Health and happiness: cross-sectional household surveys in Finland, Poland and Spain

Research Health and happiness in Finland, Poland and Spain Marta Miret et al. Bull World Health Organ 2014;92:716–725 | doi: http://dx.doi.org/10.2471/BLT.13.129254716 Health and happiness: cross-sectional household surveys in Finland, Poland and Spain Marta Miret,a Francisco Félix Caballero,a Somnath Chatterji,b Beatriz Olaya,c Beata Tobiasz-Adamczyk,d Seppo Koskinen,e Matilde Leonardi,f Josep Maria Haroc & José Luis Ayuso-Mateosg Introduction Many national surveys are in progress to evaluate well-being as an indicator of societal progress that goes beyond tradi- tional indices, such as gross domestic product (GDP). These surveys inform policy-makers about the factors that can af- fect the well-being of populations.1 The Commission on the Measurement of Economic Performance and Social Progress recommended shifting emphasis from measuring economic production to measuring people’s well-being and that this measurement be done at a national level.2 In line with these recommendations, the Better Life Initiative, launched by the Organisation for Economic Co-operation and Development, aims to measure society’s progress across eleven domains of well-being, such as life satisfaction, health, education and environment.3 Efforts are also being made at the national level in many countries.4–9 Health and well-being are interconnected, with well- being influencing health10,11 and health influencing well- being.12,13 Health is an important determinant of subjective well-being, together with other elements such as having a job, a partner and social contact.13 Good health is linked with greater well-being; while setbacks in health, such as serious diseases or disability, have negative effects on well-being.12 Research distinguishes between two different ways of as- sessing well-being. The first is to ask people to evaluate their life. The result is called evaluative well-being. The second way is to ask people to report the positive and negative emotions that they experience day-to-day. This is called experienced well-being. Evaluative well-being thus refers to a person’s overall evaluation of the quality of his or her life, whereas experienced well-being captures the positive and negative emotions that people experience from moment to moment.14 Assessing both dimensions is relevant, because these do not necessarily have the same correlates. For example, people with high income report more satisfaction with their lives when their evaluative well-being is assessed but these same people do not report better experienced well-being. Other life circumstances, such as marital status and education, are also more strongly correlated with evaluative than experienced well-being.14,15 On the other hand, ill health, caring for an adult, loneliness and smoking have been reported to be strong predictors of low experienced well-being.14 However, analysis of the correlation between health status and evaluative and experienced well-being has not been done at a population level. Therefore, we explored associations between health and evaluative and experienced well-being in three countries, and we tried to understand which part of the population has the highest risk of poor well-being. Objective To explore the associations between health and how people evaluate and experience their lives. Methods We analysed data from nationally-representative household surveys originally conducted in 2011–2012 in Finland, Poland and Spain. These surveys provided information on 10 800 adults, for whom experienced well-being was measured using the Day Reconstruction Method and evaluative well-being was measured with the Cantril Self-Anchoring Striving Scale. Health status was assessed by questions in eight domains including mobility and self-care. We used multiple linear regression, structural equation models and multiple indicators/ multiple causes models to explore factors associated with experienced and evaluative well-being. Findings The multiple indicator/multiple causes model conducted over the pooled sample showed that respondents with younger age (effect size, β = 0.19), with higher levels of education (β = −0.12), a history of depression (β = −0.17), poor health status (β = 0.29) or poor cognitive functioning (β = 0.09) reported worse experienced well-being. Additional factors associated with worse evaluative well-being were male sex (β = −0.03), not living with a partner (β = 0.07), and lower occupational (β = −0.07) or income levels (β = 0.08). Health status was the factor most strongly correlated with both experienced and evaluative well-being, even after controlling for a history of depression, age, income and other sociodemographic variables. Conclusion Health status is an important correlate of well-being. Therefore, strategies to improve population health would also improve people’s well-being. a Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain. b Department of Health Statistics and Information Systems, World Health Organization, Geneva, Switzerland. c CIBERSAM, Parc Sanitari Sant Joan de Déu, Barcelona, Spain. d Department of Medical Sociology, Jagiellonian University Medical College, Krakow, Poland. e National Institute for Health and Welfare, Helsinki, Finland. f Fondazione IRCCS, Neurological Institute Carlo Besta, Milan, Italy. g Department of Psychiatry, Universidad Autónoma de Madrid, CIBERSAM, C/ Arzobispo Morcillo 4, 28029, Madrid, Spain. Correspondence to José Luis Ayuso-Mateos (email: joseluis.ayuso@uam.es). (Submitted: 22 August 2013 – Revised version received: 11 March 2014 – Accepted: 19 March 2014 – Published online: 13 August 2014 ) Bull World Health Organ 2014;92:716–725| doi: http://dx.doi.org/10.2471/BLT.13.129254 717 Research Health and happiness in Finland, Poland and SpainMarta Miret et al. Methods Study design and data source The Collaborative Research on Ageing in Europe project16 is a cross-sectional household survey of a probabilistic sample representative of the non- institutionalized adult population of Finland, Poland and Spain. We selected these countries to give a broad repre- sentation of European populations, health characteristics and welfare systems.17 The information was collected with a face-to-face structured interview carried out at respondents’ homes, via Computer-Assisted Personal Interview- ing, between 8 April 2011 and 8 May 2012. The interviewers had participated in a training course for the administra- tion of the survey. The questionnaires were based on the ones used in the World Health Organization (WHO) Study on Global Ageing and Adult Health (SAGE)18 and they were trans- lated from English into Finnish, Polish and Spanish following the World Health Organization translation guidelines for assessment instruments.19 The translated questionnaires were piloted in 2010 in the countries and based on the feedback from the interviewers some changes and corrections were made. Quality as- surance procedures were implemented during fieldwork.20 A multistage clustered design was used to obtain nationally representa- tive samples. In Poland and Spain, a stratified multistage random sampling method was used and strata were cre- ated according to the geographical administrative regions and number of people living in the habitat. Age strata were used to select households according to the age structure of the population. The respondents were ran- domly selected among inhabitants of a household from a certain age group. In Finland, the design was a stratified two-stage cluster sampling design, and strata were created based on the largest towns and university hospital regions. A systematic sampling of people was conducted so that the sample size in each stratum was proportional to the corresponding population base. A total of 10 800 individuals par- ticipated: 1976 from Finland, 4071 from Poland and 4753 from Spain. The countries’ response rates were 53.4%, 66.5% and 69.9% respectively. Key variables We assessed experienced well-being with an abbreviated version of the Day Reconstruction Method,21 owing to its application in general population sur- veys.22,23 Participants reconstructed a portion of their previous day’s activities and reported the extent to which they experienced various emotions on a seven-point response scale ranging from 0 (not at all) to 6 (very much). Positive affect was defined as the average of the positive emotions (calm/relaxed and enjoying), weighted by the duration of the activities, with higher values indi- cating higher positive affect. Negative affect was defined as the average of the negative emotions (worried, rushed, irritated/angry, depressed and tense/ stressed), weighted by the duration of the activities, with higher values indicat- ing higher negative affect. Evaluative well-being was measured by means of the Cantril Self-Anchoring Striving Scale,24 with steps from 0 to 10, in which 0 represents the worst possible life and 10 the best possible life. Health status was assessed with a set of health-related questions grouped into eight health domains: vision, mo- bility, self-care, cognition, interpersonal activities, pain and discomfort, sleep and energy, and affect.25 For each ques- tion, the responses were recorded on a 5-point scale ranging from 1 (no diffi- culty/problem) to 5 (extreme difficulty/ inability). We obtained a global health status score from the responses using a Rasch model.26 The health status score was then transformed into a scale rang- ing from 0 to 100, with higher scores representing better health status. The presence of a depressive epi- sode was assessed by asking whether the person had been diagnosed with depression and had been receiving treat- ment during the previous 12 months. Additionally an algorithm based on The ICD-10 classification of mental and behavioural disorders: diagnostic criteria for research,27 employing a set of ques- tions based on the World Mental Health Survey Composite International Diag- nostic Interview,28 was used to include non-diagnosed cases. Cognitive functioning was assessed by evaluating verbal fluency with the animal-naming technique29 and imme- diate and delayed verbal recall was as- sessed with the Consortium to Establish a Registry for Alzheimer Disease Word List Memory.29 Short-term memory was assessed with digit span backward and forward tests from the Weschler Adult Intelligence Scale.30 A factor analysis was employed to confirm that verbal fluency, immediate verbal recall, delayed verbal recall, digit span backward and digit span forward represented one dimen- sion. Then, we calculated a global score for cognitive functioning as the average of the z-scores on each of the five vari- ables. We transformed this score into the percentile scale, with higher scores in- dicating better cognitive functioning.31 Participants were also asked to provide sociodemographic informa- tion, including age, sex, marital status, residential setting, household income, number of years of education and occupational status. We used the In- ternational Standard Classification of Occupations of the International Labour Organization,32 to code occupational status into nine subgroups, ranging from 1 (managers) to 9 ( elementary oc- cupations). For the descriptive analyses, we categorized the nine categories into three levels according to the skill level. Ethical approvals from the Ethics Re- view Committee, National Public Health Institute, Helsinki, Finland; the Bioethi- cal Committee, Jagiellonian University, Krakow, Poland; Ethics Review Com- mittee, Parc Sanitari Sant Joan de Déu, Barcelona, Spain; Ethics Review Com- mittee, La Princesa University Hospital, Madrid, Spain were obtained. Informed consent from each participant was also obtained. This study commenced in 2009, before requirements for review of all WHO-supported research by the WHO research ethics review committee had been fully implemented. Statistical analysis All data were weighted to account for sampling design in each country and to generalize the study sample to the refer- ence population. Normalized and post- stratified weights for two age groups, 18–49 and 50+ years, were used. Post- stratification corrections were made to the weights to adjust for the population distribution according to the national statistical institute’s census from each country; and for people randomly se- lected to participate in the survey but who did not finally participate.33 We calculated mean score estimates on positive affect, negative affect, evaluative well-being, health status and cognitive Bull World Health Organ 2014;92:716–725| doi: http://dx.doi.org/10.2471/BLT.13.129254718 Research Health and happiness in Finland, Poland and Spain Marta Miret et al. Table 1. Sociodemographic characteristics of the population sampled in the household survey, Finland, Poland and Spain, 2011–2012 Characteristics 18–49 years 50+ years Finland (n = 485) Poland (n = 1042) Spain (n = 962) Effect sizea Finland (n = 1491) Poland (n = 3029) Spain (n = 3791) Effect sizea Sex, no. (%) NS 0.06 Female 276 (56.91) 609 (58.45) 526 (54.68) 859 (57.61) 1844 (60.88) 2076 (54.76) Male 209 (43.09) 433 (41.55) 436 (45.32) 632 (42.39) 1185 (39.12) 1715 (45.24) Age, mean (SD) 37.08 (8.79) 32.55 (8.97) 35.91 (8.91) 0.21 66.49 (10.87) 66.25 (11.27) 66.66 (10.92) 0.02 Current marital status, no. (%) 0.05 0.06 Not in a partnership 194 (40.25) 490 (47.02) 443 (46.05) 530 (36.53) 1322 (43.64) 1465 (38.64) In a partnership 288 (59.75) 552 (52.98) 519 (53.95) 921 (63.47) 1707 (56.36) 2326 (61.36) Residential setting, no. (%) 0.31 0.31 Rural 90 (18.56) 460 (44.15) 140 (14.55) 348 (23.34) 1312 (43.31) 518 (13.66) Urban 395 (81.44) 582 (55.85) 822 (85.45) 1143 (76.66) 1717 (56.69) 3273 (86.34) Occupational status, no. (%) 0.17 0.11 Highest skill level 248 (53.91) 282 (36.15) 218 (25.98) 582 (40.33) 738 (30.85) 683 (24.40) Medium skill level 161 (35.00) 432 (55.38) 476 (56.73) 712 (49.34) 1324 (55.35) 1568 (56.02) Lowest skill level 51 (11.09) 66 (8.46) 145 (17.28) 149 (10.33) 330 (13.80) 548 (19.58) Years of education, mean (SD) 15.01 (3.25) 13.99 (3.24) 14.51 (5.25) 0.09 11.30 (4.14) 10.92 (3.67) 9.84 (6.16) 0.12 Quintile of income, no. (%) 0.12 0.08 First (Lowest) 80 (16.67) 250 (24.27) 185 (21.31) 346 (23.47) 900 (30.92) 686 (20.99) Second 38 (7.92) 132 (12.82) 110 (12.67) 347 (23.54) 552 (18.96) 694 (21.24) Third 73 (15.21) 124 (12.04) 160 (18.43) 303 (20.56) 498 (17.11) 715 (21.88) Fourth 178 (37.08) 218 (21.17) 217 (25.00) 300 (20.35) 571 (19.62) 745 (22.80) Fifth (Highest) 111 (23.13) 306 (29.71) 196 (22.58) 178 (12.08) 390 (13.40) 428 (13.10) NS: not significant; SD: standard deviation. a For categorical variables and quantitative variables effect sizes across countries were estimated using Cramer’s V for χ2 test and Cohen’s f for ANOVA test, respectively. Effect size was reported for all the differences that were found to be significant at the 95% confidence interval. Cramer´s V values of 0.10, 0.30 and 0.50 constitute small, medium and large effect sizes, whereas these values are 0.10, 0.25 and 0.40, respectively, for Cohen’s f. Note: For some variables the absolute numbers do not equal the total respondents due to missing values. Table 2. Estimates of well-being and health in Finland, Poland and Spain, 2011–2012 Variable Mean score (95% CI) Effect size (Hedges’ g)a Finland Poland Spain Finland– Poland Finland– Spain Poland– Spain 18–49 years Positive affect 4.31 (4.18 to 4.44) 4.27 (4.11 to 4.43) 4.83 (4.75 to 4.91) NS 0.52 0.38 Negative affect 0.58 (0.51 to 0.66) 0.45 (0.38 to 0.52) 0.67 (0.61 to 0.74) 0.16 NS 0.25 Evaluative well-being 7.81 (7.66 to 7.95) 6.43 (6.29 to 6.58) 6.95 (6.84 to 7.06) 0.90 0.55 0.32 Health status 74.80 (73.65 to 75.95) 71.52 (70.52 to 72.52) 75.55 (74.72 to 76.37) 0.28 NS 0.34 Cognitive functioning 66.37 (65.44 to 67.29) 58.72 (57.77 to 59.66) 58.92 (58.09 to 59.75) 0.69 0.68 NS 50+ years Positive affect 4.93 (4.86 to 5.00) 4.41 (4.33 to 4.49) 4.90 (4.85 to 4.94) 0.35 NS 0.36 Negative affect 0.26 (0.22 to 0.29) 0.51 (0.46 to 0.55) 0.66 (0.62 to 0.70) 0.28 0.49 0.16 Evaluative well-being 7.43 (7.35 to 7.52) 5.52 (5.43 to 5.61) 6.56 (6.48 to 6.63) 1.15 0.53 0.67 Health status 70.26 (69.71 to 70.81) 61.86 (61.30 to 62.42) 66.41 (65.95 to 66.86) 0.75 0.32 0.37 Cognitive functioning 58.46 (57.90 to 58.01) 46.69 (46.04 to 47.34) 47.38 (46.88 to 47.89) 0.91 0.89 NS CI: confidence interval; NS: not significant. a Only effect size associated with significant differences found at a 95% CI in pairwise comparisons after Bonferroni correction are reported between indicated countries. Hedges’ g values of 0.20, 0.50, and 0.80 constitute small, medium and large effect sizes, respectively. Note: Data are weighted and age-standardized. Bull World Health Organ 2014;92:716–725| doi: http://dx.doi.org/10.2471/BLT.13.129254 719 Research Health and happiness in Finland, Poland and SpainMarta Miret et al. functioning using the direct method of age standardization to the European standard population.34 We analysed differences in demo- graphics, well-being and health status and cognitive functioning across coun- tries using the χ2 test and the analysis of variance (ANOVA) test, using Bonfer- roni’s correction for pairwise compari- sons across countries. When differences were significant, Cramer’s V, Cohen’s f and Hedges’ g were reported as effect size measures, for χ2 tests, ANOVA tests and pairwise comparisons, respectively. Cohen’s guidelines35 were used as a standard to evaluate the magnitude of the effect size. To determine the correlation be- tween health status and the three differ- ent components of well-being, we con- ducted ordinary least squares regression analyses: one for positive affect, one for negative affect; and a third for evaluative well-being. Sociodemographic variables, the presence of a depressive episode, cognitive functioning and country were introduced as covariates to control for potential confounders. Robust standard errors were estimated using the Taylor series linearization method36 to adjust for the effects of weighting and clus- tering. The β coefficients were used to assess which variables have the highest association with the outcome variable, since β coefficients can be employed as effect size measures in regression models. The effect of health status in each well-being variable was also as- sessed separately for each quintile of household income, controlling for the rest of covariates employed in the previ- ous models. We used a structural equation model framework to examine the pos- sible predictors of well-being that could be included in a multiple indicators/ multiple causes model, accounting for relevant demographic and clinical co- variates. The latent variable well-being was constructed from experienced well- being and evaluative well-being. The maximum likelihood estimator with ro- bust standard errors was employed. This analysis was carried out on the entire sample and for each country separately. Non-standardized (B) and standardized (β) coefficients represented the effect of health status and the other covariates in the well-being construct, and the effect size associated to each coefficient, re- spectively. R2 measured the relationship between each of the construct’s three variables and the well-being construct. Finally, we employed a multiple indicators/multiple causes model to examine the correlation between health and well-being, controlling for the co- variates that were found to be significant in at least one of the previous structural equation models. Country was included as a covariate in the multiple indicators/ multiple causes model. We chose the ref- erence category according to the mean scores in positive and negative affect. The multiple indicators/multiple causes model fit was assessed by means of the following measures:37,38 (i) comparative fit index (CFI) > 0.90, indicating an acceptable fit; (ii) Tucker-Lewis index (TLI) > 0.90 indicating an acceptable fit; and (iii) root mean square error of ap- proximation (RMSEA) < 0.08 (indicat- ing an acceptable model fit) and < 0.05 (indicating a good fit).39 We performed data analysis using Mplus software, version 6 (Muthén and Muthén, Los Angeles, United States of America), for structural equation mod- els and Stata, version 11.0 (Stata Corpo- ration, College Station, USA), for the remaining analyses. Stata’s survey com- mand (svy), which fits statistical models for complex survey data, was employed. For hypothesis tests, 95% confidence intervals (CI) were generated. Results Table 1 shows the sociodemographic characteristics of the sample in each country. In general, differences in so- ciodemographic characteristics across countries were statistically significant in both age groups, but the effect sizes asso- ciated with these differences were small. Table 3. Adjusted correlation between well-being and health indicators, Finland, Poland and Spain, 2011–2012 Variable Positive affecta Negative affectb Evaluative well-beingc Coefficient (95% CI) Effect size, β coefficient Coefficient (95% CI) Effect size, β coefficient Coefficient (95% CI) Effect size, β coefficient Aged 0.13 (0.11 to 0.16) 0.16*** −0.07 (−0.09 to −0.05) −0.13*** 0.02 (−0.02 to 0.05) 0.02 Sex (Ref. = female) −0.03 (−0.10 to 0.05) −0.01 0.02 (−0.03 to 0.06) 0.01 −0.12 (−0.21 to −0.04) −0.03** Married or in partnership (Ref. = no) −0.04 (−0.13 to 0.03) −0.02 −0.01 (−0.05 to 0.04) −0.00 0.27 (0.18 to 0.36) 0.07*** Years of education −0.03 (−0.04 to −0.02) −0.09*** 0.02 (0.01 to 0.03) 0.09*** 0.01 (0.00 to 0.02) 0.03* Residential setting (Ref. = rural) 0.03 (−0.06 to 0.12) 0.01 0.03 (−0.03 to 0.09) 0.01 −0.06 (−0.16 to 0.04) −0.01 Occupational status −0.01 (−0.03 to 0.01) −0.02 −0.00 (−0.01 to 0.01) −0.00 −0.05 (−0.07 to −0.03) −0.07*** Income (Ref. = 1st/2nd quintile) 0.05 (−0.02 to 0.13) 0.02 −0.02 (−0.07 to 0.03) −0.01 0.28 (0.19 to 0.37) 0.08*** Depressive episode (Ref. = no) −0.31 (−0.44 to −0.18) −0.07*** 0.37 (0.26 to 0.48) 0.14*** −0.69 (−0.87 to −0.52) −0.13*** Health statusd 0.23 (0.19 to 0.27) 0.20*** −0.17 (−0.19 to −0.15) −0.23*** 0.44 (0.39 to 0.48) 0.29*** Cognitive functioningd 0.05 (0.01 to 0.09) 0.06** −0.04 (−0.06 to −0.02) −0.07*** 0.09 (0.05 to 0.13) 0.08*** CI: confidence interval; *P < 0.05; **P < 0.01; ***P < 0.001. a Goodness-of-fit, adjusted R2 = 0.085. b Goodness-of-fit, adjusted R2 = 0.111. c Goodness-of-fit, adjusted R2 = 0.340. d Regression coefficient is reported in 10-point increments. Note: Weighted data. Analyses were controlled for country. Bull World Health Organ 2014;92:716–725| doi: http://dx.doi.org/10.2471/BLT.13.129254720 Research Health and happiness in Finland, Poland and Spain Marta Miret et al. There were significant differences in positive and negative affect, evaluative well-being, health status and cognitive functioning across countries in each age group. Pairwise comparisons between countries indicated better evaluative well-being in Finland. Significant dif- ferences in positive and negative af- fect across countries had an effect size between small and moderate (Table 2). When both age groups were com- bined, mean scores in evaluative well- being were 7.47 (95% CI: 7.39–7.54) in Finland, 5.73 (95% CI: 5.65–5.81) in Poland, and 6.61 (95% CI: 6.55–6.67) in Spain. The mean scores for positive affect were 4.79 (95% CI: 4.73–4.86) in Finland, 4.37 (95% CI: 4.30–4.44) in Poland, and 4.90 (95% CI: 4.86–4.94) in Spain. The mean scores for negative affect were 0.32 (95% CI: 0.29–0.35) in Finland, 0.51 (95% CI: 0.47–0.54) in Poland, and 0.66 (95% CI: 0.63–0.69) in Spain. To correlate health with well- being, we ran three regression models, considering each of the components of well-being as a dependent variable (Table 3). Health status, the presence of a depressive episode, and the cognitive functioning score were significantly associated with positive affect, negative affect and evaluative well-being. The analysis indicated that health status made the largest independent contribu- tions to well-being. An older age was significantly as- sociated with higher positive affect and lower negative affect, whereas people with higher occupational status showed higher evaluative well-being. A higher income, being a woman, being married or living with a partner, and a longer period of education were all significantly associated with a better evaluative well- being. On the other hand, length of edu- cation was found to be inversely related to positive affect and directly related to negative affect (Table 3). In the analysis conducted separately for each quintile of household income, health status had a significant effect in each of the three well-being variables. Across quintiles, the β coefficients associated to health status ranged from 0.12 to 0.26 for positive affect, from −0.16 to −0.29 for negative affect and from 0.21 to 0.36 for evaluative well-being. We estimated the effect of health on well-being using structural equation models for the pooled sample and sepa- rately for each country. The well-being Ta bl e 4. Eff ec t e st im at es o f h ea lth st at us o n w el l-b ei ng u sin g st ru ct ur al e qu at io n m od el s, Fi nl an d, P ol an d an d Sp ai n, 2 01 1– 20 12 Va ria bl e Al l c ou nt rie s Fi nl an d Po la nd Sp ai n Eff ec t, B co effi cie nt (S E) Eff ec t s ize , β co effi cie nt R2 Eff ec t, B co effi cie nt (S E) Eff ec t s ize , β co effi cie nt R2 Eff ec t, B co effi cie nt (S E) Eff ec t s ize , β co effi cie nt R2 Eff ec t, B co effi cie nt (S E) Eff ec t s ize , β co effi cie nt R2 W el l-b ei ng Ev al ua tiv e w el l-b ei ng 1. 00 (0 .0 0) 0. 43 ** * 0. 19 1. 00 (0 .0 0) 0. 13 ** 0. 02 1. 00 (0 .0 0) 0. 33 ** * 0. 11 1. 00 (0 .0 0) 0. 38 ** * 0. 15 Po sit iv e aff ec t 1. 11 (0 .2 1) 0. 63 ** * 0. 40 4. 50 (1 .5 2) 0. 72 ** * 0. 51 1. 74 (0 .2 7) 0. 61 ** * 0. 38 1. 29 (0 .1 1) 0. 75 ** * 0. 57 N eg at iv e aff ec ta 0. 72 (0 .1 4) 0. 63 ** * 0. 40 2. 31 (1 .8 7) 0. 73 ** * 0. 53 1. 16 (0 .2 2) 0. 72 ** * 0. 51 1. 06 (0 .1 0) 0. 73 ** * 0. 53 H ea lt h st at us 0. 03 (0 .0 1) 0. 42 ** * N A 0. 01 (0 .0 0) 0. 33 ** * N A 0. 02 (0 .0 0) 0. 40 ** * N A 0. 02 (0 .0 0) 0. 28 ** * N A Co va ri at eb Ag e 0. 01 (0 .0 0) 0. 23 ** * N A 0. 01 (0 .0 0) 0. 38 ** * N A 0. 01 (0 .0 0) 0. 15 ** * N A 0. 01 (0 .0 0) 0. 20 ** * N A Se x − 0. 03 (0 .0 3) − 0. 02 N A − 0. 00 (0 .0 1) − 0. 01 N A − 0. 04 (0 .0 4) − 0. 04 N A 0. 03 (0 .0 3) 0. 02 N A M ar rie d or in p ar tn er sh ip 0. 02 (0 .0 3) 0. 01 N A 0. 00 (0 .0 1) 0. 01 N A 0. 05 (0 .0 4) 0. 05 N A − 0. 04 (0 .0 3) − 0. 03 N A Ye ar s o f e du ca tio n − 0. 02 (0 .0 0) − 0. 13 ** * N A − 0. 00 (0 .0 0) − 0. 09 * N A − 0. 02 (0 .0 1) − 0. 10 * N A − 0. 01 (0 .0 0) − 0. 12 ** * N A Re sid en tia l s et tin g 0. 00 (0 .0 3) 0. 00 N A 0. 02 (0 .0 2) 0. 05 N A 0. 04 (0 .0 3) 0. 03 N A − 0. 14 (0 .0 5) − 0. 08 ** N A O cc up at io n − 0. 01 (0 .0 1) − 0. 04 * N A − 0. 00 (0 .0 0) − 0. 03 N A − 0. 01 (0 .0 1) − 0. 06 N A 0. 00 (0 .0 1) 0. 01 N A In co m e 0. 07 (0 .0 4) 0. 05 * N A − 0. 01 (0 .0 2) − 0. 01 N A 0. 03 (0 .0 4) 0. 03 N A 0. 07 (0 .0 3) 0. 05 * N A D ep re ss io n − 0. 41 (0 .0 8) − 0. 17 ** * N A − 0. 00 (0 .0 3) − 0. 01 N A − 0. 40 (0 .0 9) − 0. 20 ** * N A − 0. 34 (0 .0 8) − 0. 20 ** * N A Co gn iti ve fu nc tio ni ng 0. 01 (0 .0 0) 0. 16 ** * N A − 0. 00 (0 .0 0) − 0. 04 N A 0. 01 (0 .0 0) 0. 08 * N A 0. 01 (0 .0 0) 0. 14 ** * N A N A: n ot a pp lic ab le ; R 2 : re la tio ns hi p be tw ee n ev al ua tiv e w el l-b ei ng , p os iti ve a ffe ct a nd n eg at iv e aff ec t o f t he w el l-b ei ng c on st ru ct a nd th e w ho le w el l-b ei ng c on st ru ct ; S E: st an da rd e rro r. ; * P < 0. 05 ; * * P < 0. 01 ; * ** P < 0. 00 1. a Th e sig n fo r n eg at iv e aff ec t w as c ha ng ed so th at v ar ia bl es in a sa m e co ns tru ct h ad th e sa m e di re ct io n. b Th e re fe re nc e ca te go ry fo r c at eg or ic al v ar ia bl es w as th e sa m e as in th e re gr es sio n an al ys is. N ot e: W ei gh te d da ta . C on tro lle d fo r c ov ar ia te s. Bull World Health Organ 2014;92:716–725| doi: http://dx.doi.org/10.2471/BLT.13.129254 721 Research Health and happiness in Finland, Poland and SpainMarta Miret et al. construct comprised positive affect, neg- ative affect and evaluative well-being. Health status and age had the strongest relationship with well-being in all samples (Table 4). Significant covariates in any of the structural equation models were included in the multiple indicators/ multiple causes model shown in Fig. 1. Since evaluative well-being had a lower R2 value in the well-being construct used in the structural equation model and correlated less with positive affect (r: 0.24; 95% CI: 0.22–0.26) and negative affect (r: 0.19; 95% CI: 0.17–0.21) than positive and negative affect between themselves (r: 0.45; 95% CI: 0.44–0.47), evaluative well-being was excluded from the well-being construct showed in Fig. 1. Thus, the experienced well- being construct (comprising positive and negative affect) was considered as a dependent variable in the multiple in- dicators/multiple causes model (Fig. 1). The multiple indicators/multiple causes model indicated that health status had a large and significant estimated effect on experienced well-being after adjustment for potential confounding variables. Since Finland showed the best scores in positive and negative affect (Table 2), we employed a dichotomous variable (0 for living in Poland or Spain; 1 for living in Finland) in the multiple indicators/multiple causes model to control the potential effect of country. Age, cognitive functioning and the ab- sence of depression were also related to experienced well-being, whereas length of education was found to be inversely related to experienced well-being. Moreover, people from Finland had greater experienced well-being than people from Poland and Spain. The final model presented an acceptable fit in all three measures performed (CFI: 0.95 and TLI: 0.89 RMSEA: 0.044; 90% CI: 0.037–0.050) (Fig. 1). We conducted a similar analysis for each country and found that health status, age, absence of depression (except for Finland) and lower educational level all had an esti- mated effect on experienced well-being in each country (Fig. 2). The results of the multiple indicators/multiple causes model for each country are similar to those of the structural equation mod- els showed in Table 4. The multiple indicators/multiple causes model fit by country was adequate (CFI and TLI were higher than 0.90 for each country and RMSEA ranged from 0.027 to 0.033 across countries). Fig. 1. Multiple indicators/multiple causes model of relationship between health status and experienced well-being, Finland, Poland and Spain, 2011–2012 Experienced well-being Country (Finland) Residential setting Positive affect Health status Age Income Depression Years of education Occupation Negative affect Cognitive functioning β = 0.047** β = 0.194*** β = –0.124*** β = –0.017 β = 0.018 β = 0.623*** β = 0.766*** β = –0.024 β = 0.291*** β = –0.168*** β = 0.092*** ** P < 0.01; *** P < 0.001. Note: Weighted data. Fig. 2. Multiple indicators/multiple causes model of relationship between health status and experienced well-being, by country, Finland, Poland and Spain, 2011–2012 Experienced well-being Residential setting Positive affect Health status Age Income Depression Years of education Occupation Negative affect Cognitive functioning β = 0.380*** Finland β = 0.142*** Poland β = 0.178*** Spain β = –0.089* Finland β = –0.090* Poland β = –0.138*** Spain β = 0.046 Finland β = 0.034 Poland β = –0.081*** Spain β = –0024 Finland β = 0.005 Poland β = 0.049 Spain β = 0.694*** Finland β = 0.571*** Poland β = 0.693*** Spain β = 0.754*** Finland β = 0.827*** Poland β = 0.817*** Spain β = –0.023 Finland β = –0.021 Poland β = 0.005 Spain β = 0.310*** Finland β = 0.335*** Poland β = 0.234*** Spain β = 0.001 Finland β = –0.189*** Poland β = –0.177*** Spain β = –0.039 Finland β = 0.053 Poland β = 0.143*** Spain * P < 0.05; *** P < 0.001. Note: Weighted data. Bull World Health Organ 2014;92:716–725| doi: http://dx.doi.org/10.2471/BLT.13.129254722 Research Health and happiness in Finland, Poland and Spain Marta Miret et al. صخلم اينابسأو ادنلوبو ادنلنف في تاعاطقلا ةددعتم ةيسرأ ةيئاصقتسا تاسارد :ةداعسلاو ةحصلا صاخشلأا مييقت ةقيرطو ةحصلا ينب تاطابترلاا ةسارد ضرغلا .ةيتايلحا متهابرخو متهايلح ةيئاصقتسا تاسارد نم ةدمتسلما تانايبلا ليلحتب انمق ةقيرطلا في لصلأا في تيرجأ ينطولا ديعصلا لىع ةلثمم تانيع تاذ هذه تمدقو .اينابسأو ادنلوبو ادنلنف في 2012و 2011 يماع ،غلاب صخش 10800 نع تامولعم ةيئاصقتسلاا تاساردلا مويلا ءانب ةداعإ ةقيرط مادختساب ميهدل ةيهافرلا ىوتسم سايق مت ليترناك سايقم مادختساب يمييقتلا ةيهافرلا ىوتسم سايق متو قيرط نع ةيحصلا ةلالحا مييقت متو .راهدزلال تياذلا ديدحتلل .ةيتاذلا ةياعرلاو لاقتنلاا تلمش تلاامج ةينماث في ةلئسأ حرط جذمانو ةيلكيلها ةلداعلما جذمانو ددعتلما يطلخا دادترلاا انمدختساو لماوعلا فاشكتسلا ةددعتلما بابسلأا /ةددعتلما تاشرؤ�لما .يمييقتلاو ليعفلا ةيهافرلا ىوتسمب ةطبترلما يذلا ةددعتلما بابسلأا /ةددعتلما تاشرؤلما جذومن رهظأ جئاتنلا رثلأا مجح( نسلا راغص ينكراشلما نأ ةعمجلما ةنيعلا لىع يرجأ ةيلاع ميلعت تايوتسمب نوعتمتي نيذلا ،)0.19 = اتيب لماعم[ لماعم( بائتكلاا نم خيرات ميهدل وأ ،)-0.12 = اتيب لماعم( = اتيب لماعم( ةيحصلا ةلالحا ءوس نم نوناعي وأ )-0.17 = اتيب اوغلبأ )0.09 = اتيب لماعم( ةيكاردلإا فئاظولا ءوس وأ )0.29 ةطبترلما ةيفاضلإا لماوعلا تناكو .أوسأ ةيهافر ىوتسم نع Discussion In accordance with previous studies,14,40 we showed that most people were quite happy and satisfied with their lives. Com- pared to the Gallup World Poll, which also used the Cantril Self-Anchoring Striving Scale, the three countries analysed in our study rank high on this scale.41 Further- more, positive affect scores were higher and negative affect scores were lower than results from research in the USA, indicat- ing better experienced well-being.21 We show that health status has the strongest relationship with all the three components of well-being even after controlling for sociodemographic variables, the presence of a depressive episode and cognitive functioning. Moreover, we show that health status has a higher association with evalua- tive well-being than with experienced well-being, and within experienced well-being, it has a higher association with negative affect than with positive affect. These findings can guide policy- makers to target the population at the highest risk of having poor well-being with intervention strategies aimed at improving their well-being. Our results indicate that the evalu- ative component of well-being is differ- ent from the experienced components, because the statistical model that com- prised positive affect, negative affect and evaluative well-being did not fit. Previous evidence has also found mod- est correlations between experienced and evaluative well-being.14 Therefore, if one’s aim is to describe a person’s well-being, a combined score of these three components should not to be cre- ated, since they are different, though interrelated, constructs. Furthermore, experienced well-being and evaluative well-being have different correlates. Our results show that higher occupational status, higher income, living with a partner and being a woman are associ- ated with higher evaluative well-being. However, these factors do not improve the experienced well-being.14,15 Ageing on the other hand increases the positive affect and decreases negative affect.14 Although previous studies have found that evaluative well-being declines with age,41,42 our results showed that age did not significantly correlate with evalu- ative well-being, possibly because the effect of age on well-being might be explained by other variables such as health status. This study is carried out in rep- resentative samples from different countries. It measured well-being in detail and independently from health and distinguished and captured both experienced and evaluative well-being. Nonetheless, the study’s cross-sectional design is a weakness. The results must be interpreted with caution, since cau- sality cannot be inferred from the asso- ciations. The participation rates of this study might reflect a global decrease in response rate that has been observed in many epidemiological studies.43 They are similar to the ones found in other popu- lation studies carried out in Europe.44 In all three countries, health status correlated the strongest with well-being, even stronger than income. Most policies emphasize the importance of income on well-being,12 however our results show that policy-makers should favour improvement of health status to promote the well-being of the population. Previous studies have shown an as- sociation between health and well-being in low- and middle-countries and hence our results will probably reproduce in these settings too.45,46 Our results show that the association between health status and well-being is also present in the people with the lowest income in the three countries. The importance of ensuring that every person achieves a basic standard of well-being is already included in the recommendations of the High-Level Panel on the Post-2015 Development Agenda.47 Our results indicate that clinicians should consider the well-being of the patients when developing, implement- ing and evaluating interventions. Fur- thermore, if measures of well-being are used to guide policy, both experienced well-being and evaluative well-being should be assessed. Further research might explore whether the strong asso- ciation that health status has with well- being is explained by the limitations in day-to-day activities faced by people with poor health. ■ Funding: Funding were received from the European Community’s Seventh Frame- work Programme (FP7/2007-2013) under grant agreement number 223071, from the Instituto de Salud Carlos III- FIS research grant numbers PS09/00295 and PS09/01845, and from the Spanish Ministry of Science and Innovation ACI-Promociona (ACI2009-1010). The study was supported by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III and the European Commu- nity’s Seventh Framework Programme under grant agreement number 282586 (ROAMER). BO is part of the Sara Borrell postdoctoral programme (reference no. CD12/00429) supported by the Instituto de Salud Carlos III. Competing interests: None declared. Bull World Health Organ 2014;92:716–725| doi: http://dx.doi.org/10.2471/BLT.13.129254 723 Research Health and happiness in Finland, Poland and SpainMarta Miret et al. = اتيب لماعم( عونلا ةيروكذ يه أوسلأا يمييقتلا ةيهافرلا ىوتسمب ضافخناو )0.07 = اتيب لماعم( كيشر عم شيعلا مدعو )-0.03 لماعم( لخدلا ىوتسم وأ )-0.07 = اتيب لماعم( ينهلما ىوتسلما لكب ًاطابترا ىوقلأا لماعلا ةيحصلا ةلالحا تناكو .)0.08 = اتيب تايرغتم طبض دعب ىتح ،يمييقتلاو ليعفلا ةيهافرلا ىوتسم نم ةيعماتجلاا تايرغتلما نم اهيرغو لخدلاو نسلاو بائتكلاا خيرات .ةيفارغميدلا ىوتسمب ةطبترلما ةمهلما لماوعلا دحأ ةيحصلا ةلالحا لثتم جاتنتسلاا ةحص ينستح لىإ ةيمارلا تايجيتاترسلاا يدؤتس ،مث نمو .ةيهافرلا .كلذك صاخشلأا ةيهافر ىوتسم ينستح لىإ ناكسلا 摘要 健康和幸福感：芬兰、波兰和西班牙横断面家庭调查 目的 探索健康与人们如何评价和体验其生活之间的关 联。 方法 我们分析了 2011-2012 年最初在芬兰、波兰和西 班牙进行的国家横断面家庭调查的数据。这些调查提 供了 10800 名成年人的信息，对其使用昨日重现法（Day Reconstruction Method）衡量幸福体验，并使用坎特 里尔自我定位奋斗量表（Cantril Self-Anchoring Striving Scale）衡量可评价的幸福。健康状态通过包括流动性 和自我保健等八个领域的问题进行评估。我们使用多 元线性回归、结构方程模型和多指标 / 多原因模型探 索与体验和可评价幸福感相关的因素。 结果 混合样本中执行的多指标 / 多原因模型显示， 更 年 轻（ 效 应 量 [β] = 0.19）、 教 育 水 平 更 高（β = –0.12）、抑郁史（β = –0.17）、不良健康状况（β = 0.29）或不良认知功能（β = 0.09）的受访者报告的幸 福体验更差。与更差的可评价幸福相关的其他因素有 男性（β = –0.03）、没有和家长一起生活（β = 0.07） 和更低的就业（β = –0.07）或收入水平（β = 0.08）。 健康状况是与体验和可评价幸福相关性最大的因素， 甚至在控制了抑郁史、年龄、收入和其他社会人口变 量之后也是如此。 结论 健康状况是幸福感的重要关联因素。因此，改善 人口健康的策略也将改善民生幸福度。 Résumé Santé et bonheur: enquêtes transversales sur les ménages menées en Finlande, Pologne et Espagne Objectif Étudier les associations entre la santé et la manière dont les personnes évaluent et ressentent leur vie. Méthodes Nous avons analysé les données tirées des enquêtes sur les ménages représentatifs à l’échelle nationale qui ont été menées en 2011–2012 en Finlande, Pologne et Espagne. Ces enquêtes ont fourni des informations sur 10 800 adultes, dont le bien-être a été mesuré en utilisant la méthode de reconstruction de la journée et le bien-être évaluatif a été quantifié avec l’échelle de Cantril (Cantril Self-Anchoring Striving Scale). L’état de santé a été évalué par des questions dans huit domaines, y compris la mobilité et le fait de prendre soin de soi-même. Nous avons utilisé des modèles d’équation structurelle à régression linéaire multiple et des modèles à indicateurs et à causes multiples pour étudier les facteurs associés au bien-être ressenti et évaluatif. Résultats Le modèle à indicateurs et à causes multiples appliqué sur l’échantillon groupé a montré que les répondants jeunes (ampleur de l’effet [β] = 0,19), avec un niveau d’éducation plus élevé (β = −0,12), des antécédents de dépression (β = −0,17), un mauvais état de santé (β = 0,29) ou de faibles facultés cognitives (β = 0,09) avaient signalé le plus faible bien-être ressenti. Des facteurs supplémentaires associés avec la plus mauvaise évaluation de bien-être étaient: être de sexe mâle (β = −0,03), ne pas vivre avec un partenaire (β = 0,07), un statut professionnel inférieur (β = −0,07) ou des niveaux de revenus plus faibles (β = 0,08). L’état de santé était le facteur le plus corrélé à la fois avec le bien-être ressenti et évaluatif, même après contrôle des antécédents de dépression, de l’âge, des revenus et des autres variables sociodémographiques. Conclusion L’état de santé est un facteur de corrélation important du bien-être. Par conséquent, les stratégies d’amélioration de la santé de la population devraient également améliorer le bien-être des personnes. Резюме Здоровье и счастье: перекрестные обследования домашних хозяйств в Финляндии, Польше и Испании Цель Изучить связь между здоровьем и тем, как люди оценивают и воспринимают свою жизнь. Методы Мы проанализировали данные национальных репрезентативных обследований домохозяйств, первоначально проведенных в 2011—2012 годах в Финляндии, Польше и Испании. Эти обследования представили информацию о 10 800 взрослых людях, степень фактического благополучия жизни которых измерялась с использованием метода реконструкции дня, а степень оценочного благополучия — на основе шкалы самооценки Кантрила. Состояние здоровья оценивалось по ответам на вопросы в восьми областях, включая мобильность и способность к самообслуживанию. Мы использовали модели множественной линейной регрессии, модели структурных уравнений и модели множественных индикаторов/ множественных причин для изучения факторов, связанных с фактическим и оценочным благополучием. Результаты Применение модели множественных индикаторов/ множественных причин по совокупной выборке показало, что респонденты в более молодом возрасте (величина эффекта [β] = 0,19), с более высоким уровнем образования (β = –012), ранее страдавшие от депрессии (β =–0.17), с плохим состоянием здоровья (β = 0,29) или плохими когнитивными функциями (β = 0,09) сообщали о худшем оценочном благополучии, по сравнению с другими группами. Дополнительными факторами, связанными с худшим, по сравнению с другими группами, оценочным благополучием, являлись мужской пол (β =–0,03), проживание без партнера (β= 0,07) и более низкий уровень квалификации (β= –0.07) или доходов (β= 0,08). Состояние Bull World Health Organ 2014;92:716–725| doi: http://dx.doi.org/10.2471/BLT.13.129254724 Research Health and happiness in Finland, Poland and Spain Marta Miret et al. здоровья являлось фактором, наиболее сильно коррелирующим как с фактическим, так и с оценочным благополучием, даже после учета факторов депрессии, возраста, дохода и других социально- демографических переменных. Вывод Состояние здоровья является важным фактором благополучия. Поэтому стратегии по улучшению здоровья населения также ведут к улучшению его благополучия. Resumen Salud y felicidad: encuestas transversales a hogares en Finlandia, Polonia y España Objetivo Examinar la relación entre la salud y cómo las personas evalúan y experimentan sus vidas. Métodos Se analizaron datos de encuestas a hogares representativos a nivel nacional realizadas inicialmente entre 2011 y 2012 en Finlandia, Polonia y España. Estas encuestas proporcionaron información sobre 10 800 adultos, para quienes el bienestar experiencial se midió con el método de reconstrucción de día y el bienestar evaluativo se midió con la escala Cantril. Se evaluó el estado de salud mediante preguntas de ocho ámbitos, que incluían la movilidad y el cuidado personal. Se emplearon modelos de regresión lineal múltiple y ecuaciones estructurales, así como modelos de causas múltiples/indicadores múltiples a fin de examinar los factores asociados con el bienestar experiencial y evaluativo. Resultados El modelo de causas múltiples/indicadores múltiples realizado en la muestra combinada mostró que los encuestados de menor edad (tamaño del efecto [β] = 0,19), con niveles superiores de educación (β = −0,12), antecedentes de depresión (β = −0,17), mal estado de salud (β = 0,29) o mal funcionamiento cognitivo (β = 0,09) presentaron menor bienestar experiencial. Otros factores asociados con menor bienestar evaluativofueron el sexo masculino (β = −0,03), no vivir en pareja (β = 0,07) y un nivel ocupacional β = −0,07) o de ingresos inferior (β = 0,08). El estado de salud fue el factor que más se correlacionó tanto con el bienestar experiencial como evaluativo, incluso después de examinar los antecedentes de depresión, la edad, los ingresos y otras variables sociodemográficas. Conclusión El estado de salud guarda una correlación importante con el bienestar. Por lo tanto, las estrategias para mejorar la salud de la población podrían asimismo mejorar el bienestar de las personas. References 1. Helliwell J, Layard R, Sachs J. World happiness report. New York: The Earth Institute, Colombia University; 2012. Available from: http://www. earthinstitute.columbia.edu/sitefiles/file/Sachs%20Writing/2012/World%20 Happiness%20Report.pdf [cited 2014 Mar 24]. 2. Stiglitz JE, Sen A, Fitoussi JP. Report by the Commission on the Measurement of Economic Performance and Social Progress. Paris: Commission on the Measurement of Economic Performance and Social Progress; 2009. 3. OECD guidelines on measuring subjective well-being. Paris: Organisation for Economic Co-operation and Development; 2013. 4. What makes us happy? Ten years of the Australian Unity Wellbeing Index. 2nd ed. Melbourne: Australian Unity; 2008. 5. How are Canadians really doing? The 2012 CIW Report. Waterloo: Canadian Index of Wellbeing; 2012. 6. Gallup-Healthways Well-being Index. Methodology report for indexes. Washington: Gallup Inc.; 2009. 7. Self A, Thomas J, Randall C. Measuring national well-being: life in the UK, 2012. Newport: UK Office for National Statistics; 2012. 8. Tobgay T, Dophu U, Torres CE, Na-Bangchang K. Health and gross national happiness: review of current status in Bhutan. J Multidiscip Healthc. 2011;4:293–8. doi: http://dx.doi.org/10.2147/JMDH.S21095 PMID: 21847351 9. Tobgay T, Dorji T, Pelzom D, Gibbons RV. Progress and delivery of health care in Bhutan, the land of the thunder dragon and gross national happiness. Trop Med Int Health. 2011;16(6):731–6. doi: http://dx.doi. org/10.1111/j.1365-3156.2011.02760.x PMID: 21418446 10. Cohen S, Pressman SD. Positive affect and health. Curr Dir Psychol Sci. 2006;15(3):122–5. doi: http://dx.doi.org/10.1111/j.0963-7214.2006.00420.x 11. Lyubomirsky S, King L, Diener E. The benefits of frequent positive affect: does happiness lead to success? Psychol Bull. 2005;131(6):803–55. doi: http://dx.doi.org/10.1037/0033-2909.131.6.803 PMID: 16351326 12. Graham C. Happiness and health: lessons – and questions – for public policy. Health Aff (Millwood). 2008;27(1):72–87. doi: http://dx.doi. org/10.1377/hlthaff.27.1.72 PMID: 18180481 13. Dolan P, Lee H, King D, Metcalfe R. Valuing health directly. BMJ. 2009;339:b2577. doi: http://dx.doi.org/10.1136/bmj.b2577 14. Kahneman D, Deaton A. High income improves evaluation of life but not emotional well-being. Proc Natl Acad Sci USA. 2010;107(38):16489–93. doi: http://dx.doi.org/10.1073/pnas.1011492107 PMID: 20823223 15. Kahneman D, Krueger AB, Schkade D, Schwarz N, Stone AA. Would you be happier if you were richer? A focusing illusion. Science. 2006;312(5782):1908–10. doi: http://dx.doi.org/10.1126/science.1129688 PMID: 16809528 16. COURAGE in Europe project [Internet]. Collaborative Research on Ageing in Europe; 2010. Available from: http://www.courageineurope.eu/ [cited 2013 Aug 20]. 17. Eikemo TA, Huisman M, Bambra C, Kunst AE. Health inequalities according to educational level in different welfare regimes: a comparison of 23 European countries. Sociol Health Illn. 2008;30(4):565–82. doi: http://dx.doi. org/10.1111/j.1467-9566.2007.01073.x PMID: 18298629 18. SAGE longitudinal multi-country study [Internet]. Geneva: World Health Organization; 2014. Available from: http://www.who.int/healthinfo/sage/ cohorts/en/index2.html [cited 2014 Jul 14]. 19. Process of translation and adaptation of instruments. Geneva: World Health Organization; 2013. Available from: http://www.who.int/substance_abuse/ research_tools/translation/en/ [cited 2013 Aug 20]. 20. Üstün TB, Chatterji S, Mechbal A, Murray CJL. WHS Collaborating groups. Quality assurance in surveys: standards, guidelines and procedures. In: United Nations Statistics Division. Department of Economic and Social Affairs. Household sample surveys in developing and transition countries [Series F No. 96]. New York: United Nations; 2005. 21. Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA. A survey method for characterizing daily life experience: the day reconstruction method. Science. 2004;306(5702):1776–80. doi: http://dx.doi.org/10.1126/ science.1103572 PMID: 15576620 22. Ayuso-Mateos JL, Miret M, Caballero FF, Olaya B, Haro JM, Kowal P, et al. Multi-country evaluation of affective experience: validation of an abbreviated version of the day reconstruction method in seven countries. PLoS ONE. 2013;8(4):e61534. doi: http://dx.doi.org/10.1371/journal. pone.0061534 PMID: 23626697 23. Miret M, Caballero FF, Mathur A, Naidoo N, Kowal P, Ayuso-Mateos JL, et al. Validation of a measure of subjective well-being: an abbreviated version of the day reconstruction method. PLoS ONE. 2012;7(8):e43887. doi: http:// dx.doi.org/10.1371/journal.pone.0043887 PMID: 22952801 24. Cantril H. The pattern of human concerns. New Brunswick: Rutgers University Press; 1965. 25. Salomon JA, Mathers CD, Chatterji S, Sadana R, Üstün TB, Murray CJL. Quantifying individual levels of health: definitions, concepts, and measurement levels. In: Murray CJL, Evans DB, editors. Health systems performance assessment: debates, methods, empiricism. Geneva: World Health Organization; 2003. pp. 301–18. 26. Pallant JF, Tennant A. An introduction to the Rasch measurement model: an example using the Hospital Anxiety and Depression Scale (HADS). Br J Clin Psychol. 2007;46(1):1–18. doi: http://dx.doi.org/10.1348/014466506X96931 PMID: 17472198 Bull World Health Organ 2014;92:716–725| doi: http://dx.doi.org/10.2471/BLT.13.129254 725 Research Health and happiness in Finland, Poland and SpainMarta Miret et al. 27. The ICD-10 classification of mental and behavioural disorders: diagnostic criteria for research. Geneva: World Health Organization; 1993. 28. Kessler RC, Ustün TB. The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). Int J Methods Psychiatr Res. 2004;13(2):93–121. doi: http://dx.doi.org/10.1002/mpr.168 PMID: 15297906 29. Morris JC, Heyman A, Mohs RC, Hughes JP, van Belle G, Fillenbaum G, et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology. 1989;39(9):1159–65. doi: http://dx.doi.org/10.1212/ WNL.39.9.1159 PMID: 2771064 30. The WAIS III - WMS III updated technical manual. San Antonio: The Psychological Corporation; 2002. 31. He W, Muenchrath MN, Kowal P. Shades of gray: a cross-country study of health and well-being of the older populations in SAGE countries, 2007- 2010. Washington: US Government Printing Office; 2012. 32. International Standard Classification of Occupations ISCO-08 [Internet]. Geneva: International Labour Organization; 2012. Available from: http:// www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm [cited 2014 Jul 14]. 33. Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. 2007;370(9590):851–8. doi: http://dx.doi.org/10.1016/ S0140-6736(07)61415-9 PMID: 17826170 34. World health statistics annual. Geneva: World Health Organization; 1990. 35. Cohen J. Statistical power analysis for the behavioral sciences. New York: Academic Press; 1988. 36. Wolter K. Introduction to variance estimation. New York: Springer-Verlag; 1985. 37. Hu LT, Bentler PM. Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55. doi: http://dx.doi.org/10.1080/10705519909540118 38. Reise SP, Widaman KF, Pugh RH. Confirmatory factor analysis and item response theory: two approaches for exploring measurement invariance. Psychol Bull. 1993;114(3):552–66. doi: http://dx.doi.org/10.1037/0033- 2909.114.3.552 PMID: 8272470 39. Browne MW, Cudeck R. Single sample cross-validation indexes for covariance structures. Multivariate Behav Res. 1989;24(4):445–55. doi: http://dx.doi.org/10.1207/s15327906mbr2404_4 40. Diener E, Diener C. Most people are happy. Psychol Sci. 1996;7(3):181–5. doi: http://dx.doi.org/10.1111/j.1467-9280.1996.tb00354.x PMID: 11894851 41. Deaton A. Income, health, and well-being around the world: evidence from the Gallup World Poll. J Econ Perspect. 2008;22(2):53–72. doi: http://dx.doi. org/10.1257/jep.22.2.53 PMID: 19436768 42. Stone AA, Schwartz JE, Broderick JE, Deaton A. A snapshot of the age distribution of psychological well-being in the United States. Proc Natl Acad Sci USA. 2010;107(22):9985–90. doi: http://dx.doi.org/10.1073/ pnas.1003744107 PMID: 20479218 43. Morton LM, Cahill J, Hartge P. Reporting participation in epidemiologic studies: a survey of practice. Am J Epidemiol. 2006;163(3):197–203. doi: http://dx.doi.org/10.1093/aje/kwj036 PMID: 16339049 44. Börsch-Supan A, Hank K, Jürges H. A new comprehensive and international view on ageing: introducing the “Survey of Health, Ageing and Retirement in Europe”. Eur J Ageing. 2005;2(4):245–53. doi: http://dx.doi.org/10.1007/ s10433-005-0014-9 45. Cramm JM, Møller V, Nieboer AP. Individual- and neighbourhood-level indicators of subjective well-being in a small and poor Eastern Cape township: the effect of health, social capital, marital status, and income. Soc Indic Res. 2012;105(3):581–93. doi: http://dx.doi.org/10.1007/s11205-011- 9790-0 PMID: 22247584 46. Temane QM, Wissing MP. The role of subjective perception of health in the dynamics of context and psychological well-being. S Afr J Psychol. 2006;36(3):564–81. doi: http://dx.doi.org/10.1177/008124630603600308 47. A new global partnership: eradicate poverty and transform economies through sustainable development. New York: United Nations; 2013.