Coal smoke, gestational cadmium exposure, and fetal growth.

BACKGROUND: Gestational cadmium exposure may impair fetal growth. Coal smoke has largely been unexplored as a source of cadmium exposure. We investigated the relationship between gestational cadmium exposure and fetal growth, and assessed coal smoke as a potential source of airborne cadmium, among non-smoking pregnant women in Ulaanbaatar, Mongolia, where coal combustion in home heating stoves is a major source of outdoor and indoor air pollution. METHODS: This observational study was nested within the Ulaanbaatar Gestation and Air Pollution Research (UGAAR) study, a randomized controlled trial of portable high efficiency particulate air (HEPA) filter air cleaner use during pregnancy, fetal growth, and early childhood development. We measured third trimester blood cadmium concentrations in 374 out of 465 participants who had a live birth. We used multiple linear and logistic regression to assess the relationships between log2-transformed maternal blood cadmium concentrations and birth weight, length, head circumference, ponderal index, low birth weight, small for gestational age, and preterm birth in crude and adjusted models. We also evaluated the relationships between log2-transformed blood cadmium concentrations and the density of coal-burning stoves within 5000 m of each participant's apartment as a proxy of coal smoke emissions from home heating stoves. RESULTS: The median (25th,75th percentile) blood cadmium concentration was 0.20 (0.15, 0.29) µg/L. A doubling of blood cadmium was associated with a 95 g (95% CI: 34, 155 g) reduction in birth weight in adjusted models. An interquartile range increase in coal stove density (from 3.4 to 4.9 gers/hectare) surrounding participants' apartments was associated with a 12.2% (95% CI: 0.3, 25.6%) increase in blood cadmium concentrations. CONCLUSIONS: Gestational cadmium exposure was associated with reduced birth weight. In settings where coal is a widely used fuel, cadmium may play a role in the putative association between air pollution and impaired fetal growth.

Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city.

BACKGROUND: Indoor and outdoor fine particulate matter (PM2.5) are both leading risk factors for death and disease, but making indoor measurements is often infeasible for large study populations. METHODS: We developed models to predict indoor PM2.5 concentrations for pregnant women who were part of a randomized controlled trial of portable air cleaners in Ulaanbaatar, Mongolia. We used multiple linear regression (MLR) and random forest regression (RFR) to model indoor PM2.5 concentrations with 447 independent 7-day PM2.5 measurements and 87 potential predictor variables obtained from outdoor monitoring data, questionnaires, home assessments, and geographic data sets. We also developed blended models that combined the MLR and RFR approaches. All models were evaluated in a 10-fold cross-validation. RESULTS: The predictors in the MLR model were season, outdoor PM2.5 concentration, the number of air cleaners deployed, and the density of gers (traditional felt-lined yurts) surrounding the apartments. MLR and RFR had similar performance in cross-validation (R2 = 50.2%, R2 = 48.9% respectively). The blended MLR model that included RFR predictions had the best performance (cross validation R2 = 81.5%). Intervention status alone explained only 6.0% of the variation in indoor PM2.5 concentrations. CONCLUSIONS: We predicted a moderate amount of variation in indoor PM2.5 concentrations using easily obtained predictor variables and the models explained substantially more variation than intervention status alone. While RFR shows promise for modelling indoor concentrations, our results highlight the importance of out-of-sample validation when evaluating model performance. We also demonstrate the improved performance of blended MLR/RFR models in predicting indoor air pollution.

The effect of portable HEPA filter air cleaner use during pregnancy on fetal growth: The UGAAR randomized controlled trial.

BACKGROUND: Fine particulate matter (PM2.5) exposure may impair fetal growth. AIMS/OBJECTIVES: Our aim was to assess the effect of portable high efficiency particulate air (HEPA) filter air cleaner use during pregnancy on fetal growth. METHODS: The Ulaanbaatar Gestation and Air Pollution Research (UGAAR) study is a single-blind randomized controlled trial conducted in Ulaanbaatar, Mongolia. Non-smoking pregnant women recruited at ≤18 weeks gestation were randomized to an intervention (1-2 air cleaners in homes from early pregnancy until childbirth) or control (no air cleaners) group. Participants were not blinded to their intervention status. Demographic, health, and birth outcome data were obtained via questionnaires and clinic records. We used unadjusted linear and logistic regression and time-to-event analysis to evaluate the intervention. Our primary outcome was birth weight. Secondary outcomes were gestational age-adjusted birth weight, birth length, head circumference, gestational age at birth, and small for gestational age. The study is registered at ClinicalTrials.gov (NCT01741051). RESULTS: We recruited 540 participants (272 control and 268 intervention) from January 9, 2014 to May 1, 2015. There were 465 live births and 28 losses to follow up. We previously reported a 29% (95% CI: 21, 37%) reduction in indoor PM2.5 concentrations with portable HEPA filter air cleaner use. The median (25th, 75th percentile) birth weights for control and intervention participants were 3450 g (3150, 3800 g) and 3550 g (3200, 3800 g), respectively (p = 0.34). The intervention was not associated with birth weight (18 g; 95% CI: -84, 120 g), but in a pre-specified subgroup analysis of 429 term births the intervention was associated with an 85 g (95% CI: 3, 167 g) increase in mean birth weight. CONCLUSIONS: HEPA filter air cleaner use in a high pollution setting was associated with greater birth weight only among babies born at term.