一种具有变化系数的模型，用于表征洪水相关健康影响中的持续时间驱动异质性
A varying-coefficient model for characterizing duration-driven heterogeneity in flood-related health impacts
AI总结：
本文提出EDVCM模型，用于分析洪水暴露持续时间对健康影响的异质性，通过贝叶斯方法和条件泊松回归提高估计精度。
摘要：
先前的研究揭示了洪水暴露与洪水事件期间及之后的不良健康结果之间的关联。洪水是高度异质性的事件，主要由于洪水持续时间的巨大差异，即快速洪水与缓慢移动洪水。然而，很少有研究将暴露持续时间纳入洪水相关健康影响的建模中，或调查由持续时间驱动的效果异质性。为了解决这一差距，我们提出了一种暴露持续时间变化系数建模（EDVCM）框架，用于估计连续天数环境暴露的暴露日特定健康效应。我们开发EDVCM在一个区域层面的自我匹配研究设计中，以消除时间不变的混杂因素，随后进行条件泊松回归建模以估计暴露效应并调整时间变化的混杂因素。使用贝叶斯框架，我们引入了持续时间和暴露日特定的暴露系数，在条件泊松模型中，并为它们分配二维高斯过程先验，以允许在持续时间和暴露日之间共享信息。这种方法使我们能够获得对持续时间驱动效果异质性的高分辨率见解，同时通过信息共享确保模型稳定性。通过模拟，我们证明EDVCM在效果估计和不确定性量化方面均优于传统方法。我们应用EDVCM到全国范围、多十年的医疗保险索赔数据，与高分辨率洪水暴露措施链接，以研究洪水对骨骼肌肉系统疾病住院的影响的持续时间驱动异质性。
英文摘要：
Previous work revealed associations between flood exposure and adverse health outcomes during and in the aftermath of flood events. Floods are highly heterogeneous events, largely owing to vast differences in flood durations, i.e., flash-floods versus slow-moving floods. However, little to no work has incorporated exposure duration into the modeling of flood-related health impacts or has investigated duration-driven effect heterogeneity. To address this gap, we propose an exposure duration varying coefficient modeling (EDVCM) framework for estimating exposure day-specific health effects of consecutive-day environmental exposures that vary in duration. We develop the EDVCM within an area-level self-matched study design to eliminate time-invariant confounding followed by conditional Poisson regression modeling for exposure effect estimation and adjustment of time-varying confounders. Using a Bayesian framework, we introduce duration- and exposure day-specific exposure coefficients within the conditional Poisson model and assign them a two-dimensional Gaussian process prior to allow for sharing of information across both duration and exposure day. This approach enables highly-resolved insights into duration-driven effect heterogeneity while ensuring model stability through information sharing. Through simulations, we demonstrate that the EDVCM out-performs conventional approaches in terms of both effect estimation and uncertainty quantification. We apply the EDVCM to nationwide, multi-decade Medicare claims data linked with high-resolution flood exposure measures to investigate duration-driven heterogeneity in flood effects on musculoskeletal system disease hospitalizations.
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