ArticleJAMA network open2018
Association of Chronic Opioid Use With Presidential Voting Patterns in US Counties in 2016.
Article in JAMA network open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 62 citations in OpenAlex.
- Exploring the influence of electoral political orientation on health outcomes: a scoping review.Cadernos de saude publica · 2026Article
- A Call for Measuring Partisanship in US Public Health Research.American journal of public health · 2024Article
- Experience of Personal Loss Due to Drug Overdose Among US Adults.JAMA health forum · 2024Article
- Political Ideology Direction of Policy Agendas and Maternal Mortality Outcomes in the U.S., 1915-2007.Maternal and child health journal · 2024Article
- The Imperative of Regulation: The Co-creation of a Medical and Non-medical US Opioid Crisis.Psychoactives · 2023Article
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- Political environment and mortality rates in the United States, 2001-19: population based cross sectional analysis.BMJ (Clinical research ed.) · 2022Article
- The harmful effects of partisan polarization on health.PNAS nexus · 2022Article
- State Legislators' Divergent Social Media Response to the Opioid Epidemic from 2014 to 2019: Longitudinal Topic Modeling Analysis.Journal of general internal medicine · 2021Article
- County-Level Socioeconomic and Political Predictors of Distancing for COVID-19.American journal of preventive medicine · 2021Article
- Association of Opioid Use Disorder With 2016 Presidential Voting Patterns: Cross-sectional Study in New York State at Census Tract Level.JMIR public health and surveillance · 2021Article
- Regional and temporal variation in receipt of long-term opioid therapy among older breast, colorectal, lung, and prostate cancer survivors in the United States.Cancer medicine · 2021Article
- Article
- Time Trends in Opioid Use by Dementia Severity in Long-Term Care Nursing Home Residents.Journal of the American Medical Directors Association · 2021Article
- Opioid Prescribing by Primary Care Providers: a Cross-Sectional Analysis of Nurse Practitioner, Physician Assistant, and Physician Prescribing Patterns.Journal of general internal medicine · 2020Article
- Growing sense of social status threat and concomitant deaths of despair among whites.SSM - population health · 2019Article
- Association of Disability With Mortality From Opioid Overdose Among US Medicare Adults.JAMA network open · 2019Article
- The politics hypothesis and racial disparities in infants' health in the United States.SSM - population health · 2019Article
- Frequency and Risk Factors for Prolonged Opioid Prescriptions After Surgery for Brachial Plexus Injury.The Journal of hand surgery · 2019Article
- Association between cannabis laws and opioid prescriptions among privately insured adults in the US.Preventive medicine · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
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.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.