Association of Prematurity and Low Birth Weight with Gestational Exposure to PM 2.5 and PM 10 Particulate Matter in Chileans Newborns.
Alejandra Rodríguez-FernándezNatalia Ramos-CastilloMarcela Ruíz-De la FuenteJulio Parra-FloresMaury-Sintjago EduardPublished in: International journal of environmental research and public health (2022)
Fetal growth can be affected by gestational exposure to air pollution. The aim of the study was to determine the association between prematurity and low birth weight (LBW) with gestational exposure to PM 2.5 and PM 10 particulate matter in Chileans newborns. This cross-sectional analytical study included 595,369 newborns. Data were extracted from the live newborn records of the Chilean Ministry of Health. Sex, gestational age, birth weight, and living variables were analyzed. We used the Air Quality Information System of the Chilean Ministry of the Environment to obtain mean PM 2.5 and PM 10 emissions. A multivariate logistic regression model was performed with STATA 15.0 software at α < 0.05. Prevalence was 7.4% prematurity and 5.5% LBW. Mean PM 2.5 and PM 10 concentrations were 25.5 µg/m 3 and 55.3 µg/m 3 , respectively. PM 2.5 was associated with an increased the risk of LBW (OR: 1.031; 95%CI: 1.004-1.059) when exposure occurred in the second trimester, while PM 10 affected the whole pregnancy. In addition, PM 10 exposure in any gestational trimester was associated with an increased the risk of prematurity. The PM 10 particulate matter was associated with both prematurity and LBW in all of the trimesters of exposure. The PM 2.5 particulate matter was only associated with LBW when exposure occurred in the second gestational trimester.
Keyphrases
- particulate matter
- air pollution
- low birth weight
- gestational age
- preterm birth
- birth weight
- preterm infants
- weight gain
- human milk
- pregnant women
- lung function
- cross sectional
- pregnancy outcomes
- healthcare
- public health
- mental health
- body mass index
- weight loss
- risk factors
- artificial intelligence
- human health
- social media
- big data