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

世卫组织将空气污染和气候变化列为2019年十大威胁中的两个，以及更早的研究 表明气候变化暴露和大脑健康之间的联系。此外，老年人的负担 阿尔茨海默病(AD)和相关痴呆症(ADRD)预计到2060年将翻一番，其中最大的 拉美裔美国人的人数增加了。与此同时，野火的频率、强度和持续时间都在增加。 总而言之，气候变化对环境的影响可能会成为我们现在这样的大脑健康紧急情况 没有做好铲球的准备。到目前为止，人们对高温或空气污染的影响知之甚少，包括野火烟雾， 对患有AD/ADRD的老年人。大多数关于气候变化相关脆弱性的研究都考察了单一因素 而不是以多种因素为特征的现实世界环境(同时发生的空气污染和 高温、社会经济地位、虚弱、慢性病、种族/族裔)。此外，以前的研究还没有 利用卫星图像、机器学习和因果推理方法的最新发展， 可以提高统计分析的严谨性和有效性。我们建议通过以下方式解决这些科学差距 拥有AD/ADRD的美国联邦医疗保险受益人的大量有效队列(&gt；65Y)(约这一时期为1000万美元 2000-2019年)和空间分辨率天气数据与最先进的机器学习相结合，用于 空气污染暴露的估计，利用卫星图像、土地使用数据和监测仪。我们的长- 学期目标是确定气候变化相关暴露的脆弱性和对健康的影响 一大群患有AD/ADRD的老年人。首先，我们将评估短期接触高温的影响。 和热浪对特定原因的住院、再入院、死亡率和新的以患者为中心的 居家天数的结果，并开发机器学习算法来识别哪些亚群患有 AD/ADRD在几个个人和社区层面的因素方面最脆弱(例如，性别， 慢性疾病、种族/民族、虚弱)。接下来，我们将评估患有AD/ADRD的老年人对 空气污染，包括野火烟雾，使用我们最先进的科学方法来估计空气污染和 暴露在野火烟雾中。然后，我们估计热和空气同时存在的影响和脆弱性 发展贝叶斯分层时空的污染(包括热浪和野火烟雾) 量化协同效应的模型。最后，我们将传播所有方法、暴露数据和统计数据 软件，免费向公众提供。描述增加脆弱性的因素 对于患有AD/ADRD的老年人，将使决策者能够设计有效的干预措施。调查结果将 通报气候变化的影响评估，预计气候变化会增加高温和空气污染，包括 野火，以及了解环境健康差异(即环境正义)。我们的结果将 对同时发生的高温和空气污染事件期间的健康管理产生影响，包括 并增加有关天气和空气污染的准备、反应和恢复方面的知识。

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.