An outcome-wide study of the associations of daily smoking with subsequent well-being and other outcomes in the Global Flourishing Study

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Noah Padgett, Chris Felton, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8752309/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Prior research on the relationship between tobacco use and well-being is plagued by reliance on cross-sectional data, limited measures of well-being, and a lack of cross-national comparisons and synthesis of country-specific relationships. To address these oversights, we analyze the first two waves of data from the Global Flourishing Study of over 200,000 adults being representative of 22 countries and one territory. In this outcome-wide study, we meta-analyzed country-specific effects of daily cigarette smoker status at the baseline on 78 variables of well-being and other outcomes, measured a year later, controlling for demographic and childhood factors and contemporaneous potential confounders of the daily smoker status. We repeated this analysis using a quantity measure of daily smoking for smokers only. Meta-analytic estimates were typically of similar magnitude in the total and smoker samples, although confidence intervals were wider and more likely to include zero in the latter. Specifically, daily smoking tended to be inversely associated with human flourishing and various indicators of well-being and related to worse psychological and social distress. Although those average effects were very small for all but two outcomes (subsequent daily smoking and weekly alcohol consumption), cross-national variations in country-specific effects indicated that daily smoking had more influence on well-being in some countries than others. Epidemiology cigarette smoking well-being Global Flourishing Study outcome-wide study Introduction Prior research has examined the relationship between tobacco use and physical and mental health and, to a lesser extent, other dimensions of human flourishing, such as social, spiritual, and financial well-being 1-5 . While previous studies were conducted globally, they rarely compared geographically and culturally diverse countries, let alone synthesize country-specific effects of tobacco use, or examine a large range of flourishing outcome, despite the availability of various sources of cross-national data 6 . They also tend to rely on cross-sectional data thus limiting the ability to make causal inferences. Finally, although most studies examined multiple measures of well-being simultaneously, they are generally limited in the scope of the well-being construct, which prohibits assessing differences in the relationship between tobacco use and a wide range of well-being outcomes. To address these oversights, we conduct an outcome-wide longitudinal study focusing on VanderWeele’s conceptualization of flourishing, that is, doing or being well in five broad domains of a person’s life (happiness and life satisfaction, physical and mental health, meaning and purpose, character and virtue, and close social relationships), including the contexts where the person lives 5, 7, 8 . VanderWeele proposed 10 items to measure flourishing (two items per domain), a sum of which is known as the “Flourish” index. Alternatively, adding two items of a sixth domain, financial and material security, creates the “Secure Flourish” index. In this study, we examine whether daily cigarette smoking at baseline is related to not only these two indexes of flourishing but also indicators of each domain and other associated outcomes, measured a year later, controlling for, first, sociodemographic and childhood factors and then also flourishing and other outcomes at the baseline. For this examination, we analyze data from the first two waves of Global Flourishing Study (GFS)—a panel study of over 200,000 adults sampled to be nationally representative of 22 countries and one territory—which were collected in 2022-24 (Wave 1) and 2024 (Wave 2). Specifically, we meta-analyze country-specific results from separately analyzing: (1) a binary measure of daily smoking (0 = None/Do not smoke, 1 = 1+ cigarette) for total sample and (2) a continuous measure, the number of cigarettes smoked daily, for a subsample of daily smokers at Wave 1. We begin with a review of previous studies on the relationship between tobacco use including cigarette smoking and various concepts of flourishing, followed by an introduction of our research questions along with anticipated findings based on prior research. We then describe our methods and present cross-country variations as well as results from random effects meta-analysis before discussing key results with an acknowledgement of our study’s limitations. Prior research on smoking and flourishing Happiness and life satisfaction Current smokers were found to be less happy than never smokers and/or ex-smokers among adolescents in Iran as well as adults in Hong Kong and former Soviet Union countries, while the number of cigarettes smoked was not associated with happiness among current smokers in Hong Kong 9, 10, 11, but see 12 . Life satisfaction has also been found to be lower among cigarette-smoking young adults than their non-smoking peers in 21 countries, and the results were consistent across three regions with the relationship in “Western Europe and the U.S.” and “Pacific Asia” being stronger than in “Central and Eastern Europe” 13 . A recent study found that a negative relationship between smoking and life satisfaction was in part due to self-rated health: that is, smokers were less likely to be satisfied with life partly because they were likely to have poor health than non-smokers 14 . Consistent with these cross-sectional findings, a longitudinal study of over 5,000 Dutch adults reported that quitting smoking increased subjective well-being (happiness and life satisfaction), implying smoking’s detrimental impact on this particular domain of flourishing 15 . Physical and mental health Numerous studies have examined cigarette smoking in relation to various health risks and death, and systematic reviews and meta-analyses show that current and, to a lesser extent, former smokers have a higher risk of not only respiratory diseases but also lower back pain, sciatica, chronic musculoskeletal pain, fracture, Crohn disease, stroke, cancer, and mortality than never smokers 16-26 . When adjusted for demographic, health-related (e.g., physical activity), and methodological factors (e.g., cross-sectional vs. longitudinal design), the risk tends to be reduced somewhat but remains significant. Consistent with these results, researchers report that smoking reduction and cessation decreases health risks and increases health-related quality of life 27, 28 . Prior research also found significant relationships between smoking and various health behaviors. For example, a cross-sectional study of Chinese adults found the frequency of physical exercise and smoking to be inversely related among men, though not among women 29 . A 7-year longitudinal study of males in Japan reported that current smokers were less likely to exercise than never smokers, while exercise increased among those who quit smoking but decreased among persistent smokers 30 . Drinking is the most often studied health-risk behavior in relation to smoking, and this positive relationship tends to be stronger among heavy users of both substances 31, 32 . While various explanations of the smoking-drinking link have been only partially tested 32-34 , prior research tends to suggest that smoking predicts drinking more strongly than vice versa, whereas heavy drinkers are more likely to be heavy smokers than the other way around 31 . According to cross-sectional studies based on Australian national surveys, current tobacco smokers were more likely than never smokers to have affective and anxiety disorder and psychological distress (depression) and to screen positively for psychosis, although former smokers were not different from never smokers in these measures of mental health 35, 36, 37, but see 38 . A meta-analysis of 78 cross-sectional studies revealed that current smokers were more likely to be depressed than former as well as never smokers 39 , and a meta-analysis of 63 studies on suicide, an outcome of depression, also discovered that current smokers were at higher risk of self-harm—whether measured by suicidal ideation, suicide plan/attempt, or suicide death—than non-smokers 40 . Another systematic review/meta-analysis of 26 studies investigating change in mental health after smoking cessation compared to continued smoking reported that anxiety, depression, mixed anxiety and depression, and stress significantly decreased among quitters compared to continuing smokers with psychological quality of life and positive affect significantly increasing 41 . While these findings confirm the detrimental impact of smoking on mental health, a systematic review of 148 longitudinal studies concluded that research on the association between smoking and depression and/or anxiety is inconsistent in terms of the direction of association: that is, about half of studies reviewed reported a positive relationship between depression/anxiety and later smoking, whereas over one third of them found evidence for smoking being positively associated with later depression/anxiety with few studies supporting their reciprocity 42, see also 43, 44 . Meaning and purpose According to self-medication theory, the use of licit and illicit substances including cigarettes and other tobacco products is a behavior to cope with unpleasant psychological states, such as a weak sense of meaning and purpose in life as well as negative emotions (e.g., anxiety and distress) 45 , as nicotine induces mild euphoria 33 . Consistent with this theory, meaning in life was found to be inversely related to smoking in both cross-sectional and longitudinal studies 45, 46 , although the evidence for purpose in life is limited 47, 48 . While studies of reverse causation are rare, a prospective study of a representative sample of Americans aged 50+ years found that smoking at an initial wave was inversely related to a sense of purpose in life four years later 47 . Character and virtue Like meaning and purpose, the domain of character and virtue has scarcely been studied in relation to smoking. An exception is self-control or, to be precise, lack thereof: impulsivity or delay discounting, which is the tendency for the perceived value of a reward to decrease as the length of delay to its delivery increases. A systematic review revealed that delay discounting was inversely related to smoking cessation 49 , and a longitudinal study found that delay discounting at a baseline was positively associated with an increasing trajectory of smoking, while smoking had no significant impact on delay discounting 50 . Examining prosocial behaviors, researchers found that smoking households were less likely to give to charity and, when they did, tended to give less than non-smoking households in the U.S. 51 , and a German panel study found that volunteering had negative effects on smoking at the between-individual (but not within-individual) level, although the effects were partly explained by socioeconomic status 52 . Close social relationships Social well-being, such as relational contentment and satisfaction, is likely to be inversely related to smoking because it includes social support that reduces social distress, which increases the risk of smoking 53 . Indeed, prior research found that a lack of social or relational well-being (e.g., loneliness, perceived discrimination, and distrust of others) was positively related to smoking 54-58 . Conversely, an analysis of British panel data revealed that social capital—measured by social participation and interpersonal trust—along with employment and marital status was positively associated with smoking cessation 59 . In addition, a longitudinal study in Sweden reported that daily smokers at a baseline who continued to smoke daily a year later had significantly decreased odds ratios of participating in formal and informal group and other activities in society compared to a reference group (non-daily smokers and non-smokers at the baseline), whereas the odds ratios decreased, to a lesser extent, among the baseline daily smokers who reduced the daily habit to intermittent or non-smoking 60 . Financial and material security Researchers have examined relationships between smoking and various financial factors, including socioeconomic status. For example, income was consistently related inversely to smoking across different regions, time periods, and gender groups 55, 61-63 , and a study of a national sample of Australian households found that smoking households (i.e., those reported tobacco expenditure) had higher odds of experiencing financial stress (e.g., cash flow problems, inability to pay utility bills, or going without meals) than their non-smoking counterparts 64 . Education is also inversely related to smoking 62, 63, 65, 66 , and longitudinal studies reported higher rates of smoking cessation for individuals with higher education and reciprocal relationships between smoking and educational achievement 67-69 . Consistent with these findings, smoking prevalence tends to be lower among employed than unemployed individuals 62, 70 . Longitudinal research in the U.S. reported bidirectional positive relationships between cigarette smoking and household food insecurity with cigarette cessation being inversely related to a risk of food insecurity 71, 72 . Religion and spirituality While other leading conceptualizations of flourishing tend to focus on physical, psychological, and social sources of well-being 1-4 , VanderWeele recognizes the importance of spirituality and identifies religious community as one of four institutional/community “pathways” that contribute to flourishing with the other three being family, work, and education 5 . Indeed, previous studies have found significant relationships between religious involvement and smoking as well as physical and mental health and other domains of flourishing 73-80 . Cross-sectional studies report inverse relationships between religiosity and smoking across demographic groups and countries as well as different measures of religiosity (e.g., public vs. private religiosity) 62, 81-84 . Longitudinal studies indicate that this inverse relationship is partly the protective effect of religiosity against initiating smoking and becoming a persistent smoker as well as consuming a large quantity of cigarettes among smokers 73, 85-88 . Consistent with this finding, religiously involved individuals tend to be more likely to attempt (and succeed) in smoking cessation 89-91 . While prior research has rarely examined the reversed effect (i.e., whether smoking reduces religiosity), one study found that among smokers, the quantity of cigarette consumption was inversely related to both public and private religious behaviors 81 . Composite of Flourishing Some research has examined the relationship between smoking and a composite of flourishing. For example, using the categorical diagnosis of flourishing à la Huppert and So 3 , Hone et al. found that smokers and non-smokers were no different in the odds of flourishing, controlling for demographic factors, among employed adults in New Zealand 92 . Two recent studies based on VanderWeele’s flourishing concept reported similar results. Hsu et al. found that “unhealthy behaviors,” including smoking, was not correlated with any of Secure Flourish items among Taiwanese retirees 93 . In a study of adults living in a city in Japan, Shibata et al. also found that current smokers were no different from never smokers in the Flourish index; but when its five domains were analyzed separately, they discovered that smokers fared worse than never smokers in two domains (happiness & life satisfaction and meaning & purpose) 94 , which indicates the importance of examining flourishing dimensions separately as well as jointly. In this exploratory study, we address three research questions, pre-registered with the Center for Open Science (COS) (https://osf.io/v8t5x), as follows: Research Question #1: How does daily smoking at Wave 1 of the GFS predict well-being and related outcomes in Wave 2? Research Question #2: How does the pattern of these associations vary by country? Research Question #3: To what extent are the observed associations robust to potential unmeasured confounding, as assessed by E-values? To explore these questions, we conducted two sets of random effects meta-analysis. In primary meta-analysis, we examine a total of 56 outcomes (a complete list of outcomes was preregistered with the COS; https://osf.io/v8t5x), organized into nine categories (12 variables that measure six domains of the Secure Flourishing Index, two per domain, are denoted by an asterisk): (1) human flourishing (2, Flourishing and Secure Flourishing Indexes), (2) psychological well-being (12, including happiness*, life satisfaction*, meaning in life*, purpose in life*, and mental health*), (3) psychological distress (4, including depressive and anxiety symptoms), (4) social well-being (9, including relationship contentment* and relationship satisfaction*), (5) social participation (5, including at least weekly religious service attendance), (6) social distress (2, loneliness and perceived discrimination), (7) character and prosocial behavior (9, including orientation to promote good* and delayed gratification*), (8) physical health & health behavior (6, including self-rated physical health*), and (9) socioeconomic outcomes (7, including financial security* and material security*). In secondary meta-analysis, we examined additional 22 outcomes, which consists of six domain-specific measures of Secure Flourishing, four indicators of psychological distress, and 12 indicators of religion/spirituality (e.g., religious/spiritual connection, belief in life after death, and prayer or meditation). First, based on prior research, we anticipate the following relationships (Research Question #1). Daily smoking at Wave 1 is expected to be inversely related to the outcomes of human flourishing, psychological well-being, social well-being, self-rated physical health and days exercise, socioeconomic outcomes and, to a lesser extent, character and prosocial behavior at Wave 2. Conversely, daily smoking at Wave 1 is likely to be positively related to the outcomes of psychological and social distress as well as health problems, pain, smoking, and drinking at Wave 2. Also, daily smoking at Wave 1 is expected to be inversely related to religion/spirituality-associated outcomes (e.g., religious beliefs, experiences, and practices) at Wave 2. Next, we explore whether the strength and even the direction of these relationships vary by country perhaps due to the influence of diverse sociocultural, economic, and health contexts that characterize each nation (Research Question #2). Finally, we examine the robustness of the observed relationships against potential unmeasured confounding (Research Question #3). Methods The study design, methodology, sampling, and survey development for the Global Flourishing Study (GFS) are described elsewhere 95-98 . Here we employ this data in an outcome-wide longitudinal design 99 incorporating Wave 2 data as part of a coordinated set of outcome-wide studies 100 to assess associations of predictor(s) on subsequent outcomes, minimizing individual researcher degrees of freedom, and maintaining a consistent analytic framework from which to compare across outcomes and studies. The methodology for the analyses follows that described in Padgett et al. 100, 101 and VanderWeele et al. 102 . Study sample Wave 1 of the GFS included nationally representative samples from 22 countries and one territory, also referred to as “23 countries”: Argentina, Australia, Brazil, China, Egypt, Germany, Hong Kong (Special Administrative Region of China), India, Indonesia, Israel, Japan, Kenya, Mexico, Nigeria, the Philippines, Poland, South Africa, Spain, Sweden, Tanzania, Turkey, the United Kingdom, and the United States (N = 207,919). The countries were selected to (1) maximize coverage of the world’s population, (2) ensure geographic, cultural, and religious diversity, and (3) prioritize feasibility in Gallup’s existing data collection infrastructure. The study was reviewed and approved by the institutional review boards at Baylor University (IRB reference #1841317) and Gallup (IRB reference #2021-11-02). Informed consent was obtained from all participants, and further details are available elsewhere 96 . Data for Wave 1 were collected from March 2022 to January 2024, except in China (March/April of 2024) 103 . Data for Wave 2 were collected from January 2024 to December 2024, with data in China collected at least six months after Wave 1 103 . The GFS survey assesses aspects of well-being, including happiness, health, meaning, character, relationships, and financial security 5 , along with other demographic, social, economic, political, religious, personality, childhood, community, health, and well-being variables 104, 105 . Gallup translated the GFS survey into multiple languages following the TRAPD (translation, review, adjudication, pretesting, and documentation) model for cross-cultural survey research 106 . Details about the translation, cognitive interviewing, and pilot testing phases of the GFS can be found elsewhere 97, 107-109 . Sampling design The precise sampling design varied by country to ensure samples were approximately nationally representative 96, 106 . In most countries, local field partners implemented a probability-based face‑to-face or telephone methodology to recruit panel members. Recruitment involved an intake survey gathering basic sociodemographic information and details for recontacting participants. Following recruitment, participants received invitations to participate in the annual survey via phone or online. Follow-up for Wave 2 data collection relied on the respondent-provided contact information. A minimum of three contact attempts were made on different days of the week and times of day to maximize the possibility of retention. Post-stratification and nonresponse adjustments to the Wave 1 sampling weights were performed separately within each country, using either census data or a reliable secondary source. Additional information about the sampling design and weighting scheme for Wave 2 is available elsewhere 100, 103 . Outcome-wide Analytic Design An outcome-wide analytic approach 7, 99 was employed to examine the associations of a single exposure with a range of subsequent outcomes. Compared to traditional analytic strategies focused on a single outcome, this approach provides a more holistic assessment of an exposure’s possibly differential relations to multiple life outcomes. The outcome-wide analytic design has strengths of reducing researcher subjectivity/degree-of-freedom 110 in analysis by ensuring a consistent analytic strategy and the same set of covariates across models for all outcomes; mitigates publication bias by reporting results for all examined outcomes, including null findings, simultaneously; and provides insights into beneficial, detrimental, and null associations with the exposure. Further details about the outcome-wide approach could be found elsewhere 7 . Measures Focal Wave 1 predictor variable The focal exposure from Wave 1 for this study is daily smoking, specifically, daily cigarette consumption, which is assessed with an item, asking “About how many cigarettes do you smoke each day, if any?” (0 = None/Do not smoke, 1 = one, 2 = two, … 97 = 97+). This item is first dichotomized to be analyzed as a binary variable (0 = None/Do not smoke, 1 = 1+) at Wave 1 for the total sample to see whether the prevalence of daily smoking predicts flourishing and well-being outcomes at Wave 2. Thereafter, the smoker sample is analyzed using those who reported smoking at least one cigarette per day at Wave 1. Using this sample, we will look at how the quantity of daily smoking (number of cigarettes consumed per day) at Wave 1 predicts flourishing and well-being outcomes at Wave 2. Covariates Country-specific analyses adjusted for 17 covariates (9 demographic and 8 childhood variables) unless data were not available (described below). Additional details on all variables can be found in the GFS Codebook (https://osf.io/cg76b) or Crabtree et al. 108 . Demographic covariates : Year of birth (age) was classified into 1998-2005 (18-24 years), 1993-1998 (25-29 years), 1983-1993 (30-39 years), 1973-1983 (40-49 years), 1963-1973 (50-59 years), 1953-1963 (60-69 years), 1943-1953 (70-79 years), and “1943 or earlier” (80 years or older). Gender was assessed as male, female, or other. Marital status was assessed as single/never married, married, separated, divorced, widowed, and domestic partner. Education was assessed as up to 8 years, 9-15 years, and 16 or more years. Employment was assessed as employed, self-employed, retired, student, homemaker, unemployed and looking for a job, and none of these/other. Religious service attendance was assessed as more than once a week, once a week, 1-to-3 times a month, a few times a year, and never. Immigration status was assessed with yes/no responses to: “Were you born in this country, or not?” Religious affiliation was assessed in all countries, but with considerable cross-country variation in the response categories because some religious affiliations are only applicable in certain countries. Religious affiliation response categories included Christianity, Islam, Hinduism, Buddhism, Judaism, Sikhism, Baha’i, Jainism, Shinto, Taoism, Confucianism, Primal/animist/folk religion, Spiritism, Umbanda, Candomblé, and other African-derived religions, Chinese folk/traditional religion, some other religion, or no religion/atheist/agnostic. Racial/ethnic identity was assessed in most countries but not collected in China, Germany, Japan, Spain, and Sweden. Response categories varied across countries to be locally meaningful. Country-specific analyses that adjusted for racial/ethnic identity used a binary variable based on whether an individual was in the most prominent racial/ethnic group in the sample versus a minority racial/ethnic group. Retrospective childhood covariates : 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.” “Does not apply” was treated as a dichotomous control variable for respondents who did not have a mother due to death or absence. An analogous variable was used for the relationship with father. 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?” Social isolation growing up was assessed with the question: “When you were growing up, did you feel like an outsider in your family?” Response options were yes/no. 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?” Religious service attendance during childhood was assessed with: “How often did YOU attend religious services or worship at a temple, mosque, shrine, church, or other religious building when YOU were around 12 years old?” with responses of at least once/week, 1-to-3 times/month, less than once/month, or never. Outcome variables : As explained above in detail, 56 outcomes of nine categories and 22 outcomes of three categories were examined for primary and secondary analysis, respectively. The list of Wave 2 outcomes, preregistered with the COS, is also available on the Open Science Framework (see the “W-2 Core Team Analyses” tab at https://osf.io/9kpd8). Further details on item wording can be found in Crabtree et al. 108 . In these data, the internal reliability of composite measures varies across countries, as expected. For example, inter-item reliability coefficient alpha for 12-item Secure Flourishing Index 5, 95 was 0.88, while ranging from 0.75 in Nigeria to 0.94 in Japan. Statistical analysis Regression analyses with complex survey weights Analyses were performed using R 4.5 111 and the Rglobalflourishing package 112 . Weighted descriptive statistics for the sample (N = 207,919) were estimated for each of the demographic and outcome variables at both waves. All analyses, including imputation and attrition modeling described below, accounted for complex survey design by including weights, primary sampling units, and strata. Additional methodological details, including the approach that was used to account for the complex sampling design, can be found elsewhere 100, 103 . Within each country, we conducted weighted modified Poisson multivariate regression analyses for the binary measure of daily smoking and weighted linear regression analyses for the quantity measure (i.e., the number of cigarettes smoked daily). Two models were used for each outcome, regressing each outcome on the focal exposure, first controlling only for the demographic and childhood variables, and second controlling also for principal components (PCs) extracted from all contemporaneous Wave 1 variables other than the focal exposure. PCs were used to reduce the dimensionality of predictors to mitigate multicollinearity 113 , while accommodating complex survey weights and missing data. The first seven PCs were used and accounted for an average of 51.2% of the variability in all covariates 100 , with additional PCs each explaining only 1-2%. Meta-analysis All analyses were conducted by each country (total and smoker sample results in Online Supplements 1 and 2, respectively) and random-effects meta-analyses were used to pool estimates across countries and to estimate heterogeneity (tau). For each outcome, a global p -value for an omnibus test of evidence of association in any country is reported 114 . All meta-analyses were conducted using the metafor package 115 . Sensitivity to unmeasured confounding We report E-values for all associations to evaluate the sensitivity of results to potential unmeasured confounding. An E-value is the minimum strength of the association on the risk ratio scale that an unmeasured confounder would need to have with both the outcome and the predictor, above and beyond all measured covariates, to explain away an association 116 . A high E-value signifies that an unmeasured confounder would need to have a strong association with both the predictor and the outcome to explain away the observed association. Approximate E-values can be obtained for continuous outcomes through scale conversions 116 . Missing data Primary analysis – fully imputed data . The primary analyses utilize all participants from Wave 1, including those not reporting in Wave 2 by imputing missing data 117 . Multiple imputation (MI) by chained equations 118, 119 was used for exposures, covariates, and outcomes. Twenty imputed datasets were used. Using MI with all respondents aligns with Wave 1 analyses and will also be used in Wave 3 to maximize utilization of information on those who do not report in Wave 2 but report again in Wave 3, thereby aligning the analytic sample across years, facilitating comparison of results. The imputation model utilized sampling weights, demographic characteristics, and childhood variables for Wave 1 missing data; and for Wave 2 missing data, the imputation models additionally included all Wave 1 exposures. Imputation was conducted separately by country to account for variation in the assessment of certain variables across countries (e.g., race/ethnicity and income), thereby reflecting country-specific contexts and assessment methods. Supplemental analyses – semi-complete case with attrition weights . As a sensitivity analysis for possible misspecification of the imputation models 120 , analyses were conducted using only Wave 2 respondents (semi-complete case analysis) 121 , with attrition weights 122 multiplied by the sampling weights for use in the analysis. Attrition weights were estimated using logistic regression models for retention to calculate stabilized inverse probability of retention weights 123 . Attrition predictors included sampling weight, strata, mode of survey in Wave 1, age, gender, education, income, employment status, marital status, race/ethnicity, religious service attendance, urban/rural status of participants, personality, days of exercise, depression, loneliness, and the six domains of the flourishing index, covering a range of important predictors 7, 124, 125 . Results We first present results for the total sample followed by the smoker sample. We will focus mostly on effect sizes and not place too much emphasis on using the language of statistical significance for several reasons. First, because of the large sample size, while many of the estimated effects are statistically significant, they are often small in magnitude, a point that can be obscured by describing effects with the term “significant.” Second, the difference between “significant” and “insignificant” estimates is often small, with confidence intervals for the former barely excluding zero and confidence intervals for the latter barely including zero. Finally, insignificance is often interpreted as evidence in favor of the null, whereas reporting a confidence interval that includes negative and positive values (i.e., a confidence interval for an insignificant effect) clarifies that the data is consistent with both negative and positive effects. Our focus is thus on reporting magnitudes and confidence intervals. Total sample analysis Table 1 presents weighted sample frequency distributions of the binary measure of daily smoking, year of birth (age), gender, education, and country of respondent by wave for the total sample (N = 207,919 at Wave 1 and 128,868 at Wave 2) with mean and standard deviation of daily smoking’s continuous measure being also reported. Expanded summary statistics tables of all demographic, childhood, and outcome variables are provided in Online Supplement 1 (see Tables S1-2 for the total sample and Tables S1a, S9a-31a, and S9b-31b for each country). The prevalence and average quantity of daily smoking slightly decreased between Waves 1 and 2 from 17.4% to 15.2% and from 2.0 to 1.8, respectively, indicating that daily smokers might be less likely to participate in Wave 2 than non-daily smokers and non-smokers. However, we found cross-national variations, as the prevalence and/or average quantity either remained the same or slightly increased in 12 countries (China, Egypt, India, Indonesia, Japan, Kenya, Mexico, the Philippines, South Africa, Spain, Tanzania, and Turkey; see Tables S1a and S9a-31a). Wave 2 survey participants tended to be somewhat older and more educated than their Wave 1 counterparts, but they were practically the same in gender composition. Each country’s representation in the total sample did not change drastically between the waves with the largest decrease and increase being in Brazil (‒3.1%, from 6.4% to 3.3%) and the U.S. (6.6%, from 18.4% to 25.1%), respectively. Table 1 . Weighted sample demographic summary statistics Total sample Smoker sample Characteristic Wave 1 N = 207,919 Wave 2 N = 128,868 Wave 1 N = 36,147 Wave 2 N = 18,502 Daily smoking 1 + cigarette smoked per day 36,147 (17.4%) 19,528 (15.2%) None/No smoking 168,414 (81.0%) 106,366 (82.5%) (Missing) 3,357 (1.6%) 2,974 (2.3%) Mean 2.0 1.8 11.1 9.6 Standard Deviation 5.7 5.4 9.0 9.2 Min, Max 0.0, 97.0 0.0, 97.0 1.0, 97.0 0.0, 97.0 (Missing) 3,357 (1.6%) 2,974 (2.3%) 0 (0%) 330 (1.8%) Year of birth, n (%) 1943 or earlier (current age: 80 + years) 4,047 (1.9%) 3,446 (2.7%) 207 (0.6%) 133 (0.7%) 1943–1953 (current age: 70–79 years) 16,902 (8.1%) 12,685 (9.8%) 1,818 (5.0%) 1,295 (7.0%) 1953–1963 (current age: 60–69 years) 29,031 (14.0%) 19,537 (15.2%) 5,230 (14.5%) 3,067 (16.6%) 1963–1973 (current age: 50–59 years) 32,409 (15.6%) 20,498 (15.9%) 6,688 (18.5%) 3,592 (19.4%) 1973–1983 (current age: 40–49 years) 34,970 (16.8%) 21,995 (17.1%) 7,292 (20.2%) 3,888 (21.0%) 1983–1993 (current age: 30–39 years) 40,297 (19.4%) 24,641 (19.1%) 7,687 (21.3%) 3,704 (20.0%) 1993–1998 (current age: 25–29 years) 20,325 (9.8%) 12,308 (9.6%) 3,421 (9.5%) 1,537 (8.3%) 1998–2005 (current age: 18–24 years) 29,920 (14.4%) 13,758 (10.7%) 3,801 (10.5%) 1,285 (6.9%) (Missing) 18 (< 0.0%) 1 (< 0.0%) 2 (< 0.0%) 0 (0%) Gender, n (%) Male 100,661 (48.4%) 62,159 (48.2%) 23,705 (65.6%) 12,110 (65.5%) Female 106,349 (51.1%) 66,142 (51.3%) 12,318 (34.1%) 6,352 (34.3%) Other 523 (0.3%) 370 (0.3%) 58 (0.2%) 13 (0.1%) (Missing) 386 (0.2%) 197 (0.2%) 67 (0.2%) 28 (0.2%) Education (years), n (%) Up to 8 46,842 (22.5%) 22,659 (17.6%) 8,320 (23.0%) 3,346 (18.1%) 9–15 116,015 (55.8%) 72,942 (56.6%) 22,400 (62.0%) 11,889 (64.3%) 16+ 44,904 (21.6%) 33,257 (25.8%) 5,406 (15.0%) 3,264 (17.6%) (Missing) 158 (0.1%) 11 (< 0.0%) 21 (0.1%) 4 (0.0%) Country of respondent, n (%) Argentina 6,724 (3.2%) 2,876 (2.2%) 2,216 (6.1%) 771 (4.2%) Australia 3,844 (1.8%) 2,578 (2.0%) 455 (1.3%) 218 (1.2%) Brazil 13,203 (6.4%) 4,222 (3.3%) 2,562 (7.1%) 699 (3.8%) China 5,022 (2.4%) 4,575 (3.6%) 1,355 (3.7%) 1,130 (6.1%) Egypt 4,729 (2.3%) 3,044 (2.4%) 1,073 (3.0%) 661 (3.6%) Germany 9,506 (4.6%) 5,586 (4.3%) 2,507 (6.9%) 1,278 (6.9%) Hong Kong (S.A.R. of China) 3,012 (1.4%) 608 (0.5%) 942 (2.6%) 78 (0.4%) India 12,765 (6.1%) 6,353 (4.9%) 997 (2.8%) 476 (2.6%) Indonesia 6,992 (3.4%) 2,661 (2.1%) 2,553 (7.1%) 865 (4.7%) Israel 3,669 (1.8%) 2,494 (1.9%) 856 (2.4%) 552 (3.0%) Japan 20,543 (9.9%) 13,966 (10.8%) 4,521 (12.5%) 2,870 (15.5%) Kenya 11,389 (5.5%) 7,712 (6.0%) 596 (1.6%) 360 (1.9%) Mexico 5,776 (2.8%) 2,264 (1.8%) 1,318 (3.6%) 481 (2.6%) Nigeria 6,827 (3.3%) 3,144 (2.4%) 302 (0.8%) 140 (0.8%) Philippines 5,292 (2.5%) 2,684 (2.1%) 1,126 (3.1%) 492 (2.7%) Poland 10,389 (5.0%) 6,545 (5.1%) 3,088 (8.5%) 1,682 (9.1%) South Africa 2,651 (1.3%) 962 (0.7%) 622 (1.7%) 197 (1.1%) Spain 6,290 (3.0%) 2,917 (2.3%) 1,984 (5.5%) 855 (4.6%) Sweden 15,068 (7.2%) 11,663 (9.1%) 1,431 (4.0%) 1,021 (5.5%) Tanzania 9,075 (4.4%) 5,588 (4.3%) 354 (1.0%) 179 (1.0%) Turkey 1,473 (0.7%) 497 (0.4%) 772 (2.1%) 220 (1.2%) United Kingdom 5,368 (2.6%) 3,621 (2.8%) 893 (2.5%) 540 (2.9%) United States 38,312 (18.4%) 32,310 (25.1%) 3,624 (10.0%) 2,736 (14.8%) Note. This table is based on non-imputed data; cumulative percentages for variables may not add up to 100% due to rounding; S.A.R. = Special Administrative Region. Expanded summary statistics tables of all demographic, childhood, and outcome variables are provided in the online supplement (see Tables S1, S1a, and S2 for the total sample and Tables S9a-31a and S9b-31b for each country). Table 2 shows the primary meta-analysis results, associations of daily smoking at Wave 1 with 56 measures of well-being and other outcomes at Wave 2, controlling first for demographic and childhood variables (Model 1) and then the first seven principal components (PCs) of those outcomes at Wave 1 as well as the Model 1 controls (Model 2). In each model, risk ratio (RR) for each binary outcome and effect size (ES) measure (standardized regression coefficient) for each continuous outcome are reported in separate columns. Next, 95% confidence interval (CI), heterogeneity (τ), and global p -value along with two nominal significance ( p < 0.05* and p < 0.005**) and Bonferroni-corrected thresholds ( p < 0.00089***) are presented. In Model 1, estimated associations were very small, ranging from 0.98 (e.g., weekly+ religious attendance) to 1.05 (depression symptoms composite) in RR and from ‒0.03 (e.g., secure flourishing index) to 0.02 (loneliness) in ES, with the associations being generally in the expected direction: that is, daily smoking at Wave 1 tended to be inversely related to human flourishing, psychological well-being, social well-being and participation, character and prosocial behavior, self-rated physical health, and some of socioeconomic outcomes; and positively related to psychological distress, social distress, health problems, pain in past 4 weeks, and other socioeconomic outcomes, with some exceptions. Two estimates were much larger than others: daily smoking (RR = 2.32 [95% CI; 2.04, 2.62]) and number of drinks per week (ES = 0.10 [0.07, 0.12]). That is, respondents who smoked at least one cigarette per day at Wave 1 were about 132% more likely to smoke daily at Wave 2 than those who were not daily smoker at the baseline even after controlling for the covariates. This “stability effect” is likely mostly attributable to the power of daily habits as well as nicotine addiction 126 – 128 . Also, for study participants who were 1 standard deviation (SD) above the mean on daily smoker status at Wave 1 (or a 1 SD increase in the status), there was a 0.10 SD increase in weekly alcohol consumption Table 2 Meta-analyzed associations of daily smoking at Wave 1 with well-being and other outcomes at Wave 2: Total sample analysis Model 1: Demographic and Childhood Variables as Covariates Model 2: Demographic, Childhood, and Other Wave 1 Confounding Variables (Via Principal Components) as Covariates Outcome RR ES 95% CI τ Global p-value RR ES 95% CI τ Global p-value Human Flourishing Secure flourishing index -0.03 (-0.04,-0.02) 0.02 1.71e-08*** -0.02 (-0.02,-0.01) < 0.01† 1.2e-04*** Flourishing index -0.03 (-0.04,-0.02) 0.02 1.9e-07*** -0.01 (-0.02,-0.01) < 0.01† 7.49e-04*** Psychological Well-Being Happiness -0.03 (-0.04,-0.02) 0.01 1.47e-12*** -0.02 (-0.02,-0.01) < 0.01† 3.4e-06*** Life satisfaction -0.03 (-0.04,-0.02) 0.02 4.37e-13*** -0.02 (-0.02,-0.01) < 0.01† 9.03e-07*** Current life evaluation -0.03 (-0.05,-0.02) 0.03 1.69e-09*** -0.02 (-0.03,-0.01) 0.02 1.32e-04*** Future life evaluation -0.03 (-0.04,-0.02) 0.02 7.5e-11*** -0.02 (-0.03,-0.01) 0.01 9.2e-06*** Optimism -0.01 (-0.03,-0.00) 0.02 2.61e-03** -0.00 (-0.01,0.00) < 0.01† 0.479 Freedom to pursue what's important -0.02 (-0.02,-0.01) 0.01 2.68e-03** -0.00 (-0.01,0.00) < 0.01† 0.561 Inner peace 0.99 (0.99,0.99) < 0.01† 4.14e-03** 1.00 (1.00,1.00) < 0.01† 0.958 Life balance 0.99 (0.98,0.99) < 0.01† 0.015* 1.00 (0.99,1.00) < 0.01† 0.896 Sense of mastery 1.00 (0.99,1.00) < 0.01† 0.014* 1.00 (1.00,1.00) < 0.01† 0.533 Meaningful activities -0.02 (-0.03,-0.02) 0.01 1.33e-07*** -0.01 (-0.02,-0.01) < 0.01† 1.55e-04*** Understanding purpose -0.02 (-0.02,-0.01) < 0.01† 0.016* -0.01 (-0.01,-0.00) < 0.01† 0.491 Self-rated mental health -0.02 (-0.03,-0.01) 0.02 3.94e-04*** -0.01 (-0.02,0.00) 0.01 0.036* Psychological Distress Traumatic distress 1.02 (1.01,1.03) < 0.01† 0.129 1.00 (0.99,1.01) < 0.01† 0.915 Depression symptoms composite 1.05 (1.03,1.06) 0.03 4.4e-06*** 1.02 (1.01,1.03) < 0.01† 1.65e-03** Anxiety symptoms composite 1.03 (1.02,1.05) 0.01 3.48e-04*** 1.00 (0.99,1.01) < 0.01† 0.928 Suffering 1.02 (1.01,1.03) 0.01 0.010* 1.00 (0.99,1.01) < 0.01† 0.473 Social Well-Being Relationship contentment -0.01 (-0.02,-0.00) < 0.01† 0.297 0.00 (-0.00,0.01) < 0.01† 0.835 Relationship satisfaction -0.02 (-0.02,-0.01) < 0.01† 0.095 -0.00 (-0.01,0.00) < 0.01† 0.703 Social support -0.02 (-0.03,-0.01) 0.01 3.92e-03** -0.01 (-0.02,-0.01) < 0.01† 0.253 Intimate/close friend 1.00 (0.99,1.00) < 0.01† 0.598 1.00 (0.99,1.00) < 0.01† 0.776 Government approval 1.00 (0.99,1.01) < 0.01† 0.278 1.00 (0.99,1.00) < 0.01† 0.866 Say in government 0.99 (0.98,1.01) 0.03 0.032* 0.99 (0.98,1.00) 0.02 0.117 Belonging in country -0.02 (-0.03,-0.00) 0.02 1.83e-03** -0.00 (-0.01,0.00) < 0.01† 0.088 City/place satisfaction 0.99 (0.99,0.99) < 0.01† 0.006* 1.00 (0.99,1.00) < 0.01† 0.798 Trust within country 0.99 (0.98,1.00) < 0.01† 0.006* 0.99 (0.99,1.00) < 0.01† 0.265 Social Participation Ever been married 1.00 (1.00,1.00) < 0.01† 0.958 1.00 (1.00,1.00) < 0.01† 0.948 Currently divorced 1.01 (0.99,1.02) < 0.01† 0.964 1.01 (0.99,1.02) < 0.01† 0.928 Number of children 0.01 (0.00,0.03) 0.02 3.28e-03** 0.01 (-0.00,0.02) 0.01 0.075 Weekly+ community participation 1.00 (0.98,1.02) 0.03 0.023* 0.98 (0.96,1.00) 0.03 0.060 Weekly+ religious attendance 0.98 (0.97,0.99) < 0.01† 0.048* 0.99 (0.98,1.00) < 0.01† 0.222 Social Distress Loneliness 0.02 (0.01,0.03) < 0.01† 0.012* 0.00 (-0.00,0.01) < 0.01† 0.814 Perceived discrimination 1.03 (1.02,1.04) < 0.01† 0.086 1.00 (0.99,1.02) 0.02 0.317 Character & Prosocial Behavior Orientation to promote good -0.01 (-0.01,0.00) 0.01 0.070 -0.00 (-0.01,0.01) < 0.01† 0.325 Delayed gratification -0.01 (-0.02,-0.00) < 0.01† 0.167 -0.01 (-0.01,0.00) < 0.01† 0.729 Hope -0.01 (-0.02,-0.00) < 0.01† 0.026* -0.00 (-0.01,0.00) < 0.01† 0.410 Gratitude -0.02 (-0.03,-0.01) 0.01 0.008* -0.01 (-0.02,-0.00) < 0.01† 0.227 Showing love/care -0.01 (-0.02,-0.00) 0.01 2.84e-04*** -0.01 (-0.01,0.00) < 0.01† 0.008* Forgivingness 1.00 (1.00,1.01) 0.01 0.006* 1.00 (1.00,1.01) 0.01 7.57e-04*** Charitable giving 0.99 (0.98,1.00) < 0.01† 3.65e-03** 0.98 (0.97,0.99) 0.01 0.039* Helping strangers 1.01 (1.00,1.02) < 0.01† 0.043* 1.00 (1.00,1.01) < 0.01† 0.370 Volunteering 1.00 (0.98,1.02) 0.03 0.087 0.98 (0.97,1.00) 0.03 0.016* Physical Health & Health Behavior Self-rated physical health -0.02 (-0.03,-0.01) 0.03 1.33e-06*** -0.01 (-0.02,-0.00) 0.02 0.041* Health problems 1.02 (1.01,1.04) 0.02 3.38e-04*** 1.00 (0.99,1.01) < 0.01† 0.759 Pain in past 4 weeks 1.01 (1.00,1.02) < 0.01† 4.37e-03** 0.99 (0.99,1.00) < 0.01† 0.596 Daily smoker 2.32 (2.04,2.62) 0.75 2.69e-16*** 2.30 (2.03,2.61) 0.75 2.69e-16*** Number of drinks per week 0.10 (0.07,0.12) 0.05 2.44e-15*** 0.09 (0.07,0.10) 0.04 2.55e-15*** Days exercise per week 0.00 (-0.01,0.01) 0.02 0.028* -0.00 (-0.02,0.01) 0.02 1.98e-05*** Socioeconomic Outcomes Financial security -0.03 (-0.04,-0.02) 0.02 2.21e-07*** -0.01 (-0.02,-0.01) < 0.01† 0.049* Material security -0.03 (-0.04,-0.02) < 0.01† 3.61e-05*** -0.01 (-0.02,-0.00) < 0.01† 0.746 Educational attainment (16 + years) 1.00 (1.00,1.00) < 0.01† 0.100 0.99 (0.98,1.00) < 0.01† 0.203 Currently employed 1.01 (1.00,1.01) 0.01 0.035* 1.01 (1.00,1.01) 0.01 0.052 Financially comfortable/getting by 0.98 (0.98,0.99) 0.01 5.65e-06*** 0.99 (0.98,1.00) < 0.01† 0.014* Own home 0.98 (0.98,0.99) 0.02 9.73e-06*** 0.99 (0.98,1.00) < 0.01† 8.9e-04*** Income -- top quintile 1.00 (1.00,1.00) < 0.01† 0.005* 1.00 (1.00,1.00) < 0.01† 0.252 Notes. N = 207919; Reference for focal predictor: no daily smoking; RR, risk-ratio, null effect is 1.00; ES, effect size measure for standardized regression coefficient, null effect is 0.00; CI, confidence interval; τ (tau, heterogeneity), estimated standard deviation of the distribution of effects; Global p-value, joint test of the null hypothesis that the country-specific Wald tests are null in all countries. Multiple imputation was performed to impute missing data on the covariates, exposure, and outcomes. All models controlled for sociodemographic and childhood factors assessed at Wave 1: relationship with mother growing up; relationship with father growing up; parent marital status around age 12; experienced abuse growing up (except for Israel); felt like an outsider in family growing up; self-rated health growing up; subjective financial status growing up; religious affiliation at age 12; frequency of religious service attendance around age 12; year of birth; gender; education, employment status, marital status, immigration status; and racial/ethnic identity when available. For Model 2 with PC (principal components), the first seven principal components of the entire set of contemporaneous confounders assessed at Wave 1 were included as additional covariates of the outcomes at Wave 2. An outcome-wide analytic approach was used, and a separate model was run for each outcome. A different type of model was run depending on the nature of the outcome: (1) for each binary outcome, a weighted generalized linear model (with a log link and Poisson distribution) was used to estimate a RR; and (2) for each continuous outcome, a weighted linear regression model was used to estimate an ES. All effect sizes were standardized. For continuous outcomes, the ES represents the change in SD on the outcome for a 1 SD increase in the focal predictor. For binary outcomes, the RR represents the change in risk of being in the upper category compared to the lower category for a 1 SD increase in the focal predictor. P-value significance thresholds: p < 0.05*, p < 0.005**, (Bonferroni) p < 0.00089***, correction for multiple testing to significant threshold; † Estimate of τ (tau, heterogeneity) is likely unstable. See our online supplement forest plots for more detail on heterogeneity of effects. at Wave 2 31, 32 . Alternatively, in terms of unstandardized effect size, for respondents who reported daily smoking at Wave 1, there was a 0.09 increase in the number of drinks per week at Wave 2, compared to those who did not report daily smoking, on average across countries (see Table S8 of Online Supplement 1). Model 2 shows that adding the baseline measures of well-being and other outcomes (via PCs) as covariates reduced effect sizes, but estimates were generally in the expected direction. Specifically, daily smoking at Wave 1 remained inversely related to both indexes of human flourishing, six indicators of psychological well-being, three indicators of character and prosocial behavior, two indicators of physical health and health behavior, and three indicators of socioeconomic outcomes at Wave 2, whereas it was positively associated with depressive symptoms composite and forgiveness. To the contrary, estimates for the two health-risk behaviors changed little: daily smoking (2.30 [2.03, 2.61]) and number of drinks per week (0.09 [0.07, 0.10]). To assess the robustness of the meta-analytic effect estimates to potential unmeasured confounding, we report E-values for each outcome, both for the estimate and for its confidence interval, in Table 3 (see the “Total sample” columns). For example, the E-value for secure flourishing index was 1.21 in Model 1 (1.14 in Model 2), which means that an unmeasured confounder that was associated with both higher daily smoking at Wave 1 and higher secure flourishing index at Wave 2 by risk ratios of 1.21 (1.14) each, above and beyond the demographic and childhood controls (plus the seven PCs of contemporaneous confounders at Wave 1 in Model 2) already adjusted for, could explain away the association, but weaker joint confounder associations could not. To shift the 95% CI to include the null, an unmeasured confounder that was associated with both higher daily smoking at Wave 1 and higher secure Table 3 E-value sensitivity analysis for unmeasured confounding for the association between daily smoking and subsequent well-being and other outcomes: Total sample analysis Model 1: Demographic and Childhood Variables as Covariates Model 2: Demographic, Childhood, and Other Wave 1 Confounding Variables (Via Principal Components) as Covariates Total sample Smoker sample Total sample Smoker sample Outcome E-value E-value for CI E-value E-value for CI E-value E-value for CI E-value E-value for CI Human Flourishing Secure flourishing index 1.21 1.17 1.24 1.13 1.14 1.10 1.14 1.08 Flourishing index 1.19 1.15 1.22 1.11 1.13 1.09 1.12 1.06 Psychological Well-Being Happiness 1.20 1.17 1.21 1.11 1.14 1.11 1.13 1.07 Life satisfaction 1.19 1.14 1.21 1.13 1.14 1.11 1.13 1.06 Current life evaluation 1.21 1.16 1.22 1.14 1.17 1.12 1.16 1.10 Future life evaluation 1.19 1.15 1.25 1.16 1.16 1.12 1.17 1.12 Optimism 1.13 1.06 1.17 1.07 1.07 1.00 1.07 1.00 Freedom to pursue what's important 1.13 1.08 1.16 1.05 1.06 1.00 1.05 1.00 Inner peace 1.11 1.08 1.10 1.00 1.02 1.00 1.01 1.00 Life balance 1.12 1.09 1.15 1.09 1.04 1.00 1.05 1.00 Sense of mastery 1.07 1.02 1.02 1.00 1.02 1.00 1.05 1.00 Meaningful activities 1.17 1.13 1.20 1.11 1.12 1.09 1.11 1.03 Understanding purpose 1.14 1.11 1.13 1.00 1.08 1.01 1.05 1.00 Self-rated mental health 1.17 1.12 1.12 1.00 1.08 1.00 1.05 1.00 Psychological Distress Traumatic distress 1.15 1.10 1.04 1.00 1.05 1.00 1.08 1.00 Depression symptoms composite 1.27 1.21 1.19 1.09 1.15 1.09 1.11 1.03 Anxiety symptoms composite 1.22 1.18 1.19 1.07 1.02 1.00 1.09 1.00 Suffering 1.16 1.11 1.17 1.09 1.01 1.00 1.03 1.00 Social Well-Being Relationship contentment 1.11 1.07 1.11 1.00 1.05 1.00 1.06 1.00 Relationship satisfaction 1.14 1.09 1.16 1.00 1.05 1.00 1.04 1.00 Social support 1.17 1.13 1.21 1.13 1.13 1.09 1.13 1.08 Intimate/close friend 1.07 1.03 1.12 1.05 1.06 1.00 1.06 1.01 Government approval 1.07 1.00 1.06 1.00 1.06 1.00 1.03 1.00 Say in government 1.09 1.00 1.04 1.00 1.10 1.00 1.06 1.00 Belonging in country 1.13 1.06 1.07 1.00 1.06 1.00 1.06 1.00 City/place satisfaction 1.11 1.08 1.05 1.00 1.07 1.00 1.03 1.00 Trust within country 1.11 1.06 1.17 1.10 1.08 1.02 1.09 1.05 Social Participation Ever been married 1.03 1.00 1.05 1.00 1.03 1.00 1.02 1.00 Currently divorced 1.09 1.00 1.17 1.00 1.09 1.00 1.06 1.00 Number of children 1.13 1.06 1.04 1.00 1.08 1.00 1.05 1.00 Weekly+ community participation 1.05 1.00 1.26 1.06 1.17 1.02 1.16 1.02 Weekly+ religious attendance 1.14 1.08 1.12 1.00 1.11 1.05 1.10 1.03 Social Distress Loneliness 1.16 1.13 1.15 1.00 1.06 1.00 1.06 1.00 Perceived discrimination 1.21 1.15 1.04 1.00 1.02 1.00 1.03 1.00 Character & Prosocial Behavior Orientation to promote good 1.07 1.00 1.07 1.00 1.04 1.00 1.03 1.00 Delayed gratification 1.11 1.04 1.19 1.10 1.09 1.00 1.10 1.05 Hope 1.12 1.07 1.14 1.02 1.06 1.00 1.04 1.00 Gratitude 1.15 1.10 1.17 1.08 1.11 1.04 1.10 1.00 Showing love/care 1.11 1.03 1.16 1.07 1.09 1.00 1.07 1.00 Forgivingness 1.05 1.00 1.04 1.00 1.06 1.00 1.07 1.03 Charitable giving 1.13 1.07 1.16 1.00 1.14 1.08 1.13 1.05 Helping strangers 1.12 1.07 1.06 1.00 1.07 1.00 1.07 1.00 Volunteering 1.02 1.00 1.26 1.07 1.15 1.00 1.17 1.00 Physical Health & Health Behavior Self-rated physical health 1.16 1.09 1.21 1.09 1.12 1.03 1.12 1.04 Health problems 1.17 1.08 1.23 1.11 1.03 1.00 1.05 1.00 Pain in past 4 weeks 1.09 1.00 1.15 1.03 1.09 1.01 1.05 1.00 Daily smoker 4.06 3.50 2.72 2.48 4.03 3.47 2.98 2.67 Number of drinks per week 1.41 1.34 1.34 1.28 1.38 1.32 1.39 1.33 Days exercise per week 1.04 1.00 1.24 1.16 1.07 1.00 1.11 1.00 Socioeconomic Outcomes Financial security 1.19 1.14 1.23 1.15 1.13 1.10 1.15 1.11 Material security 1.19 1.15 1.17 1.07 1.10 1.05 1.10 1.05 Educational attainment (16 + years) 1.00 1.00 1.00 1.00 1.11 1.05 1.00 1.00 Currently employed 1.09 1.02 1.06 1.00 1.09 1.03 1.07 1.00 Financially comfortable/getting by 1.15 1.11 1.17 1.07 1.11 1.06 1.10 1.02 Own home 1.14 1.09 1.07 1.00 1.11 1.07 1.10 1.03 Income -- top quintile 1.05 1.00 1.04 1.00 1.04 1.00 1.03 1.00 Notes. N = 207919; The formula for calculating E-values can be found in VanderWeele and Ding (2017). E-values for estimate are the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome to fully explain away the observed association between the exposure and outcome, conditional on the measured covariates. E-values for the 95% CI closest to the null denote the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome to shift the CI to include the null value, conditional on the measured covariates. flourishing index at Wave 2 by risk ratios of 1.17 (1.10) each could suffice, but weaker joint confounder could not. Estimates for the two health-risk behaviors (daily smoker and number of drinks per week) at Wave 2, which were relatively large and did not change much between the two models, were found to be more robust to unmeasured confounding than all other estimates, as indicated by the two highest E-values in Models 1 (4.06 and 1.41) and 2 (4.03 and 1.38). While E-values generally suggest that estimates for these two outcomes are moderately robust to unmeasured confounding, others were not. Those with E-values equal (e.g., educational attainment in Model 1) or close to 1.00 (e.g., volunteering in Model 1 and inner peace in Model 2) indicate that very little unmeasured confounding would be required to explain away the association. Thus, these effect estimates should be interpreted as having limited robustness to unmeasured confounding. Results from the secondary meta-analysis for an additional 22 outcomes—six domain-specific measures of flourishing, four items of depression and anxiety, and 12 indicators of religion/spirituality—were similar to those from its primary counterpart in that all associations were very small but in the expected direction. Confidence intervals excluded zero only for seven (31.8%) of 22 outcomes with the seven being all six domain-specific measures of flourishing and religious centrality in Model 1; and four domain-specific measure of flourishing (except for character & virtue and close social relationships), depression (loss of interest), belief in life after death, and prayer or meditation in Model 2 (see the “Multiple Imputation” column of Tables S5 and S6). Also, their E-values—ranging from 1.03 to 1.26 and 1.01 to 1.16 in Models 1 and 2, respectively—indicated that they were, in general, moderately robust to unmeasured confounding with some exceptions (see Table S7). Results from supplemental analysis (i.e., semi-complete case analysis with attrition weights), conducted as a sensitivity analysis, indicated that misspecification of imputation models for the primary and secondary meta-analyses was unlikely, as the supplemental results were identical or similar to the primary and secondary results (see Tables S5 and S6). For example, the supplemental effect estimates and their 95% CIs for the two composite and six domain-specific measures of human flourishing were practically the same as their primary and secondary counterparts in Model 1 and, to a lesser extent, Model 2. Also, while the association between daily smoking at Waves 1 and 2 from the supplemental analysis (RR = 2.61 [2.31, 2.96] and 2.60 [2.30, 2.95]) was somewhat larger in both Models 1 and 2 than those from the primary analysis (RR = 2.32 [2.04, 2.62] and 2.30 [2.03, 2.61]), that of daily smoking at Wave 1 with weekly alcohol consumption at Wave 2 remained practically the same in Models 1 (ES = 0.10 [0.07, 0.12]) and 2 (ES = 0.09 [0.07, 0.11]). E-values were similar between the two analyses as well, while supplemental effect estimates for some outcomes (e.g., perceived discrimination and daily smoking at Wave 2) were somewhat more robust to unmeasured confounding compared to their primary and secondary counterparts (see Table S7). In summary, all but two (97.4%) of 78 effect estimates for 56 primary and 22 secondary outcomes were found to be very small even when they were not adjusted for previous measures of those outcomes. This finding could lead one to conclude that daily smoking has limited impact on human well-being. However, it is important to keep in mind the limited follow-up of only one year and the extensive contemporaneous covariates control and we will return to these matters below. With this context, such a conclusion would be at odds with common knowledge about the harmful effects of cigarette smoking on health 128 , 129 as well as previous findings reviewed above. It is also important to examine cross-national variations in those estimates given that the primary estimates were meta-analytic estimates of daily smoking’s average effects on well-being and other outcomes with heterogeneity (τ; estimated SD of the distribution of effects) across 23 countries and, particularly, that those previous findings were almost always country specific. So, it is necessary to explore whether daily smoking’s effect estimates varied across those countries and, if so, how. For example, in Model 1, the average effect size of daily smoking at Wave 1 on secure flourishing index at Wave 2 was ‒0.03 [‒0.04, ‒0.02] with its SD (τ) being 0.02 (see Table 2 ). Figure S1 of Online Supplement 1 visually presents heterogeneity in country-specific effects, which varied from ‒0.11 [‒0.15, ‒0.06] (Australia) to 0.03 [‒0.00, 0.06] (China). A closer examination revealed that the effect of daily smoking on secure flourishing was at least 1 SD below the average effect size (i.e., ≤ ‒0.05) in eight countries (Australia, Brazil, Egypt, Israel, Japan, Sweden, Turkey, and the U.S.) and positive in one country (China) with the estimates for China and Turkey being not significant ( p = 0.065 and 0.067; see Table S12g and S29g, respectively), whereas the remaining 14 countries’ effects were relatively small, varying between ‒0.05 and 0.00. While this finding provides evidence of cross-national variation in the effect of daily smoking on well-being, it was significant only in 10 of 23 countries (see Tables S9-31g): seven of the eight countries (i.e., except Turkey), Argentina (‒0.04 [‒0.07, ‒0.03]), India (‒0.04 [‒0.06, ‒0.01]), and Germany (‒0.04 [‒0.06, ‒0.01]). In other words, while daily smoking had no significant impact on secure flourishing in more than half (56.5%) of GFS countries, the daily habit was found to be detrimental to well-being in some countries, though it is not immediately clear why that was the case, as those 10 countries were diverse in terms of sociocultural and religious contexts as well as geographic location. The effect of daily smoking on secure flourishing also varied across countries in Model 2, from ‒0.07 [‒0.11, ‒0.03] (Philippines) to 0.03 [‒0.01, 0.06] (China). Country-specific effects of daily smoking on all 78 well-being and other outcomes, estimated for Models 1 and 2, are presented in Tables S34 and S35, respectively, with statistically significant effects being highlighted (see Figures S1-78 for plots showing heterogeneity in those effects, separately for each outcome). For example, the top panel of Table S34 shows that daily smoking at Wave 1 had consistently larger associations with measures of human flourishing at Wave 2 in Australia, Brazil, Egypt, Germany, India, Israel, Japan, Sweden, and the U.S., whereas associations were typically smaller in other countries. Associations were in the expected (i.e., negative) direction except for China, where daily smoking was positively related to the flourishing index, physical and mental health, and character and virtue. While the next two panels tend to show similar patterns in terms of where the strongest associations were found, in other panels the pattern is less clear nor consistent. Daily smoking at Wave 1 was most strongly associated with daily smoking (all 23 countries) and weekly alcohol consumption at Wave 2 (20 countries, at least, in one model), with average effect sizes of 2.32 [2.04, 2.62] and 0.10 [0.07, 0.12] in Models 1 and 2.30 [2.03, 2.61] and 0.09 [0.07, 0.10] in Models 2. Country-specific associations between daily smoking at Waves 1 and 2 in RR widely varied, ranging from 1.33 [1.19, 1.49] (Nigeria) to 4.22 [3.97, 4.48] (Japan) in Model 1 and from 1.32 [1.19, 1.48] (Nigeria) to 4.21 [3.96, 4.47] (Japan) in Model 2 (see Tables S34-35 and Figure S57, which shows the natural logarithm of RR). Daily smokers at Wave 1 (compared to those who were not daily smokers) had a lower-than-average likelihood of smoking daily at Wave 2 in 12 countries (Nigeria, India, Tanzania, South Africa, Kenya, China, Turkey, Mexico, Poland, Egypt, Spain, and Indonesia) and a higher-than-average likelihood in 11 countries (Germany, Hong Kong, the Philippines, Brazil, Argentina, Australia, Sweden, the U.K., the U.S., Israel, and Japan). Comparing these two groups of countries seems to suggest that daily smokers were at a higher risk of continued daily smoking in western than non-western countries. As Figure S58 shows, country-specific effects of daily smoking at Wave 1 on weekly alcohol consumption at Wave 2 also widely varied from 0.02 [‒0.02, 0.07] (India) to 0.22 [0.18, 0.26] (China) and 0.22 [0.15, 0.29] (Hong Kong) in Model 1 and from 0.02 [‒0.02, 0.07] (India) to 0.21 [0.17, 0.25] (China) in Model 2. In the model without PC controls, for a 1 SD increase in the daily smoking status at Wave 1, there were a smaller-than-average (0.10 SD [0.07, 0.12]) increase in weekly alcohol consumption at Wave 2 in 14 countries (the U.K., Egypt, Nigeria, Sweden, the U.S., Argentina, Australia, Germany, Spain, Israel, Turkey, India, Tanzania, and Indonesia), the same-as-average increase in one country (Mexico), and a larger-than-average increase in eight countries (Kenya, the Philippines, South Africa, Brazil, Japan, Poland, China, and Hong Kong). Model 2 showed generally the same pattern except for Mexico (0.07 [0.02, 0.12]) moving to the group of smaller-than-average (0.09 SD [0.07, 0.10]) increase and South Africa (0.09 [0.01, 0.17]) having the same-as-average increase. Interestingly, daily smokers at Wave 1 (compared to those who were not daily smokers) tended to be at a higher risk of increasing alcohol consumption in non-western than western countries, whereas we saw above the risk of continued daily smoking tended to be greater in western than non-western countries. Smoker sample analysis The last two columns of Table 1 show weighted sample frequency distributions of the continuous measure of daily smoking (i.e., number of cigarettes smoked per day), year of birth (age), gender, education, and country of respondent by wave for the smoker sample (N = 36,147 at Wave 1 and 18,502 at Wave 2), that is, those who smoked cigarette(s) daily at Wave 1. Expanded summary statistics tables of all variables are included in Online Supplement 2 (see Tables S1-2 for the smoker sample and Tables S9a-31a and S9b-31b for each country). While the smoker sample was not different from the total sample in average age at each wave (about 45 and 47 at Waves 1 and 2, respectively; see the Table S1 of Online Supplements 1 and 2), daily smokers were less likely to be female and to have 16 + years of education than those who were not daily smokers, as expected 63 , 130 . Also, as found above for the total sample, daily smokers who participated in the Wave 2 survey were somewhat older and more educated than those who participated in the Wave 1 survey, although they were not different in gender composition, and each country’s representation in the smoker sample did not change drastically between the waves with the largest decrease and increase being in Brazil (‒3.3%, from 7.1% to 3.8%) and the U.S. (4.8%, from 10.0% to 14.8%), respectively. Table 4 presents the primary meta-analysis results that are not very different in the magnitude of RR and ES (i.e., very small) compared to the total sample results (see Table 2 ). However, because the sample is smaller, confidence intervals were slightly wider and more likely to include 0 for ES and 1 for RR. Exceptions were, again, the two health-risk behaviors at Wave 2, on which daily smoking at Wave 1 had relatively large effects: number of cigarettes per day (0.56 [0.48, 0.64]) and number of drinks per week (0.07 [0.05, 0.09]). In Model 1, for smokers who were 1 SD above the mean of daily cigarette consumption at the baseline, there was a 0.56 SD increase in number of cigarettes smoked daily and a 0.07 SD increase in the number of drinks consumed weekly a year later. In terms of unstandardized effect size (see Table S8 of Online Supplement 2), for one cigarette smoked a day at Wave 1, there was a 0.64 increase in the number of cigarettes per day and a 0.09 increase in the number of drinks per week at Wave 2. In Model 2, the effects of daily Table 4 Meta-analyzed associations of intensity of smoking at Wave 1 with well-being and other outcomes at Wave 2: Smoker sample analysis Model 1: Demographic and Childhood Variables as Covariates Model 2: Demographic, Childhood, and Other Wave 1 Confounding Variables (Via Principal Components) as Covariates Outcome RR ES 95% CI τ Global p-value RR ES 95% CI τ Global p-value Human Flourishing Secure flourishing index -0.04 (-0.07,-0.01) 0.05 1.02e-07*** -0.02 (-0.03,-0.01) 0.02 4.61e-07*** Flourishing index -0.04 (-0.06,-0.01) 0.05 2.31e-07*** -0.01 (-0.02,-0.00) 0.02 1.27e-06*** Psychological Well-Being Happiness -0.03 (-0.06,-0.01) 0.03 1.31e-03** -0.01 (-0.02,-0.01) 0.01 6.4e-06*** Life satisfaction -0.03 (-0.06,-0.01) 0.03 7.57e-05*** -0.01 (-0.02,-0.00) 0.02 1.96e-07*** Current life evaluation -0.04 (-0.06,-0.02) 0.03 7.32e-04*** -0.02 (-0.03,-0.01) 0.02 2.84e-04*** Future life evaluation -0.04 (-0.07,-0.02) 0.03 3.35e-06*** -0.02 (-0.03,-0.01) 0.02 1.16e-07*** Optimism -0.02 (-0.04,-0.00) 0.02 0.180 -0.00 (-0.02,0.01) 0.02 0.083 Freedom to pursue what's important -0.02 (-0.04,-0.00) 0.02 4.09e-04*** -0.00 (-0.01,0.01) < 0.01† 0.033* Inner peace 0.99 (0.98,1.00) < 0.01† 0.381 1.00 (1.00,1.00) < 0.01† 0.735 Life balance 0.98 (0.97,0.99) < 0.01† 0.236 1.00 (0.99,1.00) < 0.01† 0.543 Sense of mastery 1.00 (0.99,1.01) < 0.01† 0.442 1.00 (1.00,1.01) < 0.01† 0.119 Meaningful activities -0.03 (-0.05,-0.01) 0.03 2.21e-06*** -0.01 (-0.02,-0.00) 0.02 5.54e-08*** Understanding purpose -0.01 (-0.03,0.01) 0.02 1.85e-04*** -0.00 (-0.01,0.01) 0.01 0.016* Self-rated mental health -0.01 (-0.04,0.01) 0.04 0.013* -0.00 (-0.01,0.01) 0.02 0.015* Psychological Distress Traumatic distress 1.00 (0.98,1.02) < 0.01† 0.910 0.99 (0.99,1.00) < 0.01† 0.770 Depression symptoms composite 1.03 (1.01,1.05) < 0.01† 0.450 1.01 (1.00,1.02) 0.01 0.015* Anxiety symptoms composite 1.03 (1.00,1.05) < 0.01† 0.377 1.01 (1.00,1.01) < 0.01† 0.658 Suffering 1.02 (1.01,1.04) < 0.01† 0.396 1.00 (0.99,1.01) < 0.01† 0.346 Social Well-Being Relationship contentment -0.01 (-0.03,0.01) 0.01 0.054 0.00 (-0.00,0.01) < 0.01† 0.825 Relationship satisfaction -0.02 (-0.04,0.00) 0.03 0.034* -0.00 (-0.01,0.00) < 0.01† 0.433 Social support -0.03 (-0.05,-0.01) 0.02 1.6e-03** -0.02 (-0.03,-0.01) 0.01 0.051 Intimate/close friend 0.99 (0.98,1.00) < 0.01† 0.478 1.00 (0.99,1.00) < 0.01† 0.742 Government approval 1.00 (0.98,1.02) < 0.01† 0.069 1.00 (1.00,1.01) < 0.01† 0.865 Say in government 1.00 (0.97,1.02) 0.02 0.434 1.00 (0.98,1.01) 0.03 0.027* Belonging in country 0.00 (-0.02,0.03) 0.04 5.64e-04*** 0.00 (-0.00,0.01) < 0.01† 0.363 City/place satisfaction 1.00 (0.99,1.01) < 0.01† 0.860 1.00 (1.00,1.00) < 0.01† 0.786 Trust within country 0.98 (0.97,0.99) < 0.01† 0.163 0.99 (0.99,1.00) < 0.01† 0.310 Social Participation Ever been married 1.00 (0.99,1.00) < 0.01† 0.979 1.00 (1.00,1.00) < 0.01† 0.953 Currently divorced 0.98 (0.94,1.02) < 0.01† 0.981 1.00 (0.99,1.02) < 0.01† 0.965 Number of children 0.00 (-0.01,0.02) < 0.01† 0.619 0.00 (-0.01,0.01) 0.01 0.408 Weekly+ community participation 0.96 (0.92,1.00) < 0.01† 0.687 0.98 (0.96,1.00) 0.02 0.078 Weekly+ religious attendance 0.99 (0.96,1.01) < 0.01† 0.365 0.99 (0.98,1.00) < 0.01† 0.809 Social Distress Loneliness 0.02 (-0.00,0.04) 0.02 0.239 0.00 (-0.00,0.01) < 0.01† 0.916 Perceived discrimination 1.00 (0.97,1.03) < 0.01† 0.761 1.00 (0.99,1.01) < 0.01† 0.596 Character & Prosocial Behavior Orientation to promote good -0.01 (-0.02,0.01) 0.01 0.102 0.00 (-0.01,0.01) 0.02 0.041* Delayed gratification -0.03 (-0.05,-0.01) 0.02 0.045* -0.01 (-0.02,-0.00) < 0.01† 0.284 Hope -0.02 (-0.03,-0.00) 0.01 0.020* -0.00 (-0.01,0.01) < 0.01† 0.169 Gratitude -0.02 (-0.04,-0.01) 0.01 0.041* -0.01 (-0.02,0.00) 0.01 0.203 Showing love/care -0.02 (-0.04,-0.00) 0.01 0.017* -0.01 (-0.01,0.00) < 0.01† 0.333 Forgivingness 1.00 (0.99,1.01) < 0.01† 0.581 1.00 (1.00,1.01) < 0.01† 0.039* Charitable giving 0.98 (0.96,1.01) 0.03 0.284 0.99 (0.98,1.00) 0.02 0.029* Helping strangers 1.00 (0.99,1.02) 0.01 0.537 1.00 (1.00,1.01) < 0.01† 0.544 Volunteering 0.96 (0.92,1.00) 0.05 0.024* 0.98 (0.96,1.00) 0.04 0.014* Physical Health & Health Behavior Self-rated physical health -0.03 (-0.06,-0.01) 0.04 1.92e-03** -0.01 (-0.03,-0.00) 0.02 2.34e-03** Health problems 1.04 (1.01,1.06) < 0.01† 0.577 1.00 (0.99,1.01) < 0.01† 0.629 Pain in past 4 weeks 1.02 (1.00,1.03) < 0.01† 0.494 1.00 (0.99,1.00) < 0.01† 0.452 Number of cigarettes per day 0.56 (0.48,0.64) 0.18 3.63e-16*** 0.64 (0.55,0.73) 0.22 3e-16*** Number of drinks per week 0.07 (0.05,0.09) < 0.01† 1.39e-03** 0.09 (0.07,0.11) 0.03 5.11e-15*** Days exercise per week -0.04 (-0.06,-0.02) 0.03 0.006* -0.01 (-0.02,0.00) 0.02 6.27e-06*** Socioeconomic Outcomes Financial security -0.04 (-0.06,-0.02) 0.02 1.65e-03** -0.02 (-0.02,-0.01) < 0.01† 3.64e-03** Material security -0.02 (-0.04,-0.00) 0.02 0.086 -0.01 (-0.02,-0.00) < 0.01† 0.245 Educational attainment (16 + years) 1.00 (1.00,1.00) < 0.01† 0.875 1.00 (1.00,1.00) < 0.01† 0.204 Currently employed 1.00 (0.99,1.01) < 0.01† 0.938 1.00 (1.00,1.01) < 0.01† 0.016* Financially comfortable/getting by 0.98 (0.96,1.00) 0.02 0.010* 0.99 (0.98,1.00) 0.01 3.7e-03** Own home 1.00 (0.98,1.01) 0.01 0.375 0.99 (0.99,1.00) 0.01 2.82e-04*** Income -- top quintile 1.00 (1.00,1.01) < 0.01† 0.933 1.00 (1.00,1.00) < 0.01† 0.385 Notes. N = 36147; Reference for focal predictor: no daily smoking; RR, risk-ratio, null effect is 1.00; ES, effect size measure for standardized regression coefficient, null effect is 0.00; CI, confidence interval; τ (tau, heterogeneity), estimated standard deviation of the distribution of effects; Global p-value, joint test of the null hypothesis that the country-specific Wald tests are null in all countries. Multiple imputation was performed to impute missing data on the covariates, exposure, and outcomes. All models controlled for sociodemographic and childhood factors assessed at Wave 1: relationship with mother growing up; relationship with father growing up; parent marital status around age 12; experienced abuse growing up (except for Israel); felt like an outsider in family growing up; self-rated health growing up; subjective financial status growing up; religious affiliation at age 12; frequency of religious service attendance around age 12; year of birth; gender; education, employment status, marital status, immigration status; and racial/ethnic identity when available. For Model 2 with PC (principal components), the first seven principal components of the entire set of contemporaneous confounders assessed at Wave 1 were included as additional covariates of the outcomes at Wave 2. An outcome-wide analytic approach was used, and a separate model was run for each outcome. A different type of model was run depending on the nature of the outcome: (1) for each binary outcome, a weighted generalized linear model (with a log link and Poisson distribution) was used to estimate a RR; and (2) for each continuous outcome, a weighted linear regression model was used to estimate an ES. All effect sizes were standardized. For continuous outcomes, the ES represents the change in SD on the outcome for a 1 SD increase in the focal predictor. For binary outcomes, the RR represents the change in risk of being in the upper category compared to the lower category for a 1 SD increase in the focal predictor. P-value significance thresholds: p < 0.05*, p < 0.005**, (Bonferroni) p < 0.00089***, correction for multiple testing to significant threshold; † Estimate of τ (tau, heterogeneity) is likely unstable. See our online supplement forest plots for more detail on heterogeneity of effects. smoking on daily cigarette consumption (0.64 [0.55, 0.73]) and weekly alcohol consumption (0.09 [0.07, 0.11]) were somewhat larger than in Model 1, implying some of contemporaneous confounders (controlled via PCs) being suppressors for the effects. E-values, reported in the “Smoker sample” columns of Table 3 , suggest that the effect estimates were in general moderately robust to potential unmeasured confounding, while some estimates with E-values equal or close to 1.00 (e.g., suffering) should be interpreted with caution. Results from the smoker sample’s secondary meta-analysis (for additional 22 outcomes) were largely consistent with those from the total sample’s counterpart (see Tables S5-6 of Online Supplement 2). Also, their E-values—ranging from 1.01 to 1.24 and from 1.02 to 1.14 in Models 1 and 2, respectively—showed that they were, in general, moderately robust to unmeasured confounding with some exceptions (see Table S7). In addition, supplemental results from semi-complete case analysis with attrition weights indicated that misspecification of imputation models for the primary and secondary meta-analyses was unlikely, as they were generally similar to their primary and secondary counterparts (see Tables S5-6). E-values were also similar between the two analyses (see Table S7). To summarize, as was the case with the total sample meta-analysis, all but two effect estimates for the primary and secondary outcomes were found to be very small in Model 1 as well as Model 2. Country-specific effects on all 78 outcomes are summarized in Tables S34 (Model 1) and S35 (Model 2) of Online Supplement 2 (see Figures S1-78 for plots showing heterogeneity in those effects). For example, in Model 1, confidence intervals for the associations between the quantity of daily smoking at the baseline and secure flourishing a year later among daily smokers (see the first row of Table S34) were strictly negative in 10 countries (Argentina, Brazil, Germany, Hong Kong, Japan, Kenya, Mexico, Spain, Sweden, and the U.K.), strictly positive in one country (Israel), and included both positive and negative values in the remaining 12 countries. While the focal exposure’s effects on other outcomes varied differentially across GFS countries, the effect on the number of cigarettes smoked daily at Wave 2 was consistently positive. The quantity of daily cigarette consumption at Wave 1 on weekly alcohol consumption at Wave 2 was also typically positive, but estimates were smaller. Specifically, the effect varied from 0.00 [‒0.04, 0.05] (India) to 0.19 [0.01, 0.37] (the Philippines) in Model 2 and from ‒0.01 [‒0.12, 0.10] (India) to 0.38 [‒0.27, 1.02] (Nigeria) in Model 1. Discussion In this outcome-wide longitudinal study, we explored how daily cigarette smoking at Wave 1 of the GFS predicted a wide variety of well-being and other outcomes at Wave 2 (Research Questions #1), controlling for demographic and childhood factors (Model 1) and also previous measures of those outcomes at the baseline (Model 2). Model 1 results from the primary and secondary meta-analyses for the total sample showed that daily smoking predicted most of the 78 outcomes in the expected directions. That is, daily smokers at Wave 1 mostly reported a year later lower levels of human flourishing, psychological well-being, social well-being, social participation, character and prosocial behavior, self-rated physical health, socioeconomic outcomes, and, to a lesser extent, religion and spirituality; and higher levels of psychological distress, social distress, and negative measures of physical health and health behavior than their peers who did not smoke cigarette daily. While observed associations tended to be consistent with previous findings and for alcohol consumption and subsequent smoking moderately robust to potential unmeasured confounding, as assessed by E-values (Research Question #3), effect estimates were otherwise all very small, again except for daily smoker status and weekly alcohol consumption at Wave 2, which we speculate are attributable to several methodological factors and limitations of this study. First, instead of comparing individuals who were daily smokers at the baseline with those who were not, if we had examined individuals who initiated daily smoking in comparison with those who did not, using 3-wave data (i.e., no daily smoking at Wave 1 but daily smoking at Waves 2 and 3 vs. no daily smoking at Waves 1 to 3 vs. daily smoking at all three waves), larger effects may have been observed, at least, for some outcomes among those new daily smokers (e.g., social well-being, as they may isolate themselves from others as a result of the new daily habit) compared to their peers who did not smoke daily and, to a lesser extent, those who did across three waves. Many covariates were controlled for contemporaneously with the smoking exposure, which may constitute “over-control,” and attenuate effect estimates. With three waves of data, we will be able to control for covariates in the wave prior to the exposure. Second, the 1-year interval between the first two waves of the GFS might not have been long enough to assess major changes, detecting only the early effects of daily smoking over a short period of time and thus restricting its effect size. For new daily smokers, for example, the habit’s observable impact on physical health is likely to take years (i.e., a high “induction time” or the latency of smoking effects), just as, conversely, longer time after smoking cessation is related to more favorable self-rated health 131 . Also, for long-time smokers already having major health problems, one-year observation is less likely to reveal any large effect of daily smoking on self-reported physical health. Third, the very small effects of daily smoking on various outcomes, for instance, physical health may be due partly to the outcome being measured by self-report, which is potentially biased 132 . Finally, respondents who were not daily smokers at Wave 1 included not only never smokers but also occasional and former smokers, but we could not separate them into subgroups because the GFS survey included no other smoking-related item, which was likely to have contributed to daily smoking’s very small effect estimates. Specifically, given that daily smokers are more similar to occasional smokers 133 compared to former smokers and, to a greater extent, never smokers, to the extent that occasional smokers were included in those who did not smoke daily, effects of daily smoking on well-being and other outcomes would have been smaller compared to when daily smokers were compared to never smokers only. Having said that, small effect estimates may still be worth paying attention to. Arguing that effect sizes are underappreciated and often misinterpreted, Funder and Ozer 134 suggest that seemingly small effects can cumulate over time and become consequential in the long run, which is particularly relevant to research on individual differences, like the present study. They also propose that researchers should unapologetically report small effect sizes estimated based on data from a large sample because larger samples, ceteris paribus, tend to generate more precise and reliable estimates no matter how small the effect is. According to them, an alternative to establishing effects sizes by relying on single studies with very large sample sizes is meta-analysis: specifically, even if the average effect is small, all individual effect sizes that are in a narrow range and in the same direction can provide a certain level of confidence for the average effect estimate. The present study’s very small, meta-analyzed effects were the average of 23 country-specific effects, which were not only generally in the same direction with a relatively small heterogeneity but also estimated based on large samples. Thus, those very small average effects should not be overlooked but subject to further research and relevant exploration, such as examining cross-national variations (Research Question #2). We found that the effects of daily smoking on well-being and other outcomes varied across GFS countries in magnitude, with larger effects being more consistently found in certain countries than others, depending on outcomes examined. The effects of daily smoking on human flourishing, psychological well-being, and psychological distress (to a lesser extent, social well-being, social distress, and character & prosocial behavior) were generally statistically significant in the expected direction in Australia, Brazil, Egypt, Germany, Isreal, Japan, Sweden, and the U.S. but mostly not statistically significant in Hong Kong, Indonesia, Kenya, Mexico, Nigeria, the Philippines, South Africa, Spain, Tanzania, Turkey with the remaining countries (Argentina, India, Poland, and the U.K.) being in-between. Statistical insignificance for these outcomes usually the result of smaller point estimates—not wider confidence intervals—except for Hong Kong and Turkey, which showed moderate-to-large effect estimates whose confidence intervals included zero. Effects on the remaining outcomes (physical health & health behavior, socioeconomic outcomes, and religiosity/spirituality) were mostly small and statistically insignificant, with the notable exceptions of daily smoking and weekly alcohol consumption at Wave 2. Specifically, daily smokers at Wave 1 were likely to continue smoking daily in all 23 countries and to increase weekly alcohol consumption in all but three countries (India, Indonesia, and Tanzania) a year later. China was an exception, where the effects of daily smoking on some measures of human flourishing (flourishing index, physical & mental health, and character & virtue) and psychological well-being (current and future life evaluations) were found to be positive, not negative. Further research is needed to see if these unexpected findings are replicated and, if so, whether they are methodological artifacts due to response bias. For instance, Confucian modesty leading to “active indifference,” where a person is indifferent to one’s weaknesses, like the daily habit of smoking, to focus on what is perceived to be worthy of one’s attention, might have led many Chinese respondents to downplay the relevance of daily smoking to their lives and well-being and thus overreport their state of flourishing 135 , 136 . Also, while mostly cross-sectional, previous studies show that Chinese smokers, particularly, male smokers tend to rationalize their behavior as socially acceptable, being a social tradition, and also important due to the cultural value of Guanxi (which refers to relationships or social connections based on mutual interests and benefits) 137 , 138 . Smokers high on Guanxi may be actively involved not only in socializing but also charitable events, which in turn are likely to enhance their perceived well-being with heavy smokers even reporting better health than their non-heavy counterparts 139 , 140 . When we calculated the proportion of effect estimates that were statistically significant in each country, the percentages ranged from 2.6% to 60.3%: Sweden (60.3%, 47), the U.S. (59.0%, 46), Japan (48.7%, 38), Australia (47.4%, 37), Brazil (43.6%, 34), Israel (34.6%, 27), Germany (33.3%, 26), Egypt (21.8%, 17), the U.K. (19.2%, 15), Argentina (17.9%, 14), China (17.9%, 14), Poland (16.7%, 13), India (12.8%, 10), Indonesia (11.5%, 9), Hong Kong (9.0%, 7), Spain (7.7%, 6), Mexico (6.4%, 5), Tanzania (6.4%, 5), Turkey (6.4%, 5), Kenya (5.1%, 4), the Philippines (5.1%, 4), Nigeria (2.6%, 2), and South Africa (2.6%, 2). We again emphasize that, with the exceptions of Turkey and Hong Kong, effects were usually statistically insignificant because they were very close to zero. It was found that daily smoking’s statistically significant effects were more likely to be observed in western or westernized countries (i.e., Japan and Israel) than their non-western or non-westernized counterparts including all four African countries. While this finding is likely to have partly to do with sample size, we speculate that it might be also due in part to differences in smoking stigma, which tends to be greater in western than non-western cultures 141 , 142 . On the other hand, when we examined the relationship between the percentage and smoking prevalence, 62 rank-order correlation was small and noisy ( ρ = ‒0.078, p = .731), making it difficult to say whether effects on well-being and other outcomes were related to a country’s context for cigarette smoking, measured by the prevalence. Compared to results from analyzing Model 1, those from Model 2 were, as expected, less likely to be statistically significant due to the additional PC controls, and very small effect estimates in the first model became even smaller in absolute value. For example, the effect of daily smoking on flourishing index in ES, which was ‒0.03 [‒0.04, ‒0.02] in Model 1, became ‒0.01 [‒0.02, ‒0.01] in Model 2, whereas the effect on depression symptoms composite in RR reduced from 1.05 [1.03, 1.06] to 1.02 [1.01, 1.03] (see Table 2 of Online Supplement 1). While the Model 2 result of the latter can be called “conservative” estimate as typically labeled when an estimate becomes smaller with added control, it would be appropriate to frame that of the former as “optimistic” estimate as the negative effect of daily smoking on flourishing was diminished, being pulled towards zero. Regardless of which model is focused on, the results of Models 1 and 2, being “under-control” and “over-control” estimates, jointly provide a range between which estimates for the average effect across 23 GFS countries are potentially bounded, which, though, may not generalize to other countries. The same is applicable when the two models’ results are compared separately for each country. Wave 3 survey of the GFS, currently conducted, would enable us to address this issue by analyzing three waves of data. Additionally, results from analyzing the smoker sample revealed that the continuous measure of daily smoking (i.e., number of cigarettes smoked daily) was associated with outcome similarly to the binary measure. Finally, we acknowledge methodological limitations of this study. First, self-report method, widely used to measure smoking in population-based research like this one, has been found to underestimate smoking due to social desirability in reporting compared with a method using biomarkers, such as cotinine 143 , while a non-smoker can also have a high level of cotinine if the person lives with a smoker. It is worth mentioning that the underestimation bias does not necessarily render self-report-based findings invalid 144 , and, if the reporting bias tends to be constant error across observation units, it is less problematic in estimating associations between smoking and other variables. However, this may not be the case because underreporting bias is more likely in countries with anti-tobacco policies and legislations, like Brazil, Spain, and Turkey 129 . Second, childhood factors, controlled for in each model, were measured retrospectively and thus subject to potential recall bias and response error. Third, while GFS countries were selected to maximize coverage of the world’s population and to ensure geographic, cultural, and religious diversity, they are not a representative sample of nations around the globe. Thus, this study’s meta-analytic estimates are the average effects of daily smoking on well-being and other outcomes may not be generalizable beyond these 23 countries. Fourth, the smoker sample size at Wave 1 was smaller in some countries (e.g., Nigeria and Australia) than others, and a substantially high percentage of daily smokers at Wave 1 did not participate in the Wave 2 survey in some countries, like Hong Kong (91.7%), Brazil (72.7%), and Turkey (71.5%). So, while supplemental analyses using only Wave 2 respondents (semi-complete case analysis) indicated that misspecification of the imputation models was minimal, results on the smoker-only sample need to be interpreted with caution given these sample-related issues. Finally, although the Global Flourishing Study made various efforts, such as cognitive and pretest interviews 107 , 108 , 145 , to ensure that respondents would similarly understand survey items across countries, differential interpretation of some items (e.g., the meaning of “good” relationship with mother vs. father) is still a possibility among respondents in different cultures. Thus, caution is warranted in interpreting the present findings, keeping in mind various issues related to the translation of survey questions into different languages and potentially culture-dependent answers from respondents. In conclusion, based on new panel data from the first two waves of GFS survey conducted in 23 participating countries (including one territory), this outcome-wide, exploratory study contributes to global research on cigarette use by meta-analyzing country-specific effects of daily smoker status at the baseline on well-being and other outcomes a year later, controlling for demographic and childhood factors and contemporaneous confounders from the initial survey. We repeated this analysis using a quantity measure of daily cigarette use for daily smokers only. Results from synthesizing country-specific effects were, in general, consistent between the total and smoker samples, although estimates were less precise in the latter. The meta-analytic effects of daily smoking on well-being and other outcomes were generally in the expected direction: that is, the daily habit tended to be inversely associated with human flourishing and various aspects of well-being (i.e., physical, psychological, financial, and, to a lesser extent, social well-being) and positively related to psychological and social distress. Although those average effects were very small for all but two outcomes (daily smoking and weekly alcohol consumption), our exploration revealed cross-national variations in country-specific effects, indicating that daily smoking had more influence on well-being in some countries than others. Future research is called for to explain those differences across countries and also to continue studying consequences of daily smoking given that prior researchers mostly treated daily cigarette use as dependent rather than independent variable. Declarations Competing interests T.J.V. reports consulting fees from Gloo Inc., along with shared revenue received by Harvard University in its license agreement with Gloo according to the University IP policy. Funding The GFS was supported by funding from the John Templeton Foundation (grant #61665), Templeton Religion Trust (#1308), Templeton World Charity Foundation (#0605), Well-Being for Planet Earth Foundation, Fetzer Institute (#4354), Well Being Trust, Paul L. Foster Family Foundation, and the David and Carol Myers Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of these organizations. Author Contributions S.J.J. conducted the literature search and review, performed the data analysis, interpreted the results, and drafted the full manuscript. S.J.J. and P.A.D.L.R. collaborated on early versions of the manuscript. 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Advances in methods and practices in psychological science 2 , 156–168 (2019). Wang, R., Hempton, B., Dugan, J. P. & Komives, S. R. Cultural differences: Why do Asians avoid extreme responses? Survey Practice 1 (2008). Sin, W. Modesty, Confucianism, and active indifference. Educational Philosophy and Theory 55 , 158–168 (2023). Huang, X. et al . Why are male Chinese smokers unwilling to quit? A multicentre cross-sectional study on smoking rationalisation and intention to quit. BMJ open 9 , e025285 (2019). Chen, H., Fan, Y., Li, X., Gao, L. & Li, W. The relationship between smoker identity and smoking cessation among young smokers: the role of smoking Rationalization beliefs and cultural value of Guanxi. Frontiers in Psychiatry 13 , 812982 (2022). Li, X. & Bian, Y. Social networks in China: A thematic review of guanxi scholarship in the past decade. Chinese Journal of Sociology 10 , 531–562 (2024). Yen, S. T., Shaw, W. D. & Yuan, Y. Cigarette smoking and self-reported health in China. 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Assessing religion and spirituality in a cross-cultural sample: development of religion and spirituality items for the Global Flourishing Study. Religion, Brain & Behavior (2023). Additional Declarations The authors declare potential competing interests as follows: Tyler J. VanderWeele reports consulting fees from Gloo Inc., along with shared revenue received by Harvard University in its license agreement with Gloo according to the University IP policy. Supplementary Files W2Jangetal.onlinesupplement1.pdf Online Supplement 1: Total Sample Analysis W2Jangetal.onlinesupplement2.pdf Online Supplement 2: Smoker Sample Analysis Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8752309","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583619864,"identity":"8e70a772-9a46-47b8-b9d9-20f2e16f527a","order_by":0,"name":"Sung Joon Jang","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-2228-158X","institution":"Baylor University","correspondingAuthor":true,"prefix":"","firstName":"Sung","middleName":"Joon","lastName":"Jang","suffix":""},{"id":583620148,"identity":"66050521-29e3-487c-90e2-12e11a55ecea","order_by":1,"name":"Pedro A. de la Rosa","email":"","orcid":"","institution":"University of Navarra","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"A. de la","lastName":"Rosa","suffix":""},{"id":583620149,"identity":"a94c2c71-40b9-4d76-943a-9d8dc0f103c7","order_by":2,"name":"R. 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VanderWeele reports consulting fees from Gloo Inc., along with shared revenue received by Harvard University in its license agreement with Gloo according to the University IP policy.","formattedTitle":"\u003cp\u003eAn outcome-wide study of the associations of daily smoking with subsequent well-being and other outcomes in the Global Flourishing Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrior research has examined the relationship between tobacco use and physical and mental health and, to a lesser extent, other dimensions of human flourishing, such as social, spiritual, and financial well-being\u003csup\u003e1-5\u003c/sup\u003e. While previous studies were conducted globally, they rarely compared geographically and culturally diverse countries, let alone synthesize country-specific effects of tobacco use, or examine a large range of flourishing outcome, despite the availability of various sources of cross-national data\u003csup\u003e6\u003c/sup\u003e. They also tend to rely on cross-sectional data thus limiting the ability to make causal inferences. Finally, although most studies examined multiple measures of well-being simultaneously, they are generally limited in the scope of the well-being construct, which prohibits assessing differences in the relationship between tobacco use and a wide range of well-being outcomes.\u003c/p\u003e\n\u003cp\u003eTo address these oversights, we conduct an outcome-wide longitudinal study focusing on VanderWeele\u0026rsquo;s conceptualization of flourishing, that is, doing or being well in five broad domains of a person\u0026rsquo;s life (happiness and life satisfaction, physical and mental health, meaning and purpose, character and virtue, and close social relationships), including the contexts where the person lives\u003csup\u003e5, 7, 8\u003c/sup\u003e. VanderWeele proposed 10 items to measure flourishing (two items per domain), a sum of which is known as the \u0026ldquo;Flourish\u0026rdquo; index. Alternatively, adding two items of a sixth domain, financial and material security, creates the \u0026ldquo;Secure Flourish\u0026rdquo; index. In this study, we examine whether daily cigarette smoking at baseline is related to not only these two indexes of flourishing but also indicators of each domain and other associated outcomes, measured a year later, controlling for, first, sociodemographic and childhood factors and then also flourishing and other outcomes at the baseline. For this examination, we analyze data from the first two waves of Global Flourishing Study (GFS)\u0026mdash;a panel study of over 200,000 adults sampled to be nationally representative of 22 countries and one territory\u0026mdash;which were collected in 2022-24 (Wave 1) and 2024 (Wave 2). Specifically, we meta-analyze country-specific results from separately analyzing: (1) a binary measure of daily smoking (0 = None/Do not smoke, 1 = 1+ cigarette) for total sample and (2) a continuous measure, the number of cigarettes smoked daily, for a subsample of daily smokers at Wave 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe begin with a review of previous studies on the relationship between tobacco use including cigarette smoking and various concepts of flourishing, followed by an introduction of our research questions along with anticipated findings based on prior research. We then describe our methods and present cross-country variations as well as results from random effects meta-analysis before discussing key results with an acknowledgement of our study\u0026rsquo;s limitations.\u0026nbsp;\u003c/p\u003e"},{"header":"Prior research on smoking and flourishing","content":"\u003cp\u003e\u003cstrong\u003eHappiness and life satisfaction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrent smokers were found to be less happy than never smokers and/or ex-smokers among adolescents in Iran as well as adults in Hong Kong and former Soviet Union countries, while the number of cigarettes smoked was not associated with happiness among current smokers in Hong Kong\u003csup\u003e9, 10, 11, but see 12\u003c/sup\u003e. Life satisfaction has also been found to be lower among cigarette-smoking young adults than their non-smoking peers in 21 countries, and the results were consistent across three regions with the relationship in \u0026ldquo;Western Europe and the U.S.\u0026rdquo; and \u0026ldquo;Pacific Asia\u0026rdquo; being stronger than in \u0026ldquo;Central and Eastern Europe\u0026rdquo;\u003csup\u003e13\u003c/sup\u003e. A recent study found that a negative relationship between smoking and life satisfaction was in part due to self-rated health: that is, smokers were less likely to be satisfied with life partly because they were likely to have poor health than non-smokers\u003csup\u003e14\u003c/sup\u003e. Consistent with these cross-sectional findings, a longitudinal study of over 5,000 Dutch adults reported that quitting smoking increased subjective well-being (happiness and life satisfaction), implying smoking\u0026rsquo;s detrimental impact on this particular domain of flourishing\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical and mental health\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNumerous studies have examined cigarette smoking in relation to various health risks and death, and systematic reviews and meta-analyses show that current and, to a lesser extent, former smokers have a higher risk of not only respiratory diseases but also lower back pain, sciatica, chronic musculoskeletal pain, fracture, Crohn disease, stroke, cancer, and mortality than never smokers\u003csup\u003e16-26\u003c/sup\u003e. When adjusted for demographic, health-related (e.g., physical activity), and methodological factors (e.g., cross-sectional vs. longitudinal design), the risk tends to be reduced somewhat but remains significant. Consistent with these results, researchers report that smoking reduction and cessation decreases health risks and increases health-related quality of life\u003csup\u003e27, 28\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003ePrior research also found significant relationships between smoking and various health behaviors. For example, a cross-sectional study of Chinese adults found the frequency of physical exercise and smoking to be inversely related among men, though not among women\u003csup\u003e29\u003c/sup\u003e. A 7-year longitudinal study of males in Japan reported that current smokers were less likely to exercise than never smokers, while exercise increased among those who quit smoking but decreased among persistent smokers\u003csup\u003e30\u003c/sup\u003e. Drinking is the most often studied health-risk behavior in relation to smoking, and this positive relationship tends to be stronger among heavy users of both substances\u003csup\u003e31, 32\u003c/sup\u003e. While various explanations of the smoking-drinking link have been only partially tested\u003csup\u003e32-34\u003c/sup\u003e, prior research tends to suggest that smoking predicts drinking more strongly than vice versa, whereas heavy drinkers are more likely to be heavy smokers than the other way around\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAccording to cross-sectional studies based on Australian national surveys, current tobacco smokers were more likely than never smokers to have affective and anxiety disorder and psychological distress (depression) and to screen positively for psychosis, although former smokers were not different from never smokers in these measures of mental health\u003csup\u003e35, 36, 37, but see 38\u003c/sup\u003e. A meta-analysis of 78 cross-sectional studies revealed that current smokers were more likely to be depressed than former as well as never smokers\u003csup\u003e39\u003c/sup\u003e, and a meta-analysis of 63 studies on suicide, an outcome of depression, also discovered that current smokers were at higher risk of self-harm\u0026mdash;whether measured by suicidal ideation, suicide plan/attempt, or suicide death\u0026mdash;than non-smokers\u003csup\u003e40\u003c/sup\u003e. Another systematic review/meta-analysis of 26 studies investigating change in mental health after smoking cessation compared to continued smoking reported that anxiety, depression, mixed anxiety and depression, and stress significantly decreased among quitters compared to continuing smokers with psychological quality of life and positive affect significantly increasing\u003csup\u003e41\u003c/sup\u003e. While these findings confirm the detrimental impact of smoking on mental health, a systematic review of 148 longitudinal studies concluded that research on the association between smoking and depression and/or anxiety is inconsistent in terms of the direction of association: that is, about half of studies reviewed reported a positive relationship between depression/anxiety and later smoking, whereas over one third of them found evidence for smoking being positively associated with later depression/anxiety with few studies supporting their reciprocity\u003csup\u003e42, see also 43, 44\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeaning and purpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to self-medication theory, the use of licit and illicit substances including cigarettes and other tobacco products is a behavior to cope with unpleasant psychological states, such as a weak sense of meaning and purpose in life as well as negative emotions (e.g., anxiety and distress)\u003csup\u003e45\u003c/sup\u003e, as nicotine induces mild euphoria\u003csup\u003e33\u003c/sup\u003e. Consistent with this theory, meaning in life was found to be inversely related to smoking in both cross-sectional and longitudinal studies\u003csup\u003e45, 46\u003c/sup\u003e, although the evidence for purpose in life is limited\u003csup\u003e47, 48\u003c/sup\u003e. While studies of reverse causation are rare, a prospective study of a representative sample of Americans aged 50+ years found that smoking at an initial wave was inversely related to a sense of purpose in life four years later\u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacter and virtue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLike meaning and purpose, the domain of character and virtue has scarcely been studied in relation to smoking. An exception is self-control or, to be precise, lack thereof: impulsivity or delay discounting, which is the tendency for the perceived value of a reward to decrease as the length of delay to its delivery increases. A systematic review revealed that delay discounting was inversely related to smoking cessation\u003csup\u003e49\u003c/sup\u003e, and a longitudinal study found that delay discounting at a baseline was positively associated with an increasing trajectory of smoking, while smoking had no significant impact on delay discounting\u003csup\u003e50\u003c/sup\u003e. Examining prosocial behaviors, researchers found that smoking households were less likely to give to charity and, when they did, tended to give less than non-smoking households in the U.S.\u003csup\u003e51\u003c/sup\u003e, and a German panel study found that volunteering had negative effects on smoking at the between-individual (but not within-individual) level, although the effects were partly explained by socioeconomic status\u003csup\u003e52\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClose social relationships\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSocial well-being, such as relational contentment and satisfaction, is likely to be inversely related to smoking because it includes social support that reduces social distress, which increases the risk of smoking\u003csup\u003e53\u003c/sup\u003e. Indeed, prior research found that a lack of social or relational well-being (e.g., loneliness, perceived discrimination, and distrust of others) was positively related to smoking\u003csup\u003e54-58\u003c/sup\u003e. Conversely, an analysis of British panel data revealed that social capital\u0026mdash;measured by social participation and interpersonal trust\u0026mdash;along with employment and marital status was positively associated with smoking cessation\u003csup\u003e59\u003c/sup\u003e. In addition, a longitudinal study in Sweden reported that daily smokers at a baseline who continued to smoke daily a year later had significantly decreased odds ratios of participating in formal and informal group and other activities in society compared to a reference group (non-daily smokers and non-smokers at the baseline), whereas the odds ratios decreased, to a lesser extent, among the baseline daily smokers who reduced the daily habit to intermittent or non-smoking\u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial and material security\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers have examined relationships between smoking and various financial factors, including socioeconomic status. For example, income was consistently related inversely to smoking across different regions, time periods, and gender groups\u003csup\u003e55, 61-63\u003c/sup\u003e, and a study of a national sample of Australian households found that smoking households (i.e., those reported tobacco expenditure) had higher odds of experiencing financial stress (e.g., cash flow problems, inability to pay utility bills, or going without meals) than their non-smoking counterparts\u003csup\u003e64\u003c/sup\u003e. Education is also inversely related to smoking\u003csup\u003e62, 63, 65, 66\u003c/sup\u003e, and longitudinal studies reported higher rates of smoking cessation for individuals with higher education and reciprocal relationships between smoking and educational achievement\u003csup\u003e67-69\u003c/sup\u003e. Consistent with these findings, smoking prevalence tends to be lower among employed than unemployed individuals \u003csup\u003e62, 70\u003c/sup\u003e. Longitudinal research in the U.S. reported bidirectional positive relationships between cigarette smoking and household food insecurity with cigarette cessation being inversely related to a risk of food insecurity\u003csup\u003e71, 72\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReligion and spirituality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile other leading conceptualizations of flourishing tend to focus on physical, psychological, and social sources of well-being\u003csup\u003e1-4\u003c/sup\u003e, VanderWeele recognizes the importance of spirituality and identifies religious community as one of four institutional/community \u0026ldquo;pathways\u0026rdquo; that contribute to flourishing with the other three being family, work, and education\u003csup\u003e5\u003c/sup\u003e. Indeed, previous studies have found significant relationships between religious involvement and smoking as well as physical and mental health and other domains of flourishing\u003csup\u003e73-80\u003c/sup\u003e. Cross-sectional studies report inverse relationships between religiosity and smoking across demographic groups and countries as well as different measures of religiosity (e.g., public vs. private religiosity)\u003csup\u003e62, 81-84\u003c/sup\u003e. Longitudinal studies indicate that this inverse relationship is partly the protective effect of religiosity against initiating smoking and becoming a persistent smoker as well as consuming a large quantity of cigarettes among smokers\u003csup\u003e73, 85-88\u003c/sup\u003e. Consistent with this finding, religiously involved individuals tend to be more likely to attempt (and succeed) in smoking cessation\u003csup\u003e89-91\u003c/sup\u003e. While prior research has rarely examined the reversed effect (i.e., whether smoking reduces religiosity), one study found that among smokers, the quantity of cigarette consumption was inversely related to both public and private religious behaviors\u003csup\u003e81\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComposite of Flourishing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome research has examined the relationship between smoking and a composite of flourishing. For example, using the categorical diagnosis of flourishing \u0026agrave; la Huppert and So\u003csup\u003e3\u003c/sup\u003e, Hone et al. found that smokers and non-smokers were no different in the odds of flourishing, controlling for demographic factors, among employed adults in New Zealand\u003csup\u003e92\u003c/sup\u003e. Two recent studies based on VanderWeele\u0026rsquo;s flourishing concept reported similar results. Hsu et al. found that \u0026ldquo;unhealthy behaviors,\u0026rdquo; including smoking, was not correlated with any of Secure Flourish items among Taiwanese retirees\u003csup\u003e93\u003c/sup\u003e. In a study of adults living in a city in Japan, Shibata et al. also found that current smokers were no different from never smokers in the Flourish index; but when its five domains were analyzed separately, they discovered that smokers fared worse than never smokers in two domains (happiness \u0026amp; life satisfaction and meaning \u0026amp; purpose)\u003csup\u003e94\u003c/sup\u003e, which indicates the importance of examining flourishing dimensions separately as well as jointly.\u003c/p\u003e\n\u003cp\u003eIn this exploratory study, we address three research questions, pre-registered with the Center for Open Science (COS) (https://osf.io/v8t5x), as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eResearch Question #1: How does daily smoking at Wave 1 of the GFS predict well-being and related outcomes in Wave 2? \u003c/li\u003e\n\u003cli\u003eResearch Question #2: How does the pattern of these associations vary by country? \u003c/li\u003e\n\u003cli\u003eResearch Question #3: To what extent are the observed associations robust to potential unmeasured confounding, as assessed by E-values? \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo explore these questions, we conducted two sets of random effects meta-analysis. In primary meta-analysis, we examine a total of 56 outcomes (a complete list of outcomes was preregistered with the COS; https://osf.io/v8t5x), organized into nine categories (12 variables that measure six domains of the Secure Flourishing Index, two per domain, are denoted by an asterisk): (1) human flourishing (2, Flourishing and Secure Flourishing Indexes), (2) psychological well-being (12, including happiness*, life satisfaction*, meaning in life*, purpose in life*, and mental health*), (3) psychological distress (4, including depressive and anxiety symptoms), (4) social well-being (9, including relationship contentment* and relationship satisfaction*), (5) social participation (5, including at least weekly religious service attendance), (6) social distress (2, loneliness and perceived discrimination), (7) character and prosocial behavior (9, including orientation to promote good* and delayed gratification*), (8) physical health \u0026amp; health behavior (6, including self-rated physical health*), and (9) socioeconomic outcomes (7, including financial security* and material security*). In secondary meta-analysis, we examined additional 22 outcomes, which consists of six domain-specific measures of Secure Flourishing, four indicators of psychological distress, and 12 indicators of religion/spirituality (e.g., religious/spiritual connection, belief in life after death, and prayer or meditation).\u003c/p\u003e\n\u003cp\u003eFirst, based on prior research, we anticipate the following relationships (Research Question #1). Daily smoking at Wave 1 is expected to be inversely related to the outcomes of human flourishing, psychological well-being, social well-being, self-rated physical health and days exercise, socioeconomic outcomes and, to a lesser extent, character and prosocial behavior at Wave 2. Conversely, daily smoking at Wave 1 is likely to be positively related to the outcomes of psychological and social distress as well as health problems, pain, smoking, and drinking at Wave 2. Also, daily smoking at Wave 1 is expected to be inversely related to religion/spirituality-associated outcomes (e.g., religious beliefs, experiences, and practices) at Wave 2.\u003c/p\u003e\n\u003cp\u003eNext, we explore whether the strength and even the direction of these relationships vary by country perhaps due to the influence of diverse sociocultural, economic, and health contexts that characterize each nation (Research Question #2). Finally, we examine the robustness of the observed relationships against potential unmeasured confounding (Research Question #3). \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study design, methodology, sampling, and survey development for the Global Flourishing Study (GFS) are described elsewhere\u003csup\u003e95-98\u003c/sup\u003e. Here we employ this data in an outcome-wide longitudinal design\u003csup\u003e99\u003c/sup\u003e incorporating Wave 2 data as part of a coordinated set of outcome-wide studies\u003csup\u003e100\u003c/sup\u003e to assess associations of predictor(s) on subsequent outcomes, minimizing individual researcher degrees of freedom, and maintaining a consistent analytic framework from which to compare across outcomes and studies. The methodology for the analyses follows that described in Padgett et al.\u003csup\u003e100, 101\u003c/sup\u003e and VanderWeele et al.\u003csup\u003e102\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWave 1 of the GFS included nationally representative samples from 22 countries and one territory, also referred to as \u0026ldquo;23 countries\u0026rdquo;: Argentina, Australia, Brazil, China, Egypt, Germany, Hong Kong (Special Administrative Region of China), India, Indonesia, Israel, Japan, Kenya, Mexico, Nigeria, the Philippines, Poland, South Africa, Spain, Sweden, Tanzania, Turkey, the United Kingdom, and the United States (N = 207,919). The countries were selected to (1) maximize coverage of the world\u0026rsquo;s population, (2) ensure geographic, cultural, and religious diversity, and (3) prioritize feasibility in Gallup\u0026rsquo;s existing data collection infrastructure. The study was reviewed and approved by the institutional review boards at Baylor University (IRB reference #1841317) and Gallup (IRB reference #2021-11-02). Informed consent was obtained from all participants, and further details are available elsewhere\u003csup\u003e96\u003c/sup\u003e. Data for Wave 1 were collected from March 2022 to January 2024, except in China (March/April of 2024)\u003csup\u003e103\u003c/sup\u003e. Data for Wave 2 were collected from January 2024 to December 2024, with data in China collected at least six months after Wave 1\u003csup\u003e103\u003c/sup\u003e. The GFS survey assesses aspects of well-being, including happiness, health, meaning, character, relationships, and financial security\u003csup\u003e5\u003c/sup\u003e, along with other demographic, social, economic, political, religious, personality, childhood, community, health, and well-being variables\u003csup\u003e104, 105\u003c/sup\u003e. Gallup translated the GFS survey into multiple languages following the TRAPD (translation, review, adjudication, pretesting, and documentation) model for cross-cultural survey research\u003csup\u003e106\u003c/sup\u003e. Details about the translation, cognitive interviewing, and pilot testing phases of the GFS can be found elsewhere\u003csup\u003e97, 107-109\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe precise sampling design varied by country to ensure samples were approximately nationally representative\u003csup\u003e96, 106\u003c/sup\u003e. In most countries, local field partners implemented a probability-based face‑to-face or telephone methodology to recruit panel members. Recruitment involved an intake survey gathering basic sociodemographic information and details for recontacting participants. Following recruitment, participants received invitations to participate in the annual survey via phone or online. Follow-up for Wave 2 data collection relied on the respondent-provided contact information. A minimum of three contact attempts were made on different days of the week and times of day to maximize the possibility of retention. Post-stratification and nonresponse adjustments to the Wave 1 sampling weights were performed separately within each country, using either census data or a reliable secondary source. Additional information about the sampling design and weighting scheme for Wave 2 is available elsewhere\u003csup\u003e100, 103\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome-wide Analytic Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn outcome-wide analytic approach\u003csup\u003e7, 99\u003c/sup\u003e was employed to examine the associations of a single exposure with a range of subsequent outcomes. Compared to traditional analytic strategies focused on a single outcome, this approach provides a more holistic assessment of an exposure\u0026rsquo;s possibly differential relations to multiple life outcomes. The outcome-wide analytic design has strengths of reducing researcher subjectivity/degree-of-freedom\u003csup\u003e110\u003c/sup\u003e in analysis by ensuring a consistent analytic strategy and the same set of covariates across models for all outcomes; mitigates publication bias by reporting results for all examined outcomes, including null findings, simultaneously; and provides insights into beneficial, detrimental, and null associations with the exposure. Further details about the outcome-wide approach could be found elsewhere\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFocal Wave 1 predictor variable\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe focal exposure from Wave 1 for this study is daily smoking, specifically, daily cigarette consumption, which is assessed with an item, asking \u0026ldquo;About how many cigarettes do you smoke each day, if any?\u0026rdquo; (0 = None/Do not smoke, 1 = one, 2 = two, \u0026hellip; 97 = 97+). This item is first dichotomized to be analyzed as a binary variable (0 = None/Do not smoke, 1 = 1+) at Wave 1 for the total sample to see whether the prevalence of daily smoking predicts flourishing and well-being outcomes at Wave 2. Thereafter, the smoker sample is analyzed using those who reported smoking at least one cigarette per day at Wave 1. Using this sample, we will look at how the quantity of daily smoking (number of cigarettes consumed per day) at Wave 1 predicts flourishing and well-being outcomes at Wave 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCovariates\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCountry-specific analyses adjusted for 17 covariates (9 demographic and 8 childhood variables) unless data were not available (described below). Additional details on all variables can be found in the GFS Codebook (https://osf.io/cg76b) or Crabtree et al.\u003csup\u003e108\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDemographic covariates\u003c/em\u003e: Year of birth (age) was classified into 1998-2005 (18-24 years), 1993-1998 (25-29 years), 1983-1993 (30-39 years), 1973-1983 (40-49 years), 1963-1973 (50-59 years), 1953-1963 (60-69 years), 1943-1953 (70-79 years), and \u0026ldquo;1943 or earlier\u0026rdquo; (80 years or older). Gender was assessed as male, female, or other. Marital status was assessed as single/never married, married, separated, divorced, widowed, and domestic partner. Education was assessed as up to 8 years, 9-15 years, and 16 or more years. Employment was assessed as employed, self-employed, retired, student, homemaker, unemployed and looking for a job, and none of these/other. Religious service attendance was assessed as more than once a week, once a week, 1-to-3 times a month, a few times a year, and never. Immigration status was assessed with yes/no responses to: \u0026ldquo;Were you born in this country, or not?\u0026rdquo; Religious affiliation was assessed in all countries, but with considerable cross-country variation in the response categories because some religious affiliations are only applicable in certain countries. Religious affiliation response categories included Christianity, Islam, Hinduism, Buddhism, Judaism, Sikhism, Baha\u0026rsquo;i, Jainism, Shinto, Taoism, Confucianism, Primal/animist/folk religion, Spiritism, Umbanda, Candombl\u0026eacute;, and other African-derived religions, Chinese folk/traditional religion, some other religion, or no religion/atheist/agnostic. Racial/ethnic identity was assessed in most countries but not collected in China, Germany, Japan, Spain, and Sweden. Response categories varied across countries to be locally meaningful. Country-specific analyses that adjusted for racial/ethnic identity used a binary variable based on whether an individual was in the most prominent racial/ethnic group in the sample versus a minority racial/ethnic group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRetrospective childhood covariates\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 \u0026ldquo;very/somewhat good\u0026rdquo; versus \u0026ldquo;very/somewhat bad.\u0026rdquo; \u0026ldquo;Does not apply\u0026rdquo; was treated as a dichotomous control variable for respondents who did not have a mother due to death or absence. An analogous variable was used for the relationship with father. 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\u0026apos;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; Social isolation growing up was assessed with the question: \u0026ldquo;When you were growing up, did you feel like an outsider in your family?\u0026rdquo; Response options were yes/no. 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; Religious service 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, 1-to-3 times/month, less than once/month, or never.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOutcome variables\u003c/em\u003e: As explained above in detail, 56 outcomes of nine categories and 22 outcomes of three categories were examined for primary and secondary analysis, respectively. The list of Wave 2 outcomes, preregistered with the COS, is also available on the Open Science Framework (see the \u0026ldquo;W-2 Core Team Analyses\u0026rdquo; tab at https://osf.io/9kpd8). Further details on item wording can be found in Crabtree et al.\u003csup\u003e108\u003c/sup\u003e. In these data, the internal reliability of composite measures varies across countries, as expected. For example, inter-item reliability coefficient alpha for 12-item Secure Flourishing Index\u003csup\u003e5, 95\u003c/sup\u003e was 0.88, while ranging from 0.75 in Nigeria to 0.94 in Japan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRegression analyses with complex survey weights\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were performed using R 4.5\u003csup\u003e111\u003c/sup\u003e and the \u003cem\u003eRglobalflourishing\u003c/em\u003e package\u003csup\u003e112\u003c/sup\u003e. Weighted descriptive statistics for the sample (N = 207,919) were estimated for each of the demographic and outcome variables at both waves. All analyses, including imputation and attrition modeling described below, accounted for complex survey design by including weights, primary sampling units, and strata. Additional methodological details, including the approach that was used to account for the complex sampling design, can be found elsewhere\u003csup\u003e100, 103\u003c/sup\u003e. Within each country, we conducted weighted modified Poisson multivariate regression analyses for the binary measure of daily smoking and weighted linear regression analyses for the quantity measure (i.e., the number of cigarettes smoked daily). Two models were used for each outcome, regressing each outcome on the focal exposure, first controlling only for the demographic and childhood variables, and second controlling also for principal components (PCs) extracted from all contemporaneous Wave 1 variables other than the focal exposure. PCs were used to reduce the dimensionality of predictors to mitigate multicollinearity\u003csup\u003e113\u003c/sup\u003e, while accommodating complex survey weights and missing data. The first seven PCs were used and accounted for an average of 51.2% of the variability in all covariates\u003csup\u003e100\u003c/sup\u003e, with additional PCs each explaining only 1-2%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeta-analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted by each country (total and smoker sample results in Online Supplements 1 and 2, respectively) and random-effects meta-analyses were used to pool estimates across countries and to estimate heterogeneity (tau). For each outcome, a global \u003cem\u003ep\u003c/em\u003e-value for an omnibus test of evidence of association in any country is reported\u003csup\u003e114\u003c/sup\u003e. All meta-analyses were conducted using the \u003cem\u003emetafor\u0026nbsp;\u003c/em\u003epackage\u003csup\u003e115\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSensitivity to unmeasured confounding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe report E-values for all associations to evaluate the sensitivity of results to potential unmeasured confounding. An E-value is the minimum strength of the association on the risk ratio scale that an unmeasured confounder would need to have with both the outcome and the predictor, above and beyond all measured covariates, to explain away an association\u003csup\u003e116\u003c/sup\u003e. A high E-value signifies that an unmeasured confounder would need to have a strong association with both the predictor and the outcome to explain away the observed association. Approximate E-values can be obtained for continuous outcomes through scale conversions\u003csup\u003e116\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMissing data\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrimary analysis \u0026ndash; fully imputed data\u003c/em\u003e. The primary analyses utilize all participants from Wave 1, including those not reporting in Wave 2 by imputing missing data\u003csup\u003e117\u003c/sup\u003e. Multiple imputation (MI) by chained equations\u003csup\u003e118, 119\u003c/sup\u003e was used for exposures, covariates, and outcomes. Twenty imputed datasets were used. Using MI with all respondents aligns with Wave 1 analyses and will also be used in Wave 3 to maximize utilization of information on those who do not report in Wave 2 but report again in Wave 3, thereby aligning the analytic sample across years, facilitating comparison of results. The imputation model utilized sampling weights, demographic characteristics, and childhood variables for Wave 1 missing data; and for Wave 2 missing data, the imputation models additionally included all Wave 1 exposures. Imputation was conducted separately by country to account for variation in the assessment of certain variables across countries (e.g., race/ethnicity and income), thereby reflecting country-specific contexts and assessment methods.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSupplemental analyses \u0026ndash; semi-complete case with attrition weights\u003c/em\u003e. As a sensitivity analysis for possible misspecification of the imputation models\u003csup\u003e120\u003c/sup\u003e, analyses were conducted using only Wave 2 respondents (semi-complete case analysis)\u003csup\u003e121\u003c/sup\u003e, with attrition weights\u003csup\u003e122\u003c/sup\u003e multiplied by the sampling weights for use in the analysis. Attrition weights were estimated using logistic regression models for retention to calculate stabilized inverse probability of retention weights\u003csup\u003e123\u003c/sup\u003e. Attrition predictors included sampling weight, strata, mode of survey in Wave 1, age, gender, education, income, employment status, marital status, race/ethnicity, religious service attendance, urban/rural status of participants, personality, days of exercise, depression, loneliness, and the six domains of the flourishing index, covering a range of important predictors\u003csup\u003e7, 124, 125\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe first present results for the total sample followed by the smoker sample. We will focus mostly on effect sizes and not place too much emphasis on using the language of statistical significance for several reasons. First, because of the large sample size, while many of the estimated effects are statistically significant, they are often small in magnitude, a point that can be obscured by describing effects with the term \u0026ldquo;significant.\u0026rdquo; Second, the difference between \u0026ldquo;significant\u0026rdquo; and \u0026ldquo;insignificant\u0026rdquo; estimates is often small, with confidence intervals for the former barely excluding zero and confidence intervals for the latter barely including zero. Finally, insignificance is often interpreted as evidence in favor of the null, whereas reporting a confidence interval that includes negative and positive values (i.e., a confidence interval for an insignificant effect) clarifies that the data is consistent with both negative and positive effects. Our focus is thus on reporting magnitudes and confidence intervals.\u003c/p\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003eTotal sample analysis\u003c/h2\u003e\n \u003cp\u003eTable 1 presents weighted sample frequency distributions of the binary measure of daily smoking, year of birth (age), gender, education, and country of respondent by wave for the total sample (N\u0026thinsp;=\u0026thinsp;207,919 at Wave 1 and 128,868 at Wave 2) with mean and standard deviation of daily smoking\u0026rsquo;s continuous measure being also reported. Expanded summary statistics tables of all demographic, childhood, and outcome variables are provided in Online Supplement 1 (see Tables S1-2 for the total sample and Tables S1a, S9a-31a, and S9b-31b for each country). The prevalence and average quantity of daily smoking slightly decreased between Waves 1 and 2 from 17.4% to 15.2% and from 2.0 to 1.8, respectively, indicating that daily smokers might be less likely to participate in Wave 2 than non-daily smokers and non-smokers. However, we found cross-national variations, as the prevalence and/or average quantity either remained the same or slightly increased in 12 countries (China, Egypt, India, Indonesia, Japan, Kenya, Mexico, the Philippines, South Africa, Spain, Tanzania, and Turkey; see Tables S1a and S9a-31a). Wave 2 survey participants tended to be somewhat older and more educated than their Wave 1 counterparts, but they were practically the same in gender composition. Each country\u0026rsquo;s representation in the total sample did not change drastically between the waves with the largest decrease and increase being in Brazil (‒3.1%, from 6.4% to 3.3%) and the U.S. (6.6%, from 18.4% to 25.1%), respectively.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Weighted sample demographic summary statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoker sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWave 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;207,919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWave 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;128,868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWave 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;36,147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWave 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;18,502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDaily smoking\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;+\u0026thinsp;cigarette smoked per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,147 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,528 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone/No smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168,414 (81.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106,366 (82.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Missing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,357 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,974 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin, Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0, 97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0, 97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0, 97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0, 97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Missing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,357 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,974 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYear of birth, n (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1943 or earlier (current age: 80\u0026thinsp;+\u0026thinsp;years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,047 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,446 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1943\u0026ndash;1953 (current age: 70\u0026ndash;79 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,902 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,685 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,818 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,295 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1953\u0026ndash;1963 (current age: 60\u0026ndash;69 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,031 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,537 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,230 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,067 (16.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1963\u0026ndash;1973 (current age: 50\u0026ndash;59 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32,409 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,498 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,688 (18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,592 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1973\u0026ndash;1983 (current age: 40\u0026ndash;49 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34,970 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,995 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,292 (20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,888 (21.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1983\u0026ndash;1993 (current age: 30\u0026ndash;39 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40,297 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,641 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,687 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,704 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1993\u0026ndash;1998 (current age: 25\u0026ndash;29 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,325 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,308 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,421 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,537 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1998\u0026ndash;2005 (current age: 18\u0026ndash;24 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,920 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,758 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,801 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,285 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Missing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (\u0026lt;\u0026thinsp;0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u0026lt;\u0026thinsp;0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGender, n (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100,661 (48.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62,159 (48.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23,705 (65.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,110 (65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106,349 (51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66,142 (51.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,318 (34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,352 (34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Missing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e386 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEducation (years), n (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp to 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46,842 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,659 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,320 (23.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,346 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116,015 (55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72,942 (56.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,400 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,889 (64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44,904 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,257 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,406 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,264 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Missing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (\u0026lt;\u0026thinsp;0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCountry of respondent, n (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArgentina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,724 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,876 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,216 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e771 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,844 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,578 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e455 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,203 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,222 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,562 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e699 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,022 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,575 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,355 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,130 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEgypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,729 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,044 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,073 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e661 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,506 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,586 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,507 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,278 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHong Kong (S.A.R. of China)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,012 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e608 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e942 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,765 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,353 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e997 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e476 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndonesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,992 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,661 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,553 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e865 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,669 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,494 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e856 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e552 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,543 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,966 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,521 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,870 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,389 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,712 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e596 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,776 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,264 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,318 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e481 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,827 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,144 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,292 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,684 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,126 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,389 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,545 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,088 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,682 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,651 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e962 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e622 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,290 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,917 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,984 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e855 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,068 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,663 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,431 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,021 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTanzania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,075 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,588 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e354 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,473 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e497 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e772 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,368 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,621 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e893 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e540 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38,312 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32,310 (25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,624 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,736 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote. This table is based on non-imputed data; cumulative percentages for variables may not add up to 100% due to rounding; S.A.R. = Special Administrative Region. Expanded summary statistics tables of all demographic, childhood, and outcome variables are provided in the online supplement (see Tables S1, S1a, and S2 for the total sample and Tables S9a-31a and S9b-31b for each country).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the primary meta-analysis results, associations of daily smoking at Wave 1 with 56 measures of well-being and other outcomes at Wave 2, controlling first for demographic and childhood variables (Model 1) and then the first seven principal components (PCs) of those outcomes at Wave 1 as well as the Model 1 controls (Model 2). In each model, risk ratio (RR) for each binary outcome and effect size (ES) measure (standardized regression coefficient) for each continuous outcome are reported in separate columns. Next, 95% confidence interval (CI), heterogeneity (\u0026tau;), and global \u003cem\u003ep\u003c/em\u003e-value along with two nominal significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05* and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005**) and Bonferroni-corrected thresholds (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00089***) are presented. In Model 1, estimated associations were very small, ranging from 0.98 (e.g., weekly+ religious attendance) to 1.05 (depression symptoms composite) in RR and from ‒0.03 (e.g., secure flourishing index) to 0.02 (loneliness) in ES, with the associations being generally in the expected direction: that is, daily smoking at Wave 1 tended to be inversely related to human flourishing, psychological well-being, social well-being and participation, character and prosocial behavior, self-rated physical health, and some of socioeconomic outcomes; and positively related to psychological distress, social distress, health problems, pain in past 4 weeks, and other socioeconomic outcomes, with some exceptions. Two estimates were much larger than others: daily smoking (RR\u0026thinsp;=\u0026thinsp;2.32 [95% CI; 2.04, 2.62]) and number of drinks per week (ES\u0026thinsp;=\u0026thinsp;0.10 [0.07, 0.12]). That is, respondents who smoked at least one cigarette per day at Wave 1 were about 132% more likely to smoke daily at Wave 2 than those who were not daily smoker at the baseline even after controlling for the covariates. This \u0026ldquo;stability effect\u0026rdquo; is likely mostly attributable to the power of daily habits as well as nicotine addiction\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e126\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e128\u003c/span\u003e\u003c/sup\u003e. Also, for study participants who were 1 standard deviation (SD) above the mean on daily smoker status at Wave 1 (or a 1 SD increase in the status), there was a 0.10 SD increase in weekly alcohol consumption\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMeta-analyzed associations of daily smoking at Wave 1 with well-being and other outcomes at Wave 2: Total sample analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eModel 1: Demographic and Childhood Variables as Covariates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eModel 2: Demographic, Childhood, and Other Wave 1 Confounding Variables (Via Principal Components) as Covariates\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026tau;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026tau;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHuman Flourishing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecure flourishing index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71e-08***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlourishing index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9e-07***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.49e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePsychological Well-Being\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHappiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47e-12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37e-13***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.03e-07***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent life evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.05,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69e-09***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFuture life evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5e-11***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOptimism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.61e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreedom to pursue what\u0026apos;s important\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.68e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInner peace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.14e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife balance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSense of mastery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeaningful activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33e-07***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderstanding purpose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-rated mental health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePsychological Distress\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraumatic distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.01,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression symptoms composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.03,1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.01,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety symptoms composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.02,1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.48e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuffering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.01,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocial Well-Being\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelationship contentment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.00,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelationship satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.92e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntimate/close friend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment approval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSay in government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelonging in country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity/place satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust within country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocial Participation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver been married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrently divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00,0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.28e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.00,0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeekly+ community participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.96,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeekly+ religious attendance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.97,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocial Distress\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.01,0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.00,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived discrimination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.02,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCharacter \u0026amp; Prosocial Behavior\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrientation to promote good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelayed gratification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGratitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShowing love/care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.84e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForgivingness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.57e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharitable giving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.65e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.97,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHelping strangers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolunteering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.97,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePhysical Health \u0026amp; Health Behavior\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-rated physical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.01,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.38e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain in past 4 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.04,2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.69e-16***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.03,2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.69e-16***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of drinks per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.07,0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44e-15***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.07,0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55e-15***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays exercise per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98e-05***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocioeconomic Outcomes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21e-07***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaterial security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.61e-05***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment (16\u0026thinsp;+\u0026thinsp;years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrently employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancially comfortable/getting by\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.65e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwn home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.73e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.9e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome -- top quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eNotes. N\u0026thinsp;=\u0026thinsp;207919; Reference for focal predictor: no daily smoking; RR, risk-ratio, null effect is 1.00; ES, effect size measure for standardized regression coefficient, null effect is 0.00; CI, confidence interval; \u0026tau; (tau, heterogeneity), estimated standard deviation of the distribution of effects; Global p-value, joint test of the null hypothesis that the country-specific Wald tests are null in all countries.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eMultiple imputation was performed to impute missing data on the covariates, exposure, and outcomes. All models controlled for sociodemographic and childhood factors assessed at Wave 1: relationship with mother growing up; relationship with father growing up; parent marital status around age 12; experienced abuse growing up (except for Israel); felt like an outsider in family growing up; self-rated health growing up; subjective financial status growing up; religious affiliation at age 12; frequency of religious service attendance around age 12; year of birth; gender; education, employment status, marital status, immigration status; and racial/ethnic identity when available. For Model 2 with PC (principal components), the first seven principal components of the entire set of contemporaneous confounders assessed at Wave 1 were included as additional covariates of the outcomes at Wave 2.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eAn outcome-wide analytic approach was used, and a separate model was run for each outcome. A different type of model was run depending on the nature of the outcome: (1) for each binary outcome, a weighted generalized linear model (with a log link and Poisson distribution) was used to estimate a RR; and (2) for each continuous outcome, a weighted linear regression model was used to estimate an ES. All effect sizes were standardized. For continuous outcomes, the ES represents the change in SD on the outcome for a 1 SD increase in the focal predictor. For binary outcomes, the RR represents the change in risk of being in the upper category compared to the lower category for a 1 SD increase in the focal predictor.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eP-value significance thresholds: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.005**, (Bonferroni) p\u0026thinsp;\u0026lt;\u0026thinsp;0.00089***, correction for multiple testing to significant threshold; \u0026dagger; Estimate of \u0026tau; (tau, heterogeneity) is likely unstable. See our online supplement forest plots for more detail on heterogeneity of effects.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eat Wave 2\u003csup\u003e31, 32\u003c/sup\u003e. Alternatively, in terms of unstandardized effect size, for respondents who reported daily smoking at Wave 1, there was a 0.09 increase in the number of drinks per week at Wave 2, compared to those who did not report daily smoking, on average across countries (see Table S8 of Online Supplement 1).\u003c/p\u003e\n \u003cp\u003eModel 2 shows that adding the baseline measures of well-being and other outcomes (via PCs) as covariates reduced effect sizes, but estimates were generally in the expected direction. Specifically, daily smoking at Wave 1 remained inversely related to both indexes of human flourishing, six indicators of psychological well-being, three indicators of character and prosocial behavior, two indicators of physical health and health behavior, and three indicators of socioeconomic outcomes at Wave 2, whereas it was positively associated with depressive symptoms composite and forgiveness. To the contrary, estimates for the two health-risk behaviors changed little: daily smoking (2.30 [2.03, 2.61]) and number of drinks per week (0.09 [0.07, 0.10]).\u003c/p\u003e\n \u003cp\u003eTo assess the robustness of the meta-analytic effect estimates to potential unmeasured confounding, we report E-values for each outcome, both for the estimate and for its confidence interval, in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (see the \u0026ldquo;Total sample\u0026rdquo; columns). For example, the E-value for secure flourishing index was 1.21 in Model 1 (1.14 in Model 2), which means that an unmeasured confounder that was associated with both higher daily smoking at Wave 1 and higher secure flourishing index at Wave 2 by risk ratios of 1.21 (1.14) each, above and beyond the demographic and childhood controls (plus the seven PCs of contemporaneous confounders at Wave 1 in Model 2) already adjusted for, could explain away the association, but weaker joint confounder associations could not. To shift the 95% CI to include the null, an unmeasured confounder that was associated with both higher daily smoking at Wave 1 and higher secure\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eE-value sensitivity analysis for unmeasured confounding for the association between daily smoking and subsequent well-being and other outcomes: Total sample analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eModel 1: Demographic and Childhood Variables as Covariates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eModel 2: Demographic, Childhood, and Other Wave 1 Confounding Variables (Via Principal Components) as Covariates\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal sample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSmoker sample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal sample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSmoker sample\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE-value for CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE-value for CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE-value for CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE-value for CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHuman Flourishing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecure flourishing index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlourishing index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePsychological Well-Being\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHappiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent life evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFuture life evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOptimism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreedom to pursue what\u0026apos;s important\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInner peace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife balance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSense of mastery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeaningful activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderstanding purpose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-rated mental health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePsychological Distress\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraumatic distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression symptoms composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety symptoms composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuffering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocial Well-Being\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelationship contentment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelationship satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntimate/close friend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment approval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSay in government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelonging in country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity/place satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust within country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocial Participation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver been married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrently divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeekly+ community participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeekly+ religious attendance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocial Distress\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived discrimination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCharacter \u0026amp; Prosocial Behavior\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrientation to promote good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelayed gratification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGratitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShowing love/care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForgivingness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharitable giving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHelping strangers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolunteering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePhysical Health \u0026amp; Health Behavior\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-rated physical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain in past 4 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of drinks per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays exercise per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocioeconomic Outcomes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaterial security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment (16\u0026thinsp;+\u0026thinsp;years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrently employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancially comfortable/getting by\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwn home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome -- top quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNotes. N\u0026thinsp;=\u0026thinsp;207919; The formula for calculating E-values can be found in VanderWeele and Ding (2017). E-values for estimate are the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome to fully explain away the observed association between the exposure and outcome, conditional on the measured covariates. E-values for the 95% CI closest to the null denote the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome to shift the CI to include the null value, conditional on the measured covariates.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eflourishing index at Wave 2 by risk ratios of 1.17 (1.10) each could suffice, but weaker joint confounder could not. Estimates for the two health-risk behaviors (daily smoker and number of drinks per week) at Wave 2, which were relatively large and did not change much between the two models, were found to be more robust to unmeasured confounding than all other estimates, as indicated by the two highest E-values in Models 1 (4.06 and 1.41) and 2 (4.03 and 1.38). While E-values generally suggest that estimates for these two outcomes are moderately robust to unmeasured confounding, others were not. Those with E-values equal (e.g., educational attainment in Model 1) or close to 1.00 (e.g., volunteering in Model 1 and inner peace in Model 2) indicate that very little unmeasured confounding would be required to explain away the association. Thus, these effect estimates should be interpreted as having limited robustness to unmeasured confounding.\u003c/p\u003e\n \u003cp\u003eResults from the secondary meta-analysis for an additional 22 outcomes\u0026mdash;six domain-specific measures of flourishing, four items of depression and anxiety, and 12 indicators of religion/spirituality\u0026mdash;were similar to those from its primary counterpart in that all associations were very small but in the expected direction. Confidence intervals excluded zero only for seven (31.8%) of 22 outcomes with the seven being all six domain-specific measures of flourishing and religious centrality in Model 1; and four domain-specific measure of flourishing (except for character \u0026amp; virtue and close social relationships), depression (loss of interest), belief in life after death, and prayer or meditation in Model 2 (see the \u0026ldquo;Multiple Imputation\u0026rdquo; column of Tables S5 and S6). Also, their E-values\u0026mdash;ranging from 1.03 to 1.26 and 1.01 to 1.16 in Models 1 and 2, respectively\u0026mdash;indicated that they were, in general, moderately robust to unmeasured confounding with some exceptions (see Table S7).\u003c/p\u003e\n \u003cp\u003eResults from supplemental analysis (i.e., semi-complete case analysis with attrition weights), conducted as a sensitivity analysis, indicated that misspecification of imputation models for the primary and secondary meta-analyses was unlikely, as the supplemental results were identical or similar to the primary and secondary results (see Tables S5 and S6). For example, the supplemental effect estimates and their 95% CIs for the two composite and six domain-specific measures of human flourishing were practically the same as their primary and secondary counterparts in Model 1 and, to a lesser extent, Model 2. Also, while the association between daily smoking at Waves 1 and 2 from the supplemental analysis (RR\u0026thinsp;=\u0026thinsp;2.61 [2.31, 2.96] and 2.60 [2.30, 2.95]) was somewhat larger in both Models 1 and 2 than those from the primary analysis (RR\u0026thinsp;=\u0026thinsp;2.32 [2.04, 2.62] and 2.30 [2.03, 2.61]), that of daily smoking at Wave 1 with weekly alcohol consumption at Wave 2 remained practically the same in Models 1 (ES\u0026thinsp;=\u0026thinsp;0.10 [0.07, 0.12]) and 2 (ES\u0026thinsp;=\u0026thinsp;0.09 [0.07, 0.11]). E-values were similar between the two analyses as well, while supplemental effect estimates for some outcomes (e.g., perceived discrimination and daily smoking at Wave 2) were somewhat more robust to unmeasured confounding compared to their primary and secondary counterparts (see Table S7).\u003c/p\u003e\n \u003cp\u003eIn summary, all but two (97.4%) of 78 effect estimates for 56 primary and 22 secondary outcomes were found to be very small even when they were not adjusted for previous measures of those outcomes. This finding could lead one to conclude that daily smoking has limited impact on human well-being. However, it is important to keep in mind the limited follow-up of only one year and the extensive contemporaneous covariates control and we will return to these matters below. With this context, such a conclusion would be at odds with common knowledge about the harmful effects of cigarette smoking on health\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e128\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e129\u003c/span\u003e\u003c/sup\u003e as well as previous findings reviewed above.\u003c/p\u003e\n \u003cp\u003eIt is also important to examine cross-national variations in those estimates given that the primary estimates were meta-analytic estimates of daily smoking\u0026rsquo;s average effects on well-being and other outcomes with heterogeneity (\u0026tau;; estimated SD of the distribution of effects) across 23 countries and, particularly, that those previous findings were almost always country specific. So, it is necessary to explore whether daily smoking\u0026rsquo;s effect estimates varied across those countries and, if so, how.\u003c/p\u003e\n \u003cp\u003eFor example, in Model 1, the average effect size of daily smoking at Wave 1 on secure flourishing index at Wave 2 was ‒0.03 [‒0.04, ‒0.02] with its SD (\u0026tau;) being 0.02 (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Figure S1 of Online Supplement 1 visually presents heterogeneity in country-specific effects, which varied from ‒0.11 [‒0.15, ‒0.06] (Australia) to 0.03 [‒0.00, 0.06] (China). A closer examination revealed that the effect of daily smoking on secure flourishing was at least 1 SD below the average effect size (i.e., \u0026le; ‒0.05) in eight countries (Australia, Brazil, Egypt, Israel, Japan, Sweden, Turkey, and the U.S.) and positive in one country (China) with the estimates for China and Turkey being not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065 and 0.067; see Table S12g and S29g, respectively), whereas the remaining 14 countries\u0026rsquo; effects were relatively small, varying between ‒0.05 and 0.00. While this finding provides evidence of cross-national variation in the effect of daily smoking on well-being, it was significant only in 10 of 23 countries (see Tables S9-31g): seven of the eight countries (i.e., except Turkey), Argentina (‒0.04 [‒0.07, ‒0.03]), India (‒0.04 [‒0.06, ‒0.01]), and Germany (‒0.04 [‒0.06, ‒0.01]). In other words, while daily smoking had no significant impact on secure flourishing in more than half (56.5%) of GFS countries, the daily habit was found to be detrimental to well-being in some countries, though it is not immediately clear why that was the case, as those 10 countries were diverse in terms of sociocultural and religious contexts as well as geographic location. The effect of daily smoking on secure flourishing also varied across countries in Model 2, from ‒0.07 [‒0.11, ‒0.03] (Philippines) to 0.03 [‒0.01, 0.06] (China).\u003c/p\u003e\n \u003cp\u003eCountry-specific effects of daily smoking on all 78 well-being and other outcomes, estimated for Models 1 and 2, are presented in Tables S34 and S35, respectively, with statistically significant effects being highlighted (see Figures S1-78 for plots showing heterogeneity in those effects, separately for each outcome). For example, the top panel of Table S34 shows that daily smoking at Wave 1 had consistently larger associations with measures of human flourishing at Wave 2 in Australia, Brazil, Egypt, Germany, India, Israel, Japan, Sweden, and the U.S., whereas associations were typically smaller in other countries. Associations were in the expected (i.e., negative) direction except for China, where daily smoking was positively related to the flourishing index, physical and mental health, and character and virtue. While the next two panels tend to show similar patterns in terms of where the strongest associations were found, in other panels the pattern is less clear nor consistent.\u003c/p\u003e\n \u003cp\u003eDaily smoking at Wave 1 was most strongly associated with daily smoking (all 23 countries) and weekly alcohol consumption at Wave 2 (20 countries, at least, in one model), with average effect sizes of 2.32 [2.04, 2.62] and 0.10 [0.07, 0.12] in Models 1 and 2.30 [2.03, 2.61] and 0.09 [0.07, 0.10] in Models 2. Country-specific associations between daily smoking at Waves 1 and 2 in RR widely varied, ranging from 1.33 [1.19, 1.49] (Nigeria) to 4.22 [3.97, 4.48] (Japan) in Model 1 and from 1.32 [1.19, 1.48] (Nigeria) to 4.21 [3.96, 4.47] (Japan) in Model 2 (see Tables S34-35 and Figure S57, which shows the natural logarithm of RR). Daily smokers at Wave 1 (compared to those who were not daily smokers) had a lower-than-average likelihood of smoking daily at Wave 2 in 12 countries (Nigeria, India, Tanzania, South Africa, Kenya, China, Turkey, Mexico, Poland, Egypt, Spain, and Indonesia) and a higher-than-average likelihood in 11 countries (Germany, Hong Kong, the Philippines, Brazil, Argentina, Australia, Sweden, the U.K., the U.S., Israel, and Japan). Comparing these two groups of countries seems to suggest that daily smokers were at a higher risk of continued daily smoking in western than non-western countries.\u003c/p\u003e\n \u003cp\u003eAs Figure S58 shows, country-specific effects of daily smoking at Wave 1 on weekly alcohol consumption at Wave 2 also widely varied from 0.02 [‒0.02, 0.07] (India) to 0.22 [0.18, 0.26] (China) and 0.22 [0.15, 0.29] (Hong Kong) in Model 1 and from 0.02 [‒0.02, 0.07] (India) to 0.21 [0.17, 0.25] (China) in Model 2. In the model without PC controls, for a 1 SD increase in the daily smoking status at Wave 1, there were a smaller-than-average (0.10 SD [0.07, 0.12]) increase in weekly alcohol consumption at Wave 2 in 14 countries (the U.K., Egypt, Nigeria, Sweden, the U.S., Argentina, Australia, Germany, Spain, Israel, Turkey, India, Tanzania, and Indonesia), the same-as-average increase in one country (Mexico), and a larger-than-average increase in eight countries (Kenya, the Philippines, South Africa, Brazil, Japan, Poland, China, and Hong Kong). Model 2 showed generally the same pattern except for Mexico (0.07 [0.02, 0.12]) moving to the group of smaller-than-average (0.09 SD [0.07, 0.10]) increase and South Africa (0.09 [0.01, 0.17]) having the same-as-average increase. Interestingly, daily smokers at Wave 1 (compared to those who were not daily smokers) tended to be at a higher risk of increasing alcohol consumption in non-western than western countries, whereas we saw above the risk of continued daily smoking tended to be greater in western than non-western countries.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eSmoker sample analysis\u003c/h2\u003e\n \u003cp\u003eThe last two columns of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e show weighted sample frequency distributions of the continuous measure of daily smoking (i.e., number of cigarettes smoked per day), year of birth (age), gender, education, and country of respondent by wave for the smoker sample (N\u0026thinsp;=\u0026thinsp;36,147 at Wave 1 and 18,502 at Wave 2), that is, those who smoked cigarette(s) daily at Wave 1. Expanded summary statistics tables of all variables are included in Online Supplement 2 (see Tables S1-2 for the smoker sample and Tables S9a-31a and S9b-31b for each country). While the smoker sample was not different from the total sample in average age at each wave (about 45 and 47 at Waves 1 and 2, respectively; see the Table S1 of Online Supplements 1 and 2), daily smokers were less likely to be female and to have 16\u0026thinsp;+\u0026thinsp;years of education than those who were not daily smokers, as expected\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e130\u003c/span\u003e\u003c/sup\u003e. Also, as found above for the total sample, daily smokers who participated in the Wave 2 survey were somewhat older and more educated than those who participated in the Wave 1 survey, although they were not different in gender composition, and each country\u0026rsquo;s representation in the smoker sample did not change drastically between the waves with the largest decrease and increase being in Brazil (‒3.3%, from 7.1% to 3.8%) and the U.S. (4.8%, from 10.0% to 14.8%), respectively.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the primary meta-analysis results that are not very different in the magnitude of RR and ES (i.e., very small) compared to the total sample results (see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). However, because the sample is smaller, confidence intervals were slightly wider and more likely to include 0 for ES and 1 for RR. Exceptions were, again, the two health-risk behaviors at Wave 2, on which daily smoking at Wave 1 had relatively large effects: number of cigarettes per day (0.56 [0.48, 0.64]) and number of drinks per week (0.07 [0.05, 0.09]). In Model 1, for smokers who were 1 SD above the mean of daily cigarette consumption at the baseline, there was a 0.56 SD increase in number of cigarettes smoked daily and a 0.07 SD increase in the number of drinks consumed weekly a year later. In terms of unstandardized effect size (see Table S8 of Online Supplement 2), for one cigarette smoked a day at Wave 1, there was a 0.64 increase in the number of cigarettes per day and a 0.09 increase in the number of drinks per week at Wave 2. In Model 2, the effects of daily\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMeta-analyzed associations of intensity of smoking at Wave 1 with well-being and other outcomes at Wave 2: Smoker sample analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eModel 1: Demographic and Childhood Variables as Covariates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eModel 2: Demographic, Childhood, and Other Wave 1 Confounding Variables (Via Principal Components) as Covariates\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026tau;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026tau;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHuman Flourishing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecure flourishing index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.07,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02e-07***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.61e-07***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlourishing index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.06,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31e-07***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePsychological Well-Being\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHappiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.06,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.06,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.57e-05***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96e-07***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent life evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.06,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.32e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.84e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFuture life evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.07,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16e-07***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOptimism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreedom to pursue what\u0026apos;s important\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.09e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInner peace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife balance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.97,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSense of mastery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeaningful activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.05,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.54e-08***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderstanding purpose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.85e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-rated mental health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePsychological Distress\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraumatic distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression symptoms composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.01,1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety symptoms composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuffering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.01,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocial Well-Being\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelationship contentment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.00,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelationship satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.05,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntimate/close friend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment approval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSay in government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.97,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelonging in country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.64e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.00,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity/place satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust within country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.97,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocial Participation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver been married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrently divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.94,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeekly+ community participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.92,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.96,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeekly+ religious attendance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.96,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocial Distress\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.00,0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.00,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived discrimination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.97,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCharacter \u0026amp; Prosocial Behavior\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrientation to promote good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelayed gratification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.05,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGratitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShowing love/care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.01,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForgivingness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharitable giving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.96,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHelping strangers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolunteering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.92,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.96,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePhysical Health \u0026amp; Health Behavior\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-rated physical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.06,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.03,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.01,1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain in past 4 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of cigarettes per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.48,0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.63e-16***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.55,0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3e-16***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of drinks per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05,0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.07,0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.11e-15***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays exercise per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.06,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.27e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocioeconomic Outcomes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.06,-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.64e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaterial security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.04,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.02,-0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment (16\u0026thinsp;+\u0026thinsp;years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrently employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancially comfortable/getting by\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.96,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7e-03**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwn home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.99,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82e-04***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome -- top quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eNotes. N\u0026thinsp;=\u0026thinsp;36147; Reference for focal predictor: no daily smoking; RR, risk-ratio, null effect is 1.00; ES, effect size measure for standardized regression coefficient, null effect is 0.00; CI, confidence interval; \u0026tau; (tau, heterogeneity), estimated standard deviation of the distribution of effects; Global p-value, joint test of the null hypothesis that the country-specific Wald tests are null in all countries.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eMultiple imputation was performed to impute missing data on the covariates, exposure, and outcomes. All models controlled for sociodemographic and childhood factors assessed at Wave 1: relationship with mother growing up; relationship with father growing up; parent marital status around age 12; experienced abuse growing up (except for Israel); felt like an outsider in family growing up; self-rated health growing up; subjective financial status growing up; religious affiliation at age 12; frequency of religious service attendance around age 12; year of birth; gender; education, employment status, marital status, immigration status; and racial/ethnic identity when available. For Model 2 with PC (principal components), the first seven principal components of the entire set of contemporaneous confounders assessed at Wave 1 were included as additional covariates of the outcomes at Wave 2.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eAn outcome-wide analytic approach was used, and a separate model was run for each outcome. A different type of model was run depending on the nature of the outcome: (1) for each binary outcome, a weighted generalized linear model (with a log link and Poisson distribution) was used to estimate a RR; and (2) for each continuous outcome, a weighted linear regression model was used to estimate an ES. All effect sizes were standardized. For continuous outcomes, the ES represents the change in SD on the outcome for a 1 SD increase in the focal predictor. For binary outcomes, the RR represents the change in risk of being in the upper category compared to the lower category for a 1 SD increase in the focal predictor.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eP-value significance thresholds: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.005**, (Bonferroni) p\u0026thinsp;\u0026lt;\u0026thinsp;0.00089***, correction for multiple testing to significant threshold; \u0026dagger; Estimate of \u0026tau; (tau, heterogeneity) is likely unstable. See our online supplement forest plots for more detail on heterogeneity of effects.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003esmoking on daily cigarette consumption (0.64 [0.55, 0.73]) and weekly alcohol consumption (0.09 [0.07, 0.11]) were somewhat larger than in Model 1, implying some of contemporaneous confounders (controlled via PCs) being suppressors for the effects. E-values, reported in the \u0026ldquo;Smoker sample\u0026rdquo; columns of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, suggest that the effect estimates were in general moderately robust to potential unmeasured confounding, while some estimates with E-values equal or close to 1.00 (e.g., suffering) should be interpreted with caution.\u003c/p\u003e\n \u003cp\u003eResults from the smoker sample\u0026rsquo;s secondary meta-analysis (for additional 22 outcomes) were largely consistent with those from the total sample\u0026rsquo;s counterpart (see Tables S5-6 of Online Supplement 2). Also, their E-values\u0026mdash;ranging from 1.01 to 1.24 and from 1.02 to 1.14 in Models 1 and 2, respectively\u0026mdash;showed that they were, in general, moderately robust to unmeasured confounding with some exceptions (see Table S7). In addition, supplemental results from semi-complete case analysis with attrition weights indicated that misspecification of imputation models for the primary and secondary meta-analyses was unlikely, as they were generally similar to their primary and secondary counterparts (see Tables S5-6). E-values were also similar between the two analyses (see Table S7).\u003c/p\u003e\n \u003cp\u003eTo summarize, as was the case with the total sample meta-analysis, all but two effect estimates for the primary and secondary outcomes were found to be very small in Model 1 as well as Model 2. Country-specific effects on all 78 outcomes are summarized in Tables S34 (Model 1) and S35 (Model 2) of Online Supplement 2 (see Figures S1-78 for plots showing heterogeneity in those effects). For example, in Model 1, confidence intervals for the associations between the quantity of daily smoking at the baseline and secure flourishing a year later among daily smokers (see the first row of Table S34) were strictly negative in 10 countries (Argentina, Brazil, Germany, Hong Kong, Japan, Kenya, Mexico, Spain, Sweden, and the U.K.), strictly positive in one country (Israel), and included both positive and negative values in the remaining 12 countries.\u003c/p\u003e\n \u003cp\u003eWhile the focal exposure\u0026rsquo;s effects on other outcomes varied differentially across GFS countries, the effect on the number of cigarettes smoked daily at Wave 2 was consistently positive. The quantity of daily cigarette consumption at Wave 1 on weekly alcohol consumption at Wave 2 was also typically positive, but estimates were smaller. Specifically, the effect varied from 0.00 [‒0.04, 0.05] (India) to 0.19 [0.01, 0.37] (the Philippines) in Model 2 and from ‒0.01 [‒0.12, 0.10] (India) to 0.38 [‒0.27, 1.02] (Nigeria) in Model 1.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this outcome-wide longitudinal study, we explored how daily cigarette smoking at Wave 1 of the GFS predicted a wide variety of well-being and other outcomes at Wave 2 (Research Questions #1), controlling for demographic and childhood factors (Model 1) and also previous measures of those outcomes at the baseline (Model 2). Model 1 results from the primary and secondary meta-analyses for the total sample showed that daily smoking predicted most of the 78 outcomes in the expected directions. That is, daily smokers at Wave 1 mostly reported a year later lower levels of human flourishing, psychological well-being, social well-being, social participation, character and prosocial behavior, self-rated physical health, socioeconomic outcomes, and, to a lesser extent, religion and spirituality; and higher levels of psychological distress, social distress, and negative measures of physical health and health behavior than their peers who did not smoke cigarette daily. While observed associations tended to be consistent with previous findings and for alcohol consumption and subsequent smoking moderately robust to potential unmeasured confounding, as assessed by E-values (Research Question #3), effect estimates were otherwise all very small, again except for daily smoker status and weekly alcohol consumption at Wave 2, which we speculate are attributable to several methodological factors and limitations of this study.\u003c/p\u003e \u003cp\u003eFirst, instead of comparing individuals who were daily smokers at the baseline with those who were not, if we had examined individuals who initiated daily smoking in comparison with those who did not, using 3-wave data (i.e., no daily smoking at Wave 1 but daily smoking at Waves 2 and 3 vs. no daily smoking at Waves 1 to 3 vs. daily smoking at all three waves), larger effects may have been observed, at least, for some outcomes among those new daily smokers (e.g., social well-being, as they may isolate themselves from others as a result of the new daily habit) compared to their peers who did not smoke daily and, to a lesser extent, those who did across three waves. Many covariates were controlled for contemporaneously with the smoking exposure, which may constitute \u0026ldquo;over-control,\u0026rdquo; and attenuate effect estimates. With three waves of data, we will be able to control for covariates in the wave prior to the exposure. Second, the 1-year interval between the first two waves of the GFS might not have been long enough to assess major changes, detecting only the early effects of daily smoking over a short period of time and thus restricting its effect size. For new daily smokers, for example, the habit\u0026rsquo;s observable impact on physical health is likely to take years (i.e., a high \u0026ldquo;induction time\u0026rdquo; or the latency of smoking effects), just as, conversely, longer time after smoking cessation is related to more favorable self-rated health\u003csup\u003e\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e\u003c/sup\u003e. Also, for long-time smokers already having major health problems, one-year observation is less likely to reveal any large effect of daily smoking on self-reported physical health. Third, the very small effects of daily smoking on various outcomes, for instance, physical health may be due partly to the outcome being measured by self-report, which is potentially biased\u003csup\u003e\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e\u003c/sup\u003e. Finally, respondents who were not daily smokers at Wave 1 included not only never smokers but also occasional and former smokers, but we could not separate them into subgroups because the GFS survey included no other smoking-related item, which was likely to have contributed to daily smoking\u0026rsquo;s very small effect estimates. Specifically, given that daily smokers are more similar to occasional smokers\u003csup\u003e\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e\u003c/sup\u003e compared to former smokers and, to a greater extent, never smokers, to the extent that occasional smokers were included in those who did not smoke daily, effects of daily smoking on well-being and other outcomes would have been smaller compared to when daily smokers were compared to never smokers only.\u003c/p\u003e \u003cp\u003eHaving said that, small effect estimates may still be worth paying attention to. Arguing that effect sizes are underappreciated and often misinterpreted, Funder and Ozer\u003csup\u003e\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e\u003c/sup\u003e suggest that seemingly small effects can cumulate over time and become consequential in the long run, which is particularly relevant to research on individual differences, like the present study. They also propose that researchers should unapologetically report small effect sizes estimated based on data from a large sample because larger samples, ceteris paribus, tend to generate more precise and reliable estimates no matter how small the effect is. According to them, an alternative to establishing effects sizes by relying on single studies with very large sample sizes is meta-analysis: specifically, even if the average effect is small, all individual effect sizes that are in a narrow range and in the same direction can provide a certain level of confidence for the average effect estimate. The present study\u0026rsquo;s very small, meta-analyzed effects were the average of 23 country-specific effects, which were not only generally in the same direction with a relatively small heterogeneity but also estimated based on large samples. Thus, those very small average effects should not be overlooked but subject to further research and relevant exploration, such as examining cross-national variations (Research Question #2).\u003c/p\u003e \u003cp\u003eWe found that the effects of daily smoking on well-being and other outcomes varied across GFS countries in magnitude, with larger effects being more consistently found in certain countries than others, depending on outcomes examined. The effects of daily smoking on human flourishing, psychological well-being, and psychological distress (to a lesser extent, social well-being, social distress, and character \u0026amp; prosocial behavior) were generally statistically significant in the expected direction in Australia, Brazil, Egypt, Germany, Isreal, Japan, Sweden, and the U.S. but mostly not statistically significant in Hong Kong, Indonesia, Kenya, Mexico, Nigeria, the Philippines, South Africa, Spain, Tanzania, Turkey with the remaining countries (Argentina, India, Poland, and the U.K.) being in-between. Statistical insignificance for these outcomes usually the result of smaller point estimates\u0026mdash;not wider confidence intervals\u0026mdash;except for Hong Kong and Turkey, which showed moderate-to-large effect estimates whose confidence intervals included zero. Effects on the remaining outcomes (physical health \u0026amp; health behavior, socioeconomic outcomes, and religiosity/spirituality) were mostly small and statistically insignificant, with the notable exceptions of daily smoking and weekly alcohol consumption at Wave 2. Specifically, daily smokers at Wave 1 were likely to continue smoking daily in all 23 countries and to increase weekly alcohol consumption in all but three countries (India, Indonesia, and Tanzania) a year later.\u003c/p\u003e \u003cp\u003eChina was an exception, where the effects of daily smoking on some measures of human flourishing (flourishing index, physical \u0026amp; mental health, and character \u0026amp; virtue) and psychological well-being (current and future life evaluations) were found to be positive, not negative. Further research is needed to see if these unexpected findings are replicated and, if so, whether they are methodological artifacts due to response bias. For instance, Confucian modesty leading to \u0026ldquo;active indifference,\u0026rdquo; where a person is indifferent to one\u0026rsquo;s weaknesses, like the daily habit of smoking, to focus on what is perceived to be worthy of one\u0026rsquo;s attention, might have led many Chinese respondents to downplay the relevance of daily smoking to their lives and well-being and thus overreport their state of flourishing\u003csup\u003e\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e\u003c/sup\u003e. Also, while mostly cross-sectional, previous studies show that Chinese smokers, particularly, male smokers tend to rationalize their behavior as socially acceptable, being a social tradition, and also important due to the cultural value of \u003cem\u003eGuanxi\u003c/em\u003e (which refers to relationships or social connections based on mutual interests and benefits)\u003csup\u003e\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e\u003c/sup\u003e. Smokers high on \u003cem\u003eGuanxi\u003c/em\u003e may be actively involved not only in socializing but also charitable events, which in turn are likely to enhance their perceived well-being with heavy smokers even reporting better health than their non-heavy counterparts\u003csup\u003e\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e, \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhen we calculated the proportion of effect estimates that were statistically significant in each country, the percentages ranged from 2.6% to 60.3%: Sweden (60.3%, 47), the U.S. (59.0%, 46), Japan (48.7%, 38), Australia (47.4%, 37), Brazil (43.6%, 34), Israel (34.6%, 27), Germany (33.3%, 26), Egypt (21.8%, 17), the U.K. (19.2%, 15), Argentina (17.9%, 14), China (17.9%, 14), Poland (16.7%, 13), India (12.8%, 10), Indonesia (11.5%, 9), Hong Kong (9.0%, 7), Spain (7.7%, 6), Mexico (6.4%, 5), Tanzania (6.4%, 5), Turkey (6.4%, 5), Kenya (5.1%, 4), the Philippines (5.1%, 4), Nigeria (2.6%, 2), and South Africa (2.6%, 2). We again emphasize that, with the exceptions of Turkey and Hong Kong, effects were usually statistically insignificant because they were very close to zero. It was found that daily smoking\u0026rsquo;s statistically significant effects were more likely to be observed in western or westernized countries (i.e., Japan and Israel) than their non-western or non-westernized counterparts including all four African countries. While this finding is likely to have partly to do with sample size, we speculate that it might be also due in part to differences in smoking stigma, which tends to be greater in western than non-western cultures\u003csup\u003e\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e, \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e\u003c/sup\u003e. On the other hand, when we examined the relationship between the percentage and smoking prevalence,\u003csup\u003e62\u003c/sup\u003e rank-order correlation was small and noisy (\u003cem\u003eρ\u003c/em\u003e = ‒0.078, \u003cem\u003ep\u003c/em\u003e = .731), making it difficult to say whether effects on well-being and other outcomes were related to a country\u0026rsquo;s context for cigarette smoking, measured by the prevalence.\u003c/p\u003e \u003cp\u003eCompared to results from analyzing Model 1, those from Model 2 were, as expected, less likely to be statistically significant due to the additional PC controls, and very small effect estimates in the first model became even smaller in absolute value. For example, the effect of daily smoking on flourishing index in ES, which was ‒0.03 [‒0.04, ‒0.02] in Model 1, became ‒0.01 [‒0.02, ‒0.01] in Model 2, whereas the effect on depression symptoms composite in RR reduced from 1.05 [1.03, 1.06] to 1.02 [1.01, 1.03] (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e of Online Supplement 1). While the Model 2 result of the latter can be called \u0026ldquo;conservative\u0026rdquo; estimate as typically labeled when an estimate becomes smaller with added control, it would be appropriate to frame that of the former as \u0026ldquo;optimistic\u0026rdquo; estimate as the negative effect of daily smoking on flourishing was diminished, being pulled towards zero. Regardless of which model is focused on, the results of Models 1 and 2, being \u0026ldquo;under-control\u0026rdquo; and \u0026ldquo;over-control\u0026rdquo; estimates, jointly provide a range between which estimates for the average effect across 23 GFS countries are potentially bounded, which, though, may not generalize to other countries. The same is applicable when the two models\u0026rsquo; results are compared separately for each country. Wave 3 survey of the GFS, currently conducted, would enable us to address this issue by analyzing three waves of data. Additionally, results from analyzing the smoker sample revealed that the continuous measure of daily smoking (i.e., number of cigarettes smoked daily) was associated with outcome similarly to the binary measure.\u003c/p\u003e \u003cp\u003eFinally, we acknowledge methodological limitations of this study. First, self-report method, widely used to measure smoking in population-based research like this one, has been found to underestimate smoking due to social desirability in reporting compared with a method using biomarkers, such as cotinine\u003csup\u003e\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e\u003c/sup\u003e, while a non-smoker can also have a high level of cotinine if the person lives with a smoker. It is worth mentioning that the underestimation bias does not necessarily render self-report-based findings invalid\u003csup\u003e\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e\u003c/sup\u003e, and, if the reporting bias tends to be constant error across observation units, it is less problematic in estimating associations between smoking and other variables. However, this may not be the case because underreporting bias is more likely in countries with anti-tobacco policies and legislations, like Brazil, Spain, and Turkey\u003csup\u003e\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e\u003c/sup\u003e. Second, childhood factors, controlled for in each model, were measured retrospectively and thus subject to potential recall bias and response error. Third, while GFS countries were selected to maximize coverage of the world\u0026rsquo;s population and to ensure geographic, cultural, and religious diversity, they are not a representative sample of nations around the globe. Thus, this study\u0026rsquo;s meta-analytic estimates are the average effects of daily smoking on well-being and other outcomes may not be generalizable beyond these 23 countries. Fourth, the smoker sample size at Wave 1 was smaller in some countries (e.g., Nigeria and Australia) than others, and a substantially high percentage of daily smokers at Wave 1 did not participate in the Wave 2 survey in some countries, like Hong Kong (91.7%), Brazil (72.7%), and Turkey (71.5%). So, while supplemental analyses using only Wave 2 respondents (semi-complete case analysis) indicated that misspecification of the imputation models was minimal, results on the smoker-only sample need to be interpreted with caution given these sample-related issues. Finally, although the Global Flourishing Study made various efforts, such as cognitive and pretest interviews\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, 145\u003c/sup\u003e, to ensure that respondents would similarly understand survey items across countries, differential interpretation of some items (e.g., the meaning of \u0026ldquo;good\u0026rdquo; relationship with mother vs. father) is still a possibility among respondents in different cultures. Thus, caution is warranted in interpreting the present findings, keeping in mind various issues related to the translation of survey questions into different languages and potentially culture-dependent answers from respondents.\u003c/p\u003e \u003cp\u003eIn conclusion, based on new panel data from the first two waves of GFS survey conducted in 23 participating countries (including one territory), this outcome-wide, exploratory study contributes to global research on cigarette use by meta-analyzing country-specific effects of daily smoker status at the baseline on well-being and other outcomes a year later, controlling for demographic and childhood factors and contemporaneous confounders from the initial survey. We repeated this analysis using a quantity measure of daily cigarette use for daily smokers only. Results from synthesizing country-specific effects were, in general, consistent between the total and smoker samples, although estimates were less precise in the latter. The meta-analytic effects of daily smoking on well-being and other outcomes were generally in the expected direction: that is, the daily habit tended to be inversely associated with human flourishing and various aspects of well-being (i.e., physical, psychological, financial, and, to a lesser extent, social well-being) and positively related to psychological and social distress. Although those average effects were very small for all but two outcomes (daily smoking and weekly alcohol consumption), our exploration revealed cross-national variations in country-specific effects, indicating that daily smoking had more influence on well-being in some countries than others. Future research is called for to explain those differences across countries and also to continue studying consequences of daily smoking given that prior researchers mostly treated daily cigarette use as dependent rather than independent variable.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eT.J.V. reports consulting fees from Gloo Inc., along with shared revenue received by Harvard University in its license agreement with Gloo according to the University IP policy.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe GFS was supported by funding from the John Templeton Foundation (grant #61665), Templeton Religion Trust (#1308), Templeton World Charity Foundation (#0605), Well-Being for Planet Earth Foundation, Fetzer Institute (#4354), Well Being Trust, Paul L. Foster Family Foundation, and the David and Carol Myers Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of these organizations.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eS.J.J. conducted the literature search and review, performed the data analysis, interpreted the results, and drafted the full manuscript. S.J.J. and P.A.D.L.R. collaborated on early versions of the manuscript. R.N.P. and C.F. developed code for data analysis and contributed to the interpretation of the results. T.J.V. and B.R.J. contributed to the study concept and design, coordinated data collection, participated in survey design, coordinated the development of code for data analysis, contributed to the interpretation of the results. All authors contributed to critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKeyes, C. L. 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To address these oversights, we analyze the first two waves of data from the Global Flourishing Study of over 200,000 adults being representative of 22 countries and one territory. In this outcome-wide study, we meta-analyzed country-specific effects of daily cigarette smoker status at the baseline on 78 variables of well-being and other outcomes, measured a year later, controlling for demographic and childhood factors and contemporaneous potential confounders of the daily smoker status. We repeated this analysis using a quantity measure of daily smoking for smokers only. Meta-analytic estimates were typically of similar magnitude in the total and smoker samples, although confidence intervals were wider and more likely to include zero in the latter. Specifically, daily smoking tended to be inversely associated with human flourishing and various indicators of well-being and related to worse psychological and social distress. Although those average effects were very small for all but two outcomes (subsequent daily smoking and weekly alcohol consumption), cross-national variations in country-specific effects indicated that daily smoking had more influence on well-being in some countries than others.\u003c/p\u003e","manuscriptTitle":"An outcome-wide study of the associations of daily smoking with subsequent well-being and other outcomes in the Global Flourishing Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 09:01:05","doi":"10.21203/rs.3.rs-8752309/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2bc39cb5-ef49-4dc1-b70c-bf7fd5365180","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62094859,"name":"Epidemiology"}],"tags":[],"updatedAt":"2026-02-19T09:01:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 09:01:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8752309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8752309","identity":"rs-8752309","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}