Evaluating the Sensitivity of Mortality Attributable to Pollution to Modeling Choices: A Case Study for Colorado
Priyanka N. deSouza, Susan Anenberg, Neal Fann, Lisa M. McKenzie,, Elizabeth Chan, Ananya Roy, Jose L. Jimenez, William Raich, Henry Roman,, Patrick L. Kinney

TL;DR
This study assesses how different modeling choices affect estimates of mortality due to pollution in Colorado, revealing significant sensitivity to spatial scale, CRFs, and exposure assumptions.
Contribution
It systematically evaluates the impact of various modeling assumptions on pollution-related mortality estimates in Colorado, highlighting key sensitivities.
Findings
County-level NO2 estimates reduce mortality by ~50%.
Using group-specific CRFs alters mortality estimates across racial groups.
Home-based exposure estimates decrease mortality figures for NO2 and PM2.5.
Abstract
We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations. This analysis was carried out for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ~ 50% for all of Colorado for each year between 2000-2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a higher estimate of annual mortality…
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Taxonomy
TopicsAir Quality and Health Impacts · Health disparities and outcomes · Climate Change and Health Impacts
