An integrated abundance model for estimating county-level prevalence of opioid misuse in Ohio.

IF 1.5 3区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Staci A Hepler, David M Kline, Andrea Bonny, Erin McKnight, Lance A Waller
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引用次数: 0

Abstract

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.

用于估算俄亥俄州县级阿片类药物滥用流行率的综合丰度模型。
阿片类药物滥用是一种全国性流行病,也是美国面临的一个重大毒品威胁。尽管问题的严重性毋庸置疑,但对当地阿片类药物滥用流行率的估计却很缺乏,尽管这对政策制定和资源分配非常重要。这部分是由于在地方一级直接测量阿片类药物滥用所面临的挑战。在本文中,我们建立了一个贝叶斯分层时空丰度模型,该模型整合了县级阿片类药物相关结果的间接数据和州级阿片类药物滥用流行率的调查估计值,以估计县级滥用阿片类药物者的潜在流行率和人数。模拟研究表明,我们的综合模型能够准确地恢复潜在的人数和流行率。我们将模型应用于俄亥俄州阿片类药物过量死亡和入院治疗的县级监测数据。我们提出的框架可应用于对难以接触到的人群进行小范围估算的其他应用中,这在许多健康状况(如与非法行为相关的健康状况)中都很常见。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.90
自引率
5.00%
发文量
136
审稿时长
>12 weeks
期刊介绍: Series A (Statistics in Society) publishes high quality papers that demonstrate how statistical thinking, design and analyses play a vital role in all walks of life and benefit society in general. There is no restriction on subject-matter: any interesting, topical and revelatory applications of statistics are welcome. For example, important applications of statistical and related data science methodology in medicine, business and commerce, industry, economics and finance, education and teaching, physical and biomedical sciences, the environment, the law, government and politics, demography, psychology, sociology and sport all fall within the journal''s remit. The journal is therefore aimed at a wide statistical audience and at professional statisticians in particular. Its emphasis is on well-written and clearly reasoned quantitative approaches to problems in the real world rather than the exposition of technical detail. Thus, although the methodological basis of papers must be sound and adequately explained, methodology per se should not be the main focus of a Series A paper. Of particular interest are papers on topical or contentious statistical issues, papers which give reviews or exposés of current statistical concerns and papers which demonstrate how appropriate statistical thinking has contributed to our understanding of important substantive questions. Historical, professional and biographical contributions are also welcome, as are discussions of methods of data collection and of ethical issues, provided that all such papers have substantial statistical relevance.
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