用于从公开发布的压制数据中理解潜在流行病轨迹的贝叶斯ACCESS：在美国阿片类药物相关过量死亡率中的应用
Bayesian ACCESS for Understanding Latent Epidemic Trajectories from Publicly Released Suppressed Data: Application to U.S. Opioid-related Overdose Mortality
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中文总结 AI 辅助
本研究针对公开健康统计数据中因压制小单元格计数导致的推断难题，提出贝叶斯ACCESS框架，应用于美国阿片类药物过量死亡率数据，识别出特定亚组的流行病轨迹与结构性变化。
中文摘要 AI 辅助
公开发布的健康统计数据在描述时间趋势和识别人群健康的结构性变化方面发挥着核心作用。然而，疾病控制与预防中心的广泛在线流行病学研究数据系统（CDC WONDER）等系统所采用的通过压制小单元格计数来限制披露的做法，会产生部分观测的计数数据，这给统计推断带来了复杂问题。这些挑战在罕见结局和亚组分析中尤为突出，在这些分析中，压制现象普遍存在，且在不同地理区域、人口群体和时间中存在差异。我们提出了贝叶斯ACCESS（用于压制计数序列的自回归变点与聚类估计），这是一种贝叶斯层次框架，用于从受披露限制的健康统计数据中推断潜在流行病轨迹及其结构性变化。该模型通过感知压制的观测模型直接表示压制的计数数据，联合推断多个时间变点和潜在轨迹，并通过贝叶斯非参数聚类在相关地理和人口群体间借用信息，同时保留有意义的异质性。我们将贝叶斯ACCESS应用于1999年至2024年期间美国各州来自CDC WONDER的阿片类药物相关过量死亡率数据。该分析识别出了特定亚组的不同流行病轨迹和结构性变化，若不明确考虑数据压制，这些变化将难以利用公开发布的健康统计数据进行描述。
英文摘要
Publicly released health statistics play a central role in characterizing temporal trends and identifying structural changes in population health. However, disclosure limitation through suppression of small cell counts, as implemented in systems such as the Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research (CDC WONDER), produces partially observed count data that complicate statistical inference. These challenges are particularly acute for rare outcomes and subgroup analyses, where suppression is widespread and varies across geographic regions, demographic populations, and time. We propose Bayesian ACCESS (Autoregressive Change-point and Clustering Estimation for Suppressed Count Series), a Bayesian hierarchical framework for inference on latent epidemic trajectories and their structural changes from disclosure-limited health statistics. The proposed model directly represents suppressed count data through a suppression-aware observation model, jointly infers multiple temporal change points and latent trajectories, and borrows information across related geographic and demographic populations through Bayesian nonparametric clustering while preserving meaningful heterogeneity. We apply Bayesian ACCESS to opioid-related overdose mortality data from CDC WONDER for U.S. states from 1999 to 2024. The analysis identifies distinct subgroup-specific epidemic trajectories and structural changes that would be difficult to characterize using publicly released health statistics without explicitly accounting for data suppression.
