一种用于从空间错位数据估计潜在阿片类药物滥用流行率的贝叶斯多尺度综合丰度模型
A Bayesian Multiscale Integrated Abundance Model for Estimating Latent Opioid Misuse Prevalence from Spatially Misaligned Data
- Wake Forest University（维克森林大学）
- Wake Forest University School of Medicine（维克森林大学医学院）
- Yale School of Public Health（耶鲁公共卫生学院）
- Gillings School of Public Health, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校吉林斯公共卫生学院）
- Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对空间错位数据，提出贝叶斯多尺度综合丰度模型，利用Fisher非中心超几何分布和两阶段MCMC算法，联合多源指标估计潜在阿片滥用流行率，在俄亥俄州数据上揭示县内异质性。
AI中文摘要：
估计小区域阿片类药物滥用流行率对于针对性实施公共卫生干预至关重要，然而直接测量不可得，相关的监测数据往往在错位的区域单元上报告。我们提出一种贝叶斯多尺度综合丰度（MIA）模型，通过联合分析在不同地理支撑上观测的多个间接监测指标，来估计潜在阿片类药物滥用流行率。该模型通过将所有来源地理区域表示为观测区域支撑交集形成的公共原子空间单元集合，扩展了现有的综合丰度模型。原子层面的潜在流行率使用Fisher非中心超几何分布建模，该分布在尊重局部人口约束的同时保持县级流行率总数。为实现可扩展推断，我们开发了一种两阶段组合马尔可夫链蒙特卡洛算法，该算法结合了定制采样策略和并行计算。模拟研究表明，与常见的降尺度方法相比，所提方法降低了偏差和均方根误差。我们将该模型应用于2010-2023年俄亥俄州数据，整合了州级调查估计、县级阿片类药物过量死亡和治疗入院计数，以及邮政编码制表区级紧急医疗服务纳洛酮给药计数。结果揭示了显著的县内异质性，并识别出县级分析会遗漏的局部阿片类药物滥用流行率升高区域。
英文摘要：
Estimating small area prevalence of opioid misuse is critical for targeting public health interventions, yet direct measures are unavailable and related surveillance data are often reported on misaligned areal units. We propose a Bayesian Multiscale Integrated Abundance (MIA) model for estimating latent opioid misuse prevalence by jointly analyzing multiple indirect surveillance indicators observed on different geographic supports. The model extends existing integrated abundance models by representing all source geographies through a common set of atomic spatial units formed by the intersections of observed areal supports. Latent prevalence at the atomic level is modeled using a Fisher noncentral hypergeometric distribution, which preserves county-level prevalence totals while respecting local population constraints. To enable scalable inference, we develop a two-stage compositional Markov chain Monte Carlo algorithm that combines customized sampling strategies and parallel computing. Simulation studies show the proposed approach reduces bias and root mean squared error relative to common downscaling methods. We apply the model to Ohio data from 2010-2023, integrating state-level survey estimates, county-level counts of opioid overdose deaths and treatment admissions, and ZIP code tabulation area-level counts of emergency medical services naloxone administrations. Results reveal substantial within-county heterogeneity and identify localized areas of elevated opioid misuse prevalence that would be missed by county-level analyses.
