Prevention, Use of Health Services, and Genes: Implications of Genetics for Policy Formation

Genetic variation in health insurance coverage

We provide the first investigation into whether and how much genes explain having health insurance coverage or not and possible mechanisms for genetic variation. Using a twin-design that compares identical and non-identical twins from a national sample of US twins from the National Survey of Midlife Development in the United States, we find that genetic effects explain over 40% of the variation in whether a person has any health coverage versus not, and nearly 50% of the variation in whether individuals younger than 65 have private coverage versus whether they have no coverage at all. Nearly one third of the genetic variation in being uninsured versus having private coverage is explained by employment industry, self-employment status, and income, and together with education, they explain over 40% of the genetic influence. Marital status, number of children, and available measures of health status, risk preferences, and prevention effort do not appear to be important channels for genetic effects. That genes have meaningful effects on the insurance status suggests an important source of heterogeneity in insurance take up.

Why prevent when it does not pay? Prevention when health services are credence goods

This article identifies information asymmetries and the corresponding problem of overtreatment as a possible source of prevention and health disparities when patients differ with respect to their health risk. It analyzes preventive health behavior (primary prevention) and preventive health-care utilization (secondary prevention) in markets in which patients cannot determine whether they receive excessive secondary preventive treatment-that is, where health services are credence goods. The problem of overtreatment in such markets is considered as a possible pathway through which differences in health risk lead to disparities in primary and secondary prevention as well as the corresponding health outcomes. Patients with high health risks do not invest in primary prevention, because they anticipate to be provided with unnecessary secondary prevention. Patients with lower risks invest in primary but not in secondary prevention, resulting in health losses. Furthermore, when societal groups differ with respect to their exposure to overtreatment, and we consider socioeconomic status as a possible reason, we observe disparities in primary and secondary prevention as well as the resulting health outcomes, including the "social gradient." Several implications for empirical research are discussed.

Patient reported health status and all-cause mortality in patients with coronary heart disease

PURPOSE

Patients with coronary heart disease (CHD) experience reduced quality of life which may be associated with mortality in the longer term. This study explores whether patient-rated physical and mental health status was associated with mortality at 6-year follow-up among patients with CHD attending primary care in Ireland and Northern Ireland.

METHODS

This study is a secondary data analysis of patients with CHD recruited to a cluster randomized controlled trial from 2004 to 2010. Data collected included patient-rated physical component summary (PCS) and mental component summary (MCS) scores of health status (from the 12-Item Short-Form Health Survey (SF-12)), demographics and clinical parameters at baseline, and all-cause mortality at 6-year follow-up. Multivariate regression was conducted using generalized estimating equations (GEE) with a log-link function. Results are presented as odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

The study consisted of 762 individuals with mean age 67.6 years [standard deviation (SD): 9.8], and was 29% female. Mean baseline SF-12 mental (MCS) and physical (PCS) component scores were 50.0 (SD: 10.8) and 39.6 (SD: 11.2), respectively. At 6-year follow-up, the adjusted OR for the baseline MCS for mortality was 0.97 (95% CI: 0.95-0.99) and for the PCS 0.97 (95% CI: 0.95-0.99). For every five-point increase in MCS and PCS scores, there was a 14% reduction in the likelihood of all-cause mortality.

CONCLUSIONS

Overall, the magnitude of effect for both mental health status and physical health status was similar; higher scores were significantly associated with a lower risk of mortality at 6-year follow-up.

The Impact of Health Insurance on Preventive Care and Health Behaviors: Evidence from the First Two Years of the ACA Medicaid Expansions

The U.S. population receives suboptimal levels of preventive care and has a high prevalence of risky health behaviors. One goal of the Affordable Care Act (ACA) was to increase preventive care and improve health behaviors by expanding access to health insurance. This paper estimates how the ACA-facilitated state-level expansions of Medicaid in 2014 affected these outcomes. Using data from the Behavioral Risk Factor Surveillance System, and a difference-in-differences model that compares states that did and did not expand Medicaid, we examine the impact of the expansions on preventive care (e.g., dental visits, immunizations, mammograms, cancer screenings), risky health behaviors (e.g., smoking, heavy drinking, lack of exercise, obesity), and self-assessed health. We find that the expansions increased insurance coverage and access to care among the targeted population of low-income childless adults. The expansions also increased use of certain forms of preventive care, but there is no evidence that they increased ex ante moral hazard (i.e., there is no evidence that risky health behaviors increased in response to health insurance coverage). The Medicaid expansions also modestly improved self-assessed health.