Spatially varying age-period-cohort analysis with application to US mortality, 2002-2016
Spatially varying age-period-cohort analysis with application to US mortality, 2002-2016
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DOI：
10.1093/biostatistics/kxz009
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发表时间：
2020-10-01
期刊：
影响因子：
2.1
通讯作者：
Rosenberg, Philip S.
中科院分区：
文献类型：
--
作者：
Chernyavskiy, Pavel;Little, Mark P.;Rosenberg, Philip S.
许多公共卫生数据库按地理区域(如州、区或县)内的年龄组和历期编制疾病计数索引。关于小区域相对风险估计的问题已经得到了很好的研究；然而，估计不同地理区域的趋势参数受到的关注较少。此外，大多数可公开访问的数据库中的小计数(例如，<；10)被审查，这进一步使小地区的年龄-时期-队列(APC)分析复杂化。在这里，我们提出了一种新的APC模型，该模型具有左截断和空间变化的截距和趋势，并利用相邻地理区域之间的相关性进行估计。与传统模型一样，我们的模型捕捉了人口规模的趋势，但它也可以用来表征相对风险和年龄调整趋势随时间的地理差异。为了说明我们的三个空间变化参数的联合分布，我们采用了以前用于多变量疾病映射的广义多变量条件自回归先验。以这种方式指定的区域特定参数在空间上相互关联。估计是使用Stan的No-U-Turn哈密尔顿蒙特卡罗采样器进行的。我们进行了一项模拟研究，以评估所提出的模型相对于标准模型的性能，并最后应用于美国州级阿片类药物过量死亡率的15-岁的男性和女性。
Many public health databases index disease counts by age groups and calendar periods within geographic regions (e.g., states, districts, or counties). Issues around relative risk estimation in small areas are well-studied; however, estimating trend parameters that vary across geographic regions has received less attention. Additionally, small counts (e.g., < 10) in most publicly accessible databases are censored, further complicating age-period-cohort (APC) analysis in small areas. Here, we present a novel APC model with left-censoring and spatially varying intercept and trends, estimated with correlations among contiguous geographic regions. Like traditional models, our model captures population-scale trends, but it can also be used to characterize geographic disparities in relative risk and age-adjusted trends over time. To specify the joint distribution of our three spatially varying parameters, we adapt the generalized multivariate conditional autoregressive prior, previously used for multivariate disease mapping. Specified in this manner, region-specific parameters are correlated spatially, and also to one another. Estimation is performed using the No-U-Turn Hamiltonian Monte Carlo sampler in Stan. We conduct a simulation study to assess the performance of the proposed model relative to the standard model, and conclude with an application to US state-level opioid overdose mortality in men and women aged 15-64 years.