Prenatal Exposure to PM2.5 Concentration and Risk of Preterm Birth In New Mexico; A Seasonal Pattern and Spatio-Temporal Modeling.

This study investigates the association between prenatal PM2.5 exposure and preterm birth risk in New Mexico (2014–2021). Using descriptive statistics, time series models, and spatial analyses, findings show an average preterm birth rate of 14.87 per 1,000 live births, with moderate correlation (r = 0.727) between PM2.5 levels and very preterm births. While traditional models revealed no significant global effect, spatial methods such as Geographically Weighted Regression (GWR) and Multiscale GWR uncovered strong spatial heterogeneity. PM2.5 effects varied by county (coefficients: -0.00154 to 0.00150), with clustering evident in 2018–2019 (Moran’s I = 0.155–0.174). Results highlight the limitations of global models and advocate for spatially targeted interventions and seasonal risk mitigation. Recommendations include localized air quality alerts, improved monitoring, and policies tailored to high-risk areas.

Publication Details

Journal
UNM’s Digital Repository (University of New Mexico)
Published
2025-04-08
Primary Topic
Air Quality and Health Impacts
Type
article
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article

Prenatal Exposure to PM2.5 Concentration and Risk of Preterm Birth In New Mexico; A Seasonal Pattern and Spatio-Temporal Modeling.

UNM’s Digital Repository (University of New Mexico)
Air Quality and Health Impacts
article

Prenatal Exposure to PM2.5 Concentration and Risk of Preterm Birth In New Mexico; A Seasonal Pattern and Spatio-Temporal Modeling.

article en

Abstract

This study investigates the association between prenatal PM2.5 exposure and preterm birth risk in New Mexico (2014–2021). Using descriptive statistics, time series models, and spatial analyses, findings show an average preterm birth rate of 14.87 per 1,000 live births, with moderate correlation (r = 0.727) between PM2.5 levels and very preterm births. While traditional models revealed no significant global effect, spatial methods such as Geographically Weighted Regression (GWR) and Multiscale GWR uncovered strong spatial heterogeneity. PM2.5 effects varied by county (coefficients: -0.00154 to 0.00150), with clustering evident in 2018–2019 (Moran’s I = 0.155–0.174). Results highlight the limitations of global models and advocate for spatially targeted interventions and seasonal risk mitigation. Recommendations include localized air quality alerts, improved monitoring, and policies tailored to high-risk areas.

UNM’s Digital Repository (University of New Mexico)
Openalex Percentile: Top 36%
Air Quality and Health Impacts
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