Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties

Author:

Cerdá Magdalena1,Hamilton Ava D.2,Hyder Ayaz3,Rutherford Caroline2,Bobashev Georgiy4,Epstein Joshua M.5,Hatna Erez5,Krawczyk Noa1,El-Bassel Nabila6,Feaster Daniel J.7,Keyes Katherine M.2

Affiliation:

1. Department of Population Health, New York University School of Medicine, New York, NY

2. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY

3. Division of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, OH

4. Center for Data Science, RTI International, Research Triangle Park, NC

5. Department of Epidemiology, New York University School of Global Public Health, New York, NY

6. School of Social Work, Columbia University, New York, NY

7. Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL.

Abstract

Background: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. Methods: Our simulations covered 2020–2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. Results: Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250–300% increases in buprenorphine initiation coupled with 200–1000% increases in naloxone, depending on the county. Conclusions: Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county’s experience with past and current interventions.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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