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Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure.

ABSTRACT: Exposure to high levels of air pollution during the pregnancy is associated with increased probability of preterm birth (PTB), a major cause of infant morbidity and mortality. New statistical methodology is required to specifically determine when a particular pollutant impacts the PTB outcome, to determine the role of different pollutants, and to characterize the spatial variability in these results. We develop a new Bayesian spatial model for PTB which identifies susceptible windows throughout the pregnancy jointly for multiple pollutants (PM(2.5) , ozone) while allowing these windows to vary continuously across space and time. We geo-code vital record birth data from Texas (2002-2004) and link them with standard pollution monitoring data and a newly introduced EPA product of calibrated air pollution model output. We apply the fully spatial model to a region of 13 counties in eastern Texas consisting of highly urban as well as rural areas. Our results indicate significant signal in the first two trimesters of pregnancy with different pollutants leading to different critical windows. Introducing the spatial aspect uncovers critical windows previously unidentified when space is ignored. A proper inference procedure is introduced to correctly analyze these windows.

SUBMITTER: Warren J

PROVIDER: S-EPMC3422613 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure.

Warren Joshua J Fuentes Montserrat M Herring Amy A Langlois Peter P

Biometrics 20120508 4

Exposure to high levels of air pollution during the pregnancy is associated with increased probability of preterm birth (PTB), a major cause of infant morbidity and mortality. New statistical methodology is required to specifically determine when a particular pollutant impacts the PTB outcome, to determine the role of different pollutants, and to characterize the spatial variability in these results. We develop a new Bayesian spatial model for PTB which identifies susceptible windows throughout ...[more]

PMID: 22568640

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Project description:BackgroundRecent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born < 37 completed gestational weeks). In addition, windows of susceptibility during pregnancy have not been explored and potential interactions of green space with air pollution exposures during pregnancy are still unclear.ObjectivesTo evaluate the relationships between green space and PTB, identify windows of susceptibility, and explore potential interactions between green space and air pollution.MethodsBirth certificate records for all births in California (2001-2008) were obtained. The Normalized Difference Vegetation Index (NDVI) was used to characterized green space exposure. Gestational age was treated as a time-to-event outcome; Cox proportional hazard models were applied to estimate the association between green space exposure and PTB, moderately PTB (MPTB, gestational age < 35 weeks), and very PTB (VPTB, gestational age < 30 weeks), after controlling for maternal age, race/ethnicity, education, and median household income. Month-specific green space exposure was used to identify potential windows of susceptibility. Potential interactions between green space and air pollution [fine particulate matter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3)] were examined on both additive and multiplicative scales.ResultsIn total, 3,753,799 eligible births were identified, including 341,123 (9.09%) PTBs, 124,631 (3.32%) MPTBs, and 22,313 (0.59%) VPTBs. A reduced risk of PTB was associated with increases in residential NDVI exposure in 250 m, 500 m, 1000 m, and 2000 m buffers. In the 2000 m buffer, the association was strongest for VPTB [adjusted hazard ratio (HR) per interquartile range increase in NDVI: 0.959, 95% confidence interval (CI): 0.942-0.976)], followed by MPTB (HR = 0.970, 95% CI: 0.962-0.978) and overall PTB (HR = 0.972, 95% CI: 0.966-0.978). For PTB, green space during the 3rd - 5th gestational months had stronger associations than those in the other time periods, especially during the 4th gestational month (NDVI 2000 m: HR = 0.970, 95% CI: 0.965-0.975). We identified consistent positive additive and multiplicative interactions between decreasing green space and higher air pollution.ConclusionThis large study found that maternal exposure to residential green space was associated with decreased risk of PTB, MPTB, and VPTB, especially in the second trimester. There is a synergistic effect between low green space and high air pollution levels on PTB, indicating that increasing exposure to green space may be more beneficial for women with higher air pollution exposures during pregnancy.

Dataset Information

Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure.

Publications

Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure.

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