Drug overdose deaths, including from opioids, remain a significant public health threat to the United States (US). To abate the harms of opioid misuse, understanding its prevalence at the local level is crucial for stakeholders in communities to develop response strategies that effectively use limited resources. Although there exist several state-specific studies that provide county-level prevalence estimates, such estimates are not widely available across the country, as the datasets used in these studies are not always readily available in other states, which, therefore, has limited the wider applications of existing models. To fill this gap, we propose a Bayesian multi-state data integration approach that fully utilizes publicly available data sources to estimate county-level opioid misuse prevalence for all counties in the US. The hierarchical structure jointly models opioid misuse prevalence and overdose death outcomes, leverages existing county-level prevalence estimates in limited states and state-level estimates from national surveys, and accounts for heterogeneity across counties and states with counties' covariates and mixed effects. Furthermore, our parsimonious and generalizable modeling framework employs horseshoe+ prior to flexibly shrink coefficients and prevent overfitting, ensuring adaptability as new county-level prevalence data in additional states become available. Using real-world data, our model shows high estimation accuracy through cross-validation and provides nationwide county-level estimates of opioid misuse for the first time.

翻译：药物过量死亡（包括阿片类药物所致）仍然是美国面临的重大公共卫生威胁。为减轻阿片类药物滥用的危害，在地方层面了解其流行率对于社区利益相关者制定有效利用有限资源的应对策略至关重要。尽管存在一些针对特定州的研究提供了县级流行率估计值，但由于这些研究使用的数据集在其他州并非总能轻易获得，此类估计值并未在全国范围内广泛提供，这因此限制了现有模型的更广泛应用。为填补这一空白，我们提出了一种贝叶斯多州数据整合方法，该方法充分利用公开可用的数据源来估计美国所有县的县级阿片类药物滥用流行率。该层次结构模型联合建模阿片类药物滥用流行率和过量死亡结局，利用有限几个州现有的县级流行率估计值以及来自全国调查的州级估计值，并通过县的协变量和混合效应来解释县与州之间的异质性。此外，我们简洁且可推广的建模框架采用horseshoe+先验来灵活收缩系数并防止过拟合，确保随着更多州出现新的县级流行率数据时模型具有适应性。利用真实世界数据，我们的模型通过交叉验证显示出较高的估计准确性，并首次提供了全国范围的县级阿片类药物滥用估计值。