Identifying sociodemographic profiles of veterans at risk for high-dose opioid prescribing using classification and regression trees
DOI:
https://doi.org/10.5055/jom.2020.0599Keywords:
prescription opioids, social determinants of health, health disparities, machine learning, CARTAbstract
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.
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