A Bayesian Spatio-temporal Model to Optimize Allocation of Buprenorphine in North Carolina.
A Bayesian Spatio-temporal Model to Optimize Allocation of Buprenorphine in North Carolina.
复制标题
用于优化北卡罗来纳州丁丙诺啡分配的贝叶斯时空模型。
DOI：
10.1080/2330443x.2023.2218448
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Hepler,StaciA
中科院分区：
文献类型：
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作者：
Dong,Qianyu;Kline,David;Hepler,StaciA
阿片类药物的流行是一场持续的公共卫生危机。在北卡罗来纳州，由于非法阿片类药物过量而导致的过量死亡在过去5-7年中急剧增加。丁丙诺啡是美国食品和药物管理局批准用于治疗阿片类药物使用障碍的药物，并通过处方获得。在2023年1月之前，提供者必须获得豁免，并且限制他们可以开丁丙诺啡的患者数量。因此，确定增加丁丙诺啡将使过量死亡的总体减少最大的县可以帮助政策制定者针对某些地理区域，为有效的公共卫生应对提供信息。我们提出了一个贝叶斯时空模型，该模型将非法阿片类药物过量死亡率的年度县级变化与丁丙诺啡处方的变化联系起来。我们使用我们的模型来预测未来几年全州范围内非法阿片类药物过量死亡的数量和比率，我们使用非线性约束优化来确定每个县在一组可用资源约束下的最佳丁丙诺啡增加。我们的模型估计，在考虑其他协变量后，死亡率与丁丙诺啡增加之间存在负相关关系，我们确定的最佳单年分配策略估计可将阿片类药物过量死亡减少5%以上。本文的补充材料可在网上查阅。
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.