Year of Award

2024

Document Type

Dissertation

Degree Type

Doctor of Philosophy (PhD)

Degree Name

Public Health

Department or School/College

School of Public and Community Health Sciences

Committee Chair

Erin O. Semmens

Commitee Members

Jon Graham, Jon Graham, Sophia Newcomer, Curtis Noonan

Keywords

air pollution, geomasking, perinatal epidemiology

Publisher

University of Montana

Abstract

Background: Air pollution is the world’s largest single environmental health risk. Particulate matter air pollution with aerodynamic diameter ≤ 2.5 μm (PM2.5) poses significant health hazards and has been increasing in some parts of the United States in recent decades due to increased wildfire activity. Prenatal exposure to PM2.5 may increase risk of hypertensive disorders of pregnancy (HDPs), though this has not been evaluated in a rural, wildfire-prone context. To develop recommendations to mitigate harmful health effects of PM2.5, more research is needed to understand the precise timing and dose of PM2.5 that is harmful to human health. Such research should utilize highly spatially resolved data, which is not always accessible due to privacy concerns. Geomasking techniques can offer privacy protection with spatial resolutions higher than aggregation methods, but it remains unknown whether, and to what degree, geomasking introduces bias into studies on the health effects of PM2.5 exposure.

Purpose: To determine the association between prenatal exposure to PM2.5 and risk of HDP and to identify sensitive windows of exposure, and to explore whether geomasking introduces bias into risk estimates.

Methods: We used 2008-2019 Montana birth certificate records and a high-resolution spatiotemporal PM2.5 layer along with multiple logistic regression and distributed lag nonlinear models to evaluate the association between PM2.5 exposure and HDPs. We used an adaptive variable-radius mask technique on the birth certificate data and three simulated populations to evaluate the extent to which geomasking introduced bias into parameter estimates.

Results: We observed a 10% (95% CI: 1.06-1.12) increased risk of HDPs associated with each 3 μg/m3 increase in mean pregnancy PM2.5 exposure and identified two sensitive windows of exposure: (1) the week of conception and three weeks before and after conception; and (2) gestational weeks 19 through 33. We observed geomasking to have minimal effect on parameter estimates in the birth certificate study and subsequent simulations.

Conclusion: This study adds to the evidence that prenatal exposure to PM2.5 contributes to risk of HDP. Further, this study provides promising evidence that geomasking may be a valuable tool for researchers who study the health effects of PM2.5.

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© Copyright 2024 Elizabeth Rae Susanne Williams