Department of Biostatistics, The University of Iowa, Iowa City, Iowa, USA.
Department of Epidemiology, The University of Iowa, Iowa City, Iowa, USA.
Stat Med. 2021 May 30;40(12):2922-2938. doi: 10.1002/sim.8948. Epub 2021 Mar 16.
Age-adjusted rates are frequently used by epidemiologists to compare disease incidence and mortality across populations. In small geographic regions, age-adjusted rates computed directly from the data are subject to considerable variability and are generally unreliable. Therefore, we desire an approach that accounts for the excessive number of zero counts in disease mapping datasets, which are naturally present for low-prevalence diseases and are further innated when stratifying by age group. Bayesian modeling approaches are naturally suited to employ spatial and temporal smoothing to produce more stable estimates of age-adjusted rates for small areas. We propose a Bayesian hierarchical spatio-temporal hurdle model for counts and demonstrate how age-adjusted rates can be estimated from the hurdle model. We perform a simulation study to evaluate the performance of the proposed model vs a traditional Poisson model on datasets with varying characteristics. The approach is illustrated using two applications to cancer mortality at the county level.
年龄调整率经常被流行病学家用于比较不同人群的疾病发病率和死亡率。在小地理区域中，直接从数据中计算得出的年龄调整率会受到很大的变化，通常是不可靠的。因此，我们需要一种方法来处理疾病映射数据集中文本中过多的零计数，这些零计数对于低流行疾病来说是自然存在的，并且在按年龄组分层时更是如此。贝叶斯建模方法非常适合利用空间和时间平滑来产生小区域年龄调整率的更稳定估计。我们提出了一种用于计数的贝叶斯分层时空障碍模型，并展示了如何从障碍模型中估计年龄调整率。我们通过对具有不同特征的数据集进行模拟研究，来评估所提出的模型与传统泊松模型的性能。该方法通过两个县级癌症死亡率的应用示例来说明。
