Center for Biostatistics, Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH.
Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC.
Ann Epidemiol. 2019 May;33:19-23. doi: 10.1016/j.annepidem.2019.02.004. Epub 2019 Mar 12.
Opioid misuse is a national epidemic, and Ohio is one of the states most impacted by this crisis. Ohio collects county-level counts of opioid-associated deaths and treatment admissions. We jointly model these two outcomes and assess the association of each rate with social and structural factors.
We use a joint spatial rates model of death and treatment counts using a generalized common spatial factor model. In addition to covariate effects, we estimate a spatial factor for each county that characterizes structural factors not accounted for by other covariates in the model that are associated with both outcomes.
We observed an association of health professional shortage area with death rates and the rate of people 18-64 on disability with treatment rates. The proportion of single female households was associated with both outcomes. We estimated the presence of unmeasured risk factors in the southwestern part of the state and unmeasured protective factors in the eastern region.
We described associations of social and structural covariates with the death and treatment rates. We also characterized counties with latent risk that can provide a launching point for future investigations to determine potential sources of that risk.
阿片类药物滥用是一场全国性的流行病，俄亥俄州是受这场危机影响最严重的州之一。俄亥俄州收集与阿片类药物相关的死亡和治疗入院人数的县级数据。我们共同对这两个结果进行建模，并评估每个比率与社会和结构因素的关联。
我们使用广义公共空间因素模型对死亡和治疗计数进行联合空间比率模型。除了协变量效应外，我们还为每个县估计了一个空间因素，该因素描述了模型中其他协变量未涵盖的与两个结果都相关的结构因素。
我们观察到卫生专业短缺地区与死亡率以及 18-64 岁残疾人群的治疗率之间存在关联。单身女性家庭的比例与这两个结果都有关联。我们估计该州西南部存在未测量的危险因素和东部地区存在未测量的保护因素。
我们描述了社会和结构协变量与死亡率和治疗率的关联。我们还描述了具有潜在风险的县，这可以为未来的调查提供起点，以确定该风险的潜在来源。
