县级阿片类药物减害与治疗效果异质性：一项模拟建模分析
County-Level Heterogeneity in Opioid Harm Reduction and Treatment Effects: A Simulation Modeling Analysis
- University of Pittsburgh（匹兹堡大学）
- The Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过模拟模型分析宾夕法尼亚州六县，发现纳洛酮和丁丙诺啡的减害效果存在显著县级异质性，强调县级背景对资源分配决策的重要性。
AI中文摘要：
阿片类药物过量死亡仍然是美国严重的公共卫生危机，各县在流行病轨迹、基线资源和当地背景方面存在差异，负担也各不相同。虽然通过纳洛酮分发和丁丙诺啡治疗进行减害都是基于证据的策略，但县级效果信息的缺乏阻碍了决策者跨县优先分配资源的能力。我们开发了一个阿片类药物使用障碍（OUD）的模拟模型，分别针对宾夕法尼亚州的六个县进行校准，涵盖大型城市（阿勒格尼、费城）、中型城市（伊利、道芬）和农村（克利尔菲尔德、哥伦比亚）环境。我们预测了在丁丙诺啡配发和纳洛酮分发增加三个水平（高于每个县基线的10%、20%和30%）下，2025年至2029年五年期间的县级特定过量死亡轨迹。预计纳洛酮分发量高于观察到的县级基线水平30%时，将使阿勒格尼县2029年过量死亡减少约70%（95%不确定性区间，UI：55-81%），伊利县减少11%（95% UI：4-17%），克利尔菲尔德县减少28%（95% UI：2-64%）。增加丁丙诺啡所带来的过量死亡预计减少幅度始终较小（10%-23%），但伊利县除外，丁丙诺啡产生的预计减少幅度为20%，而纳洛酮为11%。纳洛酮反应性的异质性与每个县的历史纳洛酮配发变异性密切相关。纳洛酮分发的相同比例增加在不同县产生的预计死亡率降低差异显著，这取决于每个县的基线分发历史，而这一模式仅从死亡率统计数据中无法显现。县级背景对于在县级层面指导减害和治疗优先排序非常重要。
英文摘要：
Opioid overdose deaths remain a severe public health crisis in the US, with heterogeneous burden across counties that differ in epidemic trajectory, baseline resources, and local context. While harm reduction through naloxone distribution and buprenorphine treatment are both evidence-based strategies, limited information on county-level effects hinders the ability of policymakers to prioritize resources across counties. We developed a simulation model of opioid use disorder (OUD), calibrated separately to six Pennsylvania counties spanning large urban (Allegheny, Philadelphia), intermediate-sized (Erie, Dauphin), and rural (Clearfield, Columbia) settings. We projected county-specific overdose mortality trajectories under three levels of increase in buprenorphine dispensing and naloxone distribution (10%, 20%, and 30% above each county's baseline), over a five-year horizon from 2025 to 2029. A 30% increase in naloxone distribution above observed county baseline levels was projected to reduce 2029 overdose deaths by approximately 70% (95% uncertainty interval, UI: 55-81%) in Allegheny County, 11% (95% UI:4-17%) in Erie County, and 28% (95% UI:2-64%) in Clearfield County. Projected reductions in overdose deaths from increasing buprenorphine were consistently smaller (10%-23%), except that in Erie buprenorphine produced larger projected reduction by 20% vs 11% for naloxone. Heterogeneity in naloxone responsiveness was strongly associated with each county's historical naloxone dispensing variability. The same proportional increase in naloxone distribution yields substantially different projected mortality reductions across counties depending on each county's baseline distribution history, a pattern invisible from mortality statistics alone. County-level context is important for informing harm reduction and treatment prioritization at the county level.
