Count data are common in many fields such as public health. Hurdle models have been developed to model count data when the zero count could be either inflated or deflated. However, when data are repeatedly collected over time and spatially correlated, it is very challenging to model the data appropriately. For example, to study health professional shortage areas, the number of primary care physicians along with other demographic characteristics are collected at the county level in the USA and over different years. Since the data are repeatedly collected over time, counties are nested within the state, and adjacent counties are geographically correlated, the dependence structure of the data is very complex. We develop a Bayesian hurdle model with multilayered random effects to incorporate this complex structure. We use a time‐varying random effect for each state to capture the time effect at the state level, and a temporal thin plate spline to capture the spatiotemporal correlation across different counties. We use STAN to obtain samples for inference from the posterior distribution. By using the model proposed, we can identify the important factors which impact health professional shortage areas. Simulation studies also confirm the effectiveness of the model.

中文翻译：

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时空计数数据的分层混合效应障碍模型及其在识别影响卫生专业人员短缺的因素中的应用

计数数据在公共卫生等许​​多领域很常见。当零计数可以充气或放气时，已经开发了跨栏模型以对计数数据建模。但是，当数据随时间重复收集并在空间上相关时，对数据进行适当建模非常具有挑战性。例如，为了研究卫生专业人员短缺的地区，收集了美国县级和不同年份的初级保健医生的数量以及其他人口统计学特征。由于随着时间的推移重复收集数据，县嵌套在州内，相邻县在地理位置上相关，因此数据的依存结构非常复杂。我们开发了具有多层随机效应的贝叶斯跨栏模型，以纳入此复杂结构。我们对每个州使用随时间变化的随机效应来捕获状态级别的时间效应，并使用时间薄板样条来捕获不同县之间的时空相关性。我们使用STAN从后验分布中获取样本进行推断。通过使用提出的模型，我们可以确定影响卫生专业人员短缺地区的重要因素。仿真研究也证实了该模型的有效性。