Weather extremes, natural disasters, and health outcomes among vulnerable older adults: New improvements on exposure assessment, disparity identification, and risk communication strategies

极端天气、自然灾害和弱势老年人的健康结果：暴露评估、差异识别和风险沟通策略的新改进

• 批准号: 10368551
• 负责人: Shao Lin
• 金额: $ 38.86万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT ALBANY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10368551
• 关键词: Accidental Injury; Address; Adverse effects; Affect; Aging; Alzheimer' s Disease; Behavior; COVID-19 pandemic; Cardiovascular Diseases; Cessation of life; Chronic Obstructive Pulmonary Disease; Communication; Communities; Critical Care; Data; Data Science; Data Set; Death Rate; Dementia; Development; Disasters; Disease; Elderly; Environment; Environmental Health; Environmental Risk Factor; Environmental Wind; Ethics; Event; Exposure to; Face; Floods; Government Agencies; Grant; Health; Heat Waves; Hospitalization; Human; Hurricane; Ice; Individual; Injury; Interruption; Intervention; Joints; Kidney Diseases; Maps; Mediation; Meteorological Factors; Methodology; Methods; Minority; Minority Groups; Modeling; Monitor; Morbidity - disease rate; Natural Disasters; Non-linear Models; Outcome; Population; Population Heterogeneity; Positioning Attribute; Precipitation; Probability; Radiation; Readiness; Recording of previous events; Research; Research Personnel; Resolution; Resources; Respiratory Disease; Risk; Risk Factors; SARS-CoV-2 infection; Seasons; Site; Social Environment; Socioeconomic Status; Techniques; Testing; The Sun; Time Series Analysis; Vulnerable Populations; Weather; Wildfire; Work; aging population; base; climate change; cohort; community-level factor; contextual factors; data mining; deep learning; ethnic diversity; evidence base; experience; extreme heat; extreme weather; follow-up; hazard; health disparity; hospital readmission; improved; indexing; innovation; low socioeconomic status; machine learning algorithm; member; meteorological data; mortality; multidisciplinary; pandemic disease; predictive modeling; programs; public health priorities; racial diversity; remote sensing; resilience; response; rural area; simulation; social; social factors; spelling; success; vulnerable community; weather-related disaster; web site

PROJECT SUMMARY / ABSTRACT In recent decades, climate change has contributed to more frequent and extreme weather-related disasters (EWRD), such as heatwaves, floods, hurricanes, storms and power outage (PO). The impact of these EWRDs on human health has become a top public health priority. Research suggests that older adults, especially those with low socioeconomic status (SES) and minority populations, are disproportionately vulnerable to disaster hazards due to lack of access to the necessary resources for hazard mitigation or adaptation. What is now needed is a much more comprehensive way to effectively address these disparities, by considering social and contextual influences on both exposure and health responses to EWRDs. Currently, significant gaps remain in our understanding of how all meteorological factors jointly affect health, and how health effects may differ during transitional seasons. Major limitations on exposure assessment capacity, based on existing limited monitoring sites in each state (particularly in rural areas), are also apparent. In addition, few large studies have attempted to assess how the EWRDs-health may be modified by community and social contexts (e.g., greenness) in ways that produce health disparities. To fill these gaps, the proposed study will test a central hypothesis that vulnerable aging populations are particularly susceptible to the adverse health effects of extreme weather or EWRDs. Specifically, we propose to: 1) Improve exposure assessment by generating high-resolution gridded weather data; 2) Evaluate joint effects of multiple weather factors and disasters on cardio-respiratory diseases, Alzheimer/dementia, injuries, and renal diseases in vulnerable older adults, as well as the modifying effects of regional greenness and pandemic; and 3) Assess the impact of multiple community contextual factors in affecting health during EWRDs by developing predictive models and vulnerability/resilience indices. Results will serve as the basis for the development of effective communication strategies. HrGWD and weather simulations will be created using a state-of-the-art, two-stage downscaling models based on unique Mesonet data. In addition to utilizing NYS hospitalization and ED data, we will retrospectively follow-up readmission and other critical care indicators in a unique 18-year dynamic cohort in NYS, while also evaluating US COVID-19 infection/death rates after major EWRDs. We will use distributed lag non-linear models and interrupted time-series analysis to evaluate the impacts of emergent EWRDs on the most common and fatal diseases among the aging population. While causal influence analysis will be used to estimate the mediation effects from greenness and community factors, a predictive model selected from over 300 factors at the community level will be developed to identify vulnerability/resilience factors using machine-learning algorithms. Our multi-disciplinary and experienced research team, access to numerous geocoded datasets, innovative data mining/analysis methods, culturally appropriate communication materials planned for vulnerable older adults, and successful prior partnerships with government agencies maximize the feasibility of this project and our probability of success.

项目摘要/摘要 近几十年来，气候变化导致了与天气有关的更频繁、更极端的灾害 (EWRD)，如热浪、洪水、飓风、风暴和停电(PO)。这些EWRD的影响 对人类健康的关注已成为公共卫生的首要任务。研究表明，老年人，特别是那些 低社会经济地位(SES)和少数族裔人口，特别容易遭受灾难 由于无法获得必要的减灾或适应资源而造成的危险。现在是什么？ 需要一种更全面的方式来有效地解决这些差距，通过考虑社会和 环境对EWDS的暴露和健康反应的影响。目前，仍有很大差距， 我们对所有气象因素如何共同影响健康的理解，以及在 过渡季节。在现有有限监测的基础上，暴露评估能力的主要限制 每个州的地点(特别是农村地区)也很明显。此外，很少有大型研究试图 评估社区和社会环境(例如，绿色)如何改变EWRDS-Health 这会造成健康差距。为了填补这些空白，这项拟议的研究将检验一个中心假设 脆弱的老龄化人口特别容易受到极端天气或 艾尔兹。具体地说，我们建议：1)通过生成高分辨率网格来改进暴露评估 天气数据；2)评估多种天气因素和灾害对心肺疾病的联合影响， 易受伤害的老年人中的阿尔茨海默病/痴呆、损伤和肾脏疾病，以及 区域绿化和流行病；以及3)评估影响影响的多种社区背景因素的影响 通过开发预测模型和脆弱性/复原力指数，在紧急情况下保持健康。结果将 作为制定有效沟通战略的基础。HrGWD和天气模拟 将使用最先进的、基于独特的Mesonet数据的两阶段缩小模型来创建。此外 为了利用纽约的住院和急诊数据，我们将追溯再次入院和其他危重护理 在纽约一个独特的18年动态队列中的指标，同时也评估美国新冠肺炎感染/死亡 主要的EWRD之后的利率。我们将使用分布式滞后非线性模型和中断时间序列分析来 评估紧急EWRD对老龄化人口中最常见和致命的疾病的影响。 而因果影响分析则从绿化和社区两个方面对中介效果进行了评估 因素，将开发一个从社区一级的300多个因素中选择的预测模型，以确定 使用机器学习算法的脆弱性/复原力因素。我们的多学科和经验丰富 研究团队，访问大量地理编码数据集，创新的数据挖掘/分析方法，文化 为易受伤害的老年人计划了适当的宣传材料，并成功地建立了以前的伙伴关系 与政府机构合作，最大限度地提高这个项目的可行性和我们的成功几率。