Association of Chronic Opioid Use With Presidential Voting Patterns in US Counties in 2016 | Health Disparities | JAMA Network Open | JAMA Network
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A countdown of the most-viewed articles from each of the JAMA Network journals in 2018. They include articles on US trends in suicide attempts, health care spending in the US and high-income countries, the carbohydrate-insulin model of obesity, and more.

King  NB, Fraser  V, Boikos  C, Richardson  R, Harper  S.  Determinants of increased opioid-related mortality in the United States and Canada, 1990-2013: a systematic review.  Am J Public Health. 2014;104(8):e32-e42. doi:10.2105/AJPH.2014.301966PubMedGoogle ScholarCrossref
Rudd  RA, Seth  P, David  F, Scholl  L.  Increases in drug and opioid-involved overdose deaths—United States, 2010-2015.  MMWR Morb Mortal Wkly Rep. 2016;65(5051):1445-1452. doi:10.15585/mmwr.mm655051e1PubMedGoogle ScholarCrossref
Paulozzi  LJ, Mack  KA, Hockenberry  JM.  Variation among states in prescribing of opioid pain relievers and benzodiazepines—United States, 2012.  J Safety Res. 2014;51:125-129. doi:10.1016/j.jsr.2014.09.001PubMedGoogle ScholarCrossref
Morden  NE, Munson  JC, Colla  CH,  et al.  Prescription opioid use among disabled Medicare beneficiaries: intensity, trends, and regional variation.  Med Care. 2014;52(9):852-859. doi:10.1097/MLR.0000000000000183PubMedGoogle ScholarCrossref
Mundkur  ML, Rough  K, Huybrechts  KF,  et al.  Patterns of opioid initiation at first visits for pain in United States primary care settings.  Pharmacoepidemiol Drug Saf. 2018;27(5):495-503. doi:10.1002/pds.4322PubMedGoogle ScholarCrossref
Guy  GP  Jr, Zhang  K, Bohm  MK,  et al.  Vital signs: changes in opioid prescribing in the United States, 2006-2015.  MMWR Morb Mortal Wkly Rep. 2017;66(26):697-704. doi:10.15585/mmwr.mm6626a4PubMedGoogle ScholarCrossref
Painter  JT, Crofford  LJ, Talbert  J.  Geographic variation of chronic opioid use in fibromyalgia.  Clin Ther. 2013;35(3):303-311. doi:10.1016/j.clinthera.2013.02.003PubMedGoogle ScholarCrossref
Kuo  YF, Raji  MA, Chen  NW, Hasan  H, Goodwin  JS.  Trends in opioid prescriptions among Part D Medicare recipients from 2007 to 2012.  Am J Med. 2016;129(2):221.e21-221.e30. doi:10.1016/j.amjmed.2015.10.002PubMedGoogle ScholarCrossref
McDonald  DC, Carlson  KE.  The ecology of prescription opioid abuse in the USA: geographic variation in patients’ use of multiple prescribers (“doctor shopping”).  Pharmacoepidemiol Drug Saf. 2014;23(12):1258-1267. doi:10.1002/pds.3690PubMedGoogle ScholarCrossref
Keyes  KM, Cerdá  M, Brady  JE, Havens  JR, Galea  S.  Understanding the rural-urban differences in nonmedical prescription opioid use and abuse in the United States.  Am J Public Health. 2014;104(2):e52-e59. doi:10.2105/AJPH.2013.301709PubMedGoogle ScholarCrossref
McDonald  DC, Carlson  K, Izrael  D.  Geographic variation in opioid prescribing in the U.S.  J Pain. 2012;13(10):988-996. doi:10.1016/j.jpain.2012.07.007PubMedGoogle ScholarCrossref
Webster  BS, Cifuentes  M, Verma  S, Pransky  G.  Geographic variation in opioid prescribing for acute, work-related, low back pain and associated factors: a multilevel analysis.  Am J Ind Med. 2009;52(2):162-171. doi:10.1002/ajim.20655PubMedGoogle ScholarCrossref
Monnat  SM. Deaths of despair and support for Trump in the 2016 presidential election. Pennsylvania State University research brief. Published December 4, 2016. Accessed January 8 2018.
Frydl  K. The oxy electorate: a scourge of addiction and death siloed in fly-over country. Medium. Published November 16, 2016. Accessed January 8, 2018.
Jacobs  H. The revenge of the “Oxy electorate” helped fuel Trump’s election upset. Business Insider. Published November 23, 2016. Accessed January 8, 2018.
Lopez  G. Most Ohio and Pennsylvania counties that flipped from Obama to Trump are wracked by heroin: another potential explanation for Trump’s surprising win. Vox. Published November 22, 2016. Accessed January 8, 2018.
Enke B. Moral values and voting: Trump and beyond. Social Studies Research Network. National Bureau of Economic Research working paper 24268. Published June 4, 2017. Accessed January 22, 2018.
Goetz  S, Partridge  M, Stephens  H. The economic status of rural America in the Trump era. Munich Personal RePEc Archive. Published March 23, 2017. Accessed January 8, 2018.
von Elm  E, Altman  DG, Egger  M, Pocock  SJ, Gøtzsche  PC, Vandenbroucke  JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.  Ann Intern Med. 2007;147(8):573-577. doi:10.7326/0003-4819-147-8-200710160-00010PubMedGoogle ScholarCrossref
Hoadley  J, Cubanski  J, Neuman  T. Medicare Part D at ten years: the 2015 marketplace and key trends. Kaiser Family Foundation. Published October 5, 2015. Accessed March 16, 2018.
US Department of Agriculture. Rural-Urban Continuum Codes. Updated October 12, 2016. Accessed July 14, 2017.
Centers for Disease Control and Prevention. State prescription drug laws. Updated March 23, 2016. Accessed January 8, 2018.
Raji  M, Kuo  YF, Chen  NW, Hasan  H, Wilkes  D, Goodwin  JS.  Impact of laws regulating pain clinics on opioid prescribing and opioid-related toxicity among Texas Medicare Part D beneficiaries.  J Pharm Tech. 2017;33(2):60-65. doi:10.1177/8755122516686226Google ScholarCrossref
Dowell  D, Zhang  K, Noonan  RK, Hockenberry  JM.  Mandatory provider review and pain clinic laws reduce the amounts of opioids prescribed and overdose death rates.  Health Aff (Millwood). 2016;35(10):1876-1883. doi:10.1377/hlthaff.2016.0448PubMedGoogle ScholarCrossref
Wasfy  JH, Stewart  C  III, Bhambhani  V.  County community health associations of net voting shift in the 2016 U.S. presidential election.  PLoS One. 2017;12(10):e0185051. doi:10.1371/journal.pone.0185051PubMedGoogle ScholarCrossref
Bor  J.  Diverging life expectancies and voting patterns in the 2016 US presidential election.  Am J Public Health. 2017;107(10):1560-1562. doi:10.2105/AJPH.2017.303945PubMedGoogle ScholarCrossref
Rothwell  J. Economic hardship and favorable views of Trump. Gallup News Blog. Updated July 22, 2016. Accessed January 8, 2018.
Mayhew  A.  Trump through a Polanyi lens: considering community well-being.  Real-World Econ Rev. 2017;78:28-35. Accessed January 14, 2018.Google Scholar
Dalton  JE, Perzynski  AT, Zidar  DA,  et al.  Accuracy of cardiovascular risk prediction varies by neighborhood socioeconomic position.  Ann Intern Med. 2017;167(7):456-464. doi:10.7326/M16-2543PubMedGoogle ScholarCrossref
Schroeder  SA.  Shattuck Lecture. We can do better—improving the health of the American people.  N Engl J Med. 2007;357(12):1221-1228. doi:10.1056/NEJMsa073350PubMedGoogle ScholarCrossref
Brown  D. Opioids and paternalism. Am Scholar. Autumn 2017. Published September 5, 2017. Accessed January 8, 2018.
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    9 Comments for this article
    What does this mean?
    Frederick Rivara, MD, MPH | University of Washington
    This study is not siding with one political party of another. It is examining the correlation of vote for one party in November 2016 and one marker for the various factors that have been associated with the rise in opioid use in the US.
    CONFLICT OF INTEREST: Editor in Chief, JAMA Network Open
    Questionable Utility of This Analysis
    Lynn Shaffer | Mount Carmel Health System
    The scientific merit and/or the clinical or public health implications of this analysis are obscure. The importance of the paper is described as, "The causes of the opioid epidemic are incompletely understood," and their conclusion states, "Experts have struggled to explain both the root causes of the opioid epidemic and the results of the 2016 election." This paper sheds no light on the causes of the opioid epidemic and little, if any, on the reasons for the 2016 presidential election outcome.

    Why was the adjusted county opioid rate the dependent variable in the generalized linear
    mixed model? The 2016 voting occurred after the opioid prescriptions were written (or the claim for the prescriptions were submitted)? Conceptually this is somewhat contorted, since the effect of the "voting predictor" is being adjusted for by the other pre-existing county-level sociodemographic variables. It seems just as sensible to have the analysis adjust for the association of opioid prescriptions with the 2016 vote as the outcome since you can't make a case that the 2016 vote "caused" the opioid crisis (the stated focus of the paper). Could the authors clarify the use of a generalized linear mixed model? It sounds like the outcome was "adjusted county opioid rate." What link function was used?

    In their discussion the authors state that "Republican support explained 18% of the variance in county rates of opioid use in 3100 counties in the United States," however, they should clarify that this was in Model 1 which only included the percent Republican presidential vote. That statement isn't correct since when the other county-level variables were added to models 2 and 3, the percent of variance attributable to the 2016 vote dropped to 6% (the last sentence in the paragraph doesn't mention this decrease).

    The authors refer to the opioid crisis and rates of drug overdose, but the actual measurement was an opioid prescription for 90+ days. Finally, how exactly should the information surrounding the percentage of people voting for Mr. Trump be used to attack the opioid crisis? Public health practitioners are already aware that societal-level factors and socioeconomic status play a role in one's health. I doubt the authors are suggesting that physicians ask patients who they voted for before writing an opioid prescription. What further research studies are suggested based on the results of the present analysis? Even if favorable views of Mr. Trump add to the ability to measure poor health or economic hardship, it's a transient variable given that he won't be president beyond 2024. My remarks are made in the context of a medical journal offering. This information could be useful to political strategists.
    Ecologic Association of Medical Illness and Drug Use with Presidential Voting Pattern
    Marc Hochberg, MD, MPH | University of Maryland School of Medicine
    The article by Goodwin and colleagues on the association of county-level opioid prescriptions for >90 day supply with the county Presidential voting patterns among Medicare recipients reminded me of an article that noted an ecologic association between the state-specific incidence rates of Lyme disease and Presidential voting patterns in the 2004 election (1). In that study, Nadelman and Wormser noted a "remarkably similar" pattern between the Lyme disease incidence rates and Presidential voting; those states with the highest rates, >10 cases per 100,000 persons, voted Democratic (so-called "Blue" states), while those with low Lyme disease incidence rates, <10 per 100,000 persons, voted Republican (so-called "Red" states). Indeed, "the 19 states won by Kerry accounted for over 95% of the total number of cases of Lyme disease." Those authors did not perform the detailed analyses of the role of individual and county-level socioeconomic and demographic factors as reported by Goodwin and colleagues.
    When I presented these results during a "Year in Review" presentation at the 2005 annual American College of Rheumatology meeting, one of the audience members wondered whether the Kerry voters were suffering from central nervous system Lyme disease.

    1. Nadelman RB, Wormser GP: Poly-ticks: Red state versus Blue state for Lyme disease. Lancet 2005;365:280.
    Another Interpretation of the Study
    Carol Schlismann |
    I took this correlation as representing that people who believed themselves as disenfranchised would vote to rock the boat. The highest rates of opioid use and abuse in the US are in the Appalachians, areas from which people traditionally poor have been struggling mightily in the past 10-12 years.
    Even former President Obama expressed a desire that a major form of employment, coal mining, would be diminished and stopped by regulations enacted by the federal government.
    Take away the people's hope for long-term employment, reduce their access to health care, and it isn't difficult to picture voting patterns.
    Voting for Trump was a cry for help.
    "Political Science"
    Kurtis Elward, MD, MPH | None
    The JAMA Network Open article does not meet the standards and rigor of academic evaluation. The article seems to be a poorly veiled attempt to portray Trump voters as somehow impaired and convey a subtle message that one has to be on chronic narcotics or otherwise out of touch to vote for Trump.

    Within the article, however, we see less than subtle bias: "After controlling for those county characteristics, the presidential vote explained 7% of the variation in opioid use" - therefore, 93% of the variation is due to other things. Nowhere else would such conclusions see publication in
    this manner in a JAMA journal.

    It also states under its Methods section:

    "Chronic opioid use was defined as receiving a prescription for a 90-day or greater supply in 1 year." So, if someone had surgery or any level of chronic pain, they would be classified as "chronic opioid users." It's also not saying that these people voted for Trump - just that patterns of voting happened to correlate with a prescription pattern. The R values were mediocre at best.

    Moreover, under the "limitations" one reads,
    "The county presidential vote is from 2016 and includes all voters, while the information on prolonged opioid prescriptions was from 2015 and was generated only from Medicare Part D enrollees, approximately 72% of the entire Medicare population." 

    So the population was drawn from Medicare enrollees, many of whom have chronic conditions, and have any use of any opioid for any reason at any dose. The authors only superficially portray these limitations and restrictions, which limits the results and conclusions they advance.

    Kurt Elward, MD, MPH
    Thank you Dr. Goodwin and collegues,
    Nancy English, Ph.D. APRN, CHPN | University
    I appreciate the courage to publish and allow for comments on this very interesting relationship. This is indeed a crisis and one which will have consequences from the grief and often guilt of families for years and possibly generations to come. Possibly out of this there will be an increased attention to the mental health needs of communities and individuals. Nancy English
    Interesting But Skewed Premise of Opioid Use Amongst Trump Voters
    Anish Korula, M.P.A '03 | USC Public Administration Alum
    It was an interesting read, but I feel the study has an inherently biased hypothesis that poor, white voters who voted for Trump were most affected by opioid use. It supposes that political beliefs determine the type of addiction of a specific drug. A similar premise could be made with cocaine, in that wealthy Americans (mostly white) use the refined powder, while poorer Americans (mostly African-American and other minorities) have a heavier use of crack cocaine. While this poses an interesting theory, it is primarily relating to the ability to purchase the more expensive type of cocaine, which is a socio-economic one, rather than a racial one. Similarly, in the Goodwin et al study, the addiction type is prefaced as a political one.

    Goodwin, Kuo, and Brown avoided a broader study to steer their hypothesis on the fact that opioid use is a national problem and that wealthy, Democratic-heavy counties and Democratic-leaning states (West Virginia) also suffer from this crisis. Wealthy liberals and independents also get prescriptions that they abuse, but the study's authors wanted to focus more on Trump voters, rather than a larger sample involving voters across the United States. They made some interesting points, but this kind of study needs to be repeated but broadened to include a much larger sample size of opioid use across socio-economic, ethnic, gender and sexual orientation groups.
    Interesting Relationship
    William Richardson, MD | Private Practice, previous NIH T-32 researcher
    Let me say how brave the JAMA network was in publishing such an enlightening piece of research! As a previous NIH-sponsored researcher, some issues remain.

    Correlation is not causation--not that causation is expressed.  But it IS implied.  It is SO easy to throw all kinds of factors into a multivariate analysis, even to invent some novel ones (called "transgenerated" variables, mathematical constructs from the observed data), stir them up, and get what you want out of it. We see it all the time in complex sociological research.

    The choice of "support for Trump" as one of
    the factors is prima facie evidence of bias.  SURELY there are politically-neutral cultural, economic or environmental factors for which Trump support is a surrogate.

    The "Trump support" factor was the county-wide percent of Trump voters.  That's an aggregate measure, and how can you relate that to individual drug use?

    The study asks a very interesting question, but leaves a lot of questions in terms of intent, bias and confounding variables.
    False Characterization of Chronic Opioid users
    Elizabeth Payne |
    Caught in the fray of the *opioid Crisis* is the chronic pain patient. We are demonized for our pain. The CDC guidelines for dosing opioids did not study chronic pain patients. Most of us are responsible with our medications and do not abuse them. We cannot afford to run out. It is becoming harder for doctors to prescribe what we need due to state oversight.

    I am disabled from Chiari Malformation. I have been using opioids for pain for 10 years. I have been on the same dose for 8 years. My husband, an engineer, was laid off and
    was unable to find a job in his field. He had to take his social security at 62 and is working part-time stocking groceries. Out of our $3100 month income, we pay $1100 for my meds. We do not qualify for assistance. We are concerned about GOP policies towards Medicare and Social Security. We are not the only Americans in this situation, struggling to pay bills, and deal with the spectre of being in chronic pain and what it means to need opioids.

    My demographics: I live in rural Indiana. College graduate. Artist. Active Democrat. Voted for HRC.
    Original Investigation
    Public Health
    June 22, 2018

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

    Author Affiliations
    • 1Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston
    • 2Departments of Medicine, University of Texas Medical Branch, Galveston
    • 3Sealy Center on Aging, University of Texas Medical Branch, Galveston
    • 4University of Texas Medical Branch, Galveston
    • 5Department of Medicine, University of Toronto, Toronto, Ontario, Canada
    • 6Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
    • 7Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
    JAMA Netw Open. 2018;1(2):e180450. doi:10.1001/jamanetworkopen.2018.0450
    Key Points español 中文 (chinese)

    Question  To what extent do socioeconomic measurements explain the county-level association of the 2016 US Republican presidential vote with opioid use?

    Findings  This cross-sectional analysis of a national sample of Medicare claims data found that chronic use of prescription opioid drugs was correlated with support for the Republican candidate in the 2016 US presidential election. Individual and county-level socioeconomic measures explained much of the association between the presidential vote and opioid use.

    Meaning  The association of the presidential vote with chronic opioid use underscores the importance of cultural, economic, and environmental factors associated with the opioid epidemic.


    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.