Public Health in Red Counties in the 2016 Presidential Election

• In Event: Health, Equity and Democracy

Abstract

Examining 2,832 counties across the nation, this paper explores a relationship between Trump votes and public health. A preliminary regression analysis suggests that “red” counties are likely to be correlated with poor public health.

Public health and health care issues have become one of the most contentious and salient issues in presidential elections. Scholars examined presidential elections in relation to health policy issues before and after the implementation of the Affordable Care Act (Blendon et al. 2008, 2010, 2016) and more specifically with regard to health access (Derose et al. 2009), health care reform (Konisky et al. 2012), life expectancy and voting patterns (Bor 2017), public attitudes toward health care beneficiary (Malina 2017), and health of rural voters (Monnat et al. 2017).

This paper explains how electoral results, the share of Trump votes, are correlated with multiple health indicators of voters such as access to exercise opportunities, low birth weight, smoking, obesity, excessive drinking, teen birth rate, uninsured population, mental health provider, and preventable hospital rate. Controlling for education, income, unemployment, crime in the community, a preliminary regression analysis suggests that “red” counties are likely to be correlated with higher rates of teen birth rates, less access to primary care physicians, higher percentage of uninsured population and less access to regular physical activities. This paper discusses the implications of such findings in relation to health care policies and ideology. For analysis, 2017 County Health data is obtained from www.countyhealthrankings.org sponsored by Robert Wood Johnson Foundation.

Trump appealed to the “forgotten” Americans in the 2016 Presidential Election. This paper adds public health as a new dimension in electoral politics in assisting to identify a marginalized population among American voters in addition to socio-economic status (the variables of income, education, and unemployment), community quality (crime variable) and race.

Author

• Eunju Kang, National Defense University