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.

翻译：先前的研究揭示了洪水暴露与洪水事件期间及之后不良健康结果之间的关联。洪水事件具有高度异质性，这主要源于洪水持续时间的巨大差异，例如突发性洪水与缓发性洪水。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，或探究持续时间驱动的效应异质性。为填补这一空白，我们提出了一种暴露持续时间变系数建模框架，用于估计持续时间各异的连续多日环境暴露中特定暴露日的健康效应。我们在区域层面自匹配研究设计中构建该框架，以消除时间不变的混杂因素，随后采用条件泊松回归模型进行暴露效应估计和时间变化混杂因素的调整。在贝叶斯框架下，我们在条件泊松模型中引入了特定于持续时间和暴露日的暴露系数，并为它们赋予了一个二维高斯过程先验，以允许跨持续时间和暴露日的信息共享。这种方法能够在通过信息共享确保模型稳定性的同时，实现对持续时间驱动效应异质性的高分辨率洞察。通过模拟，我们证明了该框架在效应估计和不确定性量化方面均优于传统方法。我们将该框架应用于与高分辨率洪水暴露测量相关联的全国性、跨数十年医疗保险索赔数据，以研究洪水对肌肉骨骼系统疾病住院影响的持续时间驱动异质性。