Trump voters are all druggies
That's how the more extreme media outlets will headline the latest piece of research in the medical journals. But it aint so. A bit hard to know where to begin. I probably should start by congratulating the authors on their quite humble conclusions. They say nothing like my headline above. But, as Churchill said of Clement Attlee, they have much to be humble about.
They know and admit that their data is what statisticians call "ecological" (group based) but fail to mention that the correlations emerging from such data are usually much higher than what emerges in correlations using individual data. So their results are a poor guide to what individuals do.
And the fact that they had individual data but did not use it suggests that all relationships in the individual data were negligible, meaning that there was NO tendency for Trump voters to overuse prescription opiods. That is a highly critical interpretation but, in view of the revelations inspired by Ioannidis, that is actually a conservative conclusion. What Ioannidis showed can be summarized simply as "Medical researchers are crooks". Sad. And when an opportunity to bash Trump offers itself, the temptation to cheat could well be overwhelming.
But let me be charitable and assume that all the work was honestly done and all the relevant findings were reported. The big issue then with the research is the problem of control. Why was there greater use of prescription opioids in counties where the voters favoured Trump? The obvious explanation would be that Trump voters are poor and are tired of being looked down on by leading Democrats, who used to represent them (See Hillary's "deplorables"). So was that examined in the present study?
They made a good attempt at it and did find that socioeconomic variables explained two thirds of the relationship between Trump-voting and prescription opioid use. But they apparently had no data on income so they used rough proxies of it. Much error could flow from that. Better income data might have shown that opioid use was irrelevant and all the Trump voting could have been accounted for by income. I doubt that it was but the present research cannot exclude it.
On a technical note, they based their analysis on quintiles -- a common but disreputable technique. Why group your data when you can use it individually? I am afraid that the usual reason is that there is no overall relationship in the data. You can show a relationship only by throwing away three fifths of it. Sad.
Finally, let me point out that, even if we accept their findings, there are many possible interpretations of them. One that occurs to me is that Obamacare has made it more difficult for poor people to get treated for their ailments (overcrowded waiting rooms, doctors not taking welfare patients, doctors quitting medicine to go and play golf rather than spend half their day on paperwork etc.) and they blame that on the architects of Obamacare -- the Democrats. So Mr Trump's talk of dumping Obamacare would be attractive
And prescription opioids are only half the story It could be that the poor mainly use doctors to get their fix. Because of being poor, they cannot afford to buy from street dealers. So the Trump voters were actually more law abiding. I think Mr Trump might like that interpretation.
Association of Chronic Opioid Use With Presidential Voting Patterns in US Counties in 2016
James S. Goodwin et al.
Importance 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.