This study assesses effects of air pollution exposure on risk of adverse maternal pregnancy outcomes among southern California residents during and before the COVID-19. It also examines whether SARS-CoV-2 infection and time of first diagnosis of the infection, maternal comorbidity, and sociodemographic factors modify associations between air pollution and pregnancy outcomes.
This study expands the understanding of wildfire-induced health impacts by linking detailed health and covariates data in three most recent years with severe wildfire incidences with high spatiotemporal resolution wildfire smoke exposure obtained from sophisticated air quality modeling. The study team examine respiratory and cardiovascular outcomes, pregnancy outcomes, and mental health. In addition, we address specific concerns for disadvantaged communities through inclusive and informative outreaches with designated community members in California.
Background: Recent 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.
Objectives: To evaluate the relationships between green space and PTB, identify windows of susceptibility, and explore potential interactions between green space and air pollution.
Methods: Birth 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 um (PM2.5), nitrogen dioxide (NO2), and ozone (O3)] were examined on both additive and multiplicative scales.
Results: In 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.
Conclusion: This 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.
Full Text Link
The biophilia hypothesis suggests humans have an inherent need of affiliation with nature which may affect our mental health by bringing emotional stability, and helping with fast recovery. With the rapid advances in technology, researchers are seeking new ways for more immersive experience of natural environment, such as Virtual Reality (VR). However, most existing experimental studies focused on general population in working or study settings. Little is known on the link between physiological mechanisms and green space among pregnant women, who are more prone to develop mental disorders during pregnancy than at other times in their lives. To explore the potential way to improve maternal mental health and alleviate the burden of adverse pregnant outcomes, we aim to examine physiological and cognitive responses to green space in pregnant women using VR.
Pregnancy complications (e.g. gestational diabetes [GDM], gestational hypertension [GHTN], and pre- eclampsia & eclampsia [PE/E] are major causes of perinatal morbidity and mortality. Studies have examined associations of air pollution with pregnancy complications, but have major limitations, including 1) reliance on birth certificates or billing/claims data where information may be missing or of questionable validity to ascertain outcomes and co-morbidities; 2) limited consideration of effect confounding and/or modification by other environmental factors; 3) air pollution exposure misclassification due to lack of residential address history; 4) focus on individual air pollutants rather than air pollutant mixtures; 5) lack of focus on the heterogeneity of the risk from air pollution by time and place of exposures, maternal conditions, other environmental factors, and sub-types of outcomes; 6) lack of understanding of the mediation pathways linking maternal co-morbidity with outcomes. We propose a 4-year study to address these limitations and advance knowledge of the impact of air pollutant mixture on pregnancy complications. We will leverage state-of-the-art spatiotemporal air pollution modeling and novel statistical methods that examine both individual and composite exposure profiles with a longitudinal (pre-conception through postpartum) pregnancy cohort of ~400,000 singleton pregnancies in 2008-2018 that result in a live birth or fetal death after 20 weeks gestation that have prospectively-recorded high quality clinical data and residential addresses from the electronic health record (EHR) of Kaiser Permanente Southern California members in 8 southern California counties. Primary outcomes are GHTN, PE/E, GDM. We will estimate individual-level air pollutant exposures (particulate matter and its composition and traffic-related pollutants using sophisticated spatiotemporal models), weather (air temperature, relative humidity, pressure), and built environment measures (greenness, walkability, noise, neighborhood resources) based on prospectively-recorded maternal addresses. Covariates we will examine include maternal comorbidities; history of previous pregnancies and outcomes; individual and contextual socioeconomic status (SES) indicators; employment during pregnancy and job classification; self-reported physical activity and smoking. Complementary statistical methods will be used to evaluate effects of exposure to a mixture of air pollutants while accounting for co-exposure to weather, built environment, and SES. We will examine effect modifications by SES, maternal factors, and other environmental exposures, and the potential mediating role of maternal factors on associations between air pollution and the outcomes. We will elucidate risk of pregnancy complications from air pollution exposure, heterogeneity of risk due to SES, maternal conditions, and other environmental factors, potential underlying mechanisms, susceptible sub-populations, and time windows of susceptibility. Identification of modifiable environmental risk factors and high-risk subpopulation may help to design targeted interventions to reduce these risks and associated adverse maternal and fetal outcomes.
