一种考虑风险人口不确定性的贝叶斯空间Berkson误差方法用于估计小区域阿片类药物死亡率
A Bayesian Spatial Berkson error approach to estimate small area opioid mortality rates accounting for population-at-risk uncertainty
AI总结：
提出贝叶斯空间Berkson误差模型，在疾病映射中纳入风险人口不确定性，以纠正小区域阿片类药物死亡率估计偏差，并应用于佐治亚州159个县。
摘要：
监测小区域地理人口趋势中的阿片类药物死亡率，对指导预防性资源分配具有重大意义。获得小区域阿片类药物死亡率估计的常见方法是使用标准疾病映射方法，其中风险人口估计被视为固定且已知。假设固定人口忽略了小区域人口估计周围的不确定性，这可能导致风险估计产生偏差，并低估其相关的不确定性。我们提出了一种贝叶斯空间Berkson误差（BSBE）模型，以在疾病映射模型中纳入风险人口不确定性。我们通过模拟研究将BSBE方法与朴素方法（将分母视为固定）进行比较，以说明这种假设可能导致的偏差。我们展示了BSBE模型在2020年佐治亚州159个县考虑风险人口不确定性的阿片类药物死亡风险估计中的应用。利用我们提出的方法将有助于为阿片类药物相关的公共卫生应对、政策和资源分配中的干预措施提供信息。此外，我们提供了一个通用框架，以改进健康指标的估计和映射。
英文摘要：
Monitoring small-area geographical population trends in opioid mortality has large scale implications to informing preventative resource allocation. A common approach to obtain small area estimates of opioid mortality is to use a standard disease mapping approach in which population-at-risk estimates are treated as fixed and known. Assuming fixed populations ignores the uncertainty surrounding small area population estimates, which may bias risk estimates and under-estimate their associated uncertainties. We present a Bayesian Spatial Berkson Error (BSBE) model to incorporate population-at-risk uncertainty within a disease mapping model. We compare the BSBE approach to the naive (treating denominators as fixed) using simulation studies to illustrate potential bias resulting from this assumption. We show the application of the BSBE model to obtain 2020 opioid mortality risk estimates for 159 counties in GA accounting for population-at-risk uncertainty. Utilizing our proposed approach will help to inform interventions in opioid related public health responses, policies, and resource allocation. Additionally, we provide a general framework to improve in the estimation and mapping of health indicators.
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