Place-based epidemiology studies often rely on circular buffers to define ``exposure'' to spatially distributed risk factors, where the buffer radius represents a threshold beyond which exposure does not influence the outcome of interest. This approach is popular due to its simplicity and alignment with public health policies. However, buffer radii are often chosen relatively arbitrarily and assumed constant across the spatial domain. This may result in suboptimal statistical inference if these modeling choices are incorrect. To address this, we develop SVBR (Spatially-Varying Buffer Radii), a flexible hierarchical Bayesian spatial change points approach that treats buffer radii as unknown parameters and allows both radii and exposure effects to vary spatially. Through simulations, we find that SVBR improves estimation and inference for key model parameters compared to traditional methods. We also apply SVBR to study healthcare access in Madagascar, finding that proximity to healthcare facilities generally increases antenatal care usage, with clear spatial variation in this relationship. By relaxing rigid assumptions about buffer characteristics, our method offers a flexible, data-driven approach to accurately defining exposure and quantifying its impact. The newly developed methods are available in the R package EpiBuffer.

翻译：基于地点的流行病学研究通常依赖圆形缓冲区来定义对空间分布风险因素的“暴露”，其中缓冲区半径代表一个阈值，超过该阈值暴露便不会影响所关注的结果。这种方法因其简单性且与公共卫生政策相符而广受欢迎。然而，缓冲区半径的选择往往相对随意，并假设在整个空间域内保持不变。如果这些建模选择不正确，则可能导致次优的统计推断。为解决此问题，我们开发了SVBR（空间可变缓冲区半径），这是一种灵活的分层贝叶斯空间变点方法，它将缓冲区半径视为未知参数，并允许半径和暴露效应在空间上同时变化。通过模拟，我们发现与传统方法相比，SVBR改善了对关键模型参数的估计和推断。我们还将SVBR应用于马达加斯加的医疗可及性研究，发现靠近医疗设施通常会增加产前护理的使用率，且这种关系存在明显的空间变异。通过放宽关于缓冲区特征的刚性假设，我们的方法提供了一种灵活的、数据驱动的方法来准确定义暴露并量化其影响。新开发的方法可在R包EpiBuffer中获得。