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 overdose deaths and treatment admissions 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 joint model accurately recovers the latent counts and prevalence and thus overcomes known limitations with identifiability in abundance models with non-replicated observations. We apply our model to county-level surveillance data 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。在本文中,我们开发了一种巴耶斯州一级剂量过量和治疗摄入量的间接州一级数据模型,将类阿片滥用流行率的州一级间接数据与州一级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的人数。一项模拟研究表明,我们的联合模型准确地恢复了潜在的数量和流行率,从而克服了已知的在丰量模型中以非复制观察方式识别的局限性。我们把模型应用于俄亥俄州县一级的监测数据。我们提议的框架可用于难以接触到人口的小面积估计的其他应用,这是许多健康条件(例如与非法行为有关的健康条件)常见的情况。