Opioid misuse is a national epidemic and a significant drug related threat to the United States. While the scale of the problem is undeniable, estimates of the local prevalence of opioid misuse are lacking, despite their importance to policy-making and resource allocation. This is due, in part, to the challenge of directly measuring opioid misuse at a local level. In this paper, we develop a Bayesian hierarchical spatio-temporal abundance model that integrates indirect county-level data on opioid-related outcomes with state-level survey estimates on prevalence of opioid misuse to estimate the latent county-level prevalence and counts of people who misuse opioids. A simulation study shows that our integrated model accurately recovers the latent counts and prevalence. We apply our model to county-level surveillance data on opioid overdose deaths and treatment admissions from the state of Ohio. Our proposed framework can be applied to other applications of small area estimation for hard to reach populations, which is a common occurrence with many health conditions such as those related to illicit behaviors.

翻译：滥用类阿片是一个全国性的流行病,是美国面临的一个重大毒品威胁。虽然问题的规模不容否认,但当地滥用类阿片的流行程度缺乏估计,尽管对决策和资源分配很重要,部分原因是在地方一级直接测量类阿片滥用情况的挑战。在本文件中,我们开发了一个巴耶斯等级悬浮时空丰度模型,将有关类阿片结果的间接州一级数据与州一级滥用类阿片流行率调查估计数结合起来,以估计潜在的县一级滥用类阿片流行率和滥用类阿片者人数。模拟研究表明,我们的综合模型准确地恢复了潜在数量和流行率。我们把模型应用于州一级对俄亥俄州类阿片过量死亡和治疗入院情况的监测数据。我们提议的框架可用于其他难以接触到人口的小地区估算应用,这是许多健康条件(例如与非法行为有关的条件)常见现象。