Socioeconomic inequalities in cardiovascular mortality and the role of childhood socioeconomic conditions and adulthood risk factors: a prospective cohort study with 17-years of follow up

In this study, all ‘adulthood’ measures (i.e. adulthood socioeconomic position as well as adulthood risk factors) pertain to people age 40 years or older (since the analyses were restricted to respondents in this age range at baseline, see below), and ‘childhood’ refers to the age of 12 years. Adulthood socioeconomic position (SEP) was determined by the respondents’ highest attained education level, with four categories: 1-low (primary education), 2- (lower professional and intermediate general education), 3- (intermediate professional and higher general education), 4-high (higher professional education and university) [22].

Participants were asked to retrospectively recall the occupational title of their father when they were twelve years of age, or, if their father was unemployed, the title of his last occupation (childhood socioeconomic conditions). These data were classified according to the Erikson, Goldthorpe, and Portocarero scheme, [23] and three categories were created: professionals (top-level management, advanced academic competencies, high level of independence), white-collar (middle management, routine non-manual work), and blue-collar occupations (skilled and unskilled manual work).

Smoking status was categorised as never, former and current smoker based on the response to the question “Do you smoke?” [12, 13].

Physical activity was based on three questions, asking for time spent per week (never, <1 hour, 1–2 hour, 2≥ hour; analysed as 0 hour, 0.5 hour, 1.5 hour, and 2.5 hour) on transport-related activity (walking, cycling), leisure time physical activity (gardening, walking, cycling), and sports activity [13]. Time spent on transport and leisure activity was summed as ‘moderate physical activity’. Participants were classified as inactive (no sports and 0–1 hours of moderate physical activity), little active (either no sports and 1–2 hours of moderate physical activity, or <1 hours of sports and 0–1 hours of moderate physical activity), moderately active (2.5-3.5 hours of moderate physical activity and sports combined), or active (at least 3,5 hours of sports or moderate physical activity combined) [8].

Alcohol consumption was calculated from two questions, one asking for the number of days per week drinking any alcoholic drinks, and the second asking for the number of alcohol drinks (units) consumed on such a day [12, 13]. Participants were categorised as abstainers (0 units/week), light drinkers (1–7 units for women, 1–10 units for men), moderate drinkers (8–14 units for women, 11–21 units for men), and heavy drinkers (>14 units for women, >21 units for men) [8, 24].

Body mass index (BMI) was calculated from self-reported weight in kilograms/self-reported height in meters², and respondents were classified as being underweight (BMI ≤20), average weight (BMI 20–25), overweight (BMI 25–30), or obese (BMI 30≥) [25].

Four items that are indicators of the financial situation of the household were measured and have been applied in several studies among the GLOBE cohort: [12, 13]type of health insurance (private, public), car ownership (yes, no), housing tenure (rented house, house owner), and financial problems with paying bills for food, rent, electricity etc. over the preceding year (no, some, big problems) [12, 13]. Adverse neighbourhood conditions were measured by four questions about noise from neighbours, noise from traffic, smells, and vandalism in the neighbourhood (no, 1≥ adverse conditions) [12, 13]. Adverse housing conditions were measured by three questions on cold, mould, and dampness in the house (no, 1≥ adverse conditions) [12, 13].

Data describing psychosocial factors included indicators of marital status (married, single, divorced, widowed) and negative life events. Respondents were asked if they experienced each of nine negative life events in the preceding year, such as a decline in financial position, severe disease of partner, and divorce (no event, 1 event, 2≥ events) [13, 26]. Furthermore, use of medicine for anxiety (yes, no) and whether respondents had experienced depression, severe nervousness or burn-out over the last five years (yes, no) were applied as psychosocial indicators [11].

Cause-specific mortality data were obtained from Statistics Netherlands. Causes of death were coded in accordance with the 9th and 10th version of the International Classification of Diseases (ICD), with codes 390–459 (ICD 9) or I00-I99 (ICD 10) for cardiovascular diseases. For each GLOBE respondent, the mortality follow-up extended from the baseline survey (April 1st, 1991) until October 15, 2007. If respondents died in the follow-up period, their death date was used to calculate survival time. If they did not die, survival time was calculated with October 15, 2007 as the final date. If respondents moved out of the Netherlands between baseline and October 15, 2007, survival time was calculated from the baseline until the date they emigrated.

Baseline respondents were excluded from the current analyses if they were 1) younger than 40 years of age at baseline (2 946 men, 2 902 women), 2) reported in the baseline questionnaire that they had experienced severe heart problems or a heart attack (599 men, 314 women) or a stroke (100 men, 48 women) in the preceding five years, or 3) had a missing value for adulthood SEP (199 men and 234 women) (these categories partly overlapped). The current analyses were based on the remaining 11 701 participants (5 395 men and 6 306 women).

Analyses were performed in SPSS [27], the significance level used was .05, and all analyses were adjusted for age. Analyses were undertaken for men and women separately, since the socioeconomic distribution of risk factors and their relative importance for explaining CVD inequalities may differ for men and women [28]. For each of the risk factors and for childhood socioeconomic conditions, respondents with a missing value remained in the analyses as a separate category (see Additional file 1 for prevalence rates of missing values).

Using Cox proportional hazard models, we assessed associations between adulthood SEP and CVD mortality. We assessed the distribution of childhood socioeconomic conditions and adulthood risk factors by adulthood SEP using Chi-square tests, and associations of childhood socioeconomic conditions with CVD mortality (adjusted for age), and adulthood risk factors with CVD mortality (adjusted for age, childhood socioeconomic conditions and adulthood SEP) by Cox proportional hazard models.

Factors that were significantly related to cardiovascular mortality and that varied by adulthood SEP were included in the following models: 1) adulthood SEP; 2) adulthood SEP + childhood socioeconomic conditions; 3) adulthood SEP + material factors; 4) adulthood SEP + behavioural factors; 5) adulthood SEP + psychosocial factors; 6) adulthood SEP + all adulthood risk factors; 7) adulthood SEP + childhood socioeconomic conditions + all adulthood risk factors. For each model, the percent change in relative hazards for SEP-groups compared to model 1 was evaluated. A 95% CI was calculated around the percentage attenuation using a bias-corrected accelerated bootstrap method with 1000 re-samplings in the statistical program R [29].

Adulthood SEP was significantly associated with childhood socioeconomic conditions and with all adulthood material, psychosocial and behavioural factors among both men and women (see Additional file 1). Associations were mostly in expected directions, i.e. poorer childhood socioeconomic conditions, less favourable material circumstances (e.g. rent a house, no car, financial problems, problems with physical housing conditions) and more unhealthy behaviours (e.g. being physically inactive, overweight or obese, smoking) among the low SEP groups. In contrast, among women, two adverse psychosocial factors (i.e. having experienced 2≥ negative life events, and depression/nervousness) were more prevalent among those with a high adulthood SEP, but among men, adverse psychosocial factors were more prevalent among those with a low adulthood SEP.

As presented in Table 1, those with poorer childhood socioeconomic conditions were more likely to die from CVD in adulthood, but this only reached statistical significance among men from the poorest childhood socioeconomic circumstances. About half of the investigated adulthood risk factors showed significant associations with CVD mortality among both men and women, namely renting a house, experiencing financial problems, smoking, physical activity and marital status. Alcohol consumption showed a U-shaped relationship with CVD mortality among women, with an increased risk for both abstainers and heavy drinkers. Also, BMI showed a U-shaped relationship with CVD mortality among women: those who were underweight or obese were at higher risk for CVD mortality. BMI was not significant among men, although –in contrast to women- underweight seemed to have a protective effect against CVD mortality. Further, among men, being single or divorced, and using sleep/anxiety drugs increased the risk of CVD mortality, whereas among women, those who were widowed or had a history with depression/nervousness were at an increased risk. No significant associations with CVD mortality were found for type of health insurance, problems with neighbourhood and housing conditions, and negative life events (among men and women), alcohol consumption, BMI and having experienced depression/nervousness (among men), and use of sleep/anxiety drugs (among women).

Explanatory models in Table 2 (men) and Table 3 (women) show to what extent associations between adulthood SEP and CVD mortality were explained by childhood socioeconomic conditions and adulthood risk factors. The highest risk for CVD mortality as observed among those with the lowest adulthood SEP (model 1) reduced insignificantly by 15% (CI: -40% to 5%) for men and 11% (CI: -74% to 31%) for women when childhood socioeconomic conditions were taken into account (model 2). Of the three groups of adulthood risk factors, material conditions made the largest contribution to the explanation of adulthood SEP inequalities in CVD mortality among men (42%; CI: -73% to −20%), and behavioural factors among women (55%; CI: -191% to −30%). When material, behavioural and psychosocial factors were all included (model 6), the HR for the lowest adulthood SEP reduced by 52% (CI: -94% to −33%) among men, and by 73% (CI: -230% to −34%) among women. This model was further adjusted for childhood socioeconomic conditions (model 7), which led to a total reduction of the HR among those with the lowest adulthood SEP of 60% (CI: -109% to −32%) among men and 76% (CI: -232% to −29%) among women.

In sum, the total explained effect of adulthood SEP on CVD mortality (60% for men and 76% for women) was largely accounted for by risk factors in adulthood. Adulthood risk factors explained the greater part of the inequalities in CVD mortality, independent of childhood socioeconomic conditions (45% for men and 65% for women). Childhood socioeconomic conditions made a modest contribution to the explanation of inequalities in CVD mortality, i.e. mainly via their association with adulthood risk factors (7% for men and 8% for women). For men 8%, and for women 3%, of the total explained effect of adulthood SEP on CVD mortality was due to a direct effect of childhood socioeconomic circumstances on CVD mortality (i.e. independent of adulthood risk factors). Childhood socioeconomic conditions were most important for explaining the increased risk of dying from CVD among men with the second lowest adulthood SEP (12% directly, and 7% via adulthood risk factors).

To our knowledge, this is the first study to simultaneously investigate the contribution of adulthood health behaviours, material conditions, and psychosocial factors, and childhood socioeconomic conditions, to adulthood SEP inequalities in CVD mortality. The measurement of a large number of risk factors is a strength, but may also be a limitation, since, due to space limitations in the postal survey, every risk factor could only be measured with a limited number of items. Furthermore, the independent variables examined were only measured once, and were based on self-reports, which may have resulted in biased responses. Psychosocial conditions could have had more effect on the explanation of socioeconomic inequalities in CVD mortality when measured more specifically (e.g. by locus of control, number of good friends, social participation, social networks), although the psychosocial factors we included (i.e. indicators for marital status, anxiety and depression) have been used previously [11]. Father’s occupation at the respondent’s age of twelve was measured retrospectively, which may have caused underestimated associations compared to prospective measures of childhood SEP. However, retrospective reports of childhood circumstances have shown to be relatively reliable [32]. In the baseline questionnaire, participants were asked whether they experienced severe heart problems, a heart attack or stroke over the last five years. We excluded these participants from the analysis as they may have changed to healthier behaviours post-baseline while still having a higher chance of dying from CVD. However, we had no information about earlier histories of CVD, hence the analyses may still have included participants who experienced heart problems or a stroke and later changed to a healthier lifestyle, which might have underestimated the contribution of behavioural factors.