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What explains socio-economic inequality in health?

Money only explains part of the inequity.  This post explains a number of additional assumptions and new findings.

Health is a basic necessity of life. The right to the highest attainable level of health is enshrined in the charter of the World Health Organization (WHO) and in many international treaties (e.g., article 25 of the Universal Declaration of Human Rights). But the majority of people in the world do not enjoy the health that is biologically possible, with the socially and economically disadvantaged generally in worse health.

The differences in health between socioeconomic status groups are striking. For example, on average, a 20-year-old low-income male in the United States reports to be in similar health as a 60-year-old high-income male – a 40-year difference!!!

When people ask me what I do for work I explain that I research health inequality. I tell them about these stark disparities and about the “double” injustice this represents: the poor are not only poor but also unhealthy and they live substantially shorter lives. That tends to get people’s attention. Many then happily offer their favorite explanation as to why the poor are unhealthy before I have a chance to tell them what we actually know about the issue. Probably this happens because I tend to start by saying that it is complex – an academic but admittedly dull answer (or is it?) – followed by a long pause. Because, where do I start?

One favorite explanation is that money buys you good medical care. That is correct. But it turns out this explains only a relatively small part of these differences. Another favorite suggestion is that the poor engage in less healthy behaviors, and that is a good one. More affluent individuals are less likely to smoke, drink heavily, be overweight, and use illegal drugs, and are more likely to exercise and engage in preventive care. But why are these behaviors so different between socioeconomic groups? Simple explanations, such as differences in income, risk aversion, or lack of control, do not appear to explain that much. Such explanations would predict strong correlation between unhealthy behaviors within individuals, i.e. that if you do not smoke you also do not drink and that if you are a heavy drinker you are also a heavy smoker. It turns out this is not the case.


Photo: iStock

More interesting explanations are those that are not immediately apparent. For example, poor health leads to loss of income from work and therefore reduced wealth. In other words, poor health causes low socioeconomic status. Further, the physical and psychosocial demands of work impact health. For example, hard physical labor wears people out. Importantly, early childhood conditions do matter a lot. Children born to mothers of low socioeconomic status start life in worse health. In part, this is because poor, less educated, and minority mothers are less able to provide for a healthy fetal environment during pregnancy. In a series of papers, David Barker and co-authors demonstrated the importance of fetal growth on later-life health outcomes. According to Barker’s “fetal-origins” hypothesis, the intrauterine environment, nutrition in particular, programs the fetus to have particular metabolic characteristics, which lead to future disease. The fetal origins effect extends to other domains too, not just health. For example, low-birth weight children do less well in school and are less likely to be employed as adults. But it is not just fetal circumstances that matter. The emerging literature on the early determinants of health and ability has firmly established that events before the age of five can have large and long lasting impacts on a wide range of late-life health, labor and educational outcomes. The implications of these findings are beginning to receive the attention they deserve. In his 2013 State of the Union address, President Obama called upon Congress to expand access to high-quality preschool for every child in America, to help more children access the early education they need to succeed in school and in life.

All of these explanations and more play a role. An open question remains the relative importance of the various determinants of health. Some claim that 40% of early deaths are due to behavior, 30% are due to genetic predispositions, 15% are due to social circumstances, 10% are due to shortfalls in medical care, and 5% are due to environmental exposures. These domains, however, interact, suggesting this simple division is not as clear-cut. Genes may predispose individuals to certain unhealthy behaviors and to certain health conditions, but the extent to which genes are expressed depends substantially on environmental exposures (Rutter, M. 2006. Genes and behavior: Nature-nurture interplay explained. Blackwell Publishing). As another example, studies suggest that socioeconomic status and supportive social environment moderate genetic vulnerability to smoking and obesity (for example, here, here, here and here). Thus, it may be possible to counter the detrimental effects of genes by providing protective family, work, social and neighborhood environments.

As I said, it is complex. But, this complexity is what makes the topic so interesting. At the CESR/Schaeffer Center for the Study of Health Inequality (CSHI) we aim to develop integrated approaches to tackling the complexity of disparities in health, by developing theory to explain empirical findings and make predictions, and conducting empirical structural- as well as reduced-form analyses, informed by theory. The Center brings together researchers from various disciplines within the University of Southern California’s (USCs) research community as well as distinguished researchers from outside of USC, in such fields as economics (of health, human capital, and labor), epidemiology, psychology, demography, gerontology, public health, biology and genetics. Our research is moving in new directions that will contribute to a fuller understanding of health disparities. For example, the recent genotyping of participants in large panel datasets provides a unique opportunity to start using both genetic data and high-quality repeated measures of socioeconomic status. We are starting to explore such data to better understand the contribution to health disparities of gene-by-environment interplay. Who knows what we will learn and what opportunities this improved understanding may provide to better the lives of the more disadvantaged in society. The effects of genes are not as deterministic as we used to think.

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