Effects of Outdoor Wildfire PM2.5 on Alzheimer’s Disease and Related Dementia
Investigators:
Jennifer Stowell, Chad Milando, and Greg Wellenius
Funding:
NIA R61AG086854 (CACHE)
Data sources:
- Daily Emergency Department (ED) Visits & Hospitalizations. United Healthcare claims data Optum Labs Data Warehouse, 2006-2023 [https://www.optumlabs.com/].
- Wildfire Smoke (WFS) Fine Particulate Matter (PM2.5). (Childs ML, et al., 2022) Daily local estimates of WFS-specific PM2.5 for the contiguous US [https://doi.org/10.1021/acs.est.2c02934].
- Daily temperature measures and relative humidity will be obtained from the North American Land Data Assimilation System (NLDAS) and ERA5 reanalysis product [https://ldas.gsfc.nasa.gov/nldas, https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview].
- ACS 5-year Estimates. (US Census Bureau, American Community Survey, 2020) [https://www.census.gov/programs-surveys/acs].
Measures:
- Health Measures: AD/ADRD ED visits and hospitalizations will be identified using the International Classification of Diseases versions 9 and 10.
- Aging Measures: Age of event is recorded for all patients, and the analysis is restricted to persons aged at least 40 years.
- Climate Measures: Daily WFS-specific PM2.5, heat, and relative humidity will allow us to examine un-biased associations between wildfire smoke and AD/ADRD. Meteorology will include multiple measures of temperature (i.e. absolute, heat index, wet bulb globe, etc.).
Project Summary:
We will examine the impact of exposure to WFS-specific PM2.5 on emergency department visits and hospitalizations for incident AD/ADRD or exacerbations of AD/. We will link population-weighted exposure, meteorology, and demographic variables to AD/ADRD events across the contiguous US for 2006-2023 using a large medical claims dataset. We will accomplish this using distributed lag nonlinear models (DLNM) and conditional Poisson regression. We will explore multiple lag lengths to account for delays in exposure effects. These analyses will be conducted using a case-control study design where each case is matched to non-case days within the same month, year, and on the same day of the week. This design inherently controls for all time invariant confounders, and all models will include terms for confounding variables such as temperature, relative humidity, and holidays. We will repeat our analyses stratifying on measures of individual and community-level social determinants of health (SDOH) using age, sex, and select ACS variables.
This research will help to increase our understanding of the environmental factors associated with AD/ADRD. Our results will provide actionable evidence for public health practitioners, clinicians, and policymakers in future efforts to mitigate the impacts of climate change on AD/ADRD. Our future research will build on these results and inform an R01 proposal to examine the potential synergistic impacts of multiple extreme weather events (i.e. wildfire, drought, heat, etc.) and mixtures of pollutants on AD/ADRD in US adults.
Outputs:
- Poster presentations
- Grant proposal
- Code
- Future publications