Socioeconomic inequalities in mortality and repeated measurement of explanatory risk factors in a 25 years follow-up

Using repeated measures to study the contribution of alcohol consumption and smoking to the social gradient in all-cause mortality: Results from the Stockholm Public Health Cohort

INTRODUCTION

The social gradient in consumption behaviours has been suggested to partly explain health inequalities. The majority of previous studies have only included baseline measurements and not considered potential changes in behaviours over time. The study aimed to investigate the contribution of alcohol consumption and smoking to the social gradient in mortality and to assess whether the use of repeated measurements results in larger attenuations of the main association compared to using single baseline assessments.

METHODS

Longitudinal survey data from the population-based Stockholm Public Health Cohort from 2006 to 2014 was linked to register data on mortality until 2018 for 13,688 individuals and analysed through Cox regression.

RESULTS

Low socioeconomic position (SEP) was associated with increased mortality compared with high SEP; hazard ratios 1.56 (95% CI 1.30-1.88) for occupational status and 1.77 (95% CI 1.49-2.11) for education, after adjustment for demographic characteristics. Using repeated measurements, alcohol consumption and smoking explained 44% of the association between occupational status and all-cause mortality. Comparing repeated and baseline measures, the percentage attenuation due to alcohol consumption increased from 11% to 18%, whereas it remained similar for smoking (25-23%).

DISCUSSION AND CONCLUSIONS

Smoking and alcohol consumption explained a large part of the association between SEP and mortality. Comparing results from time-fixed and time-varying models, there was an increase in overall percentage attenuation that was mainly due to the increased proportion explained by alcohol consumption. Repeated measurements provide a better estimation of the contribution of alcohol consumption, but not smoking, for the association between SEP and mortality.

Mortality inequalities measured by socioeconomic indicators in Brazil: a scoping review

OBJECTIVE

Summarize the literature on the relationship between composite socioeconomic indicators and mortality in different geographical areas of Brazil.

METHODS

This scoping review included articles published between January 1, 2000, and August 31, 2020, retrieved by means of a bibliographic search carried out in the Medline, Scopus, Web of Science, and Lilacs databases. Studies reporting on the association between composite socioeconomic indicators and all-cause, or specific cause of death in any age group in different geographical areas were selected. The review summarized the measures constructed, their associations with the outcomes, and potential study limitations.

RESULTS

Of the 77 full texts that met the inclusion criteria, the study reviewed 24. The area level of composite socioeconomic indicators analyzed comprised municipalities (n = 6), districts (n = 5), census tracts (n = 4), state (n = 2), country (n = 2), and other areas (n = 5). Six studies used composite socioeconomic indicators such as the Human Development Index, Gross Domestic Product, and the Gini Index; the remaining 18 papers created their own socioeconomic measures based on sociodemographic and health indicators. Socioeconomic status was inversely associated with higher rates of all-cause mortality, external cause mortality, suicide, homicide, fetal and infant mortality, respiratory and circulatory diseases, stroke, infectious and parasitic diseases, malnutrition, gastroenteritis, and oropharyngeal cancer. Higher mortality rates due to colorectal cancer, leukemia, a general group of neoplasms, traffic accident, and suicide, in turn, were observed in less deprived areas and/or those with more significant socioeconomic development. Underreporting of death and differences in mortality coverage in Brazilian areas were cited as the main limitation.

CONCLUSIONS

Studies analyzed mortality inequalities in different geographical areas by means of composite socioeconomic indicators, showing that the association directions vary according to the mortality outcome. But studies on all-cause mortality and at the census tract level remain scarce. The results may guide the development of new composite socioeconomic indicators for use in mortality inequality analysis.

Contribution of psychosocial factors to socioeconomic inequalities in mortality among older Australian men: a population-based cohort study

BACKGROUND

Among older people, the extent to which psychosocial factors explain socioeconomic inequalities in mortality is debated. We aimed to investigate the potential mediating effect of psychosocial factors on socioeconomic inequalities in mortality.

METHODS

We used data from a prospective population-based cohort (the Concord Health and Ageing in Men Project; baseline recruitment in 2005-2007), in Sydney, Australia. The main outcomes were all-cause and cause-specific mortality. Socioeconomic status (SES; educational attainment, occupational position, source of income, housing tenure, and a cumulative SES score) was assessed at baseline. Measures of structural and functional social support, as well as depressive and anxiety symptoms were assessed three times during follow-ups. Associations were quantified using Cox regression. Mediation was calculated using "change-in-estimate method".

RESULTS

1522 men (mean age at baseline: 77·4 ± 5·5 years) were included in the analyses with a mean (SD) follow-up time of 9·0 (3·6) years for all-cause and 8·0 (2·8) years for cause-specific mortality. At baseline, psychosocial measures displayed marked social patterning. Being unmarried, living alone, low social interactions, and elevated depressive symptoms were associated with higher risk of all-cause and cardiovascular disease (CVD) mortality. Psychosocial factors explained 35% of SES inequalities in all-cause mortality, 29% in CVD mortality, 12% in cancer mortality, and 39% in non-CVD, non-cancer mortality.

CONCLUSION

Psychosocial factors may account for up to one-third of SES inequalities in deaths from all and specific causes (except cancer mortality). Our findings suggest that interventional studies targeting social relationships and/or psychological distress in older men aiming to reduce socioeconomic inequalities in mortality are warranted.

Modifiable risk factors for 9-year mortality in older English and Brazilian adults: The ELSA and SIGa-Bagé ageing cohorts

To quantify and compare 9-year all-cause mortality risk attributable to modifiable risk factors among older English and Brazilian adults. We used data for participants aged 60 years and older from the English Longitudinal Study of Ageing (ELSA) and the Bagé Cohort Study of Ageing (SIGa-Bagé). The five modifiable risk factors assessed at baseline were smoking, hypertension, diabetes, obesity and physical inactivity. Deaths were identified through linkage to mortality registers. For each risk factor, estimated all-cause mortality hazard ratios (HR) and population attributable fractions (PAF) were adjusted by age, sex, all other risk factors and socioeconomic position (wealth) using Cox proportional hazards modelling. We also quantified the risk factor adjusted wealth gradients in mortality, by age and sex. Among the participants, 659 (ELSA) and 638 (SIGa-Bagé) died during the 9-year follow-up. Mortality rates were higher in SIGa-Bagé. HRs and PAFs showed more similarities than differences, with physical inactivity (PAF 16.5% ELSA; 16.7% SIGa-Bagé) and current smoking (PAF 4.9% for both cohorts) having the strongest association. A clear graded relationship existed between the number of risk factors and subsequent mortality. Wealth gradients in mortality were apparent in both cohorts after full adjustment, especially among men aged 60-74 in ELSA. A different pattern was found among older women, especially in SIGa-Bagé. These findings call attention for the challenge to health systems to prevent and modify the major risk factors related to non-communicable diseases, especially physical inactivity and smoking. Furthermore, wealth inequalities in mortality persist among older adults.

29 recommendations to combat social inequalities in health. The Norwegian Council on Social Inequalities in Health

All political parties in Norway agree that social inequalities in health comprise a public health problem and should be reduced. Against this background, the Council on Social Inequalities in Health has taken action to provide specific advice to reduce social health differences. Our recommendations focus on the entire social gradient rather than just poverty and the socially disadvantaged. By proposing action on the social determinants of health such as affordable child-care, education, living environments and income structures, we aim to facilitate a possible re-orientation of policy away from redistribution to universalism. The striking challenges of the causes of health differences are complex, and the 29 recommendations to combat social inequality of health demand cross sectorial actions. The recommendations are listed thematically and have not been prioritized. Some are fundamental and require pronounced changes across sectors, whereas others are minor and sector-specific.

Socio-economic trajectories and cardiovascular disease mortality in older people: the English Longitudinal Study of Ageing

Background

Socio-economic status from early life has been linked to cardiovascular disease risk, but the impact of life-course socio-economic trajectories, as well as the mechanisms underlying social inequalities in cardiovascular disease risk, is uncertain.

Objectives

We assessed the role of behavioural, psychosocial and physiological (including inflammatory) factors in the association between life-course socio-economic status and cardiovascular disease mortality in older adults.

Methods

Participants were 7846 individuals (44% women) from the English Longitudinal Study of Ageing, a representative study of individuals aged ≥ 50 years, established in 2002-03. Comprising four indicators of socio-economic status (father's social class, own education, occupational position and wealth), we computed an index of socio-economic trajectory and a lifetime cumulative socio-economic score. Behavioural (smoking, physical activity, alcohol consumption, body mass index) and psychosocial (social relations, loneliness) factors, physiological (blood pressure, total cholesterol, triglycerides) and inflammatory markers (C-reactive protein, fibrinogen), measured repeatedly over time, were potential explanatory variables. Cardiovascular disease mortality was ascertained by linkage of study members to a national mortality register. Mediation was calculated using the traditional 'change-in-estimate method' and alternative approaches such as counterfactual mediation modelling could not be applied in this context.

Results

During the 8.4-year follow-up, 1301 study members died (438 from cardiovascular disease). A stable low-social-class trajectory was associated with around double the risk of cardiovascular disease mortality (hazard ratio; 95% confidence interval: 1.94, 1.37; 2.75) compared with a stable high social class across the life course. Individuals in the lowest relative to the highest life-course cumulative socio-economic status group were also more than twice as likely to die of cardiovascular disease (2.57, 1.81; 3.65). Behavioural factors and inflammatory markers contributed most to explaining this gradient, whereas the role of psychosocial and other physiological risk factors was modest.

Conclusions

In a population-based cohort of older individuals living in England, we provide evidence that disadvantage across the life course is linked to cardiovascular mortality. That behavioural factors and inflammatory markers partially explain this gradient may provide insights into the potential for intervention.

What's the difference? A gender perspective on understanding educational inequalities in all-cause and cause-specific mortality

Background

Material and behavioural factors play an important role in explaining educational inequalities in mortality, but gender differences in these contributions have received little attention thus far. We examined the contribution of a range of possible mediators to relative educational inequalities in mortality for men and women separately.

Methods

Baseline data (1991) of men and women aged 25 to 74 years participating in the prospective Dutch GLOBE study were linked to almost 23 years of mortality follow-up from Dutch registry data (6099 men and 6935 women). Cox proportional hazard models were used to calculate hazard ratios with 95% confidence intervals, and to investigate the contribution of material (financial difficulties, housing tenure, health insurance), employment-related (type of employment, occupational class of the breadwinner), behavioural (alcohol consumption, smoking, leisure and sports physical activity, body mass index) and family-related factors (marital status, living arrangement, number of children) to educational inequalities in all-cause and cause-specific mortality, i.e. mortality from cancer, cardiovascular disease, other diseases and external causes.

Results

Educational gradients in mortality were found for both men and women. All factors together explained 62% of educational inequalities in mortality for lowest educated men, and 71% for lowest educated women. Yet, type of employment contributed substantially more to the explanation of educational inequalities in all-cause mortality for men (29%) than for women (− 7%), whereas the breadwinner’s occupational class contributed more for women (41%) than for men (7%). Material factors and employment-related factors contributed more to inequalities in mortality from cardiovascular disease for men than for women, but they explained more of the inequalities in cancer mortality for women than for men.

Conclusions

Gender differences in the contribution of employment-related factors to the explanation of educational inequalities in all-cause mortality were found, but not of material, behavioural or family-related factors. A full understanding of educational inequalities in mortality benefits from a gender perspective, particularly when considering employment-related factors.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5940-5) contains supplementary material, which is available to authorized users.