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Research report
Contribution of material, occupational, and psychosocial factors in the explanation of social inequalities in health in 28 countries in Europe
  1. B Aldabe1,2,3,4,
  2. R Anderson5,
  3. M Lyly-Yrjänäinen5,
  4. A Parent-Thirion5,
  5. G Vermeylen5,
  6. C C Kelleher4,
  7. I Niedhammer1,2,3,4,5
  1. 1INSERM, U1018, CESP Centre for Research in Epidemiology and Population Health, Epidemiology of Occupational and Social Determinants of Health Team, Villejuif, France
  2. 2Université Paris Sud 11, UMRS 1018, Villejuif, France
  3. 3Université de Versailles St-Quentin, UMRS 1018, Villejuif, France
  4. 4UCD School of Public Health & Population Science, University College Dublin, Dublin, Ireland
  5. 5European Foundation for the Improvement of Living and Working Conditions, Dublin, Ireland
  1. Correspondence to Dr Isabelle Niedhammer, UCD School of Public Health & Population Science, University College Dublin, Woodview House, Belfield, Dublin 4, Ireland; isabelle.niedhammer{at}inserm.fr

Abstract

Objectives To analyse the associations between socio-economic status (SES), measured using occupation, and self-reported health, and to examine the contribution of various material, occupational and psychosocial factors to social inequalities in health in Europe.

Methods This study was based on data from the European Quality of Life Survey (EQLS) carried out in 2003. The total sample consisted of 6038 and 6383 working men and women in 28 countries in Europe (response rates: 30.3–91.2%). Each set of potential material, occupational and psychosocial mediators included between eight and 11 variables. Statistical analysis was performed using multilevel logistic regression analysis.

Results Significant social differences were observed for self-reported health, manual workers being more likely to be in poor health (OR=1.89, 95% CI 1.46 to 2.46 for men, OR=2.18, 95% CI 1.71 to 2.77 for women). Strong social gradients were found for almost all potential mediating factors, and almost all displayed significant associations with self-reported health. Social differences in health were substantially reduced after adjustment for material, occupational and psychosocial factors, with material factors playing a major role. The four strongest contributions to reducing these differences were found for material deprivation, social exclusion, financial problems and job reward. Taking all mediators into account led to an explanation of the social differences in health by 78–100% for men and women.

Conclusion The association between SES and poor health may be attributed to differential distributions of several dimensions of material, occupational and psychosocial conditions across occupational groups. Interventions targeting different dimensions might result in a reduction in social inequalities in health.

  • Social inequalities in health
  • self-reported health
  • occupation
  • Europe
  • self-rated health
  • occupational
  • social inequalities

https://doi.org/10.1136/jech.2009.102517

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    Introduction

    Social inequalities in health have been described across a range of European countries for many health indicators,1–13 including self-reported health (SRH), which is considered as a powerful predictor of subsequent morbidity and mortality.14 The association between socio-economic status (SES) and health is one of the most well-known findings in the epidemiological literature: the higher the social class, the lower the prevalence and/or incidence of health problems, illness, disease and death.

    While many studies described social inequalities in health in Europe, studies trying to explain these inequalities are still lacking. Identifying the factors that may contribute to these inequalities could be useful to reduce the prevalence of exposure to these factors, especially among the lowest social groups, and thus to reduce inequalities. Determinants of social inequalities in health are complex, and there are likely to be a number of important mediators, such as material, occupational, psychosocial and behavioural factors.

    Various theories have been developed to explain the pathways and mechanisms underlying these inequalities.15–17 These theories include the materialist explanation, that put the emphasis on material conditions (ie, access to goods and services, and exposures to material risk factors in the living and working environment), the psychosocial explanation, which focuses on psychosocial and stress related influences with a plethora of risk factors such as those related to social support or sense of control, and the behavioural explanation, which emphasises the importance of behavioural risk factors in explaining social inequalities in health. The relative contribution of these factors has been very rarely explored to date at national level.18–20 Furthermore, very few studies explored occupational factors, which include specific material and psychosocial factors of the work environment, among other mediators. As explanatory or mediating factors probably are inter-related, some authors have suggested a simplified causal model to disentangle the direct (independent) effect of mediating factors, and their indirect effect through other factors.18 19 According to this explanatory model, material factors might affect health inequalities directly or indirectly through psychosocial and behavioural factors, and psychosocial factors might work directly or indirectly through behavioural factors.

    To our knowledge, no study has attempted to explain social inequalities in health using harmonised data for Europe as a whole. Most previous studies were based on national or selected samples, and explored only a limited number of mediators that may contribute to these inequalities.19–40 In addition, some studies describing social inequalities in health in Europe were based on surveys pooling data from several surveys/countries, making comparisons between European countries and generalisations for the whole of Europe difficult.2 7 11 Mackenbach, in fact, underlined a serious lack of internationally comparable data on the topic of social inequalities in health.16

    Our study aimed at providing an original overview of social inequalities in health in Europe, and at determining mediating factors for these inequalities. Thus, the objectives of the present study were to analyse the associations between SES, measured using occupation, and health, measured using SRH, and to examine the contribution of various material, occupational and psychosocial factors to social inequalities in health in the working population of Europe.

    Methods

    This study was based on the data of the European Quality of Life Survey (EQLS), carried out by the European Foundation for the Improvement of Living and Working Conditions in 2003. Data were collected through face-to-face interviews in the respondent's home. The basic sampling design used in all countries was a multistage, random one.41 Overall, more than 26 000 interviews took place, covering 28 countries in Europe (see online appendix 1). The total response rate was 58.4% (range: 30.3–91.2% across countries; see table 1). For the purpose of this study, people who were not working at the time of the survey were excluded in order to concentrate on the role of occupational factors. Our final study sample included 12 421 workers (6038 men and 6383 women).

    View this table:
    Table 1

    Differences in response rate, self-reported health, occupation, and association between occupation and self-reported health between countries

    SRH was the health indicator studied and was assessed using the following question: ‘In general, would you say your health is …,’ and response categories were ‘excellent,’ ‘very good,’ ‘good,’ ‘fair’ and ‘poor.’ The variable was dichotomised as ‘good’ health versus ‘poor’ health (‘fair’ and ‘poor’).

    Occupation was used as an indicator of SES. Occupational groups were constructed according to the International Standard Classification of Occupations (ISCO-2008). Educational level was also used as an additional SES marker.

    Three sets of potential mediating conditions were explored: material, occupational and psychosocial factors. As we had a specific interest in occupational factors, they were distinguished from the other factors.

    Material factors included: household tenure, housing conditions (shortage of space, rot in windows/doors/floors, damp/leaks and lack of indoor flushing toilet, four items), crowding (number of persons per room), material deprivation (not able to afford at least one of these amenities or activities: car, home computer, washing machine, heating, holiday, furniture, meat/fish, clothes and drinks/meals with friends/family, nine items), financial problems (payment of bills, food and/or rent, four items), neighbourhood conditions (noise, air pollution, lack of recreational/green areas, water quality and area safety at night, five items), quality of public services (health services, education system, public transport, social services and state pension system, five items) and problems with access to medical services (distance to medical centre, delay in getting appointment, waiting time to see the doctor and cost of seeing the doctor, four items).

    Occupational factors included: sector of economy (coded using NACE classification), permanency of work contract, number of hours normally worked per week (including any paid or unpaid overtime), daily commuting time, dangerous/unhealthy working conditions, job insecurity (fear of job loss in the next 6 months), psychological demands (demanding/stressful work and tight deadlines, two items), decision latitude (extent of influence at work and interesting job, two items), reward (salary and job prospects, two items), responsibility for supervising the work of others and additional paid job.

    Psychosocial factors included: marital status, number of children, unpaid work (housework, care for children and/or elderly/disabled relatives, three items), work–life imbalance (difficulties to fulfil work and family responsibilities, three items), social support (help from family/friends/others in case of illness, advice, financial problems or feeling low, four items), social network (frequency of contacts with family/friends/neighbours, four items), social participation (voluntary work, political activities and religious services, five items), trust level (towards people in general, state pension and social benefit systems, three items), and social exclusion (feelings of inferiority or uselessness and lack of recognition or acceptance, five items).

    The associations between SES and SRH, between SES and mediating factors and between mediating factors and SRH were studied using the χ2 test. The associations between SES and SRH were also tested for each country separately, using logistic regression analysis after adjustment for sex and age, SRH being the dependent variable. Managers/professionals were used as the reference group. The associations between SES and SRH in the whole sample were then explored after adjustment for age (model 1) using multilevel logistic regression analysis with the 12 421 participants clustered within 28 countries. The potential mediating factors, which displayed social gradients and were associated with SRH, were first introduced separately to model 1. The contribution of each factor (or a set of factors) to the explanation of the social differences in SRH was estimated by the change in OR after inclusion of the variable(s) in the model, according to the formula: (ORmodel 1−ORextended model)/(ORmodel 1−1).42 Positive percentage values indicated OR reductions, and negative percentage values indicated OR increases. Only factors reducing social inequalities by more than 5% were retained in subsequent models.32 These models were adjusted for combinations of two groups of factors and finally adjusted for all factors simultaneously, allowing us to calculate independent and indirect contributions of mediators. Indirect contributions can be interpretated as the contribution of a group of factors (eg, material) through another group (eg, psychosocial). Such a method has already been presented and used by others.18 19 Analyses were carried out using SAS (SAS Institute, Cary, North Carolina) and performed for men and women separately.

    Results

    The description of the sample is presented in table 2. Many variables were significantly associated with gender, and almost all variables were also associated with SRH. Only household tenure, work contract and additional paid job were not associated with SRH. Significant differences were found for SRH and occupation between countries (table 1). The highest prevalences of poor health were observed in eartern countries, and the proportion of manual workers was the highest in these countries and the lowest in the Scandinavian countries.

    View this table:
    Table 2

    Prevalence of self-reported health according to age, occupation, and mediating factors

    The association between occupation and SRH was significant for both genders, with manual workers being at higher risk of poor health (table 2). In each country, after adjustment for sex and age (table 1), the OR of poor health for manual workers was higher than one in most cases although not significant for all countries.

    All potential mediating factors were associated with occupation, except commuting time for men and marital status among women (table 3). Strong social gradients were found for many of them, with manual workers being more likely to be exposed to housing problems, overcrowding, material deprivation, financial problems, low quality of public services, low access of medical services, temporary contract, dangerous working conditions, job insecurity, low decision latitude, low reward, low social support and network, low social participation, low level of trust and social exclusion. The factors that displayed inverse social gradients (managers/professionals were more likely to be exposed to long working hours, high psychological demands, responsability for others and additional paid job) or no clear social gradient were excluded from the analysis. The final selection of potential mediators is presented in table 4.

    View this table:
    Table 3

    Mediating factors according to occupational groups

    View this table:
    Table 4

    Contribution of mediating factors to social inequalities in health: results from multilevel logistic regression analysis

    Table 4 presents the changes in ORs for manual workers after inclusion of potential mediating factors in model 1. Among material factors, deprivation, financial problems and housing conditions contributed to the decrease in social differences for both genders. Quality of public services for men and access to medical services for women were found to be specific contributors for each sex. Taking these factors into account led to a reduction in the associations between occupation and SRH by 76% and 59% for men and women respectively. Among occupational factors, the greatest contributions to a reduction in social differences were found for reward and dangerous working conditions, and, to a lesser extent, for economic activity and decision latitude for both genders, as well as job insecurity for women only. Taking these factors into account led to a reduction in the associations between occupation and SRH by 52% and 35% for men and women respectively. Among psychosocial factors, social exclusion and social support helped to explain social differences in SRH for men and women. Trust level helped to explain social differences for men only. Taking these factors into account led to a reduction in the associations between occupation and SRH by 46% and 44% for men and women respectively.

    The ORs were further reduced when adjusted for combinations of two groups of factors (table 4), the greatest reductions being observed for the combination of material and occupational factors (91% and 73% for men and women). The independent contribution of material factors in relation to occupational factors was 39% (=91–52) and 38% (=73–35) for men and women, and was higher than the independent contribution of occupational factors in relation to material factors: 15% (=91–76) and 14% (=73–59) for men and women. The final contribution of psychosocial factors in relation to both material and occupational factors was lower, as their independent effect was 9% (=100–91) and 5% (=78–73) for men and women.

    Finally, the inclusion of the material, occupational and psychosocial factors together in model 1 reduced further the ORs by 100% in men and by 78% in women, meaning that all groups of mediators independently contributed to the explanation of social inequalities in SRH. Final ORs were consequently no longer significant.

    Additional analyses were performed using educational level as a SES marker. The results were essentially the same, initial ORs of 2.10 (95% CI 1.65 to 2.66) and 2.01 (95% CI 1.61 to 2.52) for poor health were observed for the less educated men and women (model 1), and the main mediating variables were the same. Only access to medical services was found as an additional mediator for men. Material and psychosocial factors contributed to social inequalities to the same magnitude: 75% and 60% for material factors, and 45% and 54% for psychosocial factors, for men and women, respectively. Two occupational factors were not found as mediators: economic sector for both genders and decision latitude for men. The contribution of occupational factors was found to be lower using education as an SES marker: 36% and 22% for men and women.

    Discussion

    Social inequalities in SRH were observed, with ORs of around two for manual workers. Material, occupational and psychosocial factors contributed to an explanation of these inequalities, and the four greatest contributions were found for material deprivation, social exclusion, financial problems and reward at work. Material as well as occupational factors played a major role in explaining social inequalities in health. All mediators taken into account together explained social differences in health by 78–100% for men and women, the final ORs being no longer significant for manual workers for both genders.

    Comparison with literature

    Poor health was more likely to be reported among manual workers than among workers in higher occupational groups. This inverse relationship between SES and SRH is well documented in the literature, which consistently shows a higher level of poor health for people with a lower educational level, occupational category or income.10 24 39 The result from the EQLS data covering 28 countries in Europe is in keeping with a previous study investigating educational inequalities in health in 22 European countries and showing ORs of 1.65 and 1.81 for the lowest educated men and women respectively.13

    Significant differences in SRH were observed between countries. Our study showed that the prevalence of poor health was higher in the eastern countries compared with others for both genders, as other studies had already demonstrated for both morbidity and mortality.5 43 44 A differential reporting of SRH across countries may also be suspected due to cultural differences for example. Occupational differences in health were found in almost all countries.

    Differences between genders were observed for SES, SRH and mediating factors, and this reinforces the relevance of studying each gender separately. In terms of social inequalities in health, no difference was found between genders, the magnitude of these inequalities being similar for men and women. Some previous studies have reported however that social differences in health were more pronounced in men than in women.24 37 Our study also showed that gender-specific factors may play a role in explaining social inequalities in health, such as trust level and quality of public services among men, and access to medical services among women.

    In this study, strong social gradients were found for almost all mediating factors. This is in agreement with previous studies showing that low SES groups are more likely to be exposed, for example, to high material deprivation, low decision latitude or high job insecurity than higher SES groups.22–24 Some exceptions may be worth noting such as the occupational factors of psychological demands, working hours and responsibility for others. Other studies have underlined the inverse social gradient for psychological demands.23 32 33 35

    Previous studies have explored material,19 20 22 24 34 36 psychosocial19 20 29 and/or occupational factors23 24 26 27 31–35 37 40 as mediators for social inequalities in health, but the studies exploring several dimensions at the same time are relatively rare.19 20 23 24 34 36 37 39

    In our study, material factors were the set of mediators that contributed the most to reduce social inequalities in health, confirming previous results at national levels.18 19 Material deprivation played the biggest role in the explanation of these inequalities for both genders, and this result was consistent with existing literature.34 Financial problems also accounted for a substantial part of the observed differences, as previously reported by other authors studying educational differences in mortality in The Netherlands.19 36 Housing conditions, quality of public services (men only) and access to medical services (women only) were also found to contribute to occupational differences in health in our study.

    Occupational factors contributed to the social differences in health for men and women substantially. Important mediators were reward, dangerous working conditions and economic activity—which may be an indirect marker of working conditions or occupational exposures—and also decision latitude and job insecurity (women only). Previous studies have demonstrated the contribution of occupational factors, especially decision latitude and various markers of ergonomic/physical/chemical exposures, in the association between social class and health.24 31–33 35 38 Studies reported that 50–74% of occupational differences in health for men and 38–51% for women were explained by these factors.32 35 Job insecurity was reported as a mediator of social inequalities in self-reported health in a previous study in Spain.24 In the present study, reward was also found as a mediator for social inequalities in health, something never reported before.

    Psychosocial factors also contributed to reduce social inequalities in health, although to a lesser magnitude than the two other sets of mediators. Social exclusion was the factor which contributed the most to the reduction in inequalities, a finding never reported before. Our results were in agreement with studies showing the contribution of social support to social inequalities in health.37 Our findings suggest that trust level may be an additional mediator in social inequalities in health.

    The retained mediating factors in our final models were able to explain social differences in SRH by 100% in men and by 78% for women, the final ORs being finally non-significant. A previous study21 exploring simultaneously physiological, biological and behavioural factors observed a reduction in the educational differences in SRH by 40% in a population of Danish employees. The contribution of material, psychosocial and behavioural factors was also explored in a recent study from South Korea, tending to explain 56.5–77.5% of occupational inequalities in all-cause mortality, for men and women together.20 Material, psychosocial, behavioural and biomedical factors contributed to reduce social inequalities in mortality by 73–83% in a Norwegian sample.18 Social inequalities in mortality were explained by 100% by material, psychosocial and behavioural factors in The Netherlands.19

    Limitations and strengths

    The response rate (58.4%) in the 2003 EQLS may be considered as low,41 and differed across countries. Consequently, it may be difficult to evaluate the impact on the results. However, previous studies reported that non-participants may be more likely to belong to low social groups and to have poor health behaviours and health outcomes,45 and such a bias may lead to an underestimation of the prevalence of health outcomes as well as the association between SES and health outcomes. This cross-sectional study was necessarily restricted to working men and women. A selection bias may have occurred, that is workers in poor health may have changed or left their job and/or healthier workers may be more likely to work in more difficult jobs. Thus, the associations between work factors and health outcome may be underestimated as well as the contribution of these factors to social inequalities in health. The relative importance of material and psychosocial mediators might be different if the population studied was the whole population, and not only the working population. This study did not take into account lifetime exposure to the different mediating factors. This may lead to an underestimated contribution of the mediating factors to social inequalities in health.31 Some mediating factors may have been neglected in this study; this may be especially the case for behavioural factors (eg, smoking, alcohol consumption, physical activity) which may play a role in the explanation of social inequalities in health.23 30 However, behavioural factors may also be consequences of material, occupational and psychosocial mediators, and consequently their independent effects may be low, as underlined by others previously.19 All variables studied were based on self-reports. The use of SRH might introduce a reporting bias in our study, but a previous paper46 showed that measurement errors were limited, and SRH may have many advantages as a summary health measure. Furthermore, authors have reported that the use of SRH may lead to underestimate social inequalities in health, and not the reverse.47

    The use of large-scale and comparable data across Europe allowed us to study each gender separately and to avoid problems of data comparability between countries.2 7 An additional advantage of this study was to be based on face-to-face interviews. Our study was also able to determine the specific mediators of social inequalities for each gender. Our study aimed at describing social inequalities in health and also at determining mediating factors of these inequalities. For this purpose, three different sets of mediating factors were studied, each containing various variables. The relative contributions of material and psychosocial mediators were confirmed in additional analyses using education as a SES marker. However, these analyses were less powerful to identify occupational mediators, maybe because education is a more general SES marker than occupation.

    Conclusion

    Overall, this study sheds some light on the factors that may mediate social inequalities in health among working men and women in Europe. It emphasises the disadvantages experienced by manual workers and some categories of service workers. This study also underlines the need to address material deprivation, financial problems, social exclusion—in other words all dimensions of poverty and deprivation—as well as poor working conditions, to reduce social inequalities in health. More studies are needed to evaluate prospectively the role of a large variety of mediators in these inequalities, to better understand the underlying mechanisms linking these mediators to social inequalities in health, especially as regards specific indicators of morbidity and mortality. Finally, as the mediators and their relative contributions may differ between European countries, forthcoming studies should also focus on contextual factors that may contribute to a better understanding of social inequalities in health in Europe.

    What is already known on this subject

    • There are social inequalities in health in various European countries.

    • Explanations for social inequalities in health have been reported, but studies exploring a large range of mediating factors are still lacking.

    • Explanatory studies using European harmonised data are also missing.

    What this study adds

    • Social inequalities in self-reported health have been found for both men and women across Europe.

    • Selections of mediators covering material, occupational and psychosocial aspects were able to explain these inequalities by 100% and 78%, respectively, for men and women.

    • The strongest mediators were those related to material deprivation, financial problems, social exclusion and various working conditions underlying the importance of material and occupational factors.

    • Some specific mediators were found according to gender.

    • Preventive actions focussing on these mediators may help to reduce social inequalities in health.

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    Footnotes

    • Competing interests None.

    • Ethics approval Ethics approval was provided by the Data from the European Foundation for the improvement of living and working conditions (EU Agency).

    • Provenance and peer review Not commissioned; externally peer reviewed.