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

Qianyu Dong, David Kline and Staci A. Hepler

Statistics and Public Policy, 2023, vol. 10, issue 1, 2218448

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.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/2330443X.2023.2218448 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:usppxx:v:10:y:2023:i:1:p:2218448

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uspp20

DOI: 10.1080/2330443X.2023.2218448

Access Statistics for this article

Statistics and Public Policy is currently edited by Eric Sampson

More articles in Statistics and Public Policy from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().