异质空间图上的摊销贝叶斯疾病制图与边界检测

中文摘要 AI 辅助

空间疾病图谱有助于公共卫生研究人员识别地理不平等，但标准的贝叶斯平滑在相邻社区具有截然不同的社会经济或行为特征时，可能会掩盖局部差异。因此，分析人员需要确定应在何处中断平滑，并随着图谱、邻接结构和结果的变化重复该分析。我们开发了一个协变量感知的贝叶斯边界模型，以及一个在异质区域图上训练的摊销后验近似。该模型区分了平滑中的局部中断与更广泛的残差空间依赖性；训练好的近似能够处理具有不同区域数量的图谱。模拟研究检验了后验校准、边界概率恢复、复制数据行为以及与先验匹配的MCMC在MCSE控制下的一致性。与传统方法分别分析这些数据不同，我们展示了使用单个训练好的深度学习网络来分析大格拉斯哥的呼吸系统住院、加利福尼亚的肺癌发病率以及韩国的气管、支气管和肺癌死亡率（涵盖58至241个区域）的有效性。尽管格拉斯哥和韩国都存在显著的残差空间依赖性，但所选边界的密度在格拉斯哥最高，在韩国最低，这表明局部中断和更广泛的空间持续性不必同步变化。在所有三个应用中，边级边界概率与特定数据集的分析基本一致，尽管后验分布范围和阈值化边界集有所不同。这些结果支持在所评估的疾病图谱类别中进行可重用的贝叶斯边界分析，并确定了在部署到新应用之前所需的验证。

英文摘要

Spatial disease maps help public-health researchers identify geographic inequalities, but standard Bayesian smoothing can obscure localized disparities when neighboring communities have sharply different socioeconomic or behavioral profiles. Analysts therefore need to determine where smoothing should be interrupted and repeat that analysis as maps, adjacency structures, and outcomes change. We develop a covariate-informed Bayesian boundary model and an amortized posterior approximation trained across heterogeneous areal graphs. The model distinguishes local interruptions in smoothing from broader residual spatial dependence; the trained approximation handles maps with different numbers of regions. Simulations examine posterior calibration, boundary-probability recovery, replicated-data behavior, and MCSE-controlled agreement with prior-matched MCMC. In contrast to traditional approaches that analyze these data separately, we demonstrate the effectiveness of using a single trained deep learning network to analyze respiratory hospitalizations in Greater Glasgow; lung cancer incidence in California; and tracheal, bronchial, and lung cancer mortality in South Korea, comprising 58 to 241 regions. Selected boundary density is greatest in Glasgow and lowest in South Korea despite substantial residual spatial dependence in both, showing that local interruption and broader spatial persistence need not vary together. Across all three applications, edge-level boundary probabilities agree substantially with dataset-specific analyses, although posterior spread and thresholded boundary sets differ. These results support reusable Bayesian boundary analysis across the evaluated disease-map class and identify the validation needed before deployment to new applications.

发表机构

• University of California, Los Angeles（加州大学洛杉矶分校）

机构由 AI 辅助整理，请以论文原文为准。