J. P. Mackenbach

Explaining educational differences in mortality: the role of behavioral and material factors

OBJECTIVES This study examined the role of behavioral and material factors in explaining educational differences in all-cause mortality, taking into account the overlap between both types of factors. METHODS Prospective data were used on 15,451 participants in a Dutch longitudinal study. Relative hazards of all-cause mortality by educational level were calculated before and after adjustment for behavioral factors (alcohol intake, smoking, body mass index, physical activity, dietary habits) and material factors (financial problems, neighborhood conditions, housing conditions, crowding, employment status, a proxy of income). RESULTS Mortality was higher in lower educational groups. Four behavioral factors (alcohol, smoking, body mass index, physical activity) and 3 material factors (financial problems, employment status, income proxy) explained part of the educational differences in mortality. With the overlap between both types of factors accounted for, material factors were more important than behavioral factors in explaining mortality differences by educational level. CONCLUSIONS The association between educational level and mortality can be largely explained by material factors. Thus, improving the material situation of people might substantially reduce educational differences in mortality.

Neighbourhood deprivation and overweight: the GLOBE study

BACKGROUND: Increasing morbidity and mortality rates of coronary heart disease (CHD) by increasing levels of neighbourhood deprivation may be explained by a positive association between neighbourhood deprivation and risk factors of CHD. In this study we investigated the association between neighbourhood deprivation and overweight. Further, we examined whether this association was modified by educational level, age and sex of the neighbourhood residents.METHODS: Data were used from 8897 subjects who participated in the baseline measurement of the Dutch GLOBE Study in 1991 and lived in 86 neighbourhoods of the fifth largest city in The Netherlands (Eindhoven). Neighbourhood deprivation was based on aggregated self-reported information about educational and occupational level and employment status. Self-reported body height and body weight were used to define overweight body mass index ((BMI) ≥25).RESULTS: Adjusted for educational level, age and sex of neighbourhood residents, odds ratios of overweight increased significantly by increasing neighbourhood deprivation (odds ratio=1.20, 95% CI 1.02, 1.41 in the highest vs lowest quartile of neighbourhood deprivation). Stratified analyses showed a stronger association between neighbourhood deprivation and overweight in females compared to males, and in older (≥49 y of age) compared to younger participants. The pattern of increasing odds ratios of overweight by neighbourhood deprivation was generally similar in all educational groups, the highest educational group being the exception with no association between neighbourhood deprivation and overweight.CONCLUSIONS: Increased prevalence rates of overweight in more compared to less deprived neighbourhoods are likely to contribute to the positive association between neighbourhood deprivation and CHD. Our results justify the development of a community-based strategy of primary prevention of overweight in deprived neighbourhoods, which may have a larger impact in females and older residents in particular. Prior to implementation, however, longitudinal research needs to further examine responsible mechanisms for the development of neighbourhood inequalities in overweight.

Health Disadvantage in US Adults Aged 50 to 74 Years: A Comparison of the Health of Rich and Poor Americans With That of Europeans

OBJECTIVES We compared the health of older US, English, and other European adults, stratified by wealth. METHODS Representative samples of adults aged 50 to 74 years were interviewed in 2004 in 10 European countries (n = 17,481), England (n = 6527), and the United States (n = 9940). We calculated prevalence rates of 6 chronic diseases and functional limitations. RESULTS American adults reported worse health than did English or European adults. Eighteen percent of Americans reported heart disease, compared with 12% of English and 11% of Europeans. At all wealth levels, Americans were less healthy than were Europeans, but differences were more marked among the poor. Health disparities by wealth were significantly smaller in Europe than in the United States and England. Odds ratios of heart disease in a comparison of the top and bottom wealth tertiles were 1.94 (95% confidence interval [CI] = 1.69, 2.24) in the United States, 2.13 (95% CI = 1.73, 2.62) in England, and 1.38 (95% CI = 1.23, 1.56) in Europe. Smoking, obesity, physical activity levels, and alcohol consumption explained a fraction of health variations. CONCLUSIONS American adults are less healthy than Europeans at all wealth levels. The poorest Americans experience the greatest disadvantage relative to Europeans.

Occupational class and ischemic heart disease mortality in the United States and 11 European countries.

OBJECTIVES Twelve countries were compared with respect to occupational class differences in ischemic heart disease mortality in order to identify factors that are associated with smaller or larger mortality differences. METHODS Data on mortality by occupational class among men aged 30 to 64 years were obtained from national longitudinal or cross-sectional studies for the 1980s. A common occupational class scheme was applied to most countries. Potential effects of the main data problems were evaluated quantitatively. RESULTS A north-south contrast existed within Europe. In England and Wales, Ireland, and Nordic countries, manual classes had higher mortality rates than nonmanual classes. In France, Switzerland, and Mediterranean countries, manual classes had mortality rates as low as, or lower than, those among nonmanual classes. Compared with Northern Europe, mortality differences in the United States were smaller (among men aged 30-44 years) or about as large (among men aged 45-64 years). CONCLUSIONS The results underline the highly variable nature of socioeconomic inequalities in ischemic heart disease mortality. These inequalities appear to be highly sensitive to social gradients in behavioral risk factors. These risk factor gradients are determined by cultural as well as socioeconomic developments.

Socioeconomic Status and Stroke Incidence in the US Elderly The Role of Risk Factors in the EPESE Study

Background and Purpose— This study assesses the effect of socioeconomic status on stroke incidence in the elderly, and the contribution of risk factors to stroke disparities. Methods— Data comprised a sample of 2812 men and women aged 65 years and over from the New Haven cohort of the Established Populations for the Epidemiologic Studies of the Elderly. Individuals provided baseline information on demographics, functioning, cardiovascular and psychosocial risk factors in 1982 and were followed for 12 years. Proportional hazard models were used to model survival from initial interview to first fatal or nonfatal stroke. Results— Two hundred and seventy subjects developed incident stroke. At ages 65 to 74, lower socioeconomic status was associated with higher stroke incidence for both education (HRlowest/highest=2.07, 95% CI, 1.04 to 4.13) and income (HRlowest/highest=2.08, 95% CI, 1.01 to 4.27). Adjustment for race, diabetes, depression, social networks and functioning attenuated hazard ratios to a nonsignificant level, whereas other risk factors did not change associations significantly. Beyond age 75, however, stroke rates were higher among those with the highest education (HRlowest/highest=0.42, 95% CI, 0.22 to 0.79) and income (HRlowest/highest=0.43, 95% CI, 0.22 to 0.86), which remained largely unchanged after adjustment for risk factors. Conclusions— We observed substantial socioeconomic disparities in stroke at ages 65 to 74, whereas a crossover of the association occurred beyond age 75. Policies to improve social and economic resources at early old age, and interventions to improve diabetes management, depression, social networks and functioning in the disadvantaged elderly can contribute to reduce stroke disparities.

Educational differences in leisure-time physical inactivity: a descriptive and explanatory study

In this study we aim to explain educational differences in leisure-time physical inactivity in terms of psychosocial and material factors. Cross-sectional data were obtained from the baseline of the Dutch GLOBE study in 1991, including 2598 men and women, aged 15-74 years. Physical inactivity during leisure time was defined as not participating in any activity, such as sports, gardening, walking or cycling. Psychosocial factors included in the study were coping resources, personality, and stressors. Material factors were financial situation, employment status, and living conditions. Logistic regression models were used to calculate educational differences in physical inactivity. Physical inactivity was more prevalent in lower educational groups. Psychosocial factors related to physical inactivity were locus of control, parochialism, neuroticism, emotional social support, active problem focussing, optimistic and palliative coping styles. Material factors associated with physical inactivity were income, employment status and financial problems. All correlates of physical inactivity were unequally distributed over educational groups, except optimistic and palliative coping. Personality and coping style were the main contributors to the observed educational differences in physical inactivity. That is to say, parochialism, locus of control, neuroticism and active problem focussing explained about half of elevated odds ratios of physical inactivity in the lower educational groups. The material factors, equivalent income and employment status explained about 40% of the elevated odds ratios. Psychosocial and material correlates together reduced the odds ratios of lower educational groups by on average 75%. These results have practical consequences for the design of more effective interventions to promote physical activity. In particular, personality and coping style of risk groups, such as lower educational groups, should be taken into consideration at the future development of these interventions, as well as inequalities in material restrictions related to engaging in physical activity. Supplementary interventions focussing on childhood conditions which, partly, influence both personality and physical inactivity may also contribute to a reduction of socio-economic differences in physical inactivity.

The influence of adult ill health on occupational class mobility and mobility out of and into employment in The Netherlands

In the debate about the explanation of socio-economic health inequalities one of the important issues is the relative importance of health selection. The aim of this study was to investigate to what extent occupational class mobility and mobility out of and into employment are health-related, and in addition, to estimate the contribution of health-related social mobility to socio-economic health differences in the working population. Data were taken from the Longitudinal Study on Socio-Economic Health Differences in the Netherlands, which started in 1991; follow-up data were collected in 1995. The analysis is based on 2533 persons aged 15-59 at baseline. The influence of health problems in 1991 (perceived general health, health complaints and chronic conditions) on changes in occupational class between 1991 and 1995 was negligible. Neither upward nor downward mobility was affected by health problems. However, health problems in 1991 were significantly associated with a higher risk of mobility out of employment and a lower risk of mobility into employment in 1995. For example, for mobility out of employment among persons that reported at least one chronic condition in 1991, the odds ratio was 1.46. Health-related mobility out of employment substantially influences the estimate of socio-economic health inequalities in the working population (measured by current occupation). For manual workers, as compared to non-manual workers, the odds ratio for a less-than-good perceived general health was underestimated by 34% in 1995. Selective mobility into employment overestimates socio-economic inequalities in health in the working population by 9%. Respondents that moved into and out of employment were healthier than those that remained economically inactive, but their health was worse than of those that remained employed (both manual and non-manual). Implications for health policy are that the prospects for people with health problems to stay in paid employment should be improved.

Urban-rural variations in health in The Netherlands: does selective migration play a part?

STUDY OBJECTIVE: Urban-rural health differences are observed in many countries, even when socioeconomic and demographic characteristics are controlled for. People living in urban areas are often found to be less healthy. One of the possible causes for these differences is selective migration with respect to health or health risk factors. This hypothesis is hardly ever empirically tested. This paper tries to assess the existence of selective urban-rural migration. DESIGN: Health indicators and health risk factors were measured in a 1991 population sample. Moves were registered between 1991 and 1995. Using logistic regression analyses, comparisons were made between, firstly, urban to rural movers and rural to urban movers and secondly, between movers and stayers. SETTING: Region surrounding the city of Eindhoven in south eastern part of the Netherlands. SUBJECTS: Data were used of 15,895 respondents aged 20-74 in 1991. By 1995 613 subjects had moved from urban to rural and 191 subjects from rural to urban. MAIN RESULTS: Bivariate nor multivariate analyses show hardly and differences between movers into urban and movers into rural areas. Bivariate analyses on movers and stayers show that movers are healthier than stayers. However, when socioeconomic and demographic variables are controlled for, movers appear to be less healthy, with the exception of the younger age groups. CONCLUSIONS: Areas that attract many migrants from and lose few migrants to other degrees of urbanicity will in the long run obtain healthier populations, because of demographic and socioeconomic characteristics. However, if these characteristics are accounted for, the opposite is true, with the exception of younger age groups. In extreme cases this may cause spurious findings in cross sectional research into the relation between urbanicity and health. Absolute numbers of migrants need to be very high, however, to make this noticeable at the aggregate level.

Health behaviours explain part of the differences in self reported health associated with partner/marital status in The Netherlands.

STUDY OBJECTIVE--To describe the differences in health behaviours in disparate marital status groups and to estimate the extent to which these can explain differences in health associated with marital status. DESIGN--Baseline data of a prospective cohort study were used. Directly age standardised percentages of each marital group that engaged in each of the following behaviours--smoking, alcohol consumption, coffee consumption, breakfast, leisure exercise, and body mass index--were computed. Multiple logistic regression models were fitted to estimate the health differences associated with marital status with and without control for differences in health behaviours. SETTING--The population of the city of Eindhoven and surrounding municipalities (mixed urban-rural area) in The Netherlands in March 1991. PARTICIPANTS--There were 16,311 men and women, aged 25-74 years, and of Dutch nationality. MAIN RESULTS--There were differences in relation to marital status for each health behaviour. Married people were more likely to practise positive health behaviours (such as exercise and eating breakfast) and less likely to engage in negative ones (such as smoking or drinking heavily) than the other groups. Control for all six health behaviours could explain an average of 20-36% of the differences in perceived and general health and subjective health complaints. CONCLUSIONS--Differences in health behaviours explained a considerable amount, but not all, of the health differences related to marital status. Longitudinal data are necessary to confirm these findings; to determine whether the differences in health behaviours related to marital status are caused by selection effects or social causation effects; and to learn how social control, social support, and stress inter-relate to reinforce negative or to maintain positive health behaviours.

Socio-demographic variables and 6 year change in body mass index: longitudinal results from the GLOBE study

Background: Body mass index (BMI) differs by socio-demographic variables, but the origin of these associations remains relatively unknown.Objective: To investigate the association between socio-demographic variables and the subsequent change in BMI over six years.Design: A Dutch prospective cohort study (GLOBE) from which data were used from initially 20–49-year-old subjects (males: n=362; females: n=405). BMI was calculated from self-reported body height and weight data obtained in 1991 and 1997. Socio-demographic variables used were sex, age, educational level and the occupational level of the main breadwinner, family income, marital status, religious affiliation and degree of urbanization and measured in 1991.Results: Cross-sectionally, BMI was higher in males than in females. BMI was positively associated with age and negatively associated with educational level in both sexes, after adjustment for the other socio-demographic variables. A positive association of BMI with family income was found in males and a negative association with occupational level was found in females. During follow-up, BMI increased significantly more in females (from 23.0 (s.d. 3.3) to 24.2 (s.d. 3.8)) than in males (from 24.3 (s.d. 2.9) to 25.1 (s.d. 3.5)). With the exception of a significant lesser increase in BMI in initially 30–39-year-old women compared to initially 40–49-year-old women, no other statistically significant associations were found between socio-demographic variables and the 6-year change in BMI.Conclusions: Cross-sectional differences in BMI by socio-demographic variables are not due to different 6-year changes in BMI for categories of these variables in adulthood. Cross-sectional differences in BMI by educational level are probably established at the end of adolescence.