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Objective To assess whether chronic pain increases the risk of COVID-19 complications and whether opioid use disorder (OUD) differentiates this risk among New York State Medicaid beneficiaries. Design, Setting, and Subjects This was a retrospective cohort study of New York State Medicaid claims data. We evaluated Medicaid claims from March 2019 through December 2020 to determine whether chronic pain increased the risk of COVID-19 emergency department (ED) visits, hospitalizations, and complications and whether this relationship differed by OUD status. We included beneficiaries 18–64 years of age with 10 months of prior enrollment. Patients with chronic pain were propensity score-matched to those without chronic pain on demographics, utilization, and comorbidities to control for confounders and were stratified by OUD. Complementary log–log regressions estimated hazard ratios (HRs) of COVID-19 ED visits and hospitalizations; logistic regressions estimated odds ratios (ORs) of hospital complications and readmissions within 0–30, 31–60, and 61–90 days. Results Among 773 880 adults, chronic pain was associated with greater hazards of COVID-related ED visits (HR = 1.22 [95% CI: 1.16–1.29]) and hospitalizations (HR = 1.19 [95% CI: 1.12–1.27]). Patients with chronic pain and OUD had even greater hazards of hospitalization (HR = 1.25 [95% CI: 1.07–1.47]) and increased odds of hepatic- and cardiac-related events (OR = 1.74 [95% CI: 1.10–2.74]). Conclusions Chronic pain increased the risk of COVID-19 ED visits and hospitalizations. Presence of OUD further increased the risk of COVID-19 hospitalizations and the odds of hepatic- and cardiac-related events. Results highlight intersecting risks among a vulnerable population and can inform tailored COVID-19 management. [ABSTRACT FROM AUTHOR]

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision support tools for public health practitioners. To facilitate practitioners' use of machine learning as a decision support tool for area-level intervention, we developed and applied 4 practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016–June 2020 (n  = 1,408) and neighborhood-level US Census data. We employed 2 disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5%–36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5%–20% statewide implementation capacities for neighborhood-level resource deployment. We describe the health equity implications of use of predictive modeling to guide interventions along the lines of urbanicity, racial/ethnic composition, and poverty. We then discuss considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]