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: 10705562
• 负责人: Shao Lin
• 金额: $ 38.9万
• 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/10705562
• 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; Disparity; Elderly; Environment; 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; Predisposition; 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; Time Series Analysis; Vulnerable Populations; Weather; Wildfire; Work; aging population; climate change; cohort; community setting; community-level factor; contextual factors; data integration; data mining; deep learning; ethnic diversity; evidence base; experience; extreme heat; extreme weather; follow-up; hazard; health assessment; health data; 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; resilience factor; response; risk prediction model; 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的与天气有关的灾害 （EWRD），例如热浪、洪水、飓风、风暴和停电（PO）。这些EWRD的影响 对人类健康的影响已成为公共卫生的首要任务。研究表明，老年人，尤其是那些 低社会经济地位和少数民族人口更容易遭受灾害 由于无法获得减轻或适应灾害所需的资源，造成了严重的灾害。现在的 需要一种更全面的方式来有效地解决这些差距， 环境对暴露和对EWRD的健康反应的影响。目前， 我们对所有气象因素如何共同影响健康的理解，以及健康影响在不同时期如何不同， 过渡季节根据现有的有限监测，暴露评估能力的主要局限性 每个州（特别是农村地区）的网站也很明显。此外，很少有大型研究试图 评估EWRDs-健康如何被社区和社会背景（例如，绿色）的方式 从而导致健康差异。为了填补这些空白，拟议的研究将测试一个中心假设，即 脆弱的老龄人口特别容易受到极端天气的不利健康影响， EWRD。具体而言，我们建议：1）通过生成高分辨率网格化的 2）评估多种天气因素和灾害对心肺疾病的联合影响， 老年痴呆症、损伤和肾脏疾病，以及 区域绿化和流行病;和3）评估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EWRD期间促进健康。结果将 作为制定有效传播战略的基础。HrGWD和天气模拟 将使用基于独特Mesonet数据的最先进的两阶段缩减模型创建。此外 利用纽约州住院和艾德数据，我们将回顾性随访再入院和其他重症监护 纽约州独特的18年动态队列指标，同时还评估美国COVID-19感染/死亡 主要EWRD后的费率。我们将使用分布滞后非线性模型和中断时间序列分析， 评估紧急EWRD对老龄人口中最常见和致命疾病的影响。 而因果影响分析将用于估计绿色度和社区的中介效应 从社区层面的300多个因素中选出一个预测模型，以确定 脆弱性/复原力因素使用机器学习算法。我们的多学科和经验丰富的 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适当的宣传材料，以及成功的先前伙伴关系 与政府机构最大限度地提高这个项目的可行性和我们成功的可能性。