Frontiers in endocrinology · 2026
Maternal SO2 exposure and birthweight reduction during COVID-19 lockdown and quarantine in East China.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Feng Rui, Liang Fengbing, Wang Xinhui, Shen Jiandong, Yang Cuiyu
Аннотация
BACKGROUND: Low birthweight remains a global public health challenge. While maternal exposure to certain air pollutants is known to adversely affect fetal growth, the association with sulfur dioxide (SO2) has been inconsistent. The COVID-19 lockdown provided a unique quasi-natural experiment to reassess the relationship under altered emission patterns.
METHODS: We conducted a retrospective cohort study of 21, 777 term singleton births in Southern Zhejiang Province, East China, from 2020 to 2022. We estimated maternal SO2 exposure using a time-activity weighted model based on residential and work addresses. Associations with low and high birthweight risks were evaluated using linear regression and odds ratios. Three machine learning models were used to assess whether incorporating SO2 exposure improved birthweight prediction accuracy.
RESULTS: The COVID-19 lockdown was associated with a significant 28% reduction in annual mean atmospheric SO2 levels. Maternal SO2 exposure during the third trimester showed the most substantial negative association with birthweight, particularly in female neonates, and was associated with increased odds of LBW in both sexes. Feature importance showed SO2 played a modest but nonnegligible role. Adding SO2 exposure as a predictor improved birthweight prediction, with XGBoost achieving the highest accuracy.
CONCLUSION: We found the susceptible windows of maternal SO2 exposure for birthweight reduction and LBW risks. Incorporating air pollution exposure into predictive models can enhance the identification of high-risk pregnancies, supporting targeted clinical and environmental interventions.
METHODS: We conducted a retrospective cohort study of 21, 777 term singleton births in Southern Zhejiang Province, East China, from 2020 to 2022. We estimated maternal SO2 exposure using a time-activity weighted model based on residential and work addresses. Associations with low and high birthweight risks were evaluated using linear regression and odds ratios. Three machine learning models were used to assess whether incorporating SO2 exposure improved birthweight prediction accuracy.
RESULTS: The COVID-19 lockdown was associated with a significant 28% reduction in annual mean atmospheric SO2 levels. Maternal SO2 exposure during the third trimester showed the most substantial negative association with birthweight, particularly in female neonates, and was associated with increased odds of LBW in both sexes. Feature importance showed SO2 played a modest but nonnegligible role. Adding SO2 exposure as a predictor improved birthweight prediction, with XGBoost achieving the highest accuracy.
CONCLUSION: We found the susceptible windows of maternal SO2 exposure for birthweight reduction and LBW risks. Incorporating air pollution exposure into predictive models can enhance the identification of high-risk pregnancies, supporting targeted clinical and environmental interventions.