Quantifying the opioid epidemic at the local level is a challenging problem that has important consequences on resource allocation. Adults and adolescents may exhibit different spatial trends and require different interventions and resources so it is important to examine the problem for each age group. In Ohio, surveillance data are collected at the county-level for each age group on measurable outcomes of the opioid epidemic, overdose deaths and treatment admissions. However, our interest lies in quantifying the unmeasurable construct, representing the burden of the opioid epidemic, which drives rates of the outcomes. We propose jointly modeling adult and adolescent surveillance outcomes through a multivariate spatial factor model. A generalized spatial factor model within each age group quantifies a latent factor related to the number of opioid-associated treatment admissions and deaths. By assuming a multivariate conditional autoregressive model for the spatial factors of adults and adolescents, we allow the adolescent model to borrow strength from the adult model (and vice versa), improving estimation. We also incorporate county-level covariates to help explain spatial heterogeneity in each of the factors. We apply this approach to the state of Ohio and discuss the findings. Our framework provides a coherent approach for synthesizing information across multiple outcomes and age groups to better understand the spatial epidemiology of the opioid epidemic.

中文翻译：

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估计俄亥俄州各县成人和青少年阿片类药物流行的负担

量化地方一级的阿片类药物流行是一个具有挑战性的问题，对资源分配产生重要影响。成人和青少年可能表现出不同的空间趋势，需要不同的干预措施和资源，因此检查每个年龄段的问题很重要。在俄亥俄州，在县一级收集每个年龄组的监测数据，包括阿片类药物流行、过量死亡和治疗入院的可衡量结果。然而，我们的兴趣在于量化不可测量的结构，代表阿片类药物流行的负担，这推动了结果的发生率。我们建议通过多变量空间因素模型对成人和青少年监测结果进行联合建模。每个年龄组内的广义空间因素模型量化了与阿片类药物相关治疗入院和死亡人数相关的潜在因素。通过假设成人和青少年空间因素的多元条件自回归模型，我们允许青少年模型借用成人模型的力量（反之亦然），从而改进估计。我们还纳入县级协变量来帮助解释每个因素的空间异质性。我们将这种方法应用于俄亥俄州并讨论研究结果。我们的框架提供了一种连贯的方法来综合多个结果和年龄组的信息，以更好地了解阿片类药物流行的空间流行病学。