A,The percentage of Medicare Part D enrollees who received prescriptions for at least a 90-day supply of an opioid in 2015. B, The percentage of the vote for the Republican presidential candidate in 2016. The opioid map includes 3118 of3142 U.S. counties (99.2 percent), and the voting map includes 3101 counties (98.7 percent). In each map, the rates are color coded by quintile of counties. The rates are not adjusted for any individual or county characteristics. Graphic: Goodwin, et al., 2018 / JAMA

By Paul Chisholm
23 June 2018
(NPR) – The fact that rural, economically disadvantaged parts of the country broke heavily for the Republican candidate in the 2016 election is well known. But Medicare data indicate that voters in areas that went for Trump weren’t just hurting economically — many of them were receiving prescriptions for opioid painkillers.The findings were published Friday in the medical journal JAMA Network Open. Researchers found a geographic relationship between support for Trump and prescriptions for opioid painkillers.It’s easy to see similarities between the places hardest hit by the opioid epidemic and a map of Trump strongholds. “When we look at the two maps, there was a clear overlap between counties that had high opioid use … and the vote for Donald Trump,” says Dr. James S. Goodwin, chair of geriatrics at the University of Texas Medical Branch in Galveston and the study’s lead author. “There were blogs from various people saying there was this overlap. But we had national data.”Goodwin and his team looked at data from Census Bureau, the 2016 election and Medicare Part D, a prescription drug program that serves the elderly and disabled.To estimate the prevalence of opioid use by county, the researchers used the percentage of enrollees who had received prescriptions for a three-month or longer supply of opioids. Goodwin says that prescription opioid use is strongly correlated with illicit opioid use, which can be hard to quantify.”There are very inexact ways of measuring illegal opioid use,” Goodwin says. “All we can really measure with precision is legal opioid use.”Goodwin’s team examined how a variety of factors could have influenced each county’s rate of chronic opioid prescriptions. After correcting for demographic variables such as age and race, Goodwin found that support for Trump in the 2016 election closely tracked opioid prescriptions.In counties with higher-than-average rates of chronic opioid prescriptions, 60 percent of the voters went for Trump. In the counties with lower-than-average rates, only 39 percent voted for Trump.A lot of this disparity could be chalked up to social factors and economic woes. Rural, economically-depressed counties went strongly for Trump in the 2016 election. These are the same places where opioid use is prevalent. As a result, opioid use and support for Trump might not be directly related, but rather two symptoms of the same problem – a lack of economic opportunity. […]”The types of discussions around what drove the ’16 election, and the forces that were behind that, should also be included when people are talking about the opioid epidemic,” Goodwin says. [more]

Analysis Finds Geographic Overlap In Opioid Use And Trump Support In 2016

ABSTRACT: The causes of the opioid epidemic are incompletely understood.Objective To explore the overlap between the geographic distribution of US counties with high opioid use and the vote for the Republican candidate in the 2016 presidential election.Design, Setting, and Participants A cross-sectional analysis to explore the extent to which individual- and county-level demographic and economic measures explain the association of opioid use with the 2016 presidential vote at the county level, using rate of prescriptions for at least a 90-day supply of opioids in 2015. Medicare Part D enrollees (N = 3 764 361) constituting a 20% national sample were included.Main Outcomes and Measures Chronic opioid use was measured by county rate of receiving a 90-day or greater supply of opioids prescribed in 2015.Results Of the 3 764 361 Medicare Part D enrollees in the 20% sample, 679 314 (18.0%) were younger than 65 years, 2 283 007 (60.6%) were female, 3 053 688 (81.1%) were non-Hispanic white, 351 985 (9.3%) were non-Hispanic black, and 198 778 (5.3%) were Hispanic. In a multilevel analysis including county and enrollee, the county of residence explained 9.2% of an enrollee’s odds of receiving prolonged opioids after adjusting for individual enrollee characteristics. The correlation between a county’s Republican presidential vote and the adjusted rate of Medicare Part D recipients receiving prescriptions for prolonged opioid use was 0.42 (P < .001). In the 693 counties with adjusted rates of opioid prescription significantly higher than the mean county rate, the mean (SE) Republican presidential vote was 59.96% (1.73%), vs 38.67% (1.15%) in the 638 counties with significantly lower rates. Adjusting for county-level socioeconomic measures in linear regression models explained approximately two-thirds of the association of opioid rates and presidential voting rates.Conclusions and Relevance Support for the Republican candidate in the 2016 election is a marker for physical conditions, economic circumstances, and cultural forces associated with opioid use. The commonly used socioeconomic indicators do not totally capture all of those forces.

Association of Chronic Opioid Use With Presidential Voting Patterns in US Counties in 2016