Life Expectancy Trends Tied to 2016 Voting Choices.

Ever since the unexpected results of the 2016 presidential election, political analysts have sought to understand what factors played into voters’ decision making. Now, a new study by a School of Public Health researcher shows how voting patterns correlated with the nation’s growing geographic health divides, with Donald Trump winning more votes in counties that have seen lower gains in life expectancy.

Writing in the American Journal of Public Health, Jacob Bor, assistant professor of global health, reviewed county-level voting data from the 2008 and 2016 presidential elections and county-level estimates of life expectancy at birth for 1980 and 2014 from the Institute for Health Metrics and Evaluation.

Life expectancy in the US rose by about five years between 1980 and 2014, but the gains were not even. “In the last 30 years, there’s been a sharp divergence in life expectancy across US counties,” Bor says. “Some counties have gained a full decade of life expectancy. Others have really been left behind, with zero gains or even falling life expectancy in this period.”

Bor found counties in which life expectancy rose less than three years saw a nearly 10 percentage point increase in the Republican vote share between 2008 and 2016. In counties where life expectancy rose more than seven years, Democrats saw a 3.5 percentage point increase.

Life expectancy trends also correlated with voter turnout: The total number of votes for the two major parties increased by 1.3 million in counties with above-average life expectancy gains and fell by 1.9 million in counties with below-average gains—the counties more likely to go for Trump.

Overall, from 2008 to 2016, Republicans lost 67,000 votes in counties with above-average life expectancy trends but gained 3.1 million votes in counties with below-average trends. Democrats gained 1.4 million votes in counties with above-average life expectancy gains but also lost 5.0 million votes in counties with below-average life expectancy gains—a 14 percent relative decline in the number of Democratic votes in those counties.

“In the 2016 election, in counties that had been left behind in life expectancy gains, residents abandoned the Democratic Party, voting for Trump or not voting at all,” Bor says. “What we now need to understand is why.”

Bor stresses the relationship may not be causal, and does not rule out other explanations for Trump’s margins in these counties, namely the roles of race and economic marginalization. “Regardless of the causes of this relationship, the data show that people in counties that voted for Trump are hurting, and not just metaphorically,” Bor says. “The findings signal an important opportunity for policymakers to try to address the health needs of these populations.”

Noting recent effort to repeal the Affordable Care Act and cut Medicaid, as well as proposed cuts to health research, public health surveillance, environmental protection, occupational safety, and social programs, Bor says these findings pose a critical question: “When will the Trump administration and Republicans in Congress respond to the health needs of those who put them into power, and enact policies that will support population health?”

—Michelle Samuels