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Identifying sociodemographic profiles of veterans at risk for high-dose opioid prescribing using classification and regression trees

Jacob S. Lipkin, MD, Joshua M. Thorpe, PhD, MPH, Walid F. Gellad, MD, MPH, Joseph T. Hanlon, PharmD, MS, Xinhua Zhao, PhD, Carolyn T. Thorpe, PhD, MPH, Florentina E. Sileanu, MS, John P. Cashy, PhD, Jennifer A. Hale, BA, Maria K. Mor, PhD, Thomas R. Radomski, MD, MS, Chester B. Good, MD, MPH, Michael J. Fine, MD, MSc, Leslie R. M. Hausmann, PhD


Objective: To identify sociodemographic profiles of patients prescribed high-dose opioids.

Design: Cross-sectional cohort study.

Setting/Patients: Veterans dually-enrolled in Veterans Health Administration and Medicare Part D, with 1 opioid prescription in 2012.

Main Outcome Measures: We identified five patient-level demographic characteristics and 12 community variables reflective of region, socioeconomic deprivation, safety, and internet connectivity. Our outcome was the proportion of veterans receiving >120 morphine milligram equivalents (MME) for 90 consecutive days, a Pharmacy Quality Alliance measure of chronic high-dose opioid prescribing. We used classification and regression tree (CART) methods to identify risk of chronic high-dose opioid prescribing for sociodemographic subgroups.

Results: Overall, 17,271 (3.3 percent) of 525,716 dually enrolled veterans were prescribed chronic high-dose opioids. CART analyses identified 35 subgroups using four sociodemographic and five community-level measures, with high-dose opioid prescribing ranging from 0.28 percent to 12.1 percent. The subgroup (n = 16,302) with highest frequency of the outcome included veterans who were with disability, age 18-64 years, white or other race, and lived in the Western Census region. The subgroup (n = 14,835) with the lowest frequency of the outcome included veterans who were without disability, did not receive Medicare Part D Low Income Subsidy, were >85 years old, and lived in communities within the second and sixth to tenth deciles of community public assistance.

Conclusions: Using CART analyses with sociodemographic and community-level variables only, we identified subgroups of veterans with a 43-fold difference in chronic high-dose opioid prescriptions. Interactions among disability, age, race/ ethnicity, and region should be considered when identifying high-risk subgroups in large populations.


prescription opioids, social determinants of health, health disparities, machine learning, CART

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