俄亥俄州各县阿片类药物相关死亡和治疗入院综合的潜在空间因素方法
A latent spatial factor approach for synthesizing opioid associated deaths and treatment admissions in Ohio counties
摘要：
背景：阿片类药物滥用是美国，特别是俄亥俄州的一个主要公共卫生问题。然而，由于公共卫生监测措施涵盖了问题的不同方面，因此难以量化这一流行病的负担。在这里，我们综合了县级死亡和治疗计数，以比较各县的相对负担，并评估与社会环境协变量的关联。方法：我们构建了一个广义的空间因素模型，联合模型死亡率和治疗率为每个县。对于每个结果，我们指定了一个空间变化的因素加载的泊松回归模型的空间速率参数化。我们使用条件自回归模型来解释贝叶斯框架内的空间依赖性。结果如下：估计的空间因素在该州南部和西南部县最高，这代表阿片类药物流行病的负担更高。我们发现，相对较高的治疗率促成了该州南部的因素;而西南部的死亡率相对较高。估计的因素也与18-64岁居民的残疾比例呈正相关，与报告白人种族的居民比例呈负相关。结论：我们通过空间因素模型综合了阿片类药物相关死亡和治疗计数的信息，以估计代表两种监测措施之间共识的潜在因素。我们认为，这一框架提供了一种连贯的方法来描述这一流行病，同时利用来自多种监测措施的信息。
英文摘要：
Background: Opioid misuse is a major public health issue in the United States and in particular Ohio. However, the burden of the epidemic is challenging to quantify as public health surveillance measures capture different aspects of the problem. Here we synthesize county-level death and treatment counts to compare the relative burden across counties and assess associations with social environmental covariates. Methods: We construct a generalized spatial factor model to jointly model death and treatment rates for each county. For each outcome, we specify a spatial rates parameterization for a Poisson regression model with spatially varying factor loadings. We use a conditional autoregressive model to account for spatial dependence within a Bayesian framework. Results: The estimated spatial factor was highest in the southern and southwestern counties of the state, representing a higher burden of the opioid epidemic. We found that relatively high rates of treatment contributed to the factor in the southern part of the state; whereas, relatively higher rates of death contributed in the southwest. The estimated factor was also positively associated with the proportion of residents aged 18-64 on disability and negatively associated with the proportion of residents reporting white race. Conclusions: We synthesized the information in the opioid associated death and treatment counts through a spatial factor model to estimate a latent factor representing the consensus between the two surveillance measures. We believe this framework provides a coherent approach to describe the epidemic while leveraging information from multiple surveillance measures.
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