Modeling spatially varying compliance effects of PM2.5 exposure reductions on gestational diabetes mellitus in southern California: Results from electronic health record data of a large pregnancy cohort

• Published in: Environmental research Vol. 231; no. Pt 2; p. 116091
• Main Authors: Molitor, John, Sun, Yi, Rubio, Virgilio Gómez, Benmarhnia, Tarik, Chen, Jiu-Chiuan, Avila, Chantal, Sacks, David A., Chiu, Vicki, Slezak, Jeff, Getahun, Darios, Wu, Jun
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.08.2023
• Subjects: Air pollution exposures; Bayesian modeling, multi-level models; Gestational diabetes mellitus; Spatially varying effects; Spatially varying effects; Gestational diabetes mellitus; Air pollution exposures; Bayesian modeling, multi-level models
• ISSN: 0013-9351; 1096-0953; 1096-0953
• DOI: 10.1016/j.envres.2023.116091

Gestational diabetes mellitus (GDM) is a major pregnancy complication affecting approximately 14.0% of pregnancies around the world. Air pollution exposure, particularly exposure to PM2.5, has become a major environmental issue affecting health, especially for vulnerable pregnant women. Associations between PM2.5 exposure and adverse birth outcomes are generally assumed to be the same throughout a large geographical area. However, the effects of air pollution on health can very spatially in subpopulations. Such spatially varying effects are likely due to a wide range of contextual neighborhood and individual factors that are spatially correlated, including SES, demographics, exposure to housing characteristics and due to different composition of particulate matter from different emission sources. This combination of elevated environmental hazards in conjunction with socioeconomic-based disparities forms what has been described as a “double jeopardy” for marginalized sub-populations. In this manuscript our analysis combines both an examination of spatially varying effects of a) unit-changes in exposure and examines effects of b) changes from current exposure levels down to a fixed compliance level, where compliance levels correspond to the Air Quality Standards (AQS) set by the U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality guideline values. Results suggest that exposure reduction policies should target certain “hotspot” areas where size and effects of potential reductions will reap the greatest rewards in terms of health benefits, such as areas of southeast Los Angeles County which experiences high levels of PM2.5 exposures and consist of individuals who may be particularly vulnerable to the effects of air pollution on the risk of GDM. •Study examines effects of PM2.5 exposure reduction on incidence of Gestational diabetes mellitus (GDM).· Analyses address issues related to “double jeopardy” for marginalized sub-populations.· Results suggest that interventions should target areas containing both high exposure and elevated SES-based vulnerabilities.•Effects of reducing PM2.5 exposure down to fixed compliance levels are examined.•Analyses address issues related to “double jeopardy” for marginalized sub-populations.•Results suggest that interventions should target areas containing both high exposures and elevated SES-based vulnerabilities.