Susceptibility and adverse health outcomes related to climate-sensitive events among older Medicare beneficiaries with Alzheimer and Dementia

患有阿尔茨海默症和痴呆症的老年医疗保险受益人与气候敏感事件相关的易感性和不良健康结果

• 批准号: 10607424
• 负责人: Michelle L Bell
• 金额: $ 237.85万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-15 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10607424
• 关键词: Address; Affect; Age; Aging; Air Pollutants; Air Pollution; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Area; Burn injury; Charge; Chronic; Climate; Code; Computer software; Data; Dementia; Dependence; Development; Elderly; Emergency Situation; Environmental Impact; Ethnic Origin; Event; Exposure to; Frequencies; Future; Goals; Health; Heat Waves; Hispanic Americans; Home; Hospitalization; Hospitals; Imagery; Individual; Knowledge; Late Onset Alzheimer Disease; Link; Literature; Machine Learning; Medicare; Methodology; Methods; Modeling; Monitor; Nitrogen Dioxide; Nursing Homes; Outcome; Ozone; Patient-Focused Outcomes; Persons; Physiological; Population; Predisposition; Public Health; Race; Racial Segregation; Readiness; Recovery; Regulation; Reporting; Research; Risk Estimate; Science; Series; Smoke; Socioeconomic Status; Statistical Data Interpretation; Subgroup; Symptoms; Time; Vulnerable Populations; Weather; Wildfire; Work; World Health Organization; beneficiary; brain health; climate change; climate impact; cohort; community-level factor; design; effective intervention; environmental health disparity; environmental justice; extreme heat; extreme temperature; fine particles; frailty; health management; hospital readmission; land use; loved ones; machine learning algorithm; meteorological data; mortality; mortality risk; multiple chronic conditions; novel; response; sex; spatiotemporal; structural determinants; Address; Affect; Age; Aging; Air Pollutants; Air Pollution; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Area; Burn injury; Charge; Chronic; Climate; Code; Computer software; Data; Dementia; Dependence; Development; Elderly; Emergency Situation; Environmental Impact; Ethnic Origin; Event; Exposure to; Frequencies; Future; Goals; Health; Heat Waves; Hispanic Americans; Home; Hospitalization; Hospitals; Imagery; Individual; Knowledge; Late Onset Alzheimer Disease; Link; Literature; Machine Learning; Medicare; Methodology; Methods; Modeling; Monitor; Nitrogen Dioxide; Nursing Homes; Outcome; Ozone; Patient-Focused Outcomes; Persons; Physiological; Population; Predisposition; Public Health; Race; Racial Segregation; Readiness; Recovery; Regulation; Reporting; Research; Risk Estimate; Science; Series; Smoke; Socioeconomic Status; Statistical Data Interpretation; Subgroup; Symptoms; Time; Vulnerable Populations; Weather; Wildfire; Work; World Health Organization; beneficiary; brain health; climate change; climate impact; cohort; community-level factor; design; effective intervention; environmental health disparity; environmental justice; extreme heat; extreme temperature; fine particles; frailty; health management; hospital readmission; land use; loved ones; machine learning algorithm; meteorological data; mortality; mortality risk; multiple chronic conditions; novel; response; sex; spatiotemporal; structural determinants

The WHO listed air pollution and climate change as two of the top ten threats in 2019, and earlier research indicates links between climate change exposures and brain health. Further, the burden of older persons with Alzheimer’s disease (AD) and related dementias (ADRD) is expected to double by 2060, with the largest increase for Hispanic Americans. Simultaneously, wildfires are increasing in frequency, intensity, and duration. In summary, the environmental impact of climate change could become a brain health emergency that we are unprepared to tackle. To date, little is known regarding impacts of heat or air pollution, including wildfire smoke, on the elderly with AD/ADRD. Most studies on climate change related vulnerability investigated a single factor at a time rather than the real-world settings characterized by multiple factors (co-occurring air pollution and heat, socio-economic status, frailty, chronic conditions, race/ethnicity). Further, previous studies have not leveraged recent developments in satellite imagery, machine learning, and causal inference methods, which can increase the rigor and validity of statistical analysis. We propose to address these scientific gaps using a large, validated cohort of US Medicare beneficiaries (>65y) with AD/ADRD (approx. 10 million for the period 2000-2019) and spatially resolved weather data combined with state-of-the-science machine learning for estimates of air pollution exposure, which leverages satellite imagery, land use data, and monitors. Our long- term goals are to characterize the vulnerability and health impacts of climate change-related exposures within a large cohort of older adults with AD/ADRD. First, we will estimate the impacts of short-term exposure to heat and heatwaves on cause-specific hospital admissions, readmissions, mortality, and a novel patient-centered outcomes of days-at-home, and develop machine learning algorithms to identify which subpopulations with AD/ADRD are most vulnerable with respect to several individual- and community-level factors (e.g., sex, chronic conditions, race/ethnicity, frailty). Next, we will estimate vulnerability of older persons with AD/ADRD to air pollution including wildfire smoke using our state-of-the-science approach to estimate air pollution and wildfire smoke exposure. We then estimate the impacts and vulnerabilities from co-occurring heat and air pollution (including heat waves and wildfire smoke) by developing Bayesian hierarchical spatio-temporal models to quantify synergistic effects. Finally, we will disseminate all methods, exposure data, and statistical software, making them publicly available free of charge. Characterizing the factors that increase vulnerability for older persons with AD/ADRD will allow decisionmakers to design effective interventions. Findings will inform impact assessments of climate change, which is anticipated to increase heat and air pollution including wildfires, and for understanding environmental health disparities (i.e., environmental justice). Our results will have implications for management of health during co-occurring heat and air pollution events, including wildfires, and increase knowledge on weather and air pollution preparedness, response, and recovery.

世卫组织将空气污染和气候变化列为2019年前十名威胁中的两个，以及早期的研究 指示气候变化暴露与大脑健康之间的联系。此外，老年人的燃烧 阿尔茨海默氏病（AD）和相关痴呆症（ADRD）预计到2060年将翻一番，最大 西班牙裔美国人增加。同时，野火的频率，强度和持续时间正在增加。 总而言之，气候变化的环境影响可能会成为我们正处于大脑健康的紧急情况 没有准备就绪。迄今为止 在古老的广告/adrd上。关于气候变化相关漏洞的大多数研究都研究了一个因素 一次而不是以多种因素为特征的现实环境（同时发生的空气污染和 热量，社会经济地位，脆弱，慢性病，种族/种族）。此外，以前的研究还没有 利用了卫星图像，机器学习和因果推理方法的最新发展，这些方法 可以增加统计分析的严格和有效性。我们建议使用 大型，经过验证的美国Medicare受益人（> 65y）与AD/ADRD（该期间约1000万 2000- 2019年）和空间解决的天气数据与最先进的机器学习 空气污染暴露的估计值，利用卫星图像，土地使用数据和监视器。我们的长期 术语目标是表征与气候变化相关暴露的脆弱性和健康影响 大量的AD/ADRD老年人队列。首先，我们将估计短期接触热的影响 以及针对特定原因的医院入院，再入院，死亡率和新颖的以患者为中心的热浪 在家中的成果，并开发机器学习算法，以识别哪些亚群 关于几个个人和社区级别的因素（例如，性， 慢性病，种族/民族，脆弱）。接下来，我们将估计有广告/ADRD的老年人的脆弱性 空气污染，包括使用我们最先进的方法来估计空气污染和 野火烟雾暴露。然后，我们估计同时发生的热量和空气的影响和脆弱性 通过开发贝叶斯分层空间 - 污染（包括热浪和野火烟雾） 量化协同作用的模型。最后，我们将传播所有方法，暴露数据和统计 软件，使其免费公开可用。表征增加脆弱性的因素 对于患有广告/ADRD的老年人，决策者可以设计有效的干预措施。调查结果会 告知对气候变化的影响评估，预计会增加热量和空气污染 野火和了解环境健康差异（即环境正义）。我们的结果将会 在同时发生的热量和空气污染事件中对健康的管理有影响，包括 野火，并增加对天气和空气污染准备，反应和恢复的知识。