标题： 通过考虑时空局部不连续性来改进小区域疾病风险估计 显示英文标题

标题： Improving Disease Risk Estimation in Small Areas by Accounting for Spatiotemporal Local Discontinuities

摘要： 这项工作提出了一种两步方法，通过在贝叶斯分层时空模型中整合时空聚类检测来增强小区域的疾病风险估计。 首先，我们引入了一种基于扫描统计的高效聚类算法，该算法在扫描窗口内采用贪心搜索，从而能够在大空间域中灵活地检测聚类。 然后，我们将这些检测到的聚类整合到贝叶斯时空模型中以估计相对风险，明确考虑已识别的风险不连续性。 我们将这种方法应用于西班牙大陆地区市一级的大规模癌症死亡数据。 我们的结果表明，与SaTScan相比，我们的方法在聚类检测准确性方面表现更优。 此外，将聚类信息整合到贝叶斯时空模型中显著提高了模型拟合和风险估计性能，这由比SaTScan基模型或标准BYM2模型更好的DIC、WAIC和对数评分所证明。 这种方法为流行病学分析提供了一个强大的工具，能够更精确地识别高风险和低风险区域，并提高风险估计模型的准确性。 显示英文摘要

摘要： This work proposes a two-step method to enhance disease risk estimation in small areas by integrating spatiotemporal cluster detection within a Bayesian hierarchical spatiotemporal model. First, we introduce an efficient scan-statistic-based clustering algorithm that employs a greedy search within the scan window, enabling flexible cluster detection across large spatial domains. We then integrate these detected clusters into a Bayesian spatiotemporal model to estimate relative risks, explicitly accounting for identified risk discontinuities. We apply this methodology to large-scale cancer mortality data at the municipality level across continental Spain. Our results show our method offers superior cluster detection accuracy compared to SaTScan. Furthermore, integrating cluster information into a Bayesian spatiotemporal model significantly improves model fit and risk estimate performance, as evidenced by better DIC, WAIC, and logarithmic scores than SaTScan-based or standard BYM2 models. This methodology provides a powerful tool for epidemiological analysis, offering a more precise identification of high- and low-risk areas and enhancing the accuracy of risk estimation models.