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

了解飓风对心血管健康和结果的长期影响

• 批准号: 10591959
• 负责人: Arnab Kumar Ghosh
• 金额: $ 16.81万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-04 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591959
• 关键词: Address; Affect; American; Authorization documentation; Award; Cardiovascular Diseases; Censuses; Cessation of life; Clinical; Communities; Companions; Congresses; Coronary heart disease; Cox Models; Cox Proportional Hazards Models; Data; Data Set; Development; Disaster Planning; Disasters; Disease Management; Disease Outcome; Disparity; Durable Medical Equipment; Economics; Emergency Care; Emergency Situation; Epidemiology; Event; Floods; Frequencies; Future; Goals; Health; Health Services Research; Health system; Heart failure; Hurricane; Individual; Intervention; Knowledge; Lead; Learning; Link; Long-Term Effects; Machine Learning; Measures; Medical; Medicare; Medicare claim; Mentors; Methods; Modeling; Morbidity - disease rate; Myocardial Infarction; Natural Disasters; Natural experiment; Neighborhoods; New York City; Older Population; Outcome; Physicians; Policies; Population; Prevalence; Public Health; Puerto Rico; Recording of previous events; Recovery; Research; Research Personnel; Resources; Risk; Sampling; Science; Scientist; Seasons; Services; Socioeconomic Status; Stroke; Survival Analysis; System; Techniques; Training; United States Dept. of Health and Human Services; Vulnerable Populations; Work; adverse outcome; authority; beneficiary; cardiovascular disorder risk; cardiovascular health; career; climate change; cost; empowerment; experience; extreme weather; functional disability; health care availability; health equity; high risk; high risk population; human old age (65+); improved; indexing; low socioeconomic status; mortality; neighborhood disadvantage; population health; predictive tools; programs; restoration; risk mitigation; risk prediction; risk prediction model; skills; social; social epidemiology; spatiotemporal; validation studies

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

飓风等极端天气对心血管疾病（CVD）的短期影响是有据可查的。 精神错乱然而，长期的CVD影响是未知的，可能导致不良结果，特别是对于 老年人，社会经济地位低（SES）和功能受损的居民，他们有限的资源往往 意思是呆在原地。卡特里娜飓风过后，美国卫生与公众服务部 emPOWER计划，以确定以社区为基础的，高风险的，功能受损的医疗保险受益人 依靠基本医疗服务和耐用医疗设备改善灾前撤离的人， 规划和灾后管理。一个潜在的重大限制， 高风险emPOWER人群的一个特点是，他们估计的与灾害有关的需求是基于短期的， 因为我们缺乏对长期后果的了解。本研究的目的是获得 指导培训，以量化与飓风相关的长期不利CVD结果。我假设 CVD结果的短期恶化持续超过第一年， 弱势群体尚未得到严格界定。我将评估飓风桑迪对纽约的影响 城市（纽约市）在2012年，因为造成的实质性损害，其庞大的人口和邻居- 胡德级的SES差异，以及没有进一步的飓风影响纽约市的事实。在与灾害有关的 临床专业知识和研究技能作为一个医生，科学家在卫生服务研究培训，我将学习， 应用新的空间、流行病学和机器学习分析技能，以实现以下目标： 估计飓风对65岁以上人口的短期（1年）和长期（5年）影响 年）对人口普查区级CVD患病率使用时空建模; 2）比较飓风的短期- 以及对当前高风险医疗保险受益人（emPOWER）心血管事件发生率的长期影响， 使用生存分析和考克斯模型对所有其他≥ 65岁的受益人进行评估;以及3）制定并验证 CVD风险预测工具，扩展了受飓风影响的高风险医疗保险受益人的定义 使用机器学习和风险预测方法超越emPOWER。在未来的R 01应用程序中，我将执行- 使用2017年飓风季节的100%医疗保险索赔（ 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调整灾前疏散， 评估和灾后心血管疾病管理，解决高风险群体与灾害有关的发病率， 更长期的我将得到一个全国公认的专家团队的指导和建议：马丁·夏皮罗博士（ca- Monika Safford博士（心血管疾病和社会流行病学），大卫艾布拉姆森博士（灾害科学和 Charles DiMaggio博士（空间分析）和Samuel Banerjee博士（机器学习和风险预测）。 的作用）。这个奖项将使我能够推进我成为一名独立的医生科学家的长期目标 其重点在于CVD，气候变化和健康公平的交叉点。 1