A Bayesian Spatio-temporal Model to Optimize Allocation of Buprenorphine in North Carolina

Abstract

The opioid epidemic is an ongoing public health crisis. In North Carolina, overdose deaths due to illicit opioid overdose have sharply increased over the last 5–7 years. Buprenorphine is a U.S. Food and Drug Administration approved medication for treatment of opioid use disorder and is obtained by prescription. Prior to January 2023, providers had to obtain a waiver and were limited in the number of patients that they could prescribe buprenorphine. Thus, identifying counties where increasing buprenorphine would yield the greatest overall reduction in overdose death can help policymakers target certain geographical regions to inform an effective public health response. We propose a Bayesian spatio-temporal model that relates yearly, county-level changes in illicit opioid overdose death rates to changes in buprenorphine prescriptions. We use our model to forecast the statewide count and rate of illicit opioid overdose deaths in future years, and we use nonlinear constrained optimization to identify the optimal buprenorphine increase in each county under a set of constraints on available resources. Our model estimates a negative relationship between death rate and increasing buprenorphine after accounting for other covariates, and our identified optimal single-year allocation strategy is estimated to reduce opioid overdose deaths by over 5%. Supplementary materials for this article are available online.

KEYWORDS:

• Constrained optimization
• Geographic distribution
• Health policy
• Medication-assisted treatment
• Opioid overdose
• Resource allocation

Acknowledgments

The authors would like to acknowledge the North Carolina Department of Health and Human Services for making the data used in this analysis publicly available. In particular, we thank Scott Proescholdbell and Mary Beth Cox for help accessing the data and for insightful conversations during the research process.

Additional information

Funding

Research reported in this publication was supported by the National Institute On Drug Abuse of the National Institutes of Health under Award Number R01DA052214. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.