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Socioeconomic risk factors for fatal opioid overdoses in the United States: Findings from the Mortality Disparities in American Communities Study (MDAC)

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  • Sean F Altekruse
  • Candace M Cosgrove
  • William C Altekruse
  • Richard A Jenkins
  • Carlos Blanco

Abstract

Background: Understanding relationships between individual-level demographic, socioeconomic status (SES) and U.S. opioid fatalities can inform interventions in response to this crisis. Methods: The Mortality Disparities in American Community Study (MDAC) links nearly 4 million 2008 American Community Survey responses to the 2008–2015 National Death Index. Univariate and multivariable models were used to estimate opioid overdose fatality hazard ratios (HR) and 95% confidence intervals (CI). Results: Opioid overdose was an overrepresented cause of death among people 10 to 59 years of age. In multivariable analysis, compared to Hispanics, Whites and American Indians/Alaska Natives had elevated risk (HR = 2.52, CI:2.21–2.88) and (HR = 1.88, CI:1.35–2.62), respectively. Compared to women, men were at-risk (HR = 1.61, CI:1.50–1.72). People who were disabled were at higher risk than those who were not (HR = 2.80, CI:2.59–3.03). Risk was higher among widowed than married (HR = 2.44, CI:2.03–2.95) and unemployed than employed individuals (HR = 2.46, CI:2.17–2.79). Compared to adults with graduate degrees, those with high school only were at-risk (HR = 2.48, CI:2.00–3.06). Citizens were more likely than noncitizens to die from this cause (HR = 4.62, CI:3.48–6.14). Compared to people who owned homes with mortgages, those who rented had higher HRs (HR = 1.36, CI:1.25–1.48). Non-rural residents had higher risk than rural residents (HR = 1.46, CI:1.34, 1.59). Compared to respective referent groups, people without health insurance (HR = 1.30, CI:1.20–1.41) and people who were incarcerated were more likely to die from opioid overdoses (HR = 2.70, CI:1.91–3.81). Compared to people living in households at least five-times above the poverty line, people who lived in poverty were more likely to die from this cause (HR = 1.36, CI:1.20–1.54). Compared to people living in West North Central states, HRs were highest among those in South Atlantic (HR = 1.29, CI:1.11, 1.50) and Mountain states (HR = 1.58, CI:1.33, 1.88). Discussion: Opioid fatality was associated with indicators of low SES. The findings may help to target prevention, treatment and rehabilitation efforts to vulnerable groups.

Suggested Citation

  • Sean F Altekruse & Candace M Cosgrove & William C Altekruse & Richard A Jenkins & Carlos Blanco, 2020. "Socioeconomic risk factors for fatal opioid overdoses in the United States: Findings from the Mortality Disparities in American Communities Study (MDAC)," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-16, January.
    • Handle: RePEc:plo:pone00:0227966
    DOI: 10.1371/journal.pone.0227966
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    References listed on IDEAS

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    Cited by:

    1. Janet Currie & Hannes Schwandt, 2021. "The Opioid Epidemic Was Not Caused by Economic Distress but by Factors That Could Be More Rapidly Addressed," The ANNALS of the American Academy of Political and Social Science, , vol. 695(1), pages 276-291, May.
    2. Martijn van Hasselt & Christopher R. Bollinger & Jeremy W. Bray, 2022. "A Bayesian approach to account for misclassification in prevalence and trend estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 351-367, March.
    3. Jia, Jing & Li, Zhongtian, 2024. "Opioid abuse and labor investment efficiency," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1267-1285.
    4. Janet Currie & Hannes Schwandt, 2020. "The Opioid Epidemic Was Not Primarily Caused by Economic Distress But by Other Factors that Can be More Readily Addressed," Working Papers 2020-25, Princeton University. Economics Department..
    5. Wei-Hsuan Lo-Ciganic & Julie M Donohue & Eric G Hulsey & Susan Barnes & Yuan Li & Courtney C Kuza & Qingnan Yang & Jeanine Buchanich & James L Huang & Christina Mair & Debbie L Wilson & Walid F Gellad, 2021. "Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-18, March.

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