Hannes Kröger

What causes health inequality? A systematic review on the relative importance of social causation and health selection.

BACKGROUND The social gradient in health is one of the most reliable findings in public health research. The two competing hypotheses that try to explain this gradient are known as the social causation and the health selection hypothesis. There is currently no synthesis of the results of studies that test both hypotheses. METHODS We provide a systematic review of the literature that has addressed both the health selection and social causation hypotheses between 1994 and 2013 using seven databases following PRISMA rules. RESULTS The search strategy resulted in 2952 studies, of which, we included 34 in the review. The synthesis of these studies suggests that there is no general preference for either of the hypotheses (12 studies for social causation, 10 for health selection). However, both a narrative synthesis as well as meta-regression results show that studies using indicators for socio-economic status (SES) that are closely related to the labor market find equal support for health selection and social causation, whereas indicators of SES like education and income yield results that are in favor of the social causation hypothesis. High standards in statistical modeling were associated with more support for health selection. CONCLUSIONS The review highlights the fact that the causal mechanisms behind health inequalities are dependent on whether or not the dimension being analyzed closely reflects labor market success. Additionally, further research should strive to improve the statistical modeling of causality, as this might influence the conclusions drawn regarding the relative importance of health selection and social causation.

Statistical methods for causal analysis in life course research: an illustration of a cross-lagged structural equation model, a latent growth model, and an autoregressive latent trajectories model

Abstract We present three statistical methods for causal analysis in life course research that are able to take into account the order of events and their possible causal relationship: a cross-lagged model, a latent growth model (LGM), and a synthesis of the two, an autoregressive latent trajectories model (ALT). We apply them to a highly relevant causality question in life course and health inequality research: does socioeconomic status (SES) affect health (social causation) or does health affect SES (health selection)? Using retrospective survey data from SHARELIFE covering life courses from childhood to old age, the cross-lagged model suggests an equal importance of social causation and health selection; the LGM stresses the effect of education on health growth; whereas the ALT model confirms no causality. We discuss examples, present short and non-technical introduction of each method, and illustrate them by highlighting their relative strengths for causal life course analysis.

The Association of Levels of and Decline in Grip Strength in Old Age with Trajectories of Life Course Occupational Position.

Background The study of the influence of life course occupational position (OP) on health in old age demands analysis of time patterns in both OP and health. We study associations between life course time patterns of OP and decline in grip strength in old age. Methods We analyze 5 waves from the Survey of Health Ageing and Retirement in Europe (n = 5108, ages 65–90). We use a pattern-mixture latent growth model to predict the level and decline in grip strength in old age by trajectory of life course OP. We extend and generalize the structured regression approach to establish the explanatory power of different life course models for both the level and decline of grip strength. Results Grip strength declined linearly by 0.70 kg (95% CI -0.74;-0.66) for men and 0.42 kg (95% CI -0.45;-0.39) for women per year. The level of men’s grip strength can best be explained by a critical period during midlife, with those exposed to low OP during this period having 1.67 kg (95% CI -2.33;-1.00) less grip strength. These differences remain constant over age. For women, no association between OP and levels of or decline in grip strength was found. Conclusions Men’s OP in midlife seems to be a critical period for the level of grip strength in old age. Inequalities remain constant over age. The integration of the structured regression approach and latent growth modelling offers new possibilities for life course epidemiology.

The stratifying role of job level for sickness absence and the moderating role of gender and occupational gender composition

The study investigates whether sickness absence is stratified by job level - understood as the authority and autonomy a worker holds - beyond the association with education, income, and occupation. A second objective is to establish the moderating role of gender and occupational gender composition on this stratification of sickness absence. Four competing hypotheses are developed that predict different patterns of moderation. Associations between job level and sickness absence are estimated for men and women in three groups of differing occupational gender composition, using data from the German Socio-Economic Panel Study (SOEP). For the purpose of moderation analysis, this study employs a new method based on Bayesian statistics, which enables the testing of complex moderation hypotheses. The data support the hypothesis that the stratification of sickness absence by job level is strongest for occupational minorities, meaning men in female-dominated and women in male-dominated occupations.

Retrospective life course data from European countries on how early life experiences determine health in old age and possible mid-life mediators

The data presented in this article is related to the research paper entitled “The long arm of childhood circumstances on health in old age: Evidence from SHARELIFE” (E. Pakpahan, R. Hoffmann, H. Kröger, 2016) [1]. It presents the distribution of socioeconomic status (SES) and health from childhood until old age in thirteen European countries. In order to capture the characteristics of longitudinal data, which resembles life course data, we divide the data into three schematic periods: childhood (up to 15 years old), adulthood (30 to 60 years old), and old age (61 to 90 years old). This data set contains respondents’ life histories, ranging from childhood conditions (such as housing and health) to detailed questions on education, adult SES (working history, income, and wealth) and old age health. The data can be used not only to understand on how early life experiences determine health in old age, but also to recognise the importance of possible mid-life mediators.

Health Inequalities and the Interplay of Socioeconomic Factors and Health in the Life Course

In this chapter, we present health as an intersection between biology and society, and between medical/biological science and sociology. We discuss the examples of health inequalities according to socioeconomic status (SES), race and gender, before considering in more detail from a life course perspective the causal direction between SES and health. Our empirical analysis investigates the explanatory power of social causation and health selection, using retrospective survey data from ten European countries (SHARELIFE), and structural equations models in a cross-lagged panel design. Between childhood and adulthood both mechanisms seem equally important, but in older ages, social causation is much more important than health selection. The contribution of both mechanisms to health inequality illustrates the co-evolution of social and biological factors in the human life course.

Comparing Observed and Unobserved Components of Childhood: Evidence From Finnish Register Data on Midlife Mortality From Siblings and Their Parents

In this study, we argue that the long arm of childhood that determines adult mortality should be thought of as comprising an observed part and its unobserved counterpart, reflecting the observed socioeconomic position of individuals and their parents and unobserved factors shared within a family. Our estimates of the observed and unobserved parts of the long arm of childhood are based on family-level variance in a survival analytic regression model, using siblings nested within families as the units of analysis. The study uses a sample of Finnish siblings born between 1936 and 1950 obtained from Finnish census data. Individuals are followed from ages 35 to 72. To explain familial influence on mortality, we use demographic background factors, the socioeconomic position of the parents, and the individuals’ own socioeconomic position at age 35 as predictors of all-cause and cause-specific mortality. The observed part—demographic and socioeconomic factors, including region; number of siblings; native language; parents’ education and occupation; and individuals’ income, occupation, tenancy status, and education—accounts for between 10 % and 25 % of the total familial influence on mortality. The larger part of the influence of the family on mortality is not explained by observed individual and parental socioeconomic position or demographic background and thus remains an unobserved component of the arm of childhood. This component highlights the need to investigate the influence of childhood circumstances on adult mortality in a comprehensive framework, including demographic, social, behavioral, and genetic information from the family of origin.