Novel Assessments of the Health Impacts of Tropical Cyclones

项目摘要/摘要 在美国，飓风和热带风暴等热带气旋对 社会。然而，除了一些有限的研究外，在理解完整的 热带气旋对健康的影响程度。此K99/R00应用程序的目的是fi所有这项研究 这与对热带气旋健康影响的几项新评估存在差距。为了能够充分fiL这一目标， 这项K99/R00应用是跨学科的，涉及环境流行病学专家的合作， 暴露评估、贝叶斯统计、机器学习、计算机视觉和社会流行病学。K99 这一阶段旨在通过课程学习、导师指导和 定向读物，特别是fic培训(1)与气候有关的灾害流行病学和暴露评估； (2)先进的贝叶斯统计方法；。(3)用于公共卫生的机器学习和计算机视觉；以及。 (4)灾害和公共卫生背景下的社会流行病学。在颁奖过程中获得的技能对 候选人的长期目标是成为一名领先的、在方法上很强的环境流行病学家 世卫组织进行严谨的大规模研究，有助于社会了解热带气旋和 其他环境危害，帮助为美国和世界各地的政策提供信息。拟议中的项目 将利用丰富的住院数据来源(联邦医疗保险和医疗补助队列)；死亡(美国国家医疗保险中心 卫生统计)；热带气旋暴露；以及卫星和地面图像，所有这些图像都跨越几个 近几十年来，美国所有地区都受到热带气旋的影响。目标1(K99阶段)将有所改善 并协调每个命名飓风后过度住院和死亡的估计，方法是(A)应用 一组贝叶斯模型到住院和死亡率数据，以估计每周住院和 如果没有发生飓风暴露，预计的死亡率；然后(B)比较实际的 历史住院率和死亡率以计算超额住院率和死亡率。目标2(R00阶段) 将(A)通过分析确定反复接触热带气旋对慢性健康结果的影响 应用贝叶斯方法研究热带气旋暴露与月住院或死亡的关系 然后(B)利用结果准确地预测健康影响。目标3(R00阶段) 将通过以下方式描述物理邻居特征如何解释热带气旋对健康影响的差异 (A)利用机器学习和计算机视觉技术识别各种物理邻域特征 使用卫星和街道图像的热带气旋暴露区；然后(B)将要素转换为 健康模型，以调查热带气旋对健康的影响是否以及如何根据这些指标变化。建议数 培训和研究计划都与NIEHS的使命和战略计划紧密结合，并做出反应 NIEHS的数据科学和大数据优先事项(主题一，目标7)、环境健康差距和 环境正义(主题二，目标4)和新出现的环境健康问题(主题二，目标5)。

PROJECT SUMMARY/ABSTRACT In the United States, tropical cyclones, such as hurricanes and tropical storms, have a devastating impact on society. However, beyond some limited studies, there remains a critical research gap in understanding the full extent of the impact of tropical cyclones on health. The objective of this K99/R00 application is to fill this research gap with several novel assessments of the health impacts of tropical cyclones. To be able to fulfil this objective, this K99/R00 application is interdisciplinary, involving the collaboration of experts in environmental epidemiology, exposure assessment, Bayesian statistics, machine learning, computer vision, and social epidemiology. The K99 phase is designed to augment the candidate's prior research experience through coursework, mentorship, and directed readings, with specific training in (1) climate-related disaster epidemiology and exposure assessment; (2) advanced Bayesian statistics methodology; (3) machine learning and computer vision for public health; and (4) social epidemiology in a disaster and public health context. The skills gained during this award are critical to the candidate's long-term goal to become a leading and methodologically strong environmental epidemiologist who conducts rigorous large-scale research that contributes to society's understanding of tropical cyclones and other environmental hazards to help inform policies in the United States and worldwide. The proposed project will draw on rich data sources on hospitalization (Medicare and Medicaid cohorts); death (National Center for Health Statistics); tropical cyclone exposure; and satellite- and ground-based imagery, all of which span several recent decades and cover all of the United States exposed to tropical cyclones. Aim 1 (K99 phase) will improve and harmonize estimation of excess hospitalizations and deaths after each named hurricane by (a) applying an ensemble of Bayesian models to hospitalization and mortality data to estimate weekly hospitalization and deaths rates that would have been expected had hurricane exposure not occurred; then (b) comparing the actual historical hospitalization and death rates to calculate excess hospitalizations and deaths. Aim 2 (R00 phase) will (a) determine the impact of repeated tropical cyclone exposure on chronic health outcomes by analyzing the association between tropical cyclone exposure and monthly hospitalizations or deaths by applying Bayesian spatio-temporal hazard models; then (b) accurately forecast health impacts by using results. Aim 3 (R00 phase) will characterize how physical neighborhood features explain differences in health impacts of tropical cyclones by (a) utilizing machine learning and computer vision techniques to identify various physical neighborhood features in tropical cyclone-exposed areas using satellite and street-level imagery; then (b) converting features into metrics in health models to investigate if and how health impacts of tropical cyclones vary by those metrics. The proposed training and research program both closely align with NIEHS's mission and Strategic Plan, and is responsive to NIEHS's priorities of Data Science and Big Data (Theme I, Goal 7), Environmental Health Disparities and Environmental Justice (Theme II, Goal 4), and Emerging Environmental Health Issues (Theme II, Goal 5).