Background
Evidence is accumulating on social inequalities in health. [
1‐
3]. These social inequalities are characterized by higher risk of poor physical and mental health among people in more disadvantaged socioeconomic position (SEP). Social inequalities in mental health problems (MHP) have previously been documented in several studies [
4,
5]. MHP are the leading cause of disability worldwide [
6]. Their prevalence, long duration and high risk of recurrence [
7] place a considerable burden on health and social care systems and important productivity losses for employers [
6]. Understanding the pathways that link SEP to MHP is therefore of important public health significance [
3].
Work environment has been suggested to act as one such pathway [
8,
9]. Factors from the work environment, including adverse psychosocial work factors, were shown to contribute to the development of MHP, including psychological distress [
10‐
13]. Evidences also suggest that low SEP workers tend to concentrate in jobs where the prevalence of exposure to adverse psychosocial work factors is high [
14,
15].
Two theoretical models have been widely used to measure the effect of psychosocial work factors on health, the Demand-Control-Support (DCS) [
16] and the Effort-Reward Imbalance (ERI) models [
17]. The DCS model states that workers simultaneously exposed to high psychological demands and low job control, i.e.
job strain, are more at risk to develop health problems. A third component, low social support from colleagues and supervisor may act directly or amplify the effect of job strain [
18]. The ERI model proposes that workers are in a state of detrimental imbalance when high efforts are accompanied by low reward (respect, esteem, and promotion prospect), and thus more susceptible to health problems [
17,
19]. The proportions of working men and women exposed to these adverse factors have been found to be about 20–25% in previous prospective studies conducted in industrialized countries [
20]. These factors were also shown to be modifiable through workplace interventions [
21,
22].
Previous studies that have examined the contribution of psychosocial work factors to social inequalities in MHP showed inconsistent results [
15,
23‐
36]. Three previous studies have examined the contribution of ERI exposure at work [
23,
34,
36], which deleterious effect on mental health is well-documented and showed to be independent from other adverse psychosocial factors at work [
37‐
40]. Only one previous study has examined the relative and complementary contribution of DCS and ERI exposures in explaining social inequalities in mental health, using four dimensions covered by these theoretical models [
36].
The primary aim of this study was to evaluate the contribution of psychosocial work factors from the DCS and the ERI models in the SEP inequalities of psychological distress. We also examined the additional contribution of other psychosocial work-related factors and other works-related factors in these inequalities. We hypothesized that psychosocial work factors from the DCS and the ERI models are important contributors to social inequalities of psychological distress. The contribution was evaluated for three SEP indicators – education, occupation and household income, and for men and women separately.
Discussion
The aims of the present study were to examine the contributions of psychosocial work factors from the DCS and the ERI models and of other work-related factors to social inequalities in psychological distress. The strongest social inequalities were observed in men, using household income as the SEP indicator. Psychosocial work factors from the DCS and the ERI models partly explained these inequalities. This contribution was higher in magnitude for reward, JC and SS. After considering psychosocial work factors from the DCS and ERI models, other psychosocial work-related factors and other work-related factors did not further contribute.
In the present study, social inequalities in psychological distress observed were of higher magnitude using household income. This is consistent with the findings of a meta-analysis which identified income as the socioeconomic indicator having the strongest inverse dose–response association with depression [
5]. Income represents the flow of economic resources available to an individual [
52], and persons with lower income are likely to have fewer resources for material needs [
14]. Poor material living conditions may affect mental health through different mechanisms including poor social networks and a decreased access to health care services [
53].
The findings of the present study indicate that psychosocial work factors are important contributors to SEP inequalities in psychological distress among men. In our study, income inequalities in psychological distress were attenuated after adjustment for reward, JC and SS, which is consistent with findings from previous studies [
25,
27,
30,
34,
36]. (The results were similar with education inequalities, see Additional file
2).
The important contribution of reward found in the present study was in line with Niedhammer et al. who reported that reward contributed to explain 12.8% to 48.8% of social inequalities in depression among men [
36]. However, in the present study, this component of the ERI model had the highest relative contribution. In a recent study in older workers, the contribution of ERI exposure was found to be higher in magnitude than that of job control, which is in line with our results [
34]. While job control covers task-level characteristics, reward includes broader socioeconomic conditions, such as salaries, promotion prospects and job stability. It has been hypothesized that the adverse effects could be amplify when one feels that the ‘injustice’ is attributable to ‘out of control’ conditions [
54]. Our findings suggest that insufficient reward at work could be an important pathway by which working in low-paid jobs leads to mental health problems. Studies with prospective design are needed to further test this hypothesis.
The contributions of the DCS dimensions, considered separately, were comparable to those reported in previous studies. JC was found to make the greatest contribution in explaining social inequalities in well-being and depression [
30,
31,
36]. SS has also been shown to partly explain social inequalities in mental functioning [
28,
36]. It is also noteworthy that previous studies have also observed an opposite effect of PD [
15,
26,
28,
31,
36]. It suggests that high PD might not be particularly prevalent among workers with low SEP [
28,
30,
31]. Consistent with this hypothesis, we found that PD was higher among people in the highest household income category (36% in the >100 000$/year category, compared to 20% in the 0–39 999$//year category), which could likely explain the inverse contribution found for PD.
In the current study, social inequalities in psychological distress were of smaller magnitude in women than in men. This finding is consistent with those of previous studies measuring SEP based on household income, occupation and/or education [
55‐
57]. A potential explanation is that the relation between SEP and mental health for men and women differ depending on the SEP indicator used. While the SEP indicators used in the current study had little or no association with women’s mental health, other indicators such as the experience of current or childhood economic difficulties [
58] and relative financial deprivation [
59,
60] have been highlighted as important markers of mental health in women. Furthermore, other gender-related variables such as marital status and family responsibilities have previously been associated with SEP, working conditions and mental health. Indeed, among women, being a single parent has been associated with holding lower-grade hourly jobs [
61] along with moderate to severe mental health disability [
62]. Therefore, future research on social inequalities in women’s mental health may benefit from measuring a wider range of SEP indicators and stratifying or controlling for personal life factors (e.g., marital status, family responsibilities, and parity).
The addition of other work-related factors to the statistical models, including other psychosocial dimensions of the work environment, did not further explain social inequalities in psychological distress, over and above the contribution of DCS and ERI exposures. Previous studies have reported that work-related factors such as physical conditions [
15,
31,
36] and working hours [
29,
35] could contribute. However, in these previous studies, these factors were considered in separate models; therefore conclusions cannot be drawn on their independent effect. The present study suggests that adverse psychosocial work factors from the DCS and the ERI models might co-occur with those other deleterious exposures, partly explaining why they were shown to explain social inequalities in previous studies.
Strengths and limitations of the study
The strengths of this study include: 1- It was conducted on a large sample of men and women working in a wide range of occupations, 2- the use of validated models to measure psychosocial work factors and psychological distress, 3- the use of three complementary SEP indicators, and 4- the evaluation of the contribution of the work environment on men and women health inequalities using an exhaustive set of work-related factors.
This study has some limitations. First, the study relies on a cross-sectional design. SEP and psychosocial work factors may contribute to the development of mental health, but workers with MHPs could also have limited access to more satisfying jobs and social status. Furthermore, since the study is cross-sectional, there is a potential for reverse causality, either by real changes in the work environment or by changes in the evaluation of the same work environment for individuals with MHP [
63] However, it should be noted that available prospective studies strongly support the temporal precedence of adverse psychosocial work exposures, prior to the onset of MHP [
10‐
13]. Second, information on some MHP risk factors, such as personal and family history of MHP, was not available possibly leading to residual confounding. Third, the exposure and outcome were self-reported, possibly introducing a common method bias [
64] leading to inflated measures of effect. However, using measures of psychological distress rather than the use of a formal diagnosis or other measures of a diagnosed mental health problem is very pertinent for screening and prevention of mental health problems before the onset of more severe form of diseases. Fourth, the participation was suboptimal. We can therefore not exclude the possibility of selection bias. Estimates were weighted to maintain the representativeness of the Quebec workforce in terms of socioeconomic distribution, minimizing this possibility [
64]. Lastly, workers exposed to adverse working conditions as well as prevalent cases of MHP might have switched to less exposed jobs or left employment. This healthy worker survivor effect could have led to an underestimation of the contribution of psychosocial work factors and other work characteristics to mental health inequalities [
65].
Acknowledgements
We are grateful to the Quebec National Institute of Public Health for granted the access to the EQCOTESST data. We also thank all the participants of this study.