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，气候变化导致了更频繁和极端与天气有关的灾难 （EWRD），例如热浪，洪水，飓风，风暴和停电（PO）。这些EWRD的影响 关于人类健康已成为公共卫生的重点。研究表明，老年人，尤其是那些 具有低社会经济地位（SES）和少数民族人口，不成比例地遭受灾难的影响 由于缺乏获得必要的减轻危害或适应资源而造成的危害。现在是什么 所需的是一种更全面的方法，可以通过考虑社会和 上下文对EWRD的暴露和健康反应都有影响。目前，仍然存在重大差距 我们对所有气象因素如何共同影响健康的理解，以及在健康期间的健康影响如何差异 过渡季节。基于现有有限监控的暴露评估能力的主要限制 每个州（尤其是在农村地区）的地点也很明显。此外，很少有大型研究尝试 评估如何通过社区和社会环境（例如绿色）修改EWRDS-Health 产生健康差异。为了填补这些空白，拟议的研究将检验一个中心假设，即 脆弱的老龄化人口特别容易受到极端天气或 ewrds。具体而言，我们建议：1）通过产生高分辨率网格来改善暴露评估 天气数据； 2）评估多种天气因素和灾难对心脏呼吸疾病的关节影响， 易受伤害的老年人的阿尔茨海默氏症/痴呆症，伤害和肾脏疾病以及修改作用 区域绿色和大流行； 3）评估多个社区背景因素影响影响的影响 通过开发预测模型和脆弱性/弹性指数，在EWRD期间的健康状况。结果将 作为制定有效沟通策略的基础。 HRGWD和天气模拟 将使用基于唯一的中音数据的最先进的，两阶段的降尺度模型来创建。此外 为了利用NYS住院和ED数据，我们将回顾性跟进再入院和其他重症监护 在纽约州独特的18年动态队列中的指标，同时还评估了美国Covid-19感染/死亡 主要EWRD之后的费率。我们将使用分布式滞后非线性模型和中断的时间序列分析 评估新兴EWRD对老龄化人群中最常见和致命疾病的影响。 尽管因果影响分析将用于估计绿色和社区的调解效应 因素，将开发从社区一级的300多个因素中选择的预测模型以识别 使用机器学习算法的脆弱性/弹性因素。我们的多学科和经验丰富的 研究团队，访问众多地理编码数据集，创新数据挖掘/分析方法，文化上 计划针对脆弱的老年人计划的适当沟通材料，并成功的先前合作伙伴关系 随着政府机构，该项目的可行性和我们成功的可能性最大化。