differences in the effects of the factors. Many of the studies cited above were in a US context, which indeed is characteristic of the psychological literature as a whole, as elucidated in the introduction. Thus, a particular strength of our research is its multinational reach, covering 22 diverse countries. And, as per our second hypothesis, there was indeed considerable variation in the impact of childhood health. The following are the respective RRs for the four health categories (relative to the middle category of “good”) for the 22 countries (with details for each country provided in the Supplementary Tables), together with the respective E-values, followed by 95% CIs, in square parentheses: Argentina (poor = 0.89 [1.50; 0.72, 1.09], fair = 1.12 [1.48; 1.02, 1.22], very good = 1.01 [1.08; 0.94, 1.07], excellent = 0.99 [1.12; 0.93, 1.05]); Australia (0.88 [1.54; 0.72, 1.07], 0.96 [1.27; 0.84, 1.09], 1.03 [1.21; 0.96, 1.11], 1.09 [1.41; 1.02, 1.17]); Brazil (0.99 [1.12; 0.86, 1.13], 0.93 [1.36; 0.87, 1.00], 1.07 [1.34; 1.02, 1.12], 1.14 [1.53; 1.09, 1.18]); Egypt (0.94 [1.33; 0.84, 1.05], 0.95 [1.29; 0.88, 1.02], 0.97 [1.22; 0.92, 1.02], 0.98 [1.16; 0.94, 1.03]); Germany (1.06 [1.32; 0.94, 1.20], 0.88 [1.55; 0.81, 0.95], 1.06 [1.31; 1.02, 1.10], 1.10 [1.44; 1.06, 1.15]); Hong Kong (0.73 [2.09; 0.57, 0.92], 0.85 [1.63; 0.79, 0.91], 1.04 [1.23; 1.00, 1.08], 1.03 [1.22; 0.97, 1.10]); India (0.93 [1.36; 0.84, 1.03], 0.87 [1.57; 0.82, 0.92], 0.97 [1.20; 0.93, 1.02], 1.05 [1.27; 0.99, 1.11]); Indonesia (0.76 [1.96; 0.53, 1.08], 0.97 [1.20; 0.92, 1.03], 0.99 [1.11; 0.94, 1.04], 1.02 [1.18; 0.97, 1.08]); Israel (1.02 [1.16; 0.74, 1.42], 0.89 [1.51; 0.75, 1.04], 0.96 [1.24; 0.91, 1.02], 0.98 [1.15; 0.93, 1.04]); Japan (0.75 [2.00; 0.68, 0.83], 0.86 [1.61; 0.82, 0.90], 1.12 [1.49; 1.09, 1.15], 1.20 [1.70; 1.17, 1.24]); Kenya (1.03 [1.19; 0.90, 1.17], 0.96 [1.27; 0.89, 1.02], 1.02 [1.15; 0.96, 1.08], 1.04 [1.24; 0.99, 1.09]); Mexico (0.91 [1.43; 0.75, 1.10], 1.09 [1.39; 1.01, 1.17], 0.97 [1.20; 0.91, 1.04], 0.95 [1.28; 0.90, 1.01]); Nigeria (1.19 [1.67; 1.08, 1.31], 1.03 [1.21; 0.92, 1.15], 1.04 [1.25; 0.98, 1.10], 1.01 [1.14; 0.96, 1.08]); Philippines (1.01 [1.11; 0.86, 1.18], 0.96 [1.25; 0.87, 1.05], 1.09 [1.40; 0.98, 1.21], 1.17 [1.62; 1.08, 1.27]); Poland (1.03 [1.21; 0.81, 1.32], 0.88 [1.55; 0.77, 1.00], 1.00 [1.03; 0.96, 1.05], 1.05 [1.25; 1.00, 1.11]); South Africa (0.90 [1.47; 0.78, 1.04], 0.94 [1.33; 0.84, 1.04], 1.03 [1.22; 0.95, 1.12], 0.99 [1.09; 0.93, 1.06]); Spain (1.06 [1.32; 0.86, 1.31], 0.96 [1.23; 0.79, 1.18], 0.97 [1.22; 0.89, 1.05], 0.98 [1.17; 0.90, 1.07]); Sweden (0.80 [1.82; 0.71, 0.90], 0.92 [1.40; 0.86, 0.97], 1.15 [1.56; 1.11, 1.18], 1.19 [1.68; 1.16, 1.23]); Tanzania (0.98 [1.18; 0.86, 1.11], 1.08 [1.38; 1.01, 1.16], 1.10 [1.43; 1.04, 1.17], 1.11 [1.46; 1.05, 1.17]); Türkiye (0.37 [4.82; 0.18, 0.77], 0.96 [1.25; 0.76, 1.21], 1.12 [1.48; 0.94, 1.33], 1.32 [1.97; 1.12, 1.57]); United Kingdom (0.87 [1.57; 0.69, 1.10], 0.84 [1.65; 0.73, 0.98], 1.11 [1.47; 1.03, 1.20], 1.14 [1.55; 1.06, 1.23]); and United States (0.97 [1.20; 0.78, 1.21], 0.89 [1.49; 0.79, 1.01], 1.10 [1.43; 1.04, 1.16], 1.16 [1.58; 1.10, 1.21]). As one can see, there are many notable country-level nuances. For a start, compared to the overall RR range of 0.14 (spanning 0.93 for poor to 1.07 for excellent health), some nations had a much larger range – as much as 0.95 in Türkiye – implying that childhood health is a much more significant factor there compared to other countries. More research is needed to explore why this regional variation exists, but it will almost certainly involve considerations such as the provision of healthcare in the various countries. There were also intriguing patterns that are harder to explain and certainly merit further investigation, especially the fact that, in some countries, the RRs seemed ‘out of order.’ One would expect, based on the overall RRs, that relative to people with “good” childhood health, people with worse health (“poor” or “fair”) would have lower levels of peace (RR 1.00). Indeed, seven countries did conform to this linear escalating pattern (Australia, Brazil, Hong Kong, Sweden, Türkiye, UK and USA). However, in the remaining countries, this pattern was subverted in various ways, where compared to those with good childhood health, some groups with worse health (either poor and/or fair) had higher levels of peace (RR > 1.00), while conversely others with better health (very good and/or excellent) had lower levels of peace (RR < 1.00). Consider Nigeria, for instance, where people with poor childhood health had an RR of 1.19 (95% CI [1.08, 1.31]): i.e., here, not only does poor childhood health not detract from inner peace in adulthood, the data imply it actively helps . Moreover, the E-value for this particular observation is 1.67 (while the E-value for the 95% CI is 1.38), suggesting this finding is very robust to potential confounding. We cannot know from our data why this effect is observed, i.e., what is special about Nigeria that childhood poor health seems to actually facilitate inner peace in adulthood. One could speculate that, at least in some countries, experiencing poor health in childhood either encourages or compels people to develop a certain resilience or other psychological qualities, that may give rise to inner peace. But this of course begs the question, namely what is it that is different about these countries that this effect is observed, and why are similar effects not found elsewhere. Certainly, this is something that demands more in-depth study. Let us now consider the other variables. While we do not have the space to discuss these in comparable depth to childhood health, we can nevertheless highlight some notable patterns that merit further study. Indeed, to reiterate, every factor – apart from immigration status – had a significant effect on inner peace in adulthood. To begin with, family dynamics are very important, including having a good relationship with one’s mother (RR = 1.06; E = 1.34; 95% CI [1.03, 1.09]) and father (1.03; 1.21; 1.01, 1.06), as is having parents who were married compared to either being divorced (0.97; 1.22; 0.94, 1.00), single or never married (0.95; 1.29; 0.92, 0.99), or one or both parents having died during childhood (0.95; 1.30; 0.91, 0.99). The financial situation of the family also matters: relative to people whose families “got by,” those who “lived comfortably” fared better (1.03; 1.22; 1.02, 1.05), while people did worse whose families found it either “difficult” (0.98; 1.17; 0.96, 0.99) or “very difficult” (0.96; 1.25; 0.93, 0.99). These findings accord with a vast existing literature on the importance of these factors for wellbeing, both in childhood itself and moreover in later life. Thus, for example, with respect to the quality of relationships with parents, a considerable literature on attachment styles shows the positive impact of “secure” bonds – generally regarded as the optimal type of attachment – on mental health later in life 65 . So too with marriage: overall, research has consistently shown this to be beneficial for children relative to other possibilities such as divorce/separation, both during childhood itself 66 and over the life course 67 , though one notes that in some situations – such as conflicted or abusive marriages – divorce may indeed be better option all round 68 . And again, with the financial aspect, research consistently finds that economic security in childhood is associated with better long term mental health prospects 69 . Until now, however, these factors had not been linked to inner peace in adulthood, and thus our work now extends the literature to encompass this. Moreover, perhaps of even greater value in this study is the way it highlights national variation, showing that the impact of these factors differs considerably based on the location. Thus, the effect of having a good relationship with one’s mother ranged from (RR =) 0.89 in India (95% CI [0.79, 1.00]) to 1.26 in Indonesia (95% CI [0.99, 1.60]), while the impact of having a good relationship with one’s father ranged from 0.93 in Nigeria (95% CI [0.82, 1.05]) to 1.16 in Türkiye (95% CI [0.92, 1.45]). Likewise, there was considerable variation pertaining to parental marital status, where compared to having parents who were married, the effect of parents: being divorced ranged from 0.81 in Nigeria (95% CI [0.72, 0.91]) to 1.36 in Türkiye (95% CI [1.03, 1.80]); being single or never married ranged from 0.79 in Egypt (95% CI [0.58, 1.06]) to 1.15 in Australia (95% CI [1.01, 1.31]); and one or both parents having died ranged from 0.74 in South Africa (95% CI [0.61, 0.90]) to 1.16 in Mexico (95% CI [1.02, 1.32]) and the Philippines (95% CI [0.91,1.48]). Finally, there was also variation in relation to finances, albeit less so than the other familial dynamics, implying this factor is somewhat less susceptible to cultural influence. Thus, compared to those whose families “got by” financially, the effect of one’s family having “lived comfortably” ranged from 0.95 in Poland (95% CI [0.91, 1.00]) to 1.18 in Türkiye (95% CI [1.03, 1.34]), while for those who found it “difficult” ranged from 0.91 in Japan (95% CI [0.87, 0.95]) to 1.05 in the US (95% CI [1.00, 1.09]), and for those who found it “very difficult” ranged from 0.76 in Türkiye (95% CI [0.55, 1.07]) to 1.14 in Spain (95% CI [0.94, 1.38]). Again, these regional differences are fascinating and deserve further study, and will require in-depth enquiry into cultural dynamics to help explain them. Consider for example the impact of having parents who were divorced, with a 0.55 RR differential between Nigeria, where such divorce has a markedly negative impact on the likelihood of experiencing peace in adulthood, and Türkiye, where it means one is more likely to have peace compared to people whose parents were married. Accounting for such findings will require detailed exploration into the traditions, values and practices pertaining to both marriage and divorce in the respective countries. It may be relevant, for instance, that Nigeria has large numbers of both Christians (45.9% of the population) and Muslims (53.5%), whereas Türkiye is overwhelmingly Muslim (99%) 70 . In that respect, it is possible that Islam is more accommodating of divorce – albeit still describing it as a “necessary evil” 71 – than Christianity, and hence overall may be less destabilising to the future equanimity of Muslims than Christians. However, when comparing results across countries, it is also possible that subtle culturally-influenced linguistic nuances are playing a role, influencing the data. When developing translations of the original English-language scale for use in the non-English-speaking countries, Gallup used their considerable experience and expertise to ensure the translations were as accurate and comparable as possible, such that the rendering of “inner peace” in Turkish would signify the same phenomenological state as do its equivalents in the languages of Nigeria. It is nevertheless possible that these terms were not precisely equivalent, and perhaps – even if only very subtly – were actually assessing slightly different outcomes. This is not a possibility we can investigate in the present paper, and would require in depth qualitative research to explore, which indeed we hope this paper will inspire. But it is still worth bearing in mind as we seek to understand apparent differences between nations. Another important variable is religious attendance at age 12. Not only was this associated with adult inner peace, but moreover an increasing amount depended on the frequency of attendance. So, compared to those who never attended, the impact of attending rose from RR = 1.03 for those attending less than once a month (95% CI [1.02, 1.05]), to 1.05 for those attending 1–3 times a month (95% CI [1.02, 1.08]), to 1.06 for those attending at least weekly (95% CI [1.04, 1.09]). This aligns with an extensive body of work on the positive impact of childhood religious attendance on subsequent physical and mental health 72 and also with scholarship that explores the centrality of peace to many religious traditions 18 . Again though, our study seems to be the first to link childhood religious service attendance to inner peace specifically. Also again, however, perhaps even more striking is the regional variation, where the impact of attending less than once a month ranged from 0.84 in Nigeria (95% CI [0.67, 1.05]) to 1.17 in Türkiye (95% CI [0.94, 1.46]), of attending 1–3 times a month ranged from 0.93 in South Africa (95% CI [0.82, 1.05]) to 1.32 in Türkiye (95% CI [1.08, 1.61]), and attending weekly ranged from 0.92 in Nigeria (95% CI [0.79, 1.07]) to 1.33 in Türkiye (95% CI [1.12, 1.57]). Thus, we see a striking comparison between – as above – Nigeria and Turkey in particular, where childhood religious attendance in the former seems potentially detrimental to adult inner peace, while in the latter it strongly supports this later outcome. Thus, as with all factors here, the impact of attendance may not be uniformly positive, and depends on cultural factors. In that respect, in-depth work in places like Nigeria will help us better understand why this country in particular seems to buck the overall trend. One wonders, for example, about the relevance, as noted above, of Nigeria having two main religions – which moreover can often be in tension and even conflict with one another in the country 73 – while Türkiye is nearly all Muslim, and hence lacks comparable internal divisions. It does therefore seem plausible that religious involvement in Nigeria could bring a level of adversity or friction that is mostly absent in Türkiye, thus accounting for the significant disparities in the impact of that involvement on adult inner peace. The final set of factors that seem impactful for inner peace are adverse experiences, namely experiencing abuse and being an outsider growing up, both with an RR of 0.94 (and 95% CIs of 0.92, 0.96, and 0.91, 0.97, respectively). These of course connect with a now vast literature on the long-term detrimental impact of Adverse Childhood Experiences, which are documented to negatively impact a panoply of outcomes later in life, ranging from substance use 74 and food insecurity 75 to depression 76 and even frailty in older adults 77 . Thus, to this literature we can also add that such adversities also lower the likelihood of experiencing inner peace as an adult. Again though, the regional variation is striking, where the impact of abuse ranges from 0.80 in Poland (95% CI [0.69, 0.91]) to 1.01 in Mexico (95% CI [0.95, 1.07]), while the impact of being an outsider ranges from 0.83 in Brazil (95% CI [0.78, 0.88]) to 1.07 in Türkiye (95% CI [0.87, 1.33]). Here it seems that, in certain countries, experiencing abuse or being an outsider can make it more likely one will experience peace later in life. This seems to echo the finding above regarding poor childhood health, where in select countries, like Nigeria, this raised the chances of people having inner peace in adulthood. As in that health case, it would appear that, at least in some cultural contexts, adversity can lead people to develop the aptitude or fortitude that leads to a greater propensity to attain peace later in life. Again, we cannot tell from our data what it is about these particular contexts that does perhaps enable that, but this would be a fruitful avenue for future research to investigate. Finally, there are three factors that are not necessarily about childhood per se, but are nevertheless relevant to childhood, namely, people’s age, sex, and immigration status. In one sense of course, these are childhood factors (in that they tell us something about people’s childhood), but from another perspective they are factors that pertain to the individual at all life stages. Nevertheless, they are worth briefly noting here. Of these, age had the strongest impact. Essentially, the older the participant, the more likely they are to have inner peace. Compared to people aged 18–24 (i.e., born between 1998 and 2005), those aged 25–29 (1993–1998) had just a marginally higher RR of 1.01 (95% CI [0.99, 1.03]), but the RRs rise in a linear way with the age categories, culminating in an RR of 1.19 (95% CI [1.12, 1.27]) for people aged over 80 (born in 1943 or earlier). These findings could be regarded as reflecting a childhood factor, especially if we interpret the data as being about the time period when people were born , hence being a cohort effect. However, the emergent literature on inner peace suggests it tends to increase as a function of age 30 . As such, it is perhaps more realistic to interpret the findings here as simply being more a question of the actual current age of the participants. Nevertheless, it is again still interesting to note regional variation, where the RR of this oldest category ranged from 0.77 in Poland (95% CI [0.60, 1.00]) to 1.37 for the UK (95% CI [1.20, 1.56]) and US (95% CI [1.22, 1.54]), showing that the relationship between age and peace is not universally observed, and like the other factors here is affected by socio-cultural dynamics. The penultimate variable is gender, which ranked second last in terms of impact, where compared to men, women had an RR of 0.98 (95% CI [0.96, 1.00]). That said, we should note a very small percentage of the sample stated their sex was neither male nor female but “other”, with this group having considerably lower inner peace (RR = 0.44, 95% CI [0.13, 1.50]). We do need to be cautious in interpreting this finding, as this group was very small (< 0.1% of the observed sample) within several countries, leading to complete separation and large uncertainty in this estimate. Nevertheless, it is a strikingly low RR that does demand further study. There is by now an extensive literature showing that people who identify as LGBTQ + tend to have lower levels of mental health across the lifespan, from youth 78 to older adults 79 . It is perhaps unsurprising then that this factor then would also affect inner peace. It is not certain whether the data here constitutes a childhood factor per se, since the item asks people their current gender, not their gender as a child, and it is possible that some percentage who answered “other” now would not have done so in childhood. That said, even if the latter were the case, it is likely that some relevant dynamics may have manifested during childhood (e.g., a sense of gender dysphoria). Thus, more research will be needed to look into this finding. Also, as with other factors, it will also be important to investigate the regional variation, where the RR for females ranged from 0.90 in Kenya (95% CI [0.87, 0.93]) to 1.13 in Türkiye (95% CI [1.00, 1.27]), and for those answering “other” ranging from 0.40 in Indonesia (95% CI [0.10, 1.61]) to 1.36 in Mexico (95% CI [0.89, 1.96]). It would be helpful to know, for instance, what it is about Mexico that means people who answer “other” tend to be much more likely to have inner peace than men or women. Lastly, there was one factor with no significant impact on peace, namely immigration status: compared to people born in the country in which they live, those born elsewhere had a RR that was basically equal (1.01, 95% CI [0.98, 1.03]). As with age and gender, this is not necessarily a childhood factor, since it reflects a person’s current immigrant status, not that of when they were a child. Nevertheless, it is still intriguing to note that such status does not seem to have any bearing on inner peace, which is notable, given that being an immigrant is frequently perceived as presenting challenges that can be detrimental to mental health 80 . That said, research has often found immigrant mental health is “better than expected” 81 , and may even be better than native people, a phenomenon remarked on often enough to have a label – the “healthy immigrant effect” – which “suggests that immigrants have a health advantage over the domestic-born,” though this usually “vanishes with increased length of residency” 82 . In our case, while we didn’t observe this kind of effect, neither were immigrants disadvantaged when it comes to peace. Again though, there were also significant regional disparities, with RR ranging from 0.92 in Egypt [0.69, 1.23] and India (95% CI [0.76, 1.10]) to 1.19 in Tanzania (95% CI [0.85,1.65]), so in some countries at least the healthy immigrant effect does seem to play out. Conclusion Inner peace is a low arousal positive state that has received relatively little attention amidst the proliferation of research into the various aspects of flourishing ever recent decades. Our paper is one of the first to explore country-level variations in the childhood predictors of this important constituent of flourishing. Using a retrospective assessment of childhood experiences in 22 countries, combined with several important current demographics, we found that many aspects of a child's upbringing do in fact predict peace in adulthood, although there are important country-level variables that require further research to understand. The most impactful factor that we found was self-rated health growing up, while the least impactful predictor was immigration status (which indeed was the only factor with a non-significant effect). All the significant relationships documented in this study were robust to potential unmeasured confounding, as assessed by E-values. We hope that future research will help to explain the reasons why we found some quite divergent patterns across countries, and more generally that researchers will pay closer attention to inner peace as an important constituent of human flourishing. Declarations Author Contribution T.L. wrote the main manuscript text. R.N.P. prepared all tables and figures. B.R.J. and T.V.J. led the overall study on which this paper reports. All authors reviewed the manuscript and contributed edits and additions to the text. Acknowledgement This is not an acknowledgment, but rather a statement we have been asked to include with our submission (and I cannot find another suitable location): This submission is part of the Global Flourishing collection. We were invited to submit this manuscript, following a peer review offer by the Chief Editor. Data Availability The study design was pre-registered with the Open Science Framework on November 18th, 2023 (see https://osf.io/5yr62/). The datasets generated and/or analysed during the current study are available in the Open Science Framework repository upon submission of pre-registration (https://www.cos.io/gfs-access-data), as is the methodology for the analyses (https://osf.io/pv93c), and all code to reproduce the analyses (https://osf.io/9egpr). References Solmi, M. et al. Risk and protective factors for mental disorders with onset in childhood/adolescence: An umbrella review of published meta-analyses of observational longitudinal studies. 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Elshahat, S., Moffat, T. & Newbold, K. B. Understanding the healthy immigrant effect in the context of mental health challenges: A systematic critical review. J Immigr Minor Health 24, 1564–1579 (2022). Additional Declarations No competing interests reported. Supplementary Files Innerpeacechildhoodonlinesupplement.docx Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviews received at journal 05 Aug, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers invited by journal 22 Jul, 2024 Editor assigned by journal 22 Jul, 2024 Editor invited by journal 15 Jul, 2024 Submission checks completed at journal 15 Jul, 2024 First submitted to journal 18 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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VanderWeele","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Tyler","middleName":"J.","lastName":"VanderWeele","suffix":""}],"badges":[],"createdAt":"2024-06-18 23:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4602277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4602277/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-83353-z","type":"published","date":"2025-04-30T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81988109,"identity":"9dfefa6d-ccce-4c57-9425-60217060d7cf","added_by":"auto","created_at":"2025-05-05 16:07:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1372529,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4602277/v1/771a8c81-9433-4037-ae78-9deb65415f31.pdf"},{"id":61935824,"identity":"3da7f341-952c-4e28-8872-b9b90fe23c89","added_by":"auto","created_at":"2024-08-07 09:10:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5907082,"visible":true,"origin":"","legend":"","description":"","filename":"Innerpeacechildhoodonlinesupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-4602277/v1/73b29f783d5416314bdd600d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Childhood predictors of inner peace: A cross-national analysis of the Global Flourishing Study","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eThe Roots of Flourishing\u003c/h2\u003e \u003cp\u003eFor decades, indeed centuries, scholars have been intrigued by how a person\u0026rsquo;s childhood affects outcomes later in life. Answering this question ideally involves, (a) longitudinal studies that, (b) begin tracking participants during childhood, including (c) with attention to numerous contextual factors (e.g., details of their family), and (d) follow participants into adulthood, with (e) relevant outcomes of interest (e.g., variables relating to wellbeing). For some outcomes, a wealth of studies meet these criteria. On the whole though, these tend to focus on \u0026lsquo;negative\u0026rsquo; (i.e., undesirable) outcomes, such as mental health problems. Such is the depth of longitudinal research on environmental factors associated with their onset during childhood/adolescence, for example, that sufficient meta-analyses exist to allow an umbrella review of them\u003csup\u003e1\u003c/sup\u003e. Indeed, this focus on negative phenomena has characterised academia for most of the past century. However, this is gradually changing, with increasing attention to more \u0026lsquo;positive\u0026rsquo; (i.e., desirable) outcomes. This interest is not new per se; humanistic scholars like Maslow advocated such an approach over 80 years ago\u003csup\u003e2\u003c/sup\u003e. However, not until the emergence of positive psychology in the late 1990s did this work begin to receive more widespread hearing, with Martin Seligman using his ascension to the presidency of the American Psychological Association to encourage greater attention to positive phenomena as serious and legitimate topics of scientific enquiry.\u003c/p\u003e \u003cp\u003eAs a result, outcomes like happiness \u0026ndash; which we briefly discuss here as an exemplar of the wider literature on flourishing, with the latter being a more comprehensive term that includes states like happiness as facets \u0026ndash; have now received extensive attention. This includes the kind of longitudinal work mentioned above that allows scholars to explore the childhood roots of such outcomes. One study for example used data from the UK National Child Development Study to study a cohort of 4,400 children born in 1958 who had been repeatedly surveyed for 50 years, including a question on life satisfaction (\u0026ldquo;how satisfied or dissatisfied you are with the way life has turned out so far\u0026rdquo;) at four points in adulthood (ages 33, 42, 46, and 50)\u003csup\u003e3\u003c/sup\u003e. Notably, demographic and socio-economic factors in early childhood had a relatively negligible effect, predicting just 1.2% of the variance in adult life satisfaction. By contrast, more individual characteristics \u0026ndash; especially childhood behavioural-emotional problems and social maladjustment \u0026ndash; were \u0026ldquo;powerful predictors\u0026rdquo; of adult satisfaction. Similar findings were obtained in an analysis of 5,124 young participants (aged 11\u0026ndash;15) from the British Household Panel Survey\u003csup\u003e4\u003c/sup\u003e. The study began by assessing the determinants of youth happiness \u0026ndash; on a scale of 1 (completely happy) to 7 (completely unhappy) \u0026ndash; and found contextual factors like family structure had a significant impact. Living with both natural parents, for example, was linked to greater happiness, both relative to a step family (for boys and girls) and living with a single parent (evidence for boys). Most relevantly here, a subset of participants (1,825) went on to participate in the adult panel (i.e., 18+), which features a question on life satisfaction. While satisfaction was influenced by demographic and socio-economic conditions in childhood, their impact was much smaller than a \u0026ldquo;youthful personality trait for happiness\u0026rdquo; (i.e., an \u0026ldquo;individual effect\u0026rdquo; in the analysis of youth happiness that captures individual heterogeneity). Specifically, youth happiness predicted from socio-economic factors had a coefficient of 0.15 and 0.14 for adult satisfaction for women and men respectively, whereas the coefficient for the youth happiness \u003cem\u003etrait\u003c/em\u003e was 0.27 (for both men and women). Moreover, such work is not limited to happiness; research has also indicated that perceived childhood experiences of, for example, parental warmth subsequently predict a range of domains of flourishing, including more positive relationships and a variety of forms of social wellbeing, including social contribution and integration\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThrough such research we are gaining a better understanding of topics like happiness and other domains of flourishing. However, other desiderata have received relatively little attention, in general, and certainly in any comparable longitudinal way to the happiness studies cited above. These neglected topics include \u0026ldquo;low arousal positive states\u0026rdquo; (LAPS) such as inner peace. The theoretical context for understanding these is Russell\u0026rsquo;s influential circumplex model of affective states\u003csup\u003e6\u003c/sup\u003e, which construes them through the intersection of two parameters: valence (experientially pleasant and approach-inducing, versus unpleasant and withdrawal-inducing), and arousal (high versus low, or active versus passive). Thus, affective states are understood as being generated \u0026ndash; physiologically, mentally, experientially, etc. \u0026ndash; by their interaction\u003csup\u003e7\u003c/sup\u003e. Juxtaposing the parameters, Russell created a two-dimensional state space with four quadrants: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) low arousal and negative valence (e.g., depression); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) high arousal and negative valence (e.g., anxiety); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) low arousal and positive valence (e.g., calmness); and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) high arousal and positive valence (e.g., elation). Most relevantly here, when it comes to research on positively-valenced states, the majority of attention has focused on \u003cem\u003ehigh\u003c/em\u003e arousal forms, like enjoyment. For example, among the most influential constructs in this arena is subjective wellbeing (SWB)\u003csup\u003e8\u003c/sup\u003e, comprising a cognitive component (usually understood and measured using constructs of life satisfaction or evaluation) and an affective component (viewed through the prism of positive affect). However, most research on the latter has concentrated on high arousal forms. For instance, since 2005 the Gallup World Poll (GWP) has included items pertaining to both components of SWB; most relevantly here, until very recently, positive affect had just been assessed through high arousal notions like enjoyment and laughter. Significantly though, since 2020 these items have been augmented by items pertaining to LAPS, as we explore below. First though, our next section considers why LAPS have historically been overlooked.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eNeglecting LAPS\u003c/h2\u003e \u003cp\u003eIn attempting to understand the tendency in wellbeing research to focus on high rather than low arousal states, two plausible interlinked explanations are, (a) the Western-centric nature of psychology, and (b) greater importance being placed on HAPS than LAPS in Western cultures, and hence psychology. Let\u0026rsquo;s briefly consider each in turn. To begin with, it is increasingly recognized that psychology has historically been Western-centric, as argued influentially by Henrich and colleagues\u003csup\u003e9\u003c/sup\u003e, who observed that most work has been conducted by and on people in societies that are relatively \u0026ldquo;WEIRD\u0026rdquo; (Western, Educated, Industrialised, Rich, and Democratic). While one ought not to simplistically classify societies in a binary way as WEIRD versus non-WEIRD\u003csup\u003e10\u003c/sup\u003e \u0026ndash; since each element of the acronym is a spectrum upon which countries may be variously situated \u0026ndash; most of the world is certainly not \u003cem\u003eas\u003c/em\u003e WEIRD as places like the USA, where most scholarship in top journals takes place. As a result, critics have questioned the extent to which such research is generalizable and universally valid. Although some scholars might retort that people are relatively similar across cultures and share a common human nature, and hence findings from Western contexts \u003cem\u003ecan\u003c/em\u003e be extrapolated to other locales, many academics would likely agree that the conditions of life can vary dramatically among cultures. As a result, it can be problematic to draw conclusions about human experience based only on comparatively WEIRD contexts. Moreover, the issue is not only about participants, but scholars themselves, who are likewise shaped by their context, which will thus influence all aspects of their work, from their choice of topics and methodologies to subsequent analyses and interpretation of data.\u003c/p\u003e \u003cp\u003eMoreover, the Western-centric bias of academia has been implicated by Tsai and colleagues in the relative neglect of LAPS, who suggest the preference for HAPS is a Western-centric concern, whereas by contrast, Eastern cultures place greater value on LAPS. Tsai described such preferences as \u0026ldquo;ideal affect\u0026rdquo;\u003csup\u003e11\u003c/sup\u003e \u0026ndash; \u0026ldquo;the affective states that people strive for or ideally want to feel\u0026rdquo; (p.243) \u0026ndash; and has observed these across an extensive series of studies, mostly involving college students in America and China\u003csup\u003e12,13,14,15\u003c/sup\u003e, with similar patterns observed by others\u003csup\u003e16,17,18,19\u003c/sup\u003e. That said, new items on LAPS in the GWP \u0026ndash; mentioned above and discussed further below \u0026ndash; indicate such emotions are more universally valued and experienced than these East-West generalisations imply\u003csup\u003e20\u003c/sup\u003e. Even so, notwithstanding such findings, one can still argue that LAPS have historically received greater valorization and attention in Eastern cultures, for which various explanations have been mooted.\u003c/p\u003e \u003cp\u003eOne prominent interpretation invokes another distinction often noted vis-\u0026agrave;-vis East-West differences, namely between individualism and collectivism. Generalisations along these lines have been aired for centuries, often by Western scholars seeking to disparage the East, as notably charted by Said in his critical text \u003cem\u003eOrientalism\u003c/em\u003e\u003csup\u003e21\u003c/sup\u003e. However, in the modern era, this binary has been harnessed in a relatively neutral way by Hofstede\u003csup\u003e22\u003c/sup\u003e, who deployed it to differentiate cultural contexts, and then Markus and Kitayama\u003csup\u003e23\u003c/sup\u003e, who shifted the emphasis to self-construal (i.e., how people view themselves). Subsequently, this distinction has been explored in hundreds of studies, with numerous meta-analyses, not only of the distinction per se, but specific facets, such as its link to subjective wellbeing\u003csup\u003e24\u003c/sup\u003e. Most relevantly here, it has been suggested that Eastern cultures tend to appraise HAPS as relatively self-aggrandizing and hence disruptive of social harmony, whereas LAPS are more conducive to such harmony\u003csup\u003e16,25\u003c/sup\u003e. Scholars have also pointed towards other cultural trends, such as the rich history of contemplative practices in Eastern cultures, that may also contribute to their greater valorisation of LAPS\u003csup\u003e26\u003c/sup\u003e. Whatever the explanation, there has been a relative inattention to LAPS in academia, with research tending to focus on HAPS\u003csup\u003e27\u003c/sup\u003e. However, the situation may be changing amidst a broader concern with redressing the Western-centricity of psychology: a review of positive psychology interventions found that although 78.2% were in Western countries, there was \u0026ldquo;a strong and steady increase in publications from non-Western countries since 2012,\u0026rdquo; indicating an encouraging \u0026ldquo;trend towards globalization\u0026rdquo; of happiness research\u003csup\u003e28\u003c/sup\u003e. These dynamics also mean LAPS are beginning to receive more attention, as exemplified by the Global Wellbeing Initiative.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGlobal Wellbeing Initiative and the Gallup World Poll\u003c/h3\u003e\n\u003cp\u003eSince 2005 the GWP has collected data annually on wellbeing (and many aspects of life) worldwide. However, it has still been subject to the Western-centricism that characterises wellbeing research more broadly, with its main metrics being Cantril\u0026rsquo;s \u0026ldquo;ladder\u0026rdquo;\u003csup\u003e29\u003c/sup\u003e item on life evaluation and several pertaining to HAPS. To redress these issues, the Global Wellbeing Initiative (GWI), a partnership between Gallup and the Japan-based Wellbeing for Planet Earth foundation, was launched in 2019, focusing on developing items related to Eastern cultures (given the Japanese location of the foundation). The first iteration was in the 2020 GWP, as analysed in a chapter for the 2022 World Happiness Report\u003csup\u003e30\u003c/sup\u003e. Subsequently, the module has been through two substantive iterations\u003csup\u003e31\u003c/sup\u003e, and by the 2022 GWP it was centred exclusively on balance/harmony and LAPS, collectively described as \u0026ldquo;harmonic principles of wellbeing\u0026rdquo;\u003csup\u003e32\u003c/sup\u003e. Most notably, this has included an item on inner peace: \u0026ldquo;Did you feel at peace most of the day yesterday, or not?\u0026rdquo; (2020); \u0026ldquo;In general, how often do you feel you are at peace with your thoughts and feelings?\u0026rdquo; (2021); and \u0026ldquo;In general, how often can you find inner peace during difficult times?\u0026rdquo; (2022, 2023, 2024). In a forthcoming analysis (under review), numerous notable patterns have emerged.\u003c/p\u003e \u003cp\u003eOf particular relevance here are the contextual factors associated with peace, as revealed by a regression analysis. In that regard, there were intriguing differences between the three peace items, leading to an interpretation that while the latter two are more directly about \u0026ldquo;inner peace,\u0026rdquo; the 2020 item is more ambiguous, straddling inner and \u0026ldquo;outer peace\u0026rdquo; (i.e., the peacefulness of one\u0026rsquo;s societal context). Consider for example that poverty only had a significant impact on the 2020 item, both in terms of lacking money for food (B = -0.37) and shelter (B = -0.49). One might suggest that people lacking money for these necessities are indeed likely to experience a lack of outer peace, living in situations that are challenging, which perhaps lies behind these variables having a significant impact on being \u0026ldquo;at peace most of the day.\u0026rdquo; By contrast, and perhaps counter expectations, poverty appeared to have no discernible impact on the two items that tapped more directly into inner peace. Similarly, the 2020 item was more strongly associated with life factors such as finding it \u0026ldquo;very difficult to get by\u0026rdquo; on present income (B = -1.81, versus \u0026minus;\u0026thinsp;0.30 for 2021 and \u0026minus;\u0026thinsp;0.37 for 2022), and having other people to count on (B\u0026thinsp;=\u0026thinsp;0.58, versus 0.07 for 2021 and 0.15 for 2022). Conversely, the more \u0026ldquo;inner-oriented\u0026rdquo; factor of negative emotions had a stronger association with the 2021 (B = -0.44) and 2022/2023/2024 (B = -0.45) items than 2020 (B = -0.18). There were also interesting demographic patterns, but most saliently these had an overall larger impact on the 2020 item.\u003c/p\u003e \u003cp\u003eWhile these kinds of contextual analyses are interesting and relevant, here we are especially interested in the \u003cem\u003echildhood\u003c/em\u003e predictors of peace. However, the GWP data is not conducive to that kind of assessment, since it neither has respondents who are children nor asks adults about childhood. Indeed, although academia has paid increasing attention to the childhood predictors of certain flourishing outcomes, such as happiness, this has so far not extended into the domain of LAPS. That is, among the kinds of rigorous longitudinal work that allows assessment of the childhood predictors of positive outcomes, metrics pertaining to LAPS have mostly been absent from such research, reflecting their general omission from scholarship on flourishing more generally. That said, there has been \u003cem\u003esome\u003c/em\u003e relevant work: it has been suggested, for example, that childhood trauma may lead to a diminished sense that attainment of inner peace is possible\u003csup\u003e33\u003c/sup\u003e; similarly, maladaptive stress coping mechanisms, along with disrupted patterns of homeostasis, may contribute to neuropsychiatric disorders that are inimical to inner peace\u003csup\u003e34\u003c/sup\u003e. Conversely, it has been argued that \u0026ldquo;Positive early interpersonal experience lays the groundwork for a more peaceful individual life,\u0026rdquo; drawing on a synthesis of relevant studies to conclude that a \u0026ldquo;cycle of positive early experience\u0026rdquo; fosters secure relationships that \u0026ldquo;promote cognitive and social skills, in turn leading to more peaceful relationships within families and beyond\u0026rdquo;\u003csup\u003e35\u003c/sup\u003e. Likewise, inner peace is also part of a set of spiritual factors and experiences that partially mediate the effect of Adverse Childhood Experiences on quality of life in adulthood\u003csup\u003e36\u003c/sup\u003e, which suggests relationships between inner peace and a variety of social factors may be reciprocal over the life course. Such research, while relatively sparse, is sufficient to suggest that conditions and experiences in childhood may well have a bearing on inner peace later in life. However, more work is needed \u0026ndash; especially rigorous longitudinal designs \u0026ndash; to explore this neglected area of inquiry more fully.\u003c/p\u003e \u003cp\u003eTo that end, the present paper reports on an assessment of inner peace that has been included in the Global Flourishing Study (GFS), an ambitious intended five-year longitudinal study investigating the predictors of human flourishing across over 200,000 participants from 22 geographically and culturally diverse countries. The particular distinguishing feature of this study is its longitudinal nature. While there are various laudable endeavours researching flourishing in a global context \u0026ndash; such as the GWP \u0026ndash; these are mostly cross-sectional, so cannot provide much insight into causal dynamics. Hence the value of the GFS, which includes a comprehensive battery of items relating to all aspects of flourishing, which will be assessed longitudinally. Furthermore, even the first-wave demographic intake form included retrospective enquires into participants\u0026rsquo; childhood experiences, allowing for a synthetic longitudinal study of sorts based on the first year of data alone, which is the focus of the present paper. Our dependent variable of interest is an item, as an adult outcome, on inner peace, adapted from the 2021 GWI item: \u0026ldquo;In general, how often do you feel you are at peace with your thoughts and feelings?\" (always, often, rarely, never). The analysis here is guided by three research questions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) how do different aspects of a child's recalled upbringing predict inner peace in adulthood; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) do these associations vary by country; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) are the observed relationships robust to potential unmeasured confounding, as assessed by E-values? Specifically, we look at 13 different childhood predictors: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age (year of birth); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) gender; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) marital status / family structure; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) age 12 religious service attendance; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) religious affiliation at age 12; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) relationship with mother; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) relationship with father; (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) outsider growing up; (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) abuse; (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) self-rated health growing up; (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) immigration status; (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) subjective financial status of family growing up; and (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) race/ethnicity (when available). We have three main hypotheses: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) among the 13 childhood predictors, certain ones will show meaningful associations with an individual\u0026rsquo;s inner peace in adulthood; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the strength of associations between the predictors and inner peace in adulthood will vary by country, reflecting the influence of diverse sociocultural, economic, and health contexts that characterize each nation; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the observed associations between the predictors and inner peace in adulthood will be robust against potential unmeasured confounding (as assessed through E-values, in some cases suggesting that the observed associations would require strong confounding effects by unmeasured variables to explain away, thus enhancing the credibility of our findings).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe description of the methods below has been adapted from VanderWeele and colleagues\u003csup\u003e37\u003c/sup\u003e. Further methodological detail is available elsewhere\u003csup\u003e38,39,40,41,42,43,44,45\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003e The GFS is a study of 202,898 participants (in this first year) from 22 geographically and culturally diverse countries, with nationally representative sampling within each country, concerning the distribution of determinants of well-being. Wave 1 of the data included the following countries and territories: Argentina, Australia, Brazil, Hong Kong [S.A.R of China, with mainland China also included from 2024 onwards], Egypt, Germany, India, Indonesia, Israel, Japan, Kenya, Mexico, Nigeria, Philippines, Poland, South Africa, Spain, Sweden, Tanzania, Turkey, United Kingdom, and United States. (Note: Data from Hong Kong (S.A.R. of China) is available in the first wave of data collection. Data from mainland China were not included in the first data release due to fieldwork delays. The first wave of fieldwork in mainland China began in February 2024, and a second wave is expected to occur in November-December 2024. All wave 1 and 2 data from mainland China will be part of the second dataset release in March 2025.) 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\u003e44\u003c/sup\u003e. Four additional waves of panel data on the participants will be collected annually from 2024\u0026ndash;2027. The precise sampling design to ensure nationally representative samples varied by country and further details are available\u003csup\u003e44\u003c/sup\u003e. Survey items included aspects of flourishing such as happiness, health, meaning, character, relationships, and financial stability\u003csup\u003e46\u003c/sup\u003e, plus other demographic, social, economic, political, religious, personality, childhood, community, health, and wellbeing variables. 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\u003e47\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eChildhood Antecedents.\u003c/em\u003e Relationship with mother during childhood was assessed with the question: \u0026ldquo;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?\u0026rdquo; Responses were dichotomized to very/somewhat good versus very/somewhat bad. An analogous variable was used for relationship with father. \u0026ldquo;Does not apply\u0026rdquo; 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: \u0026ldquo;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?\u0026rdquo; Responses were lived comfortably, got by, found it difficult, and found it very difficult. Abuse was assessed with yes/no responses to \u0026ldquo;Were you ever physically or sexually abused when you were growing up?\u0026rdquo; Participants were separately asked: \u0026ldquo;When you were growing up, did you feel like an outsider in your family?\u0026rdquo; Childhood health was assessed by: \u0026ldquo;In general, how was your health when you were growing up? Was it excellent, very good, good, fair, or poor?\u0026rdquo; Immigration status was assessed with: \u0026ldquo;Were you born in this country, or not?\u0026rdquo; Religious attendance during childhood was assessed with: \u0026ldquo;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?\u0026rdquo; with responses of at least once/week, one-to-three times/month, less than once/month, or never. Gender was assessed as male, female, or other. Continuous age (year of birth) was classified as 18\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;39, 40\u0026ndash;49, 50\u0026ndash;59, 60\u0026ndash;69, 70\u0026ndash;79, and 80 or older. Childhood religious tradition/affiliation was had response categories of Christianity, Islam, Hinduism, Buddhism, Judaism, Sikhism, Baha\u0026rsquo;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 (Johnson et al., 2023). Racial/ethnic identity were assessed in some, but not all, countries, and response categories were unique to each country. For additional details on the assessments see the COS GFS codebook\u003csup\u003e47\u003c/sup\u003e or Crabtree et al.\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eOutcome variable.\u003c/em\u003e Inner peace is assessed with one question: \"In general, how often do you feel you are at peace with your thoughts and feelings?\" The response categories are: always, often, rarely, never. In our analyses, we dichotomized inner peace as always/often [1] vs rarely/never [0].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics for the observed sample, weighted to be nationally representative within country, were estimated for each childhood demographic category. A weighted modified Poisson regression model with complex survey adjusted standard errors was fit within each country of inner peace on all of the aforementioned childhood predictor variables simultaneously. In the primary analyses, random effects meta-analyses of the regression coefficients \u003csup\u003e48,49\u003c/sup\u003e along with confidence intervals, estimate proportions of effects across countries with effect sizes (risk-ratios) larger than 1.1 and smaller than 0.9, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}^{2}\\)\u003c/span\u003e\u003c/span\u003e for evidence concerning variation within a given demographic category across countries\u003csup\u003e50\u003c/sup\u003e. Forest plots of estimates are available in the online supplement. Religious affiliation/tradition and race/ethnicity were used within country as control variables, when available, but these coefficients themselves were not included in the meta-analyses since categories/responses varied by country. All meta-analyses were conducted in R\u003csup\u003e51\u003c/sup\u003e using the metafor package\u003csup\u003e52\u003c/sup\u003e. Within each country, a global test of association of each childhood predictor variable group with outcome was conducted, and a pooled p-value\u003csup\u003e53\u003c/sup\u003e across countries reported concerning evidence for association within any country. Bonferroni corrected p-value thresholds are provided based on the number of childhood demographic variables \u003csup\u003e54,55\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\u003e56\u003c/sup\u003e. As a supplementary analysis, population weighted meta-analyses of the regression coefficients were estimated. All analyses were pre-registered with COS prior to data access, with only slight subsequent modification in the regression analyses due to multicollinearity (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/ZTM7R\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/ZTM7R\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); all code to reproduce analyses are openly available in an online repository\u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMissing Data\u003c/h3\u003e\n\u003cp\u003eMissing data on all variables was imputed using multivariate imputation by chained equations, and five imputed datasets were used\u003csup\u003e57,58\u003c/sup\u003e. To account for variation in the assessment of certain variables across countries (e.g., religious affiliation/tradition and race/ethnicity), the imputation process was conducted separately in each country. This within-country imputation approach ensured that the imputation models accurately reflected country-specific contexts and assessment methods. Sampling weights were included in the imputation model to account for missingness to be related to probability of inclusion.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\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\u003e44\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\u003e40\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe study design was pre-registered with the Open Science Framework on November 18th, 2023 (see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/5yr62/\u003c/span\u003e\u003cspan address=\"https://osf.io/5yr62/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The datasets generated and/or analysed during the current study are available in the Open Science Framework repository upon submission of pre-registration (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cos.io/gfs-access-data\u003c/span\u003e\u003cspan address=\"https://www.cos.io/gfs-access-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), as is the methodology for the analyses (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/pv93c\u003c/span\u003e\u003cspan address=\"https://osf.io/pv93c\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and all code to reproduce the analyses (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/9egpr\u003c/span\u003e\u003cspan address=\"https://osf.io/9egpr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the distribution of descriptive statistics (weighted counts and proportions). Participant ages ranged the entire adult lifespan (18\u0026ndash;80+). The gender distribution was nearly balanced with 51% female, 48% male, along with a small representation from other gender identities (0.3%). Most participants reported either having a somewhat good or very good relationship with either parent while growing up. The distribution of individuals reporting attending religious services growing up shows that 41% of participants report attending at least once a week and 23% of participants reporting never attending. Counts and proportions for demographic characteristics weighted to be representative of each country\u0026rsquo;s population are reported on in supplemental Tables S1a-S22a.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNationally representative descriptive statistics of the observed sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;202,898\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRelationship with mother\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127,836 (63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomewhat good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52,439 (26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomewhat bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,060 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,642 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes not apply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,965 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e956 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelationship with father\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107,742 (53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomewhat good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55,714 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomewhat bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,807 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,278 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes not apply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,985 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,372 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParent marital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParents married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152,001 (75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,726 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParents were never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,534 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne or both parents had died\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,794 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,843 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubjective financial status of family growing up\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLived comfortably\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70,861 (35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGot by\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82,905 (41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFound it difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35,852 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFound it very difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,606 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e674 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbuse\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29,139 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167,279 (82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,479 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutsider growing up\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28,732 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170,577 (84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,589 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-rated health growing up\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67,121 (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63,086 (31%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47,378 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,877 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,906 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e530 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImmigration status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorn in this country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190,998 (94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorn in another country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,791 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,110 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge 12 religious service attendance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least 1/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83,237 (41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,308 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36,928 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47,445 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,980 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear of birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1998\u0026ndash;2005; age 18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27,007 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1993\u0026ndash;1998; age 25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,700 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1983\u0026ndash;1993; age 30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40,256 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1973\u0026ndash;1983; age 40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34,464 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1963\u0026ndash;1973; age 50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,793 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1953\u0026ndash;1963; age 60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27,763 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1943\u0026ndash;1953; age 70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,776 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1943 or earlier; age 80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,119 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98,411 (49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103,488 (51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e602 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e397 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCountry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArgentina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,724 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,844 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,204 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,729 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,506 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHong Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,012 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,765 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,992 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsrael\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,669 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,543 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,389 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,776 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,827 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhilippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,292 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,389 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,651 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,290 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,068 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,075 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u0026uuml;rkiye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,473 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,368 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38,312 (19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%); History of abuse was not collected in Israel.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eChildhood Experiences Predicting Inner Peace\u003c/h2\u003e \u003cp\u003eThe meta-analytic estimates of how childhood experiences predict inner peace are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These results show an association between 10 of the 11 childhood candidate predictors and responses about inner peace (race and religious affiliation categories varied by country and so no meta-analysis is given but these are available by country in the Online Supplement). Childhood experiences associated with endorsing a sense of inner peace more often included having a good relationship with parents, having a sense of a comfortable financial status growing up, being in good or excellent health growing up, and more frequent attendance at religious services. These factors were, on average across countries, associated with a higher frequency of inner peace as an adult. However, these positive associations were not universal across all countries. In India, for example, the effect of having a very/somewhat good relationship with one\u0026rsquo;s mother was slightly negative to null (RR\u0026thinsp;=\u0026thinsp;0.89, 95% CI [0.79,1.00]). All country-specific results and variation are given in the Online Supplement, and we comment on the variation across countries further in the Discussion below. For most of these effects, though on average were positive, there was commonly no evidence for the effect being statistically different than zero within the country-specific analyses. The forest plots provided in our Online Supplement provide additional evidence for the heterogeneity of these effects across countries (see Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S27).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRandom effects meta-analysis of regressing inner peace on childhood predictors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eEstimated Proportion of Effects by Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGlobal p-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship with mother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Very bad/somewhat bad)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good/somewhat good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.03,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship with father\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Very bad/somewhat bad)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good/somewhat good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.01,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Parents married)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.94,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle, never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.92,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne or both parents had died\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.91,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective financial status of family growing up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Got by)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLived comfortably\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.02,1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFound it difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.96,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFound it very difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.93,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.92,0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutsider growing up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.91,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health growing up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Good)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.04,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.02,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.91,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.88,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmigration status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Born in this country)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorn in another country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.98,1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 12 religious service attendance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Never)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least 1/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.04,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.02,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.02,1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.1ǂ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: 1998\u0026ndash;2005; age 18\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1993\u0026ndash;1998; age 25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.99,1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1983\u0026ndash;1993; age 30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.00,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1973\u0026ndash;1983; age 40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.99,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1963\u0026ndash;1973; age 50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.02,1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1953\u0026ndash;1963; age 60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.04,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1943\u0026ndash;1953; age 70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.07,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1943 or earlier; age 80+\u003csup\u003eǂ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.12,1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.96,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003csup\u003eǂ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.13,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;202,898. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;.004 (Bonferroni corrected threshold); \u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;\u0026thinsp;0.1% of the observed sample) within several countries leading large uncertainty in this estimate or even complete separation\u0026mdash;be cautious about interpreting this estimate; CI\u0026thinsp;=\u0026thinsp;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\u003c/sup\u003e is 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 \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity of Effects to Unmeasured Confounding\u003c/h2\u003e \u003cp\u003eSensitivity to unmeasured confounding was assessed using E-values suggested that some of the observed associations were moderately robust to unmeasured confounding (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Thus, for example, to explain away the estimate for good/somewhat good relationship with mother, an unmeasured confounder associated with both higher inner peace and a good/somewhat good relationship with mother with risk ratios of 1.31 each, above and beyond the measured covariates, could suffice, but weaker joint confounder associations could not. To shift the confidence interval to include the null, an unmeasured confounder associated with both higher inner peace and a good/somewhat good relationship with mother with risk ratios of 1.23 each, above and beyond the measured covariates, could suffice, but weaker joint confounder associations could not. Further, country-specific sensitivity analyses are reported on in the Online Supplement (see Tables S1c-S23c).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity of meta-analyzed childhood predictors to unmeasured confounding.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE-value for\u003c/p\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE-value for\u003c/p\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship with mother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Very bad/somewhat bad)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good/somewhat good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship with father\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Very bad/somewhat bad)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good/somewhat good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Parents married)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle, never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne or both parents had died\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective financial status of family growing up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Got by)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLived comfortably\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFound it difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFound it very difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutsider growing up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health growing up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Good)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmigration status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Born in this country)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorn in another country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 12 religious service attendance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Never)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least 1/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: 1998\u0026ndash;2005; age 18\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1993\u0026ndash;1998; age 25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1983\u0026ndash;1993; age 30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1973\u0026ndash;1983; age 40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1963\u0026ndash;1973; age 50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1953\u0026ndash;1963; age 60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1943\u0026ndash;1953; age 70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1943 or earlier; age 80+\u003csup\u003eǂ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ref: Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003csup\u003eǂ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote. N\u0026thinsp;=\u0026thinsp;202,898; the E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome (inner peace) and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association (VanderWeele \u0026amp; Ding, 2017, p.\u0026nbsp;269\u0026ndash;270); and \u003csup\u003eǂ\u003c/sup\u003eGroup is very small (\u0026lt;\u0026thinsp;0.1% of the observed sample) within several countries potentially leading to complete separation and large uncertainty in this estimate\u0026mdash;be cautious about interpreting this estimate.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe analysis has shed unique light on the childhood predictors of inner peace. As indicated above, this outcome has received relatively little attention \u003cem\u003eper se\u003c/em\u003e, with low arousal emotions in generally being understudied and underappreciated in research on flourishing and its various aspects. It is thus unsurprising that there has been barely any research into its childhood predictors, hence the value of our study. In summary, all three of our main hypotheses were supported, often strikingly so. As a reminder, our first was that among the 13 childhood predictors, certain ones will show meaningful associations with inner peace in adulthood. Indeed, every predictor \u0026ndash; with the sole but striking exception of immigration status \u0026ndash; had a significant association with inner peace when meta-analyzed over the 22 countries. Second, the strength of associations between the predictors and inner peace in adulthood will vary by country, reflecting the influence of diverse sociocultural, economic, and health contexts that characterize each nation. Third, some of the observed associations between the predictors and an individual's inner peace in adulthood will be robust against potential unmeasured confounding (as assessed through E-values). Here we shall touch in turn on the predictors, beginning with the one with the strongest impact, namely self-rated health growing up. We discuss this factor in some detail as a way of illustrating the nature and nuances of the data. We then consider the other factors more briefly, referencing and extrapolating the points made in relation to health.\u003c/p\u003e \u003cp\u003eOverall, the most impactful factor on average was self-rated health growing up, as assessed on a five-point scale: poor; fair; good; very good; and excellent. Relative to the middle category of \u0026ldquo;good,\u0026rdquo; the Risk Ratios (RRs) range from 0.93 for poor (95% CI [0.88, 0.99]) and 0.94 for fair (95% CI [0.91, 0.98]), to 1.04 for very good (95% CI [1.02, 1.06]) to 1.07 for excellent (95% CI [1.04, 1.11]), with all results significant at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 level. An RR can be interpreted as the relative percentage in each category, which in the present paper is calculated in relation to the proportion of people reporting experiencing inner peace. In that respect, although peace was assessed on a four-point scale \u0026ndash; never, rarely, often, or always at peace \u0026ndash; in our analysis and interpretation we aggregate this into two binary categories, whereby people either \u003cem\u003ehave\u003c/em\u003e inner peace (endorsing either \u0026ldquo;often\u0026rdquo; or \u0026ldquo;always\u0026rdquo; on the peace item) or \u003cem\u003edo not have\u003c/em\u003e it (endorsing either \u0026ldquo;rarely\u0026rdquo; or \u0026ldquo;never\u0026rdquo;). Thus, taking the RR of 1.07 (95% CI [1.04, 1.11]) for excellent health as an example, this means that, compared to people who reported that they \u0026ldquo;only\u0026rdquo; had good health growing up, the proportion of people with excellent health who have inner peace is 1.07 times greater than those who do not have inner peace. Put another way, there is a 7% increase in having inner peace for those who reported excellent health in childhood relative to those who reported good health (conditional on all other variables in the model). Although this effect size is modest, at the population level it is quite meaningful, and moreover, in some countries the effect size was considerably higher.\u003c/p\u003e \u003cp\u003eOne can also examine the robustness of these associations through their E-value\u003csup\u003e56\u003c/sup\u003e, which pertains to our third main hypothesis. An E-value measures the strength that an \u0026ldquo;unmeasured confounder\u0026rdquo; \u0026ndash; a variable not included in the analyses \u0026ndash; would \u003cem\u003eneed to be\u003c/em\u003e to \u0026ldquo;explain away\u0026rdquo; the observed relationship. In the case of excellent health, the E-value for the estimate was 1.36. This would mean an unmeasured confounder would need to be both, (a) related to peace with a RR of 1.36 (meaning that being one unit higher on the confounder is associated with a 36% increase in having peace), \u003cem\u003eand\u003c/em\u003e (b) \u003cem\u003esimultaneously\u003c/em\u003e related to childhood health with a RR of 1.36 (where a one unit increase on the confounder is associated with a 36% increase in being in the excellent health category over the good category). It is this \u003cem\u003esimultaneous\u003c/em\u003e association of the unmeasured confounder with our outcome (peace) \u003cem\u003eand\u003c/em\u003e our predictor (health) that makes the unknown variable a \u0026ldquo;confounder.\u0026rdquo; The usefulness of the E-value is that it serves as a benchmark for thinking \u0026ndash; in light of existing knowledge and theory \u0026ndash; about whether a confounder \u003cem\u003ecould\u003c/em\u003e exist that does indeed fulfil this criterion of simultaneous association with responses to the health and peace items. Essentially, the closer the E-value to 1, the more likely it is that such a confounder could indeed exist. Conversely, the higher the E-value, the less likely that it does. In the present case, an E-value of 1.36 is judged as high: especially given observed associations, it may be difficult to envisage an unmeasured variable that could plausibly have an RR of 1.36 with both inner peace \u003cem\u003eand\u003c/em\u003e childhood health. That is, this RR would need to both be, (a) much higher than the one observed between excellent childhood health and inner peace (i.e., 1.07), and (b) apply separately to both childhood health \u003cem\u003eand\u003c/em\u003e inner peace. So, in terms of our third hypothesis, we have some evidence that the observed RR between inner peace and childhood health \u003cem\u003eis\u003c/em\u003e robust to potential unmeasured confounding, so is a \u0026ldquo;real\u0026rdquo; effect (i.e., rather than a statistical artefact produced by us not including enough relevant variables in our analysis).\u003c/p\u003e \u003cp\u003eThis finding that childhood health is associated with inner peace in adulthood is unique: we could find no previous study that has explored such a connection, so simply observing it here is a notable addition to the literature. However, it is worth emphasizing up front a caveat, namely that we did not actually assess people\u0026rsquo;s health \u003cem\u003ein\u003c/em\u003e childhood itself, but rather their retrospective recollections \u003cem\u003eabout\u003c/em\u003e their childhood. Crucially, there are indications that people sometimes \u003cem\u003echange\u003c/em\u003e their ratings of childhood health over time; one analysis found nearly one half of their sample revised this during a 10-year observation period. Older adults who were relatively advantaged (e.g., with socioeconomic resources and better memory) were less likely to revise it, whereas those with multiple childhood health problems were more likely to (either positively or negatively)\u003csup\u003e59\u003c/sup\u003e. As such, we must be somewhat cautious in interpreting our data, given we did not measure health in childhood per se, and recall bias might be present. However, for recall bias to completely explain the observed associations of the childhood predictors with adult inner peace, the effect of adult inner peace on the retrospective assessments of the childhood predictors would have to be at least as strong as the observed associations themselves\u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoroever, numerous longitudinal studies have actually measured health in childhood then traced its impact on later outcomes, with a substantial literature showing it \u003cem\u003edoes\u003c/em\u003e have a substantive effect on myriad aspects of adult life. Given that context, it is reasonable to think \u0026ndash; based on our data \u0026ndash; that inner peace is indeed one of the variables affected by it. Much of this existing longitudinal work focuses either on physical health or socio-economic status, with poor childhood health having a long-term detrimental health on these outcomes\u003csup\u003e61,62\u003c/sup\u003e. However, there is some work with more direct relevance to inner peace, with various studies connecting poor childhood health to \u003cem\u003emental\u003c/em\u003e health issues specifically in later life, particularly depression. A study involving a nationally representative sample of late midlife adults in the US, for example, found childhood disability was significantly associated with higher levels of depressive symptoms, suggesting such people may accumulate more physical impairment over the life course, thus suffering worse mental health in late midlife\u003csup\u003e63\u003c/sup\u003e. Similarly, another study found people with childhood disability exhibited more depressive symptoms at age 50 compared to those who did not, although there was no difference in the progression of depressive symptoms over time between the two groups, suggesting an initial inequality which was then \u003cem\u003emaintained\u003c/em\u003e over the life course\u003csup\u003e64\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLet us now turn to our second hypothesis, namely that we would observe national differences in the effects of the factors. Many of the studies cited above were in a US context, which indeed is characteristic of the psychological literature as a whole, as elucidated in the introduction. Thus, a particular strength of our research is its multinational reach, covering 22 diverse countries. And, as per our second hypothesis, there was indeed considerable variation in the impact of childhood health. The following are the respective RRs for the four health categories (relative to the middle category of \u0026ldquo;good\u0026rdquo;) for the 22 countries (with details for each country provided in the Supplementary Tables), together with the respective E-values, followed by 95% CIs, in square parentheses: Argentina (poor\u0026thinsp;=\u0026thinsp;0.89 [1.50; 0.72, 1.09], fair\u0026thinsp;=\u0026thinsp;1.12 [1.48; 1.02, 1.22], very good\u0026thinsp;=\u0026thinsp;1.01 [1.08; 0.94, 1.07], excellent\u0026thinsp;=\u0026thinsp;0.99 [1.12; 0.93, 1.05]); Australia (0.88 [1.54; 0.72, 1.07], 0.96 [1.27; 0.84, 1.09], 1.03 [1.21; 0.96, 1.11], 1.09 [1.41; 1.02, 1.17]); Brazil (0.99 [1.12; 0.86, 1.13], 0.93 [1.36; 0.87, 1.00], 1.07 [1.34; 1.02, 1.12], 1.14 [1.53; 1.09, 1.18]); Egypt (0.94 [1.33; 0.84, 1.05], 0.95 [1.29; 0.88, 1.02], 0.97 [1.22; 0.92, 1.02], 0.98 [1.16; 0.94, 1.03]); Germany (1.06 [1.32; 0.94, 1.20], 0.88 [1.55; 0.81, 0.95], 1.06 [1.31; 1.02, 1.10], 1.10 [1.44; 1.06, 1.15]); Hong Kong (0.73 [2.09; 0.57, 0.92], 0.85 [1.63; 0.79, 0.91], 1.04 [1.23; 1.00, 1.08], 1.03 [1.22; 0.97, 1.10]); India (0.93 [1.36; 0.84, 1.03], 0.87 [1.57; 0.82, 0.92], 0.97 [1.20; 0.93, 1.02], 1.05 [1.27; 0.99, 1.11]); Indonesia (0.76 [1.96; 0.53, 1.08], 0.97 [1.20; 0.92, 1.03], 0.99 [1.11; 0.94, 1.04], 1.02 [1.18; 0.97, 1.08]); Israel (1.02 [1.16; 0.74, 1.42], 0.89 [1.51; 0.75, 1.04], 0.96 [1.24; 0.91, 1.02], 0.98 [1.15; 0.93, 1.04]); Japan (0.75 [2.00; 0.68, 0.83], 0.86 [1.61; 0.82, 0.90], 1.12 [1.49; 1.09, 1.15], 1.20 [1.70; 1.17, 1.24]); Kenya (1.03 [1.19; 0.90, 1.17], 0.96 [1.27; 0.89, 1.02], 1.02 [1.15; 0.96, 1.08], 1.04 [1.24; 0.99, 1.09]); Mexico (0.91 [1.43; 0.75, 1.10], 1.09 [1.39; 1.01, 1.17], 0.97 [1.20; 0.91, 1.04], 0.95 [1.28; 0.90, 1.01]); Nigeria (1.19 [1.67; 1.08, 1.31], 1.03 [1.21; 0.92, 1.15], 1.04 [1.25; 0.98, 1.10], 1.01 [1.14; 0.96, 1.08]); Philippines (1.01 [1.11; 0.86, 1.18], 0.96 [1.25; 0.87, 1.05], 1.09 [1.40; 0.98, 1.21], 1.17 [1.62; 1.08, 1.27]); Poland (1.03 [1.21; 0.81, 1.32], 0.88 [1.55; 0.77, 1.00], 1.00 [1.03; 0.96, 1.05], 1.05 [1.25; 1.00, 1.11]); South Africa (0.90 [1.47; 0.78, 1.04], 0.94 [1.33; 0.84, 1.04], 1.03 [1.22; 0.95, 1.12], 0.99 [1.09; 0.93, 1.06]); Spain (1.06 [1.32; 0.86, 1.31], 0.96 [1.23; 0.79, 1.18], 0.97 [1.22; 0.89, 1.05], 0.98 [1.17; 0.90, 1.07]); Sweden (0.80 [1.82; 0.71, 0.90], 0.92 [1.40; 0.86, 0.97], 1.15 [1.56; 1.11, 1.18], 1.19 [1.68; 1.16, 1.23]); Tanzania (0.98 [1.18; 0.86, 1.11], 1.08 [1.38; 1.01, 1.16], 1.10 [1.43; 1.04, 1.17], 1.11 [1.46; 1.05, 1.17]); T\u0026uuml;rkiye (0.37 [4.82; 0.18, 0.77], 0.96 [1.25; 0.76, 1.21], 1.12 [1.48; 0.94, 1.33], 1.32 [1.97; 1.12, 1.57]); United Kingdom (0.87 [1.57; 0.69, 1.10], 0.84 [1.65; 0.73, 0.98], 1.11 [1.47; 1.03, 1.20], 1.14 [1.55; 1.06, 1.23]); and United States (0.97 [1.20; 0.78, 1.21], 0.89 [1.49; 0.79, 1.01], 1.10 [1.43; 1.04, 1.16], 1.16 [1.58; 1.10, 1.21]). As one can see, there are many notable country-level nuances. For a start, compared to the overall RR range of 0.14 (spanning 0.93 for poor to 1.07 for excellent health), some nations had a much larger range \u0026ndash; as much as 0.95 in T\u0026uuml;rkiye \u0026ndash; implying that childhood health is a much more significant factor there compared to other countries. More research is needed to explore why this regional variation exists, but it will almost certainly involve considerations such as the provision of healthcare in the various countries.\u003c/p\u003e \u003cp\u003eThere were also intriguing patterns that are harder to explain and certainly merit further investigation, especially the fact that, in some countries, the RRs seemed \u0026lsquo;out of order.\u0026rsquo; One would expect, based on the overall RRs, that relative to people with \u0026ldquo;good\u0026rdquo; childhood health, people with worse health (\u0026ldquo;poor\u0026rdquo; or \u0026ldquo;fair\u0026rdquo;) would have lower levels of peace (RR\u0026thinsp;\u0026lt;\u0026thinsp;1.00), while people with better health (\u0026ldquo;very good\u0026rdquo; or \u0026ldquo;excellent\u0026rdquo;) would have higher levels (RR\u0026thinsp;\u0026gt;\u0026thinsp;1.00). Indeed, seven countries did conform to this linear escalating pattern (Australia, Brazil, Hong Kong, Sweden, T\u0026uuml;rkiye, UK and USA). However, in the remaining countries, this pattern was subverted in various ways, where compared to those with good childhood health, some groups with worse health (either poor and/or fair) had \u003cem\u003ehigher\u003c/em\u003e levels of peace (RR\u0026thinsp;\u0026gt;\u0026thinsp;1.00), while conversely others with better health (very good and/or excellent) had \u003cem\u003elower\u003c/em\u003e levels of peace (RR\u0026thinsp;\u0026lt;\u0026thinsp;1.00). Consider Nigeria, for instance, where people with poor childhood health had an RR of \u003cem\u003e1.19\u003c/em\u003e (95% CI [1.08, 1.31]): i.e., here, not only does poor childhood health not detract from inner peace in adulthood, the data imply it actively \u003cem\u003ehelps\u003c/em\u003e. Moreover, the E-value for this particular observation is \u003cem\u003e1.67\u003c/em\u003e (while the E-value for the 95% CI is 1.38), suggesting this finding is \u003cem\u003every\u003c/em\u003e robust to potential confounding. We cannot know from our data \u003cem\u003ewhy\u003c/em\u003e this effect is observed, i.e., what is special about Nigeria that childhood poor health seems to actually facilitate inner peace in adulthood. One could speculate that, at least in some countries, experiencing poor health in childhood either encourages or compels people to develop a certain resilience or other psychological qualities, that may give rise to inner peace. But this of course begs the question, namely what is it that is different about these countries that this effect is observed, and why are similar effects not found elsewhere. Certainly, this is something that demands more in-depth study.\u003c/p\u003e \u003cp\u003eLet us now consider the other variables. While we do not have the space to discuss these in comparable depth to childhood health, we can nevertheless highlight some notable patterns that merit further study. Indeed, to reiterate, every factor \u0026ndash; apart from immigration status \u0026ndash; had a significant effect on inner peace in adulthood. To begin with, family dynamics are very important, including having a good relationship with one\u0026rsquo;s mother (RR\u0026thinsp;=\u0026thinsp;1.06; E\u0026thinsp;=\u0026thinsp;1.34; 95% CI [1.03, 1.09]) and father (1.03; 1.21; 1.01, 1.06), as is having parents who were married compared to either being divorced (0.97; 1.22; 0.94, 1.00), single or never married (0.95; 1.29; 0.92, 0.99), or one or both parents having died during childhood (0.95; 1.30; 0.91, 0.99). The financial situation of the family also matters: relative to people whose families \u0026ldquo;got by,\u0026rdquo; those who \u0026ldquo;lived comfortably\u0026rdquo; fared better (1.03; 1.22; 1.02, 1.05), while people did worse whose families found it either \u0026ldquo;difficult\u0026rdquo; (0.98; 1.17; 0.96, 0.99) or \u0026ldquo;very difficult\u0026rdquo; (0.96; 1.25; 0.93, 0.99). These findings accord with a vast existing literature on the importance of these factors for wellbeing, both in childhood itself and moreover in later life. Thus, for example, with respect to the quality of relationships with parents, a considerable literature on attachment styles shows the positive impact of \u0026ldquo;secure\u0026rdquo; bonds \u0026ndash; generally regarded as the optimal type of attachment \u0026ndash; on mental health later in life\u003csup\u003e65\u003c/sup\u003e. So too with marriage: overall, research has consistently shown this to be beneficial for children relative to other possibilities such as divorce/separation, both during childhood itself\u003csup\u003e66\u003c/sup\u003e and over the life course\u003csup\u003e67\u003c/sup\u003e, though one notes that in some situations \u0026ndash; such as conflicted or abusive marriages \u0026ndash; divorce may indeed be better option all round\u003csup\u003e68\u003c/sup\u003e. And again, with the financial aspect, research consistently finds that economic security in childhood is associated with better long term mental health prospects\u003csup\u003e69\u003c/sup\u003e. Until now, however, these factors had not been linked to inner peace in adulthood, and thus our work now extends the literature to encompass this.\u003c/p\u003e \u003cp\u003eMoreover, perhaps of even greater value in this study is the way it highlights national variation, showing that the impact of these factors differs considerably based on the location. Thus, the effect of having a good relationship with one\u0026rsquo;s mother ranged from (RR =) 0.89 in India (95% CI [0.79, 1.00]) to 1.26 in Indonesia (95% CI [0.99, 1.60]), while the impact of having a good relationship with one\u0026rsquo;s father ranged from 0.93 in Nigeria (95% CI [0.82, 1.05]) to 1.16 in T\u0026uuml;rkiye (95% CI [0.92, 1.45]). Likewise, there was considerable variation pertaining to parental marital status, where compared to having parents who were married, the effect of parents: being divorced ranged from 0.81 in Nigeria (95% CI [0.72, 0.91]) to 1.36 in T\u0026uuml;rkiye (95% CI [1.03, 1.80]); being single or never married ranged from 0.79 in Egypt (95% CI [0.58, 1.06]) to 1.15 in Australia (95% CI [1.01, 1.31]); and one or both parents having died ranged from 0.74 in South Africa (95% CI [0.61, 0.90]) to 1.16 in Mexico (95% CI [1.02, 1.32]) and the Philippines (95% CI [0.91,1.48]). Finally, there was also variation in relation to finances, albeit less so than the other familial dynamics, implying this factor is somewhat less susceptible to cultural influence. Thus, compared to those whose families \u0026ldquo;got by\u0026rdquo; financially, the effect of one\u0026rsquo;s family having \u0026ldquo;lived comfortably\u0026rdquo; ranged from 0.95 in Poland (95% CI [0.91, 1.00]) to 1.18 in T\u0026uuml;rkiye (95% CI [1.03, 1.34]), while for those who found it \u0026ldquo;difficult\u0026rdquo; ranged from 0.91 in Japan (95% CI [0.87, 0.95]) to 1.05 in the US (95% CI [1.00, 1.09]), and for those who found it \u0026ldquo;very difficult\u0026rdquo; ranged from 0.76 in T\u0026uuml;rkiye (95% CI [0.55, 1.07]) to 1.14 in Spain (95% CI [0.94, 1.38]). Again, these regional differences are fascinating and deserve further study, and will require in-depth enquiry into cultural dynamics to help explain them.\u003c/p\u003e \u003cp\u003eConsider for example the impact of having parents who were divorced, with a \u003cem\u003e0.55\u003c/em\u003e RR differential between Nigeria, where such divorce has a markedly negative impact on the likelihood of experiencing peace in adulthood, and T\u0026uuml;rkiye, where it means one is \u003cem\u003emore\u003c/em\u003e likely to have peace compared to people whose parents were married. Accounting for such findings will require detailed exploration into the traditions, values and practices pertaining to both marriage and divorce in the respective countries. It may be relevant, for instance, that Nigeria has large numbers of both Christians (45.9% of the population) and Muslims (53.5%), whereas T\u0026uuml;rkiye is overwhelmingly Muslim (99%)\u003csup\u003e70\u003c/sup\u003e. In that respect, it is possible that Islam is more accommodating of divorce \u0026ndash; albeit still describing it as a \u0026ldquo;necessary evil\u0026rdquo;\u003csup\u003e71\u003c/sup\u003e \u0026ndash; than Christianity, and hence overall may be less destabilising to the future equanimity of Muslims than Christians. However, when comparing results across countries, it is also possible that subtle culturally-influenced linguistic nuances are playing a role, influencing the data. When developing translations of the original English-language scale for use in the non-English-speaking countries, Gallup used their considerable experience and expertise to ensure the translations were as accurate and comparable as possible, such that the rendering of \u0026ldquo;inner peace\u0026rdquo; in Turkish would signify the same phenomenological state as do its equivalents in the languages of Nigeria. It is nevertheless possible that these terms were not precisely equivalent, and perhaps \u0026ndash; even if only very subtly \u0026ndash; were actually assessing slightly different outcomes. This is not a possibility we can investigate in the present paper, and would require in depth qualitative research to explore, which indeed we hope this paper will inspire. But it is still worth bearing in mind as we seek to understand apparent differences between nations.\u003c/p\u003e \u003cp\u003eAnother important variable is religious attendance at age 12. Not only was this associated with adult inner peace, but moreover an increasing amount depended on the frequency of attendance. So, compared to those who never attended, the impact of attending rose from RR\u0026thinsp;=\u0026thinsp;1.03 for those attending less than once a month (95% CI [1.02, 1.05]), to 1.05 for those attending 1\u0026ndash;3 times a month (95% CI [1.02, 1.08]), to 1.06 for those attending at least weekly (95% CI [1.04, 1.09]). This aligns with an extensive body of work on the positive impact of childhood religious attendance on subsequent physical and mental health\u003csup\u003e72\u003c/sup\u003e and also with scholarship that explores the centrality of peace to many religious traditions\u003csup\u003e18\u003c/sup\u003e. Again though, our study seems to be the first to link childhood religious service attendance to inner peace specifically. Also again, however, perhaps even more striking is the regional variation, where the impact of attending less than once a month ranged from 0.84 in Nigeria (95% CI [0.67, 1.05]) to 1.17 in T\u0026uuml;rkiye (95% CI [0.94, 1.46]), of attending 1\u0026ndash;3 times a month ranged from 0.93 in South Africa (95% CI [0.82, 1.05]) to 1.32 in T\u0026uuml;rkiye (95% CI [1.08, 1.61]), and attending weekly ranged from 0.92 in Nigeria (95% CI [0.79, 1.07]) to 1.33 in T\u0026uuml;rkiye (95% CI [1.12, 1.57]). Thus, we see a striking comparison between \u0026ndash; as above \u0026ndash; Nigeria and Turkey in particular, where childhood religious attendance in the former seems potentially \u003cem\u003edetrimental\u003c/em\u003e to adult inner peace, while in the latter it strongly supports this later outcome. Thus, as with all factors here, the impact of attendance may not be \u003cem\u003euniformly\u003c/em\u003e positive, and depends on cultural factors. In that respect, in-depth work in places like Nigeria will help us better understand why this country in particular seems to buck the overall trend. One wonders, for example, about the relevance, as noted above, of Nigeria having \u003cem\u003etwo\u003c/em\u003e main religions \u0026ndash; which moreover can often be in tension and even conflict with one another in the country\u003csup\u003e73\u003c/sup\u003e \u0026ndash; while T\u0026uuml;rkiye is nearly all Muslim, and hence lacks comparable internal divisions. It does therefore seem plausible that religious involvement in Nigeria could bring a level of adversity or friction that is mostly absent in T\u0026uuml;rkiye, thus accounting for the significant disparities in the impact of that involvement on adult inner peace.\u003c/p\u003e \u003cp\u003eThe final set of factors that seem impactful for inner peace are adverse experiences, namely experiencing abuse and being an outsider growing up, both with an RR of 0.94 (and 95% CIs of 0.92, 0.96, and 0.91, 0.97, respectively). These of course connect with a now vast literature on the long-term detrimental impact of Adverse Childhood Experiences, which are documented to negatively impact a panoply of outcomes later in life, ranging from substance use\u003csup\u003e74\u003c/sup\u003e and food insecurity\u003csup\u003e75\u003c/sup\u003e to depression\u003csup\u003e76\u003c/sup\u003e and even frailty in older adults\u003csup\u003e77\u003c/sup\u003e. Thus, to this literature we can also add that such adversities also lower the likelihood of experiencing inner peace as an adult. Again though, the regional variation is striking, where the impact of abuse ranges from 0.80 in Poland (95% CI [0.69, 0.91]) to 1.01 in Mexico (95% CI [0.95, 1.07]), while the impact of being an outsider ranges from 0.83 in Brazil (95% CI [0.78, 0.88]) to 1.07 in T\u0026uuml;rkiye (95% CI [0.87, 1.33]). Here it seems that, in certain countries, experiencing abuse or being an outsider can make it \u003cem\u003emore\u003c/em\u003e likely one will experience peace later in life. This seems to echo the finding above regarding poor childhood health, where in select countries, like Nigeria, this \u003cem\u003eraised\u003c/em\u003e the chances of people having inner peace in adulthood. As in that health case, it would appear that, at least in some cultural contexts, adversity can lead people to develop the aptitude or fortitude that leads to a greater propensity to attain peace later in life. Again, we cannot tell from our data what it is about these particular contexts that does perhaps enable that, but this would be a fruitful avenue for future research to investigate.\u003c/p\u003e \u003cp\u003eFinally, there are three factors that are not necessarily about childhood per se, but are nevertheless relevant to childhood, namely, people\u0026rsquo;s age, sex, and immigration status. In one sense of course, these \u003cem\u003eare\u003c/em\u003e childhood factors (in that they tell us something about people\u0026rsquo;s childhood), but from another perspective they are factors that pertain to the individual at \u003cem\u003eall\u003c/em\u003e life stages. Nevertheless, they are worth briefly noting here. Of these, age had the strongest impact. Essentially, the older the participant, the more likely they are to have inner peace. Compared to people aged 18\u0026ndash;24 (i.e., born between 1998 and 2005), those aged 25\u0026ndash;29 (1993\u0026ndash;1998) had just a marginally higher RR of 1.01 (95% CI [0.99, 1.03]), but the RRs rise in a linear way with the age categories, culminating in an RR of 1.19 (95% CI [1.12, 1.27]) for people aged over 80 (born in 1943 or earlier). These findings could be regarded as reflecting a childhood factor, especially if we interpret the data as being about the time period when people were \u003cem\u003eborn\u003c/em\u003e, hence being a cohort effect. However, the emergent literature on inner peace suggests it tends to increase as a function of age\u003csup\u003e30\u003c/sup\u003e. As such, it is perhaps more realistic to interpret the findings here as simply being more a question of the actual current age of the participants. Nevertheless, it is again still interesting to note regional variation, where the RR of this oldest category ranged from 0.77 in Poland (95% CI [0.60, 1.00]) to 1.37 for the UK (95% CI [1.20, 1.56]) and US (95% CI [1.22, 1.54]), showing that the relationship between age and peace is not universally observed, and like the other factors here is affected by socio-cultural dynamics.\u003c/p\u003e \u003cp\u003eThe penultimate variable is gender, which ranked second last in terms of impact, where compared to men, women had an RR of 0.98 (95% CI [0.96, 1.00]). That said, we should note a very small percentage of the sample stated their sex was neither male nor female but \u0026ldquo;other\u0026rdquo;, with this group having \u003cem\u003econsiderably\u003c/em\u003e lower inner peace (RR\u0026thinsp;=\u0026thinsp;0.44, 95% CI [0.13, 1.50]). We do need to be cautious in interpreting this finding, as this group was \u003cem\u003every\u003c/em\u003e small (\u0026lt;\u0026thinsp;0.1% of the observed sample) within several countries, leading to complete separation and large uncertainty in this estimate. Nevertheless, it is a strikingly low RR that does demand further study. There is by now an extensive literature showing that people who identify as LGBTQ\u0026thinsp;+\u0026thinsp;tend to have lower levels of mental health across the lifespan, from youth\u003csup\u003e78\u003c/sup\u003e to older adults\u003csup\u003e79\u003c/sup\u003e. It is perhaps unsurprising then that this factor then would also affect inner peace. It is not certain whether the data here constitutes a childhood factor per se, since the item asks people their \u003cem\u003ecurrent\u003c/em\u003e gender, not their gender as a child, and it is possible that some percentage who answered \u0026ldquo;other\u0026rdquo; now would not have done so in childhood. That said, even if the latter were the case, it is likely that some relevant dynamics may have manifested during childhood (e.g., a sense of gender dysphoria). Thus, more research will be needed to look into this finding. Also, as with other factors, it will also be important to investigate the regional variation, where the RR for females ranged from 0.90 in Kenya (95% CI [0.87, 0.93]) to 1.13 in T\u0026uuml;rkiye (95% CI [1.00, 1.27]), and for those answering \u0026ldquo;other\u0026rdquo; ranging from 0.40 in Indonesia (95% CI [0.10, 1.61]) to 1.36 in Mexico (95% CI [0.89, 1.96]). It would be helpful to know, for instance, what it is about Mexico that means people who answer \u0026ldquo;other\u0026rdquo; tend to be much \u003cem\u003emore\u003c/em\u003e likely to have inner peace than men or women.\u003c/p\u003e \u003cp\u003eLastly, there was one factor with \u003cem\u003eno\u003c/em\u003e significant impact on peace, namely immigration status: compared to people born in the country in which they live, those born elsewhere had a RR that was basically equal (1.01, 95% CI [0.98, 1.03]). As with age and gender, this is not necessarily a childhood factor, since it reflects a person\u0026rsquo;s \u003cem\u003ecurrent\u003c/em\u003e immigrant status, not that of when they were a child. Nevertheless, it is still intriguing to note that such status does not seem to have any bearing on inner peace, which is notable, given that being an immigrant is frequently perceived as presenting challenges that can be detrimental to mental health\u003csup\u003e80\u003c/sup\u003e. That said, research has often found immigrant mental health is \u0026ldquo;better than expected\u0026rdquo;\u003csup\u003e81\u003c/sup\u003e, and may even be better than native people, a phenomenon remarked on often enough to have a label \u0026ndash; the \u0026ldquo;healthy immigrant effect\u0026rdquo; \u0026ndash; which \u0026ldquo;suggests that immigrants have a health advantage over the domestic-born,\u0026rdquo; though this usually \u0026ldquo;vanishes with increased length of residency\u0026rdquo;\u003csup\u003e82\u003c/sup\u003e. In our case, while we didn\u0026rsquo;t observe this kind of effect, neither were immigrants disadvantaged when it comes to peace. Again though, there were also significant regional disparities, with RR ranging from 0.92 in Egypt [0.69, 1.23] and India (95% CI [0.76, 1.10]) to 1.19 in Tanzania (95% CI [0.85,1.65]), so in some countries at least the healthy immigrant effect does seem to play out.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eInner peace is a low arousal positive state that has received relatively little attention amidst the proliferation of research into the various aspects of flourishing ever recent decades. Our paper is one of the first to explore country-level variations in the childhood predictors of this important constituent of flourishing. Using a retrospective assessment of childhood experiences in 22 countries, combined with several important current demographics, we found that many aspects of a child's upbringing do in fact predict peace in adulthood, although there are important country-level variables that require further research to understand. The most impactful factor that we found was self-rated health growing up, while the least impactful predictor was immigration status (which indeed was the only factor with a non-significant effect). All the significant relationships documented in this study were robust to potential unmeasured confounding, as assessed by E-values. We hope that future research will help to explain the reasons why we found some quite divergent patterns across countries, and more generally that researchers will pay closer attention to inner peace as an important constituent of human flourishing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.L. wrote the main manuscript text. R.N.P. prepared all tables and figures. B.R.J. and T.V.J. led the overall study on which this paper reports. All authors reviewed the manuscript and contributed edits and additions to the text.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis is not an acknowledgment, but rather a statement we have been asked to include with our submission (and I cannot find another suitable location): This submission is part of the Global Flourishing collection. We were invited to submit this manuscript, following a peer review offer by the Chief Editor.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe study design was pre-registered with the Open Science Framework on November 18th, 2023 (see https://osf.io/5yr62/). The datasets generated and/or analysed during the current study are available in the Open Science Framework repository upon submission of pre-registration (https://www.cos.io/gfs-access-data), as is the methodology for the analyses (https://osf.io/pv93c), and all code to reproduce the analyses (https://osf.io/9egpr).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSolmi, M. \u003cem\u003eet al.\u003c/em\u003e Risk and protective factors for mental disorders with onset in childhood/adolescence: An umbrella review of published meta-analyses of observational longitudinal studies. Neurosci Biobehav Rev 120, 565\u0026ndash;573 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaslow, A. H. A theory of human motivation. Psychol Rev 50, 370\u0026ndash;396 (1943).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrijters, P., Johnston, D. W. \u0026amp; Shields, M. A. 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X., Hill, J. \u0026amp; McDaniel, P. N. A scoping review of literature about mental health and well-being among immigrant communities in the United States. Health Promot Pract 22, 181\u0026ndash;192 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlegr\u0026iacute;a, M., \u0026Aacute;lvarez, K. \u0026amp; DiMarzio, K. Immigration and mental health. Curr Epidemiol Rep 4, 145\u0026ndash;155 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElshahat, S., Moffat, T. \u0026amp; Newbold, K. B. Understanding the healthy immigrant effect in the context of mental health challenges: A systematic critical review. J Immigr Minor Health 24, 1564\u0026ndash;1579 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"peace, wellbeing, flourishing, global, cross-cultural, Global Flourishing Study","lastPublishedDoi":"10.21203/rs.3.rs-4602277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4602277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGreat efforts have been expended studying how people\u0026rsquo;s childhood affects outcomes later in life. Although attention has mostly focused on \u0026lsquo;negative\u0026rsquo; outcomes, such as mental illness, paradigms like positive psychology have encouraged interest in desirable phenomena too. Yet amidst this \u0026lsquo;positive turn\u0026rsquo; some desiderata have still received scant engagement, including inner peace. This lacuna perhaps reflects the Western-centric nature of academia, with low arousal positive emotions being relatively undervalued in the West. But aligning with broader efforts to redress this Western-centricity is an emergent literature on this topic. This report adds to this by presenting the most ambitious study to date of inner peace, namely as an item \u0026ndash; \u0026ldquo;In general, how often do you feel you are at peace with your thoughts and feelings?\u0026rdquo; \u0026ndash; in the Global Flourishing Study, an intended five-year study investigating the predictors of human flourishing involving (in this first year) 202,898 participants from 22 countries. This paper looks at the \u003cem\u003echildhood predictors\u003c/em\u003e of peace, using random effects meta-analysis to aggregate all findings, focusing on three research questions. First, how do recalled aspects of a child's upbringing predict peace in adulthood, for which the most impactful factor on average was self-rated health growing up, with Risk Ratios spanning, relative to \u0026ldquo;good\u0026rdquo;, 0.93 for \u0026ldquo;poor\u0026rdquo; (95% CI [0.88,0.99]) to 1.07 for \u0026ldquo;excellent\u0026rdquo; (95% CI [1.04,1.11]). Second, do associations vary by country, with the effect of poor self-rated health spanning 0.37 in T\u0026uuml;rkiye (95% CI [0.18,0.77]) to 1.19 in Nigeria (95% CI [1.08,1.31]). Third, are relationships robust to potential unmeasured confounding, as assessed by E-values, for which the effect of poor health growing up is robust up to unmeasured confounder association risk ratios of 1.36 with inner peace. These results shed new valuable light on the long-term causal dynamics of this overlooked topic.\u003c/p\u003e","manuscriptTitle":"Childhood predictors of inner peace: A cross-national analysis of the Global Flourishing Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-07 09:10:12","doi":"10.21203/rs.3.rs-4602277/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-21T11:32:35+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"193897851704090922336928561526795126769","date":"2024-08-14T12:57:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-05T19:27:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85062043977102263412393404927661186842","date":"2024-07-22T20:50:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-22T07:09:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-22T07:02:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-15T19:10:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-15T04:43:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-18T23:43:08+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":"August 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35624835,"name":"Biological sciences/Psychology"},{"id":35624836,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-05-05T16:06:27+00:00","versionOfRecord":{"articleIdentity":"rs-4602277","link":"https://doi.org/10.1038/s41598-024-83353-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-30 15:57:21","publishedOnDateReadable":"April 30th, 2025"},"versionCreatedAt":"2024-08-07 09:10:12","video":"","vorDoi":"10.1038/s41598-024-83353-z","vorDoiUrl":"https://doi.org/10.1038/s41598-024-83353-z","workflowStages":[]},"version":"v1","identity":"rs-4602277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4602277","identity":"rs-4602277","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}