An important challenge to addressing the opioid overdose crisis is the lack of information on the size of the population of people who misuse opioids (PWMO) in local areas. This estimate is needed for better resource allocation, estimation of treatment and overdose outcome rates using appropriate denominators (i.e., the population at risk), and proper evaluation of intervention effects. In this study, we used a Bayesian hierarchical spatio-temporal integrated abundance model that integrates multiple types of county-level surveillance outcome data, state-level information on opioid misuse, and covariates to estimate the latent (hidden) counts and prevalence of PWMO across New York State counties (2007-2018). The model assumes that each opioid-related outcome reflects a partial count of the number of PWMO, and leverages these multiple sources of data to circumvent limitations of parameter estimation associated with other types of abundance models. Model estimates showed a reduction in the prevalence of PWMO during the study period, with important spatial and temporal variability. The model also provided county-level estimates of rates of treatment and opioid overdoses using the PWMO as denominators. This modeling approach can identify the size of hidden populations to guide public health efforts to confront the opioid overdose crisis across local areas.

中文翻译：

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2007-2018 年纽约州各县阿片类药物滥用流行率估计。贝叶斯时空丰度模型方法

解决阿片类药物过量危机的一个重要挑战是缺乏有关当地滥用阿片类药物 (PWMO) 人口规模的信息。为了更好地分配资源、使用适当的分母（即面临风险的人群）估计治疗和用药过量结果率以及正确评估干预效果，需要进行这种估计。在本研究中，我们使用贝叶斯分层时空综合丰度模型，该模型整合了多种类型的县级监测结果数据、州级阿片类药物滥用信息和协变量来估计 PWMO 的潜在（隐藏）计数和流行率。纽约州各县（2007-2018 年）。该模型假设每个阿片类药物相关结果反映了 PWMO 数量的部分计数，并利用这些多个数据源来规避与其他类型丰度模型相关的参数估计的限制。模型估计显示，研究期间 PWMO 的患病率有所下降，且具有重要的空间和时间变异性。该模型还使用 PWMO 作为分母，提供了县级治疗率和阿片类药物过量的估计值。这种建模方法可以确定隐藏人群的规模，以指导公共卫生工作，应对当地地区的阿片类药物过量危机。