Predicting the Future Course of Opioid Overdose Mortality: An Example From Two US States

• Type of Material: Article
• Authors: Natalie Sumetsky, University of Pittsburgh; Christina Mair, University of Pittsburgh; Katherine Wheeler-Martin, New York University; Magdalena Cerda, New York University; Lance Waller, Emory University; William R Ponicki, Pacific Institute for Research and Evaluation; Paul J Gruenewald, Pacific Institute for Research and Evaluation
• Language: English
• Date: 2021-01-01
• Publisher: LIPPINCOTT WILLIAMS & WILKINS
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2020 Wolters Kluwer Health, Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1097/EDE.0000000000001264
• Title of Journal or Parent Work: EPIDEMIOLOGY
• Volume: 32
• Issue: 1
• Start Page: 61
• End Page: 69
• Grant/Funding Information: This research was performed under a subcontract to National Institute on Drug Abuse research grant R01-DA039962, “Prescription Drug Monitoring Programs and Opioid-Related Harm”, Magdalena Cerdá, P.I., and National Institute on Alcohol Abuse and Alcoholism Research Center grant P60-AA006282, “Environmental Approaches to Prevention”, Paul J. Gruenewald, P.I.
• Keywords: HOSPITALIZATIONS; Science & Technology; Opioid-related disorders; UNITED-STATES; Spatial analysis; GROWTH; Life Sciences & Biomedicine; TRENDS; DEATHS; DRUG OVERDOSES; TIME; Epidemiologic methods; NONLINEAR MODELS; Public, Environmental & Occupational Health; Spatiotemporal analysis

Abstract

Background: The rapid growth of opioid abuse and the related mortality across the United States has spurred the development of predictive models for the allocation of public health resources. These models should characterize heterogeneous growth across states using a drug epidemic framework that enables assessments of epidemic onset, rates of growth, and limited capacities for epidemic growth. Methods: We used opioid overdose mortality data for 146 North and South Carolina counties from 2001 through 2014 to compare the retrodictive and predictive performance of a logistic growth model that parameterizes onsets, growth, and carrying capacity within a traditional Bayesian Poisson space-time model. Results: In fitting the models to past data, the performance of the logistic growth model was superior to the standard Bayesian Poisson space-time model (deviance information criterion: 8,088 vs. 8,256), with reduced spatial and independent errors. Predictively, the logistic model more accurately estimated fatality rates 1, 2, and 3 years in the future (root mean squared error medians were lower for 95.7% of counties from 2012 to 2014). Capacity limits were higher in counties with greater population size, percent population age 45-64, and percent white population. Epidemic onset was associated with greater same-year and past-year incidence of overdose hospitalizations. Conclusion: Growth in annual rates of opioid fatalities was capacity limited, heterogeneous across counties, and spatially correlated, requiring spatial epidemic models for the accurate and reliable prediction of future outcomes related to opioid abuse. Indicators of risk are identifiable and can be used to predict future mortality outcomes.