Based on nationally representative panel data (N person-years=40,020; N persons=18,704; Panel Labour Market and Social Security; PASS) from 2018 to 2022, we investigate how mental health changed during and after the COVID-19 pandemic. We employ time-distributed fixed effects regressions to show that mental health (Mental Health Component Summary Score of the SF-12) decreased from the first COVID-19 wave in 2020 onward, leading to the most pronounced mental health decreases during the Delta wave, which began in August 2021. In the summer of 2022, mental health had not returned to baseline levels. An analysis of the subdomains of the mental health measure indicates that long-term negative mental health changes are mainly driven by declines in psychological well-being and calmness. Furthermore, our results indicate no clear patterns of heterogeneity between age groups, sex, income, education, migrant status, childcare responsibilities or pre-COVID-19 health status. Thus, the COVID-19 pandemic appears to have had a uniform effect on mental health in the German adult population and did not lead to a widening of health inequalities in the long run.

Many European countries have experienced sustained educational expansion. Although the subjective well-being of upwardly mobile individuals has been studied, less is known about the implications of intergenerational mobility in education on parental well-being. Using multivariate regression models based on the Survey of Health, Ageing and Retirement in Europe (SHARE), this study examines whether children’s educational mobility is associated with their parents’ well-being, as measured by life satisfaction, and if so, whether low-educated parents profit more than middle-educated parents, and through which mechanisms. Parents with upwardly mobile children reported higher life satisfaction than those with nonmobile and downwardly mobile children. The effect was slightly stronger for parents with low education than for those with medium education. For parents with more than one child, each additional upwardly mobile child amplified the positive association with life satisfaction. However, we were unable to identify the substantial mechanisms responsible for the association between children’s educational mobility and parents’ life satisfaction. Emotional closeness, financial support, and instrumental help were associated with parental life satisfaction in general but did not differ enough between parents with and without upwardly mobile children to explain the mobility-related difference in parental life satisfaction. Intergenerational mobility in education represents a potentially relevant and “new” category of social inequality in old age, as social class in later life may also be affected by the educational capital of adult children. We discuss the need for further studies to understand the role of educational mobility for parental well-being in later life.

The main objective of this study is to investigate whether different medical attitudes relate to COVID-19 vaccination uptake and approval of vaccine mandates. The theory of planned behavior and the health belief model suggest that individual attitudes towards medical approaches are important for vaccination uptake. We use data from a German online cross-sectional study comprising 4065 respondents conducted between September and October in 2022 on the use and acceptance of five pre-defined medical approaches: conventional medicine, Traditional European Medicine (Naturheilkunde), complementary medicine, integrative medicine, and alternative medicine. The two main outcome measures are: (1) COVID-19 vaccination uptake, differentiating between (a) rejected, (b) socially pressured and (c) endorsed vaccination; (2) attitudes towards mandatory COVID-19 vaccination, i.e., whether or not individuals endorse vaccination mandates. We employ logistic and multinomial logistic regressions to calculate average marginal effects (AME) and to account for the influence of different medical attitudes and for confounding variables. While vaccination uptake in general is high (91.0 % in the analytical sample), our multivariate results reveal that individuals with a positive disposition towards Traditional European Medicine (AME = 0.05; p < 0.01) and alternative medicine (AME = 0.02; p < 0.10) were, comparatively, more likely to reject COVID-19 vaccination. A positive disposition towards conventional medicine is associated with higher vaccination uptake (AME = 0.17; p < 0.001). Positive attitudes towards alternative medicine correlate with increased levels of feeling socially pressured into accepting the vaccination (AME = 0.05; p < 0.01). Approval levels for universal mandatory vaccination are low (43.9 %). Positive attitudes towards alternative (AME = -0.03; p < 0.1) and Traditional European Medicine (AME = -0.04; p < 0.05) negatively correlate with approval of vaccination mandates, while positive attitudes towards conventional medicine (AME = 0.05; p < 0.01) increase approval. Our findings suggest that different medical attitudes are simultaneously associated with vaccination uptake and mandate approval. This provides important knowledge for policy makers when designing vaccination schemes and for health professionals when consulting their heterogeneous group of patients.

One way to approach causality in health research is the instrumental variable approach, which is used to avoid confounding and measurement error in observational studies (as opposed to experiments). Beginning with Angrist and colleagues’ studies of mortality differentials using the U.S. draft lotteries during the Vietnam War, instrumental variables have been widely applied in the health literature. Examples of instrumental variables range from birth month, which was used as early as the 1930s to study effects of early life on health outcomes later in life, to famines, wars, German reunification, or - more recently - genes. This chapter introduces the idea of instrumental variables and reviews their definition. It provides an overview of the study questions and instruments used in health research. It discusses what makes a good instrument, the advantages and disadvantages of the approach, and outlines how this approach can be combined with other study designs used to examine causality.

This introduction to the Handbook of Health Inequalities Across the Life Course reviews the most important features of and developments in the research on health inequalities throughout the life course. It contextualizes this interdisciplinary field of research within sociology and introduces some prominent research topics. The major achievements of life course research on health inequalities, as well as a number of unresolved challenges, will be outlined in brief sections from the perspective of the concepts of complexity, causality, biology, and policy, thus neatly summarising the state of the art in this important field of research. Subsequently, the motivation behind the structure and content of the book as a whole is described, before each chapter is summarised to give an overview and orientation for the reader.

We aim to give an overview of the state of the art of causal analysis of demographic issues related to morbidity and mortality. We will systematically introduce strategies to identify causal mechanisms, which are inherently linked to panel data from observational surveys and population registers. We will focus on health and mortality, and on the issues of unobserved heterogeneity and reverse causation between health and (1) retirement, (2) socio-economic status, and (3) characteristics of partnership and fertility history. The boundaries between demographic research on mortality and morbidity and the neighbouring disciplines epidemiology, public health and economy are often blurred. We will highlight the specific contribution of demography by reviewing methods used in the demographic literature. We classify these methods according to important criteria, such as a design-based versus model-based approach and control for unobserved confounders. We present examples from the literature for each of the methods and discuss the assumptions and the advantages and disadvantages of the methods for the identification of causal effects in demographic morbidity and mortality research. The differentiation between methods that control for unobserved confounders and those that do not reveal a fundamental difference between (1) methods that try to emulate a randomised experiment and have higher internal validity and (2) methods that attempt to achieve conditional independence by including all relevant factors in the model. The latter usually have higher external validity and require more assumptions and prior knowledge of relevant factors and their relationships. It is impossible to provide a general definition of the sort of validity that is more important, as there is always a trade-off between generalising the results to the population of interest and avoiding biases in the estimation of causal effects in the sample. We hope that our review will aid researchers in identifying strategies to answer their specific research question. * This article belongs to a special issue on "Identification of causal mechanisms in demographic research: The contribution of panel data".

Less-educated persons have worse cardiovascular health. We compare the educational gradients in three disease-specific health measures (biomarkers, self-reported doctors’ diagnoses and cause-specific mortality) in order to compare their relevance in different stages of the disease process. We study 14,102 people aged 50–89 from the US Health Retirement Study (HRS) in the period 2006–17. We use six CVD biomarkers (systolic/​diastolic blood pressure, ratio total/HDL cholesterol, C-reactive protein, body mass index, HbA1c) and two self-reported doctors’ diagnoses (stroke, heart attack). We estimate the gradient in biomarkers using log-binomial regression and the hazard of diagnoses and CVD mortality with Cox survival models. Among those without pre-diagnosed CVD conditions, the educational gradient in mortality is highest (RR 1.97), the gradient for those who receive a CVD diagnosis is in the middle (RR 1.46), and the gradient in biomarkers is lowest (RR 1.32). Among those with recent/​older diagnoses, the biomarker gradient is comparable to levels among the non-diagnosed, while the mortality gradient is much lower (RR 1.35). The gradients in diagnoses and mortality are only slightly explained by differences in biomarkers. The comparison of the three gradients and the mediation analysis suggest that in each of the steps to diagnosis and death there are social factors involved that increase the gradient and go beyond what biomarkers can predict. Having a CVD diagnosis leads to smaller mortality gradients, presumably because of the convergence of educational differences in behaviour and during treatment and monitoring. Our findings support prevention as a strategy against social inequalities in CVD.

Differences in mortality by socio-economic position (SEP) are well established, but there is uncertainty as to which dimension of SEP is most important in what context. This study compares the relationship between three SEP dimensions and mortality in Finland, during the periods 1990–97 and 2000–07, and to existing results for Sweden. We use an 11% random sample from the Finnish population with information on education, occupational class, individual income and mortality (age groups 35–59 and 60–84) (n = 810,902; 274,316 deaths). Cox proportional hazard models produce hazard ratios (HR) for categories of SEP variables in bivariate and multivariate models. Multivariate HRs are smaller than bivariate HRs, but all dimensions have a net effect on mortality. Overall, income shows the steepest mortality gradient: HR = 2.49 among men in the lowest income quintile aged 35–59 in the 1990s. The importance of the various SEP dimensions is modified by gender and age group, reflecting the significance of gendered life course differences in analyses of health inequality. Except for the declining disadvantage of poor men aged 35–59, inequalities are very stable over time and similar between Finland and Sweden. In such studies, the use of only one SEP indicator functions well as a broad marker of SEP. However, only analyses of multiple dimensions allow for comprehensive measurements of SEP, take into account the fact that some SEP dimensions are mediated by others, and provide insights into the social mechanisms underlying the stable structure of inequalities in mortality.

A person's socioeconomic status (SES) can affect health (social causation) and health can affect SES (health selection). The findings for each of these pathways may depend on how SES is measured. We study (1) whether social causation or health selection is more important for overall health inequalities, (2) whether this differs between stages of the life course, and (3) between measures of SES. Using retrospective survey data from 10 European countries (SHARELIFE, n=18,734), and structural equation models in a cross-lagged panel design, we determine the relative explanatory power of social causation and health selection through childhood, adulthood, and old age. We use three ways to measure SES: First, as a latent variable capturing different aspects of SES, second as material wealth, and third as occupational skill level. Between childhood and adulthood, social causation and health selection are equally important. In the transition from adulthood to old age, social causation becomes more important than health selection, making it the dominant mechanism in old age. The three measures of SES produce similar results. Only material wealth shows a stronger effect on health (between childhood and adulthood); it is also more affected by health (between adulthood and old age) than the other measures.

Differences in mortality between groups with different socioeconomic positions (SEP) are well-established, but the relative contribution of different SEP measures is unclear. This study compares the correlation between three SEP dimensions and mortality, and investigates differences between gender and age groups (35-59 vs. 60-84). We use an 11% random sample with an 80% oversample of deaths from the Finnish population with information on education, occupational class, individual income, and mortality (n=496,658; 274,316 deaths between 1995 and 2007). We estimate bivariate and multivariate Cox proportional hazard models and population attributable fractions. The total effects of education are substantially mediated by occupation and income, and the effects of occupation is mediated by income. All dimensions have their own net effect on mortality, but income shows the steepest mortality gradient (HR 1.78, lowest vs. highest quintile). Income is more important for men and occupational class more important among elderly women. Mortality inequalities are generally smaller in older ages, but the relative importance of income increases. In health inequality studies, the use of only one SEP indicator functions well as a broad marker of SEP. However, only analyses of multiple dimensions allow insights into social mechanisms and how they differ between population subgroups.

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.

The widely established health differences between people with greater economic resources and those with fewer resources can be attributed to both social causation (material factors affecting health) and health selection (health affecting material wealth). Each of these pathways may have different intensities at different ages, because the sensitivity of health to a lack of material wealth and the degree to which health can influence economic resources may change. We study the relative importance, in terms of explanatory power, of social causation and health selection, comparing the transitions from childhood to adulthood and from adulthood to old age. We use retrospective survey data from ten European countries from the Survey of Health, Ageing and Retirement in Europe (SHARELIFE, n = 18,734) and the English Longitudinal Study of Ageing (ELSA, n = 6117), and structural equations models in a cross-lagged panel design. Material wealth and health depend on their prior status, wealth more so than health. In the transition from childhood to adulthood, social causation and health selection are equally important: the standardized coefficients for men in SHARE are 0.07 and 0.06, respectively, i.e. one standard deviation increase in material wealth in childhood is associated with a 0.07 standard deviation increase in adult health. In the transition from adulthood to old age, social causation is more important than health selection (0.52 vs. 0.01), across gender and data sets. Both pathways contribute to the creation of health inequalities—however, their relative importance changes with age, which is important for understanding how health inequalities develop and how policies can address them.

Health differences which correspond to socioeconomic status (SES) can be attributed to three causal mechanisms: SES affects health (social causation), health affects SES (health selection), and common background factors influence both SES and health (indirect selection). Using retrospective survey data from 10 European countries (SHARELIFE, n = 20,227) and structural equation models in a cross-lagged panel design, we determine the relative importance in terms of explanatory power of social causation and health selection in the life course from childhood to old age. Both SES and health heavily depend on their prior status, albeit more for SES than health. During the transition from childhood to working ages, social causation and health selection are equally weak. Turning to the second phase (transition from working ages to old age) causation increases while selection decreases which makes causation the dominant mechanism in older age. While the contribution of common background factors remains difficult to assess, this study shows that both social causation and health selection are responsible for health inequalities; however, their relative importance changes with age. Life course modelling can complement causal analysis by revealing interactions between the processes of SES and health and their contribution to health inequality.

Background: It has become increasingly common in multiple purpose general population surveys to integrate different kinds of biomarker in the data collection process. Objective: In this article we test the predictive power of five different functional forms of CVD-related biomarkers for all-cause and CVD mortality in the Health and Retirement Study (HRS). Methods: We use five different functional forms of biomarker: A risk factor index, risk factors separately, continuous biomarkers, risk groups comprising every possible combination of risk factors, and a cluster analytic approach to identify risk profiles in the sample. We use data from the Health and Retirement Study (HRS) with information on four collected biomarkers (glycated hemoglobin (hbA1c), high-density lipoprotein (HDL), total cholesterol, and C-reactive protein (CRP)) with an eight-year mortality follow-up period. Results: The results show that the additive index has comparatively high predictive power, relative to its simplicity. Risk profiles were identified in the data, with substantial differences in mortality risk between the profiles. The more complex functional forms improve prediction only moderately compared to the simple index, although we can identify groups with an elevated mortality risk that are not identified in more parsimonious approaches. Conclusions: Depending on the specific research question, both a very simple modeling of biomarker information and more detailed examinations of specific complex risk profiles can be appropriate. Contribution: The study provides initial guidelines for the measurement of commonly used biomarkers, which can be a reference for other studies that use biomarkers as health indicators or for mortality prediction.

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.

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.

Socioeconomic status (SES) and health during childhood have been consistently observed to be associated with health in old age in many studies. However, the exact mechanisms behind these two associations have not yet been fully understood. The key challenge is to understand how childhood SES and health are associated. Furthermore, data on childhood factors and life course mediators are sometimes unavailable, limiting potential analyses. Using SHARELIFE data (N=17230) we measure childhood SES and health circumstances, and examine their associations with old age health and their possible pathways via education, adult SES, behavioural risks, and labour market deprivation. We employ structural equation modelling to examine the mechanism of the long lasting impact of childhood SES and health on later life health, and how mediators partly contribute to these associations. The results show that childhood SES is substantially associated with old age health, albeit almost fully mediated by education and adult SES. Childhood health and behavioural risks have a strong effect on old age health, but they do not mediate the association between childhood SES and old age health. Childhood health in contrast retains a strong association with old age health after taking adulthood characteristics into account. This paper discusses the notion of the ‘long arm of childhood’, and concludes that it is a lengthy, mediated, incremental progression rather than a direct effect. Policies should certainly focus on childhood, especially when it comes to addressing childhood health conditions, but our results suggest other important entry points for improving old age health when it comes to socioeconomic determinants.