Do socioeconomic mortality differences decrease with rising age?

The impact of SES on mortality is an established finding in mortality research. I examine, whether this impact decreases with age. Most research finds evidence for this decrease but it is unknown whether the decline is due to mortality selection. My data come from the US-HRS Study and includes 9376 persons aged 59+, which are followed over 8 years. The variables allow a time varying measurement of SES, health and behavior. Event-history-analysis is applied to analyze mortality differentials. My results show that socioeconomic mortality differences are stable across ages whereas they decline clearly with decreasing health. The first finding that health rather than age is the equalizer combined with the second finding of unequally distributed health leads to the conclusion that in old age, the impact of SES is transferred to health and is stable across ages.health, HRS, mortality, old age, socio-economic differentials, socioeconomic status, USA

Approaches and Methods for Causal Analysis of Panel Data in the Area of Morbidity and Mortality

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"

A systematic literature review of studies analyzing the effect of sex, age, education, marital status, obesity, and smoking on health transitions

Sex, age, education, marital status, obesity, and smoking have been found to affect health transitions between non-disabled, disabled, and death. Our aim is to review the research literature on this topic and provide structured information, first on the availability of results for each risk factor and transition, and then on detailed study characteristics and disability measures. We use expert recommendations and the electronic databases Medline, PsycINFO, and SOCA. The search is confined to the years 1985-2005, and produced a total of 7,778 articles. Sixty-three articles met the selection criteria regarding study population, longitudinal design, risk factors, transition, and outcome measures.gender, health, mortality, obesity, review, sex, smoking, systematic review, transition

Making space with data: Data politics, statistics and urban governance in Denmark

In this article we engage with the contemporary data moment by exploring how particular data practices – consisting of census data and statistics - have become embroiled in the making of urban space and governance in Denmark. By focusing on the controversial case of Danish “ghettos” - a state-sanctioned list of marginalised urban areas– we show how Danish data practices of routinely collecting and aggregating extensive census data have become central to ascribing particular urban neighbourhoods as ghetto areas. These data practices spatialise residential housing areas as problematic and influence Danish urban governance. We explore how new forms of data practices for monitoring urban areas arise, and argue that these practices help to maintain the spatialisation of the “ghetto list”. They do so by drawing multiple forms of data together, that visualise and monitor “at risk” areas making them governable and amenable to physical changes. Finally, we show how the state uses data practices to make citizens (and municipalities) accountable; yet, this accountability cuts both ways, as citizens and municipalities also use data to hold the state accountable. We end with a discussion of how our analysis of data practices has implications for how we imagine the scalar hierarchy of the state and the politics of data

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

First published online 18 June 2015. 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. 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. 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. 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

The long arm of childhood circumstances on health in old age: Evidence from SHARELIFE

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

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

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