Understanding the long-term effects of hurricanes on cardiovascular health and outcomes

飓风等极端天气对心血管疾病的短期影响是有据可查的。 分心的。然而，心血管疾病的长期影响是未知的，可能会导致不良后果，特别是对 年龄较大、社会经济地位较低和功能受损的居民，他们的有限资源往往 意味着呆在原地不动。卡特里娜飓风过后，美国卫生与公众服务部启动 旨在识别社区、高风险、功能受损的医疗保险受益人的授权计划 他们依靠基本医疗服务和耐用的医疗设备，以改善灾前疏散- 国家规划和灾后管理。一个潜在的重大限制是一般和 高风险人群的赋权是，他们估计的与灾害相关的需求是基于短期外 这是因为我们对更长期的后果缺乏了解。这项研究的目的是接受 指导培训，以量化与飓风有关的长期不利的心血管疾病后果。我假设 心血管疾病结果的短期恶化将持续到第一年以后，较高的发病率发生在 尚未严格界定的弱势群体。我将评估飓风桑迪对纽约的影响 纽约市(NYC)，因为造成了巨大的物理破坏，其庞大的人口和邻居- 引擎盖级别的SES差异，以及没有进一步的飓风影响纽约市的事实。建立在与灾难相关的基础上 临床专业知识和研究技能作为一名在卫生服务研究方面接受培训的内科科学家，我将学习和 应用新的空间、流行病学和机器学习分析技能来实现以下目标：1) 估计飓风在短期(1年)和长期(5年)对≥65岁人口的影响 年)使用时空模型对人口普查区域级别的心血管疾病患病率进行了比较；2)比较了飓风的短期 以及对当前高危医疗保险受益人(Empower)心血管事件发生率的长期影响进行比较 对于所有其他受益人，≥65岁，使用生存分析和COX模型；以及3)制定和验证 心血管疾病风险预测工具，扩展受飓风影响的高风险医疗保险受益人的定义 Beyond使用机器学习和风险预测方法增强能力。在未来的R01应用程序中，我将执行- 使用2017年飓风季节(The)100%的联邦医疗保险索赔对风险预测模型进行永久验证 美国历史上最昂贵的)。总之，这项工作将使公共卫生当局能够更好地定制灾难前的疏散- 评估和灾后心血管疾病管理，解决高危人群灾害相关发病率问题 从长远来看。我将得到一个由全国公认的专家组成的团队的指导和建议：马丁·夏皮罗博士(加州- REER导师)、Monika Safford博士(心血管疾病和社会流行病学)、David Abramson博士(灾害科学和 政策)、Charles DiMaggio博士(空间分析)和Samprt Banerjee博士(机器学习和风险预测- Tion)。这个奖项将使我能够推进我成为一名独立内科医生-科学家的长期目标 其重点在于心血管疾病、气候变化和健康公平的交汇点。 1

The short-term effects of extreme weather such as hurricanes on cardiovascular disease (CVD) are well-docu- mented. However, the long-term CVD effects are unknown, likely leading to adverse outcomes, particularly for older, low socioeconomic status (SES), and functionally impaired residents, whose limited resources often mean staying in place. After Hurricane Katrina, the US Department of Health and Human Services launched the emPOWER Program to identify community-based, high-risk, functionally impaired Medicare beneficiaries who rely on essential medical services and durable medical equipment in order to improve pre-disaster evacu- ation planning and post-disaster management. One potentially significant limitation for both the general and high-risk emPOWER population is that their estimated disaster-related needs are based on short-term out- comes since we lack knowledge about longer-term consequences. The objective of this study is to receive mentored training to quantify the long-term adverse CVD outcomes associated with hurricanes. I hypothesize that the short-term worsening of CVD outcomes endures beyond the first year, with higher rates occurring in vulnerable groups that are yet to be rigorously defined. I will evaluate Hurricane Sandy’s impact on New York City (NYC) in 2012 because of the substantial physical damage wrought, its large population and neighbor- hood-level SES disparities, and the fact that no further hurricanes affected NYC. Building on disaster-related clinical expertise and research skills as a physician-scientist trained in health services research, I will learn and apply new spatial, epidemiological, and machine learning analytical skills to accomplish the following aims: 1) estimate the hurricane’s impact on the ≥ 65 years old population over the short- (1 year) and long-term (5 years) on census tract-level CVD prevalence using spatiotemporal modeling; 2) compare the hurricane’s short- and long-term impact on CVD event rates for current high-risk Medicare beneficiaries (emPOWER) compared to all other beneficiaries ≥ 65 years old using survival analyses and Cox models; and 3) develop and validate a CVD risk prediction tool that expands the definition of high-risk Medicare beneficiaries impacted by hurricanes beyond emPOWER using machine learning and risk prediction methods. In a future R01 application, I will ex- ternally validate the risk prediction model using 100% Medicare claims from the 2017 hurricane season (the costliest in US history). Together, this work will allow public health authorities to better tailor pre-disaster evac- uation and post-disaster CVD management that address disaster-related morbidity for high-risk groups into the longer term. I will be mentored and advised by a team of nationally recognized experts: Dr. Martin Shapiro (ca- reer mentor), Dr. Monika Safford (CVD and social epidemiology), Dr. David Abramson (disaster science and policy), Dr. Charles DiMaggio (spatial analysis), and Dr. Samprit Banerjee (machine learning and risk predic- tion). This Award will enable me to advance my long-term goal of becoming an independent physician-scientist whose focus lies at the intersection of CVD, climate change, and health equity. 1