Susceptibility and adverse health outcomes related to climate-sensitive events among older Medicare beneficiaries with Alzheimer and Dementia
Funded Grant
Overview
Affiliation
View All
Overview
description
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.
