Quality of Life Research (2021) 30:2171–2185 https://doi.org/10.1007/s11136-021-02813-5 Mental well‑being of the general population: direct and indirect effects of socioeconomic, relational and health factors Natalia Soldevila‑Domenech1,2,3 · Carlos G. Forero4 · Itxaso Alayo1,5 · Jordina Capella6,10 · Joan Colom6 · Davide Malmusi5,7 · Anna Mompart8 · Philippe Mortier1,5 · Beatriz Puértolas1,5 · Néstor Sánchez4 · Anna Schiaffino8,9 · Gemma Vilagut1,5 · Jordi Alonso1,3,5 Accepted: 3 March 2021 / Published online: 13 April 2021 © The Author(s) 2021 Abstract Purpose The aim of this study was to analyse the association between individual mental well-being and social, economic, lifestyle and health factors. Methods Cross-sectional study on a representative sample of 13,632 participants (> 15y/o) from the Catalan Health Inter- view Survey 2013–2016 editions. Mental well-being was assessed with the Warwick–Edinburg Mental Well-being Scale (WEMWBS). Linear regressions were fitted to associate well-being and sociodemographic, relational, lifestyle and health variables according to minimally sufficient adjustment sets identified using directed acyclic graphs. Predictors entered the model in blocks of variable types and analysed individually. Direct and total effects were estimated. Results Health factors significantly contributed to mental well-being variance. Presence of a mental disorder and self- reported health had the largest effect size ­(eta2 = 13.4% and 16.3%). The higher individual impact from a variable came from social support (β = − 12.8, SE = 0.48, ­eta2 = 6.3%). A noticeable effect gradient (­ eta2 = 4.2%) from low to high mental well- being emerged according to economic difficulties (from β = 1.59, SE = 0.33 for moderate difficulties to β = 6.02 SE = 0.55 for no difficulties). Younger age (β = 5.21, SE = 0.26, ­eta2 = 3.4%) and being men (β = 1.32, SE = 0.15, ­eta2 = 0.6%) were associated with better mental well-being. Direct gender effects were negligible. Conclusions This study highlights health and social support as the most associated factors with individual mental well-being over socioeconomic factors. Interventions and policies aimed to these factors for health promotion would improve popula- tion mental well-being. Keywords Well-being · WEMWBS · Mental health · Health determinants · Health survey · Directed acyclic graphs Plain English summary health operates in the population beyond mental illness. In this study, we explored the impact of demographic, socio- Mental well-being is typically understood as ‘feeling good’ economic, relational, lifestyle and health factors on the men- and ‘functioning well’ and it is considered an indicator of tal well-being, in a representative general population sam- societal progress. However, there is a lack of knowledge ple of 13,632 individuals. This study indicates that mental about its risk and protective factors. The study of drivers of well-being is sensitive to demographic and socioeconomic mental well-being is important to understand how mental factors such as gender, age, education, employment and economic difficulties, as well as, to relational and health factors. Adverse health factors and self-reported health are Natalia Soldevila-Domenech and Carlos G. Forero these authors most strongly associated with mental well-being over socio- are contributed equally. economic factors, which would have an indirect impact on mental well-being. Finally, the lack of social support appears * Gemma Vilagut gvilagut@imim.es as a critical risk factor of decreased mental well-being. Find- ings from this study suggest that interventions and policies * Jordi Alonso jalonso@imim.es aimed for health promotion would improve population men- tal well-being. Extended author information available on the last page of the article 13 Vol.:(0123456789) 2172 Quality of Life Research (2021) 30:2171–2185 Introduction Identifying the causal direction among these factors is not straightforward. Also, to date, determinants of mental The evaluation of well-being at the individual level well-being as an outcome have been researched with various receives increasing attention for its potential impact on instruments more focussed on mental disorders rather than health, economy and societal progress [1, 2]. The World on positive aspects of mental health. In recent years, the Health Organisation (WHO) emphasizes that ‘there is no Warwick Edinburgh Mental Well-being Scale (WEMWBS) health without mental health’, which includes aspects of has emerged as an increasingly popular individual-level psychological, emotional and social well-being [3]. Men- measure of positive mental well-being [21]. Studies using tal well-being, defined as ‘feeling good’ and ‘functioning WEMWBS have shown that mental well-being does not mir- well’ [4], may generate resilience to mental and physical ror the traditional gradients reported for mental illness [22] illnesses, boost educational achievement, enhance per- and that its predictors differ from those of psychopathology formance in the workplace and increase longevity in the [23], making it a potentially informative instrument for ana- general population [5, 6]. Consequently, promoting mental lysing population mental well-being. well-being may also be a useful approach to health promo- A better comprehension of the drivers of positive men- tion and disease prevention [7]. To achieve this, we need tal health would increase our understanding of how mental more evidence on its risk and protective factors [8, 9]. health operates in the population beyond mental illness. In the WHO model of social determinants of health and Such understanding would boost our ability to monitor well-being [10], the socioeconomic and political context public health policies and interventions for promoting pop- gives rise to structural determinants of health (gender, age, ulation health. In this study, we aim to identify potential ethnicity, social class), responsible of health inequalities determinants of population mental well-being, as measured by influencing how people live and work and affecting with the WEMWBS, using data from a large representative the exposure to risk and protective factors over the life population sample of the Catalan population (Spain). Spe- course. The socioeconomic status (education, employment cifically, and based on the general WHO model of health and money) puts people in economic difficulties at “risk of determinants, we intend to estimate the associations between risk” [11], conditioning proximal determinants of mental mental well-being and demographic, socioeconomic, rela- well-being, including relational factors (e.g. social sup- tional, lifestyle and health-related blocks of variables, as port), health factors (e.g. physical and mental disorders, well as analysing the direct and indirect effect of each spe- disability) and perceived health [12]. Similarly, the path- cific variable on mental well-being. We hypothesized (1) the way from lifestyle factors to well-being would act as risk existence of social inequalities in the distribution of mental or protective factors of mental and physical disorders [13]. well-being; (2) a higher contribution of relational and health Among relational factors, functional social support pro- factors to the mental well-being over lifestyle, socioeco- vides emotional, instrumental and informational resources, nomic and demographic factors; and (3) a substantial posi- which have been identified as affected by unemployment, tive contribution of health factors and self-perceived health retirement or economic difficulties [11, 12]. Social support on the indirect effect of each variable on mental well-being. may directly impact mental well-being by promoting the sense of belonging, enhancing self-realization and increas- ing coping abilities [14, 15]. Methods Some authors suggest that mental disorders may have stronger effects on mental well-being than physical disorders Design, information sources and study population due to the higher personal uncertainty and compromised ability to display adaptive conducts associated with such Repeated cross-sectional design using data from 7 biannual conditions [16]. Also because of adaptation, recent acute waves of the Catalan Health Interview Survey (ESCA) dur- health problems may have a higher impact on well-being ing 2013–2016 (N = 13,632). Each wave ensues an independ- than long-term chronic conditions [17, 18]. Disability would ent representative sample of the general population (over mediate the impact of physical and mental conditions on 15 years old) of Catalonia, a north-eastern region of Spain perceived health [19], and could also impact mental well- (7.5 million inhabitants). The sampling frame is the non- being directly due to its pervasive effects on major areas institutionalized adult population in the Population Registry of everyday life. Self-perceived health status reflects both of Catalonia from the Catalan Institute of Statistics (IDES- the actual physical condition and its emotional impact on CAT). Individuals are selected through stratified three- general living conditions [20]. Actually, there is evidence stage random sampling with different probabilities within that the relative effect of self-reported health on well-being strata. The ESCA has the rank of official statistics by the is larger than that of income and social relationships [15]. Government of the Catalonia, so participants must answer the survey in a complete and truthful way (Law 23/1998, 13 Quality of Life Research (2021) 30:2171–2185 2173 December 30, of statistics of Catalonia). Data are obtained I (directors, managers and university professionals), through face-to-face interviews by trained interviewers at the class II (intermediate occupations and self-employed respondent’s home using computer-assisted personal inter- workers), class III (manual workers) and not classifi- viewing (CAPI). Interview time takes about 45 min [24]. able (never worked and living alone). Study’s population features were as follows: 50.9% (3) Relational factors: Perceived social support was women; mean age 47.4 years; 21.3% had up to primary stud- assessed with the DUKE-UNC-11 Functional Social ies; 14.4% were born in non-high income countries; 10.7% Support Scale, covering confidant support (e.g. chances were unemployed; 15.1% were retired, and 20.6% had dif- to talk about work or money problems), and affective ficulties in making monthly ends meet. Low social support support (e.g. displays of affection, love, and empathy) was present in 2.5% women and 1.7% men. Regarding life- [29]. It has 11 Likert-scaled items, with total score style factors, 14.7% were obese, and 25.5% were current ranging 11–55 points, with higher scores indicating smokers. As for health factors, 17.0% reported life-time lower social support. In the Spanish validation of the history of at least one mental disorder (22.9% women and DUKE-UNC-11, a cut-off point at the 15th percentile 10.9% men); 63.9% reported at least one physical disor- was chosen to categorize subjects as ‘low’ (≥ 32 points) der (70.6% women and 56.9% men); 8.0% reported lack of and ‘adequate’ (< 32 points) social support [30]. The autonomy, and 3.5% reported “poor” perceived health status. Spanish version of the DUKE-UNC-11 shows high See Table 2 for detailed sample characteristics. internal consistency and reliability (Cronbach’s alpha of 0.90) [30]. Variables (4) Lifestyle factors: Body mass index (BMI, kg/m 2) categorized as: underweight (BMI < 18.5), normal Mental well‑being (18.5 ≥ BMI < 25), overweight (25 ≥ BMI < 30), and obesity (BMI ≥ 30); hours of sleep; and smoking status. The primary outcome was mental well-being as measured (5) Health factors: Life-time history of mental disorders with Spanish and Catalan versions of the WEMWBS [25, (anxiety, depression or other mental disorders); lifetime 26]. It is a unidimensional measure of mental well-being history of long-term (≥ 6 months) physical disorders in the previous two weeks [21] using 14 Likert-scaled from a list of 28 [24]; and lack of autonomy, as need positively-worded items (e.g. “I’ve been feeling optimistic of help to perform routine activities due to a health about the future”), with five categories from “None of the problem. time” to “All of the time”. Sumscore ranges 14–70, with (6) Self-Perceived health: Using the question: “In general higher scores indicating higher levels of mental well-being. how would you say your health is?” in a 5-point ordinal The Spanish and English versions of the WEMWBS have scale (from Excellent to Poor). shown high internal consistency and reliability (Cronbach’s alpha higher than 0.93 and 0.91, respectively) and adequate discriminative capacity between socioeconomic groups and Statistical analysis health-related conditions to perform studies about health, social or economic inequalities [21, 25]. We computed descriptive statistics and standard errors (SE) of WEMWBS scores and study variables, stratified by sex. Predictive factors of mental well‑being We tested score differences between categories using one- way analysis of variance (ANOVA), adjusting for multiple We explored the WEMWBS relationship with six blocks comparisons using Benjamini–Hochberg procedure [31] at of factors: false discovery rate of 5%. Effect sizes were computed as either Cohen’s d with cut-offs of 0.2 (small), 0.5 (medium), (1) Demographic factors: Sex; Age; and Country of origin, 0.8 (large) and 1.2 (very large) [32, 33] or, in regression categorized as Spain, high income and other accord- models, as eta-squared with cut-offs of 1% (small), 6% ing to the Organisation for Economic Co-operation and (moderate) and 14% (large) [34]. Development (OECD) classification [27]. To explore the contribution of each block on mental (2) Socioeconomic factors: Educational level (primary well-being, we fitted multivariable linear block regres- or less, secondary, and higher); Employment status sion models. Models were devised in a block-nested man- (student; employed; unemployed; housework; retired; ner, added from distal to proximal. The first block included other); Economic difficulties, obtained from a question demographic factors; the following models added blocks about the presence of family economic difficulties to consecutively: socioeconomic factors; relational factors; make monthly ends meet; and Social class of the house- lifestyle factors; health factors and perceived health. Models hold reference person, based on occupation [28]: class increase in explained variance was assessed by block using 13 2174 Quality of Life Research (2021) 30:2171–2185 R-squared, and tested using F-test at 0.05 nominal level. The two suitable MSAS identified from the DAG-1 were final full model included six blocks and all variables. entered in two different linear regression models for each We estimated multivariable adjusted linear regression predictor, to estimate total and direct effects. Finally, to models for each predictor variable. To avoid overadjustment study whether health factors and self-reported health medi- bias [35], we used directed acyclic graph (DAG) methodol- ated the effect of all other factors on mental well-being, a ogy to determine the variables for which it is sufficient to second DAG (DAG-2) was devised (Supplementary Fig- control to obtain unbiased estimates of causal effects [36]. ure 2), identical to DAG-1 but excluding health factors DAGs are especially useful for using a priori knowledge (i.e. and self-reported health from the pathways to well-being. they use no actual data) on the relation between variables, For each predictor, the rate between direct effect differ- thus displaying graphically explicit and transparent assump- ences between DAG-1 and DAG-2 over indirect effects tions based on former available evidence. Relationships are from DAG-1 was computed to study the contribution of “directed” as variables imply a causal sequence, which are health factors and self-perceived health on the indirect represented by arrows. When there is insufficient evidence effect of each variable on mental well-being. All analyses to exclude a potential effect between variables, such relation- were adjusted by survey year. ship is also assumed and so is reflected in the DAG. Once a Inverse probability sampling weights and post-stratifi- relationship framework is developed, DAGs apply algebraic cation weights were applied to achieve representativeness methods (Pearl’s back- and single-door criterion [37–39]) to in terms of geographic areas, age, sex, and strata sizes. trim model-biasing pathways, yielding two minimally suf- Weights were normalized to the total sample size so that ficient adjustment sets (MSAS) for each predictor, one for the samples of each year had equal weights in the analy- estimating the total effect and the other for the direct effects ses. Missing values were < 1.6% so each specific analysis (unmediated paths directly connecting a predictor variable was performed on individuals with complete information and an outcome). The MSAS represent the lowest number on the variables involved. DAGs and MSAS for total and of covariates needed for adjusting after excluding irrelevant direct effects were devised using the ‘dagitty’ package in variables that can actually cause bias when included in the R [41]. Data management were done with Stata version multivariable regression model [40]. Once total and direct 13 [42], and statistical analyses with R version 3.5.2 [43]. effects are estimated, it is possible to decompose total effects in direct and indirect (i.e. remaining effects through paths including mediators and other variables). Ethics We devised a first directed acyclic graph (DAG-1) (Fig. 1), where we assumed that all predictor variables The ESCA has the rank of official statistics carried out were directly related to mental well-being. Table 1 details by the Government of the Catalonia region, and it must on other assumed relationships between covariates. The ensure the confidentiality of the data (Law 23/1998, December 30th of statistics of Catalonia). Fig. 1  Directed acyclic graph (DAG-1). Variable names are abbreviated: origin country of origin; education educational level; work employment status; income family economic dif- ficulties; BMI body mass index; smoke smoking status; sleep hours of sleep; dependency lack of autonomy. Node colours rep- resent the group to which each variable belongs: demographic factors (in blue), socioeconomic factors (in green), relational factors (in purple), lifestyle fac- tors (in grey), health factors (in red), and self-reported health (in orange). (Color figure online) 13 Quality of Life Research (2021) 30:2171–2185 2175 Table 1  Assumed relationships Directed edges Potential bidi- References between covariables of mental rectionality* well-being when developing From To the DAG Sex All variables except age and country of origin No [61, 66, 67] Age All variables except sex and country of origin No Country of origin Education No Social class No Social support No Education Employment status Yes ( +) [68, 69] Social class Yes (+ +) BMI No Smoking status No Hours of sleep No Mental disorder Yes ( +) Social class Employment status Yes (+ +) [70–72] Social support No BMI No Smoking status No Hours of sleep No Physical disorder No Mental disorder No Employment status Economic difficulties Yes (+ +) Social support Yes ( +) BMI Yes ( +) Smoking status Yes ( +) Hours of sleep Yes ( +) Mental disorder Yes ( +) Economic difficulties Social support No BMI No Smoking status No Hours of sleep No Physical disorder Yes ( +) Mental disorder Yes ( +) Social support Mental disorder Yes (+ +) [14, 55, 73] Perceived health Yes ( +) Body mass index (BMI) Physical disorder Yes (+ + +) [61, 66] Mental disorder Yes (+ + +) Smoking status Physical disorder Yes (+ +) Mental disorder Yes ( +) Physical disorder Hours of sleep Yes ( +) [19, 60, 74, 75] Mental disorder Yes (+ + +) Lack of autonomy Yes ( +) Perceived health Yes (+ +) Mental disorder Hours of sleep Yes (+ +) Lack of autonomy Yes ( +) Perceived health Yes (+ +) Lack of autonomy Perceived health Yes (+ +) *Threatens to the face-validity (plausibility of the posited relationship) are assessed according to evi- dence, and “Yes” is given in the cases where reverse directionality is plausible. In such cases, the chance of reverse causality is qualitatively evaluated with (+ , +  + , +  + +), indicating less or more probability of bidirectionality, respectively 13 2176 Quality of Life Research (2021) 30:2171–2185 Results demographic, socioeconomic and lifestyle factors showed low association with mental well-being. Table 2 shows descriptive statistics of mental well-being Noticeably, there was no direct association between sex across categories of studied variables (see Supplementary and mental well-being (β = − 0.05, SE 0.14). The direct Figure 1 for a visual representation of effect sizes strati- effect of smoking habits was also negligible (­ eta2 = 0.1%). fied by sex). As expected from the large sample size, all Age, education, social class, social support, BMI, hours variables showed significant differences in WEMWBS of sleep, mental disorder, lack of autonomy and perceived scores. Demographic, socioeconomic and lifestyle factors health status had higher effects in women than in men; the had small effects on mental well-being. Women presented opposite was true in the case of employment status (results slightly lower mental well-being than men (58.2 vs 59.5, available upon request). Cohen’s d = 0.13). Negative effect gradients emerged Figure 2a shows total and decomposed (direct and indi- according to age (from 60.2 in the 15–44y/o group to 55.0 rect) effects for variable categories on mental well-being. over 75y/o) and family economic difficulties from least to Interestingly, lower levels of well-being due to age were most difficulties (ranging from 61.7 to 54.9). Those who more explained by indirect rather than direct associations: declared low social support had lower WEMWBS scores middle-aged subjects (45–64y) presented lower direct scores (44.0 vs 59.2, Cohen’s d = − 1.54). Perceived health sta- (β = 1.24, SE 0.33) than the younger group (β = 1.33, SE tus also yielded a negative gradient of WEMWBS scores, 0.36). Contrarily, for socioeconomic factors, direct effects from 63.4 for excellent health to 46.1 for poor health. represented about 50% of the total. In fact, the gradient in Small differences in effect sizes appeared when stratify- well-being according to economic difficulties was mostly ing by sex: being student, employed or having a mental direct. Regarding relational factors, 77.9% of the total effects disorder had larger effect in men than in women. On the came from direct effects. Mental disorders and lack of auton- contrary, lack of autonomy had higher effect in women omy showed similar results. than in men (Cohen’s d = 0.76 and 0.64, respectively). Figure 2b shows total effects (decomposed into direct and Table 3 displays model fit and explained variance in indirect effects) when health factors were removed from the block regression models. All blocks had impact on men- DAG to study the contribution of health factors and self- tal well-being. By block, socioeconomic factors explained perceived health on the indirect effects of each variable on 9.8% of mental well-being variance; relational factors mental well-being (see Supplementary Table 1 for compari- explained 6.3%, and lifestyle factors explained only 2.2%. son of direct and total effects when excluding health factors Health factor block had the most substantial contribution in the DAG-2 and Supplementary Figure 4 for their respec- to mental well-being with 20.4% of explained variance, tive MSAS). The relative contribution of health factors var- and perceived health status explained 16.3%. Sequential ied across predictors. For example, health factors explained block entry showed that, after adjusting by year of survey over 65% of the indirect effects of age on well-being and less and demographic factors, the incremental contribution of than 10% of the indirect effect of education. Interestingly, socioeconomic factors was 7.8%. Successive block contri- health factors had lower indirect effects on well-being at bution was: relational factors 4.4%; lifestyle factors 0.1%; higher levels of economic difficulties (values ranging from health factors 10.0%, and self-reported health 2.9%. The 30 to 50%). Finally, health factors explained all the indirect full model with all variables explained 29.4% of mental effects of social support on well-being. well-being variance. Similar results were observed when stratifying by sex (results available upon request). Table 4 includes regression coefficients for total, direct and indirect effects of individual variables adjusted by Discussion MSAS from DAG-1. Supplementary Figure 2 summarizes the variables used to build each model. As hypothesized, In this study we analysed the association of individual fac- the largest effect sizes were those of health and relational tors and mental well-being. Three main findings emerged: factors. Low social support had a moderate association first, differences in the distribution of mental well-being with well-being ­( eta 2 = 6.3%), implying 12 points less according to gender and age were mostly due to indirect mental well-being. Self-reported health had the highest effects, which suggest social inequalities, given that other effect ­(eta2 = 16.3%) with a 10-point difference from excel- factors mediate their relationship; second, health factors lent to poor health. Being dependent and having a mental and self-perceived health were highly associated to mental disorder also implied lower well-being ­(eta2 = 10.6% and well-being; third, the lack of perceived functional social 13.4%, respectively). Except for employment ­(eta2 = 6.4%), support is itself a critical risk factor for lower mental well-being. A novelty of this study is the decomposition of effects for each hypothesis of exposure-outcome. This 13 Quality of Life Research (2021) 30:2171–2185 2177 Table 2  Distribution of sample characteristics (N, %) and description of WEMWBS scores (mean, SE) across categories of study variables Variable Category Total (N# = 13,632) Women (N# = 6814) Men (N# = 6818) N# (%) WEMWBS score N# (%) WEMWBS score N# (%) WEMWBS score Mean* (SE) Mean* (SE) Mean* (SE) Sex Men 6818 (49.1) 59.5 (0.12) Women 6814 (50.9) 58.2 (0.13) Age (years) 15—44 6372 (48.5) 60.2 (0.11) 3113 (46.5) 59.9 (0.16) 3259 (50.7) 60.5 (0.15) 45—64 4311 (31.2) 58.3 (0.16) 2141 (30.9) 57.6 (0.23) 2170 (31.6) 59.1 (0.21) 65—74 1377 (10.5) 57.6 (0.30) 720 (11.1) 57.0 (0.43) 657 (10.0) 58.4 (0.41) ≥ 75 1572 (9.7) 55.0 (0.31) 840 (11.5) 54.4 (0.42) 732 (7.8) 56.0 (0.42) Country of origin Spain 11,406 (83.7) 58.7 (0.10) 5700 (84.0) 58.1 (0.14) 5706 (83.3) 59.4 (0.13) High income 287 (1.9) 60.0 (0.50) 143 (1.7) 59.8 (0.68) 144 (2.1) 60.2 (0.73) Other 1936 (14.4) 59.6 (0.22) 970 (14.3) 59.0 (0.32) 966 (14.6) 60.2 (0.30) Educational level Up to Primary 3085 (21.3) 56.2 (0.21) 1588 (22.8) 55.5 (0.30) 1497 (19.7) 57.0 (0.30) Secondary 7926 (57.8) 59.3 (0.11) 3808 (55.2) 58.7 (0.17) 4118 (60.6) 59.9 (0.15) Higher 2615 (20.8) 60.4 (0.16) 1415 (22.0) 60.0 (0.23) 1200 (19.7) 60.9 (0.21) Social class I 2623 (20.5) 60.1 (0.16) 1274 (20.2) 59.8 (0.23) 1349 (20.9) 60.5 (0.22) II 2256 (17.3) 59.2 (0.20) 1207 (18.2) 58.7 (0.30) 1049 (16.4) 59.7 (0.26) III 8304 (58.8) 58.5 (0.12) 4005 (56.9) 57.8 (0.17) 4299 (60.8) 59.2 (0.16) Has never worked 294 (2.0) 54.1 (0.77) 236 (3.1) 53.3 (0.80) 58 (0.9) 57.1 (1.95) Employment status Student 1150 (8.7) 61.0 (0.24) 571 (8.3) 60.4 (0.36) 579 (9.0) 61.5 (0.32) Employed 6857 (50.4) 60.4 (0.10) 3158 (46.0) 59.8 (0.16) 3699 (55.0) 60.9 (0.13) Unemployed 1330 (10.7) 57.1 (0.29) 551 (9.0) 57.0 (0.45) 779 (12.6) 57.1 (0.38) Housework 1401 (10.3) 56.9 (0.30) 1401 (20.1) 56.9 (0.30) 0 (0.0) Retired 2263 (15.1) 57.5 (0.23) 842 (12.2) 56.7 (0.39) 1421 (18.1) 58.1 (0.28) Other conditions 592 (4.6) 51.7 (0.55) 254 (3.8) 50.0 (0.84) 338 (5.4) 53.0 (0.72) Family economic Great difficulty 851 (7.3) 54.9 (0.44) 433 (7.4) 54.3 (0.62) 418 (7.1) 55.6 (0.62) difficulties to make Difficulty 1603 (13.3) 56.5 (0.27) 851 (14.0) 55.7 (0.38) 752 (12.6) 57.5 (0.38) monthly ends meet Some difficulty 3216 (24.9) 58.4 (0.18) 1644 (25.4) 57.9 (0.25) 1572 (24.4) 58.9 (0.24) Some ease 5533 (37.7) 59.7 (0.12) 2687 (36.6) 59.1 (0.17) 2846 (38.8) 60.3 (0.15) Ease 2135 (14.8) 61.1 (0.19) 1071 (14.7) 60.4 (0.28) 1064 (14.8) 61.9 (0.25) Great ease 294 (2.1) 61.7 (0.54) 128 (1.9) 61.6 (0.88) 166 (2.4) 61.8 (0.66) Social support Low 263 (2.1) 44.0 (0.81) 157 (2.5) 43.3 (1.05) 106 (1.7) 45.1 (1.26) Adequate 13,369 (97.9) 59.2 (0.08) 6657 (97.5) 58.6 (0.12) 6712 (98.3) 59.8 (0.11) BMI Underweight 330 (2.4) 58.4 (0.60) 256 (3.7) 58.6 (0.68) 74 (1.1) 57.7 (1.29) Normal weight 6426 (47.6) 59.6 (0.12) 3537 (52.1) 59.1 (0.16) 2889 (43.0) 60.2 (0.17) Overweight 4642 (33.7) 58.8 (0.15) 1849 (26.9) 57.8 (0.24) 2793 (40.7) 59.5 (0.18) Obesity 2041 (14.7) 57.0 (0.25) 1035 (14.9) 56.2 (0.36) 1006 (14.6) 58.0 (0.34) Hours of sleep <6 h 1064 (8.4) 55.6 (0.37) 619 (9.7) 55.0 (0.48) 445 (7.0) 56.4 (0.57) 6-8 h 11,231 (82.6) 59.3 (0.09) 5549 (81.4) 58.7 (0.13) 5682 (83.8) 59.9 (0.12) >8 h 1320 (8.9) 58.1 (0.33) 635 (8.7) 57.4 (0.50) 685 (9.1) 58.7 (0.44) Smoking status Current smoker 3488 (25.5) 58.6 (0.17) 1419 (20.7) 57.8 (0.29) 2069 (30.5) 59.1 (0.22) Ex-smoker 2551 (19.5) 59.3 (0.20) 897 (14.4) 59.5 (0.31) 1654 (24.8) 59.2 (0.25) Non-smoker 7593 (55.0) 58.9 (0.12) 4498 (65.0) 58.1 (0.16) 3095 (44.7) 60.0 (0.16) Mental disorder Yes 2170 (17.0) 51.8 (0.27) 1484 (22.9) 52.2 (0.32) 686 (10.9) 51.0 (0.48) No 11,462 (83.0) 60.3 (0.08) 5330 (77.1) 60.0 (0.12) 6132 (89.1) 60.6 (0.11) Physical disorder Yes 8798 (63.9) 57.6 (0.12) 4847 (70.6) 57.2 (0.16) 3951 (56.9) 58.2 (0.17) No 4834 (36.1) 61.0 (0.11) 1967 (29.4) 60.6 (0.17) 2867 (43.1) 61.3 (0.15) Lack of autonomy No 12,525 (92.0) 59.7 (0.08) 6160 (90.1) 59.3 (0.12) 6365 (94.0) 60.1 (0.11) Yes 1107 (8.0) 49.3 (0.39) 654 (9.9) 48.3 (0.50) 453 (6.0) 51.0 (0.62) 13 2178 Quality of Life Research (2021) 30:2171–2185 Table 2  (continued) Variable Category Total (N# = 13,632) Women (N# = 6814) Men (N# = 6818) N# (%) WEMWBS score N# (%) WEMWBS score N# (%) WEMWBS score Mean* (SE) Mean* (SE) Mean* (SE) Perceived health status Excellent 1044 (8.2) 63.4 (0.21) 439 (6.8) 63.2 (0.33) 605 (9.6) 63.5 (0.27) Very good 4410 (32.4) 60.7 (0.11) 2122 (30.8) 60.4 (0.17) 2288 (34.0) 60.9 (0.16) Good 5678 (40.9) 59.4 (0.12) 2848 (41.2) 59.1 (0.17) 2830 (40.6) 59.8 (0.17) Fair 2032 (15.0) 54.0 (0.26) 1127 (17.0) 53.4 (0.36) 905 (13.0) 54.8 (0.38) Poor 467 (3.5) 46.1 (0.61) 277 (4.2) 45.5 (0.78) 190 (2.8) 47.1 (0.95) BMI body mass index, N number, SE standard error *All variables have a significant effect on mental well-being (one-way ANOVA; p < 0.05) after the Benjamini–Hochberg correction with false discovery rate 0.05 # Unweighted N Table 3  Model fit and % Block effect Incremental block effects explained variance by (a) individual blocks of factors Explained variance (%) Total explained vari- Increase in and (b) incremental block ance (%) explained vari- effects when blocks entered ance (%) sequentially Year of survey 0.4 0.4 – Demographic factors 3.8 4.2 3.8 Socioeconomic factors 9.8 12.0 7.8 Relational factors 6.3 16.4 4.4 Lifestyle factors 2.2 16.5 0.1 Health factors 20.4 26.5 10.0 Perceived health* 16.3 29.4 2.9 Each model contained all variables within each block All models are significant (F statistic p < 0.001). The % variance is computed as, adjusted R2*100 (%) *The final model with the seven blocks factors contains all studied variables approach may add valuable information to the study of Our results revealed associations between economic con- health disparities and social determinants of health [44]. ditions and mental well-being. Unemployment was a sub- We found that men had a marginally higher mental stantial risk factor for lower mental well-being, and a distinct well-being than women, in line with previous studies negative gradient in mental well-being appeared according based on WEMWBS [22, 45, 46]. Most of sex association to family economic difficulties. Stewart-Brown et al. [22], was indirect, via socioeconomic and health factors. This found that the group with higher economic resource pre- is in contrast with the consistent findings on sex differ- sented better odds of higher mental well-being; however, ences in mental health and psychopathology [47, 48]. Our no differences existed among other groups. In our study, results may imply that sex impacts differently in positive economic effects were direct and had no sizeable mediating and negative mental states. A similar result was found for role. However, additional analyses showed that, as economic age: increasing age was negatively associated with mental difficulties increased, health factors contributed less to the well-being, but the effect was mostly indirect and mediated indirect effect on well-being (see Supplementary Table 1). by health factors. Also, age and well-being associations This result is most relevant when considering that a direct were linear, in contrast with previous studies reporting a effect of socioeconomic factors on mental well-being in U-shape relation, in which young and the elderly people groups with economic difficulties would imply a source of present higher well-being than middle-aged adults [49, social inequality. Such result aligns with previous research: 50]. Additional analyses are needed to test potential quad- job type and job context characteristics may determine the ratic effects of age on mental well-being in the population. relationship between work, income and well-being [51, 52]. 13 Table 4  Total and direct effects of each variable on mental well-being in all population Total effects Direct effects 2 β SE P eta (%) β SE P eta2 (%) Demographic Sex Men 1.32 0.15 < 0.001 0.6 − 0.05 0.14 0.741 0.6 factors (Ref: Women) Age (years) 15–44 5.21 0.26 < 0.001 3.4 1.33 0.36 < 0.001 3.4 (Ref: ≥ 75) 45–64 3.35 0.27 < 0.001 1.24 0.33 < 0.001 65–74 2.67 0.32 < 0.001 1.47 0.29 < 0.001 Country of origin High income 1.36 0.54 0.012 0.2 0.68 0.50 0.173 0.2 (Ref: Spain) Other 0.90 0.21 < 0.001 1.01 0.21 < 0.001 Quality of Life Research (2021) 30:2171–2185 Socioeco- Educational level Secondary 1.99 0.20 < 0.001 2.8 0.84 0.19 < 0.001 1.7 nomic fac- (Ref: Primary) Higher 3.05 0.24 < 0.001 0.71 0.26 0.006 tors Social class I 0.68 0.22 0.002 1.2 − 0.42 0.20 0.037 1.2 (Ref: III) II 0.16 0.21 0.444 − 0.46 0.18 0.013 Never worked − 3.75 0.52 < 0.001 − 2.58 0.50 < 0.001 Employment status Student 3.35 0.34 < 0.001 6.5 1.67 0.32 < 0.001 6.4 (Ref: Unemployed) Employed 3.16 0.24 < 0.001 1.81 0.23 < 0.001 Housework 2.34 0.36 < 0.001 1.50 0.33 < 0.001 Retired 2.65 0.39 < 0.001 1.90 0.37 < 0.001 Other − 4.11 0.41 < 0.001 − 1.81 0.38 < 0.001 Family economic difficulties Difficulty 1.59 0.33 < 0.001 4.2 0.60 0.31 0.05 4.2 to make monthly ends meet Some dif- 3.14 0.30 < 0.001 1.52 0.29 < 0.001 (Ref: Great difficulty) ficulty Some ease 4.36 0.30 < 0.001 2.24 0.29 < 0.001 Ease 5.64 0.33 < 0.001 3.70 0.32 < 0.001 Great ease 6.02 0.55 < 0.001 3.98 0.52 < 0.001 Relational Social support (Ref: Low − 12.80 0.48 < 0.001 6.3 − 9.97 0.45 < 0.001 5.9 factors Adequate) Lifestyle fac- BMI Underweight − 0.38 0.50 0.446 1.0 − 0.80 0.46 0.086 1.0 tors (Ref: Obesity) Normal 0.78 0.22 < 0.001 0.34 0.20 0.097 weight Overweight 0.72 0.22 < 0.001 0.37 0.20 0.068 Hours of sleep (Ref: < 6 h) 6-8 h 1.37 0.24 < 0.001 1.5 1.37 0.24 < 0.001 1.5 >8 h 1.51 0.32 < 0.001 1.51 0.32 < 0.001 Smoking status (Ref: Smoker) Ex-smoker 0.96 0.22 < 0.001 0.1 0.70 0.20 < 0.001 0.1 Non-smoker 0.65 0.18 < 0.001 0.31 0.16 0.058 13 2179 2180 Quality of Life Research (2021) 30:2171–2185 Literature also points at less frequent health-oriented behav- Regression coefficients represent WEMWBS change relative to the variable reference category. Each variable has been adjusted by its minimally sufficient adjustment set (MSAS) identified iours in persons with limited resources, which may contrib- eta2 (%) ute to social differences in social well-being [53]. In our study, low functional social support played a key 13.3 10.5 16.3 3.5 role, and had the most substantial effect in mental well- being scores among all groups. Our findings add to the existing evidence that social support plays a decisive role < 0.001 0.006 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 in the maintenance of psychological well-being and that poor social relationship negatively impact mental health P [14, 54, 55]. Moreover, our results suggest that the most important effects of social support on well-being are direct, and their small indirect effect go through health factors. Literature abounds in social support interventions on dif- 0.19 0.15 0.26 0.45 0.40 0.38 0.38 SE ferent outcomes and specific groups [56, 57]. Our findings agree with previous studies showing a strong relationship Direct effects between physical, psychological health and subjective well- 4.89 0.40 5.16 10.07 7.73 7.14 4.17 being [17]. Under the model, health factors were the most important contributors to mental well-being. There was also β a strong association between self-reported health and mental well-being, those reporting better health also showed higher levels of mental well-being. Qualitative studies show that eta (%) health is at the core of what constitutes well-being [20]. Suf- 13.4 10.6 16.3 2 3.6 β regression coefficient, SE standard error, eta2 (%) eta squared*100, BMI body mass index, Ref reference category fering from a mental disorder had a huge direct association with well-being, which has been found in previous studies on the relationship between WEMWBS and mental illness [58]. < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 The debate on wether well-being or mental illness are part of the same dual continuum or separate constructs is still open P [59]. Our model cannot distinguish whether mental well- being and mental illness are part of the same construct or its outcome. Yet, under our causal assumptions, well-being 0.19 0.16 0.25 0.45 0.40 0.38 0.38 comes after health factors, so promoting population health SE may be a way of promoting well-being. with the DAG-1 (see Fig. 1 and Supplementary Figure 2), and by year of survey As hypothesized, when decomposing variable effects on Total effects well-being through health variables, we found they mediated 6.36 2.15 7.84 10.07 7.73 7.14 4.17 the associations. This result matches well-known models of disability where it comes as a consequence of health [19, 60, β 61]. In spite of potential reverse causality effects between Very good mental well-being and health, our results add to the evidence Excellent Absence Absence Absence supporting the sensitivity of mental well-being as a health Good Fair outcome. In our study, WEMWBS was sensitive to a variety of socioeconomic, relational and health factors. Mental well- Lack of autonomy (Ref: Pres- Physical disorder (Ref: Pres- being overcomes the disadvantages of instruments designed to be sensitive in the population fraction with mental health problems. This approach potentially capture changes in well- being that otherwise would have gone unnoticed [21, 62]. Perceived health Health factors Mental disorder (Ref: Presence) Such relationships must be explored in future research, ide- (Ref: Poor) ally in longitudinal studies. ence) ence) Our results must be interpreted in light of some limita- Table 4  (continued) tions. Firstly, the temporal ambiguity entailed by cross- sectional designs regarding causality involves that results Self-reported are as correct as DAG assumptions. It is worth noting that health other models may be devised, and that we are not propos- ing a theoretical framework of mental well-being. Our DAG 13 Quality of Life Research (2021) 30:2171–2185 2181 Fig. 2  Variable total effects, as regression coefficient value, decom- health factors. Values over bars represent the percentage of direct posed into direct (light area) and indirect (dark area). Values adjusted effects over total effects. Only variables significantly associated with by each variable’s minimally sufficient adjustment set (MSAS) a from mental well-being are represented (P < 0.05). (Color figure online) DAG-1 that includes health factors; and b from DAG-2 that excludes just intends to systematize [63] and make transparent our multivariable analysis, reducing noise parameter estimates assumptions on exposures-outcome relationships. As of [65]. We have also tested separate models of direct and indi- date, no theoretical framework exists on mental well-being rect effects for each exposure-outcome hypothesis, so that to guide DAG-building, so we based our model on the results inform of pathways that can be intervened directly widespread and commonly accepted WHO model of deter- on the variable or averted at some point along causal chains. minants of health and disability [19, 60, 61]. Traditional Future models using this kind of effect decomposition may methods based on fit criteria (e.g. R2 or Akaike information help to refine the study of modifiable effects of health dis- criterion) would take the model as a whole, thus ignoring parities and social determinants of health. directionality in the relationships. Secondly, conceptually relevant variables were not available (e.g. social participa- tion [23] or dispositional traits such personality or character Conclusions variables) [64]). Personality is itself a most relevant variable in behaviour analyses and would indeed affect well-being This study identified a variety of factors associated with the outcomes. However, mental and physical health are also levels of mental well-being, ranging from structural factors affected by such variables, so that they might encompass such as gender, age and employment, to more proximal fac- these effects up to a certain point. Finally, the ESCA survey tors such as social support and health factors. These results assesses lifetime presence of disorders, so their reporting can are consistent with previous studies focussing on disability either be affected by recall bias or absent at the time of the and disease. Our results support that mental well-being, and interview, yielding results which are likely to average both especially WEMWBS, could be an essential tool for moni- of these influences. toring population health and general well-being. The focus However, this study is not without strengths: its repre- on positive mental health offers an opportunity to expand sentativeness, sample size, and various variables make it research on aspects of promotion rather than prevention. comprehensive and able to detect small effects. Also, the Finally, policies aimed at reducing social inequalities are use of MSAS disconnect irrelevant pathways from the also required to promote the well-being of the population. 13 2182 Quality of Life Research (2021) 30:2171–2185 Supplementary Information The online version contains supplemen- 2. New Economics Foundation. (2012). Measuring Well-being A tary material available at https://d​ oi.o​ rg/1​ 0.1​ 007/s​ 11136-0​ 21-0​ 2813-5. guide for practitioners. 3. WHO. (2018). Mental health: Strengthening our response. Author contributions JC, AM, JC, AS were responsible for data acqui- Retrieved November 14, 2018, from http://​www.​who.​int/​news-​ sition. GV, CGF, JA were responsible of study conceptualization and room/f​ act-s​ heets/d​ etail/m ​ ental-h​ ealth-s​ treng​ theni​ ng-o​ ur-r​ espon​ se design. NSD analysed the data. IA contributed analysis tools. PM, 4. Ryan, R. M., & Deci, E. L. (2001). On happiness and human BP, DM and NS, made substantial contributions in data interpretation. potentials: A review of research on hedonic and eudaimonic well- NSD, GV, CGF, JA, wrote the first draft. All authors provided critical being. Annual Review of Psychology, 52(1), 141–166. https://​doi.​ revisions. All authors read and approved the submitted manuscript. org/​10.​1146/​annur​ev.​psych.​52.1.​141 5. Huppert, F. A. (2009). Psychological well-being: Evidence regard- ing its causes and consequences. Applied Psychology: Health and Funding The study has been funded by grant from Generalitat de Cata- Well-Being, 1(2), 137–164. https://​doi.​org/​10.​1111/j.​1758-​0854.​ lunya (2017 SGR 452) and by ISCIII-FEDER (PI19/00109). NSD was 2009.​01008.x recipient of a predoctoral fellowship 2021 FI_B 00104 from the DIUE- 6. Chida, Y., & Steptoe, A. (2008). Positive psychological well-being AGAUR of the “Generalitat de Catalunya”. The authors are indebted and mortality: A quantitative review of prospective observational to the Department of Health of Generalitat de Catalunya for providing studies. Psychosomatic Medicine, 70, 741–756. https://d​ oi.o​ rg/1​ 0.​ raw data from the ESCA dataset for the analysis. 1097/​PSY.​0b013​e3181​8105ba 7. CDC. (2018). Well-being concepts. Retrieved November 15, 2018, Data availability The raw data of this study are available from Depart- from https://​www.​cdc.​gov/​hrqol/​wellb​eing.​htm ment of Health of Generalitat de Catalunya. Restrictions apply to the 8. Mehta, N., Croudace, T., & Davies, S. C. (2015). Public mental availability of these data, which were used under license for this study. health: evidenced-based priorities. The Lancet, 385, 1472–1475. 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M. (2009). Addressing the determinants of positive mental health: concepts, evidence and practice. International Ethical approval Data from the present cross-sectional study were Journal of Mental Health Promotion, 11(3), 4–7. obtained from the Catalan Health Interview Survey (ESCA) that has 13. Wootton, R. E., Lawn, R. B., Millard, L. A. C., Davies, N. M., the rank of official statistics carried out by the Government of the Taylor, A. E., Munafò, M. R., … Haworth, C. M. A. (2018). Catalonia region, and it must ensure the confidentiality of the data Evaluation of the causal effects between subjective wellbeing (Law 23/1998, December 30, of statistics of Catalonia). The content and cardiometabolic health: Mendelian randomisation study. BMJ of the ESCA and the whole interview process follows the European (Online). http://doi.org/https://​doi.​org/​10.​1136/​bmj.​k3788 Statistics Code of Practice adopted by the European Statistical System 14. Tough, H., Siegrist, J., & Fekete, C. (2017). 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(2013). “I’m afraid I have bad news for as you give appropriate credit to the original author(s) and the source, you.” Estimating the impact of different health impairments on provide a link to the Creative Commons licence, and indicate if changes subjective well-being. Social Science and Medicine, 87, 155–167. were made. The images or other third party material in this article are https://​doi.​org/​10.​1016/j.​socsc​imed.​2013.​03.​025 included in the article’s Creative Commons licence, unless indicated 17. Dolan, P., Peasgood, T., & White, M. (2008). Do we really know otherwise in a credit line to the material. If material is not included in what makes us happy? A review of the economic literature on the article’s Creative Commons licence and your intended use is not the factors associated with subjective well-being. 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V., Thomson, H., Green, M. J., Smith, D. J., & Lewsey, J. D. (2019). Evidence syn- Publisher’s Note Springer Nature remains neutral with regard to thesis for constructing directed acyclic graphs (ESC-DAGs): A jurisdictional claims in published maps and institutional affiliations. novel and systematic method for building directed acyclic graphs. International Journal of Epidemiology, dyz150, 1–8. https://​doi.​ org/​10.​1093/​ije/​dyz150 13 Quality of Life Research (2021) 30:2171–2185 2185 Authors and Affiliations Natalia Soldevila‑Domenech1,2,3 · Carlos G. Forero4 · Itxaso Alayo1,5 · Jordina Capella6,10 · Joan Colom6 · Davide Malmusi5,7 · Anna Mompart8 · Philippe Mortier1,5 · Beatriz Puértolas1,5 · Néstor Sánchez4 · Anna Schiaffino8,9 · Gemma Vilagut1,5 · Jordi Alonso1,3,5 1 6 Health Services Research Group, Epidemiology and Public Programme on substance Abuse, Agency of Public Health Health Program, Hospital del Mar Medical Research of Catalonia, Carrer Roc Boronat, 81‑95, 08005 Barcelona, Institute (IMIM), Carrer del Doctor Aiguader, 88, PRBB Spain Building, 08003 Barcelona, Spain 7 Ajuntament de Barcelona, Barcelona, Spain 2 Integrative Pharmacology and Systems Neurosciences 8 Direcció General de Planificació en Salut, Departament de Research Group, Neurosciences Research Program, Hospital Salut, Generalitat de Catalunya, Travessera de les Corts, del Mar Medical Research Institute (IMIM), Carrer del 131‑159, 08028 Barcelona, Spain Doctor Aiguader, 88, PRBB Building, 08003 Barcelona, 9 Spain Institut Català D’Oncologia, Gran Via de l’Hospitalet 3 199‑203, 08908 l’Hospitalet de Llobregat, Spain Department of Experimental and Health Sciences, Pompeu 10 Fabra University (UPF), Carrer del Doctor Aiguader, 88, Direcció General d’Ordenació i Regulació Sanitàries, PRBB Building, 08003 Barcelona, Spain Departament de Salut, Generalitat de Catalunya, Travessera 4 de les Corts, 131‑159, 08028 Barcelona, Spain School of Medicine, Universitat Internacional de Catalunya (UIC), Barcelona, Spain 5 CIBER en Epidemiología y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3‑5, 28029 Madrid, Spain 13