Challenges in studying the interplay of genes and environment. A study of childhood financial distress moderating genetic predisposition for peak smoking.

Smoking is one of the leading causes of preventable disease and death in the U.S., and it is strongly influenced both by genetic predisposition and childhood adversity. Using polygenic indices (PGIs) of predisposition to smoking, we evaluate whether childhood financial distress (CFD; a composite measure of financial adversity) moderates genetic risk in explaining peak-cigarette consumption in adulthood. Using the Health and Retirement Study (HRS), we find a substantial reduction in the relationship between genetic risk and peak smoking for those who did not suffer financial adversity in childhood. Among adult smokers who grew up in high-CFD households, a one standard deviation higher PGI is associated with 2.9 more cigarettes smoked per day at peak. By contrast, among smokers who grew up in low-CFD households, this gradient is reduced by 37 percent (or 1.1 fewer). These results are robust to controlling for a host of prime confounders. By contrast, we find no evidence of interactions between the PGI and typical measures of childhood SES such as parental education - a null result that we replicate in the Wisconsin Longitudinal Study (WLS) and the English Longitudinal Study of Aging (ELSA). This suggests the role of childhood financial distress in the relationship with peak smoking is distinct from that of low childhood SES, with high CFD potentially reflecting more acute distress than do measures of low childhood SES. Our evidence also suggests low childhood SES is a weaker proxy for acute distress, providing an alternative explanation for the childhood SES null result.

Overcoming attenuation bias in regressions using polygenic indices.

Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.

Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits.

Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

A Theory of Socio-economic Disparities in Health over the Life Cycle.

Motivated by the observation that medical care explains only a relatively small part of the SES-health gradient, we present a life-cycle model that incorporates several additional behaviours that potentially explain (jointly) a large part of observed disparities. As a result, the model provides not only a conceptual framework for the SES-health gradient but more generally an improved framework for the production of health. We derive novel predictions from the theory by performing comparative dynamic analyses. More generally, our comparative dynamic method can be applied to models of similar form, e.g., human capital, health deficits, firm investment, to name a few.

Wealth and Health Behavior: Testing the Concept of a Health Cost.

Wealthier individuals engage in healthier behavior. This paper seeks to explain this phenomenon by exploiting both inheritances and lottery winnings to test a theory of health behavior. We distinguish between the direct monetary cost and the indirect health cost (value of health lost) of unhealthy consumption. The health cost increases with wealth and the degree of unhealthiness, leading wealthier individuals to consume more healthy and moderately unhealthy, but fewer severely unhealthy goods. The empirical evidence presented suggests that differences in health costs may indeed partially explain behavioral differences, and ultimately health outcomes, between wealth groups.

Health Inequalities through the Lens of Health Capital Theory: Issues, Solutions, and Future Directions.

We explore what health-capital theory has to offer in terms of informing and directing research into health inequality. We argue that economic theory can help in identifying mechanisms through which specific socioeconomic indicators and health interact. Our reading of the literature, and our own work, leads us to conclude that non-degenerate versions of the Grossman model (1972a;b) and its extensions can explain many salient stylized facts on health inequalities. Yet, further development is required in at least two directions. First, a childhood phase needs to be incorporated, in recognition of the importance of childhood endowments and investments in the determination of later-life socioeconomic and health outcomes. Second, a unified theory of joint investment in skill (or human) capital and in health capital could provide a basis for a theory of the relationship between education and health.

Is there empirical evidence for decreasing returns to scale in a health capital model?

We estimate a health investment equation, derived from a health capital model that is an extension of the well-known Grossman model. Of particular interest is whether the health production function has constant returns to scale, as in the standard Grossman model, or decreasing returns to scale, as in the Ehrlich-Chuma model and extensions thereof. The model with decreasing returns to scale has a number of theoretically and empirically desirable characteristics that the constant returns model does not have. Although our empirical equation does not point-identify the decreasing returns to scale curvature parameter, it does allow us to test for constant versus decreasing returns to scale. The results are suggestive of decreasing returns and in line with prior estimates from the literature. But when we attempt to control for the endogeneity of health by using instrumental variables, the results become inconclusive. This brings into question the robustness of prior estimates in this literature.