Department of Biomedical Informatics, Center for Biostatistics, The Ohio State University, Columbus, Ohio.
Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, North Carolina.
Biometrics. 2021 Jun;77(2):765-775. doi: 10.1111/biom.13295. Epub 2020 Jun 2.
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.
量化局部地区的阿片类药物流行情况是一个具有重要资源分配后果的挑战问题。成年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，因此分别检查每个年龄组的问题很重要。在俄亥俄州，针对每个年龄组，在县一级收集阿片类药物流行、过量死亡和治疗入院等可衡量结果的监测数据。然而，我们的兴趣在于量化不可衡量的结构，即阿片类药物流行的负担，它驱动着结果的比率。我们通过多元空间因子模型联合建模成年和青少年的监测结果。每个年龄组中的广义空间因子模型量化了与阿片类药物相关的治疗入院和死亡数量有关的潜在因子。通过假设成年人和青少年的空间因子的多元条件自回归模型，我们允许青少年模型从成年模型（反之亦然）中汲取力量，从而提高估计效果。我们还纳入了县级协变量，以帮助解释每个因子的空间异质性。我们将这种方法应用于俄亥俄州，并讨论了结果。我们的框架提供了一种连贯的方法，可用于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。
