The paper describes a Bayesian spatial discrete time survival model to estimate the effect of air pollution on the risk of preterm birth. The standard approach treats prematurity as a binary outcome and cannot effectively examine time varying exposures during pregnancy. Time varying exposures can arise either in short-term lagged exposures due to seasonality in air pollution or long-term cumulative exposures due to changes in length of exposure. Our model addresses this challenge by viewing gestational age as time-to-event data where each pregnancy becomes at risk at a prespecified time (e.g. the 28th week). The pregnancy is then followed until either a birth occurs before the 37th week (preterm), or it reaches the 37th week, and a full-term birth is expected. The model also includes a flexible spatially varying baseline hazard function to control for unmeasured spatial confounders and to borrow information across areal units. The approach proposed is applied to geocoded birth records in Mecklenburg County, North Carolina, for the period 2001–2005. We examine the risk of preterm birth that is associated with total cumulative and 4-week lagged exposure to ambient fine particulate matter.
Maternal exposure to environmental chemicals during pregnancy can alter birth and children's health outcomes. Research seeks to identify critical windows, time periods when exposures can change future health outcomes, and estimate the exposure–response relationship. Existing statistical approaches focus on estimation of the association between maternal exposure to a single environmental chemical observed at high temporal resolution (e.g., weekly throughout pregnancy) and children's health outcomes. Extending to multiple chemicals observed at high temporal resolution poses a dimensionality problem and statistical methods are lacking. We propose a regression tree–based model for mixtures of exposures observed at high temporal resolution. The proposed approach uses an additive ensemble of tree pairs that defines structured main effects and interactions between time‐resolved predictors and performs variable selection to select out of the model predictors not correlated with the outcome. In simulation, we show that the tree‐based approach performs better than existing methods for a single exposure and can accurately estimate critical windows in the exposure–response relation for mixtures. We apply our method to estimate the relationship between five exposures measured weekly throughout pregnancy and birth weight in a Denver, Colorado, birth cohort. We identified critical windows during which fine particulate matter, sulfur dioxide, and temperature are negatively associated with birth weight and an interaction between fine particulate matter and temperature. Software is made available in the R package dlmtree.
more » « less- NSF-PAR ID:
- 10364254
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 79
- Issue:
- 1
- ISSN:
- 0006-341X
- Format(s):
- Medium: X Size: p. 449-461
- Size(s):
- ["p. 449-461"]
- Sponsoring Org:
- National Science Foundation
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There is substantial interest in assessing how exposure to environmental mixtures, such as chemical mixtures, affects child health. Researchers are also interested in identifying critical time windows of susceptibility to these complex mixtures. A recently developed method, called lagged kernel machine regression (LKMR), simultaneously accounts for these research questions by estimating the effects of time‐varying mixture exposures and by identifying their critical exposure windows. However, LKMR inference using Markov chain Monte Carlo (MCMC) methods (MCMC‐LKMR) is computationally burdensome and time intensive for large data sets, limiting its applicability. Therefore, we develop a mean field variational approximation method for Bayesian inference (MFVB) procedure for LKMR (MFVB‐LKMR). The procedure achieves computational efficiency and reasonable accuracy as compared with the corresponding MCMC estimation method. Updating parameters using MFVB may only take minutes, whereas the equivalent MCMC method may take many hours or several days. We apply MFVB‐LKMR to Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS), a prospective cohort study in Mexico City. Results from a subset of PROGRESS using MFVB‐LKMR provide evidence of significant and positive association between second trimester cobalt levels and
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Abstract Objective To assess whether exposure to high temperatures in pregnancy is associated with increased risk for preterm birth, low birth weight, and stillbirth. Design Systematic review and random effects meta-analysis. Data sources Medline and Web of Science searched up to September 2018, updated in August 2019. Eligibility criteria for selecting studies Clinical studies on associations between high environmental temperatures, and preterm birth, birth weight, and stillbirths. Results 14 880 records and 175 full text articles were screened. 70 studies were included, set in 27 countries, seven of which were countries with low or middle income. In 40 of 47 studies, preterm births were more common at higher than lower temperatures. Exposures were classified as heatwaves, 1°C increments, and temperature threshold cutoff points. In random effects meta-analysis, odds of a preterm birth rose 1.05-fold (95% confidence interval 1.03 to 1.07) per 1°C increase in temperature and 1.16-fold (1.10 to 1.23) during heatwaves. Higher temperature was associated with reduced birth weight in 18 of 28 studies, with considerable statistical heterogeneity. Eight studies on stillbirths all showed associations between temperature and stillbirth, with stillbirths increasing 1.05-fold (1.01 to 1.08) per 1°C rise in temperature. Associations between temperature and outcomes were largest among women in lower socioeconomic groups and at age extremes. The multiple temperature metrics and lag analyses limited comparison between studies and settings. Conclusions Although summary effect sizes are relatively small, heat exposures are common and the outcomes are important determinants of population health. Linkages between socioeconomic status and study outcomes suggest that risks might be largest in low and middle income countries. Temperature rises with global warming could have major implications for child health. Systematic review registration PROSPERO CRD 42019140136 and CRD 42018118113.more » « less
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Abstract Objectives Maternal experiences before pregnancy predict birth outcomes, a key indicator of health trajectories, but the timing and pathways for these effects are poorly understood. Here we test the hypothesis that maternal pre‐adult growth patterns predict pregnancy glucose and offspring fetal growth in Cebu, Philippines.
Methods Using multiple regression and path analysis, gestational age‐adjusted birthweight and variables reflecting infancy, childhood, and post‐childhood/adolescent weight gain (conditional weights) were used to predict pregnancy HbA1c and offspring birth outcomes among participants in the Cebu Longitudinal Health and Nutrition Survey.
Results Maternal early/mid‐childhood weight gain predicted birth weight, length, and head circumference in female offspring. Late‐childhood/adolescent weight gain predicted birth length, birth weight, skinfold thickness, and head circumference in female offspring, and head circumference in male offspring. Pregnancy HbA1c did not mediate relationships between maternal growth and birth size parameters.
Discussion In Cebu, maternal growth patterns throughout infancy, childhood, and adolescence predict fetal growth via a pathway independent of circulating glucose, with stronger impacts on female than male offspring, consistent with a role of developmental nutrition on offspring fetal growth. Notably, the strength of relationships followed a pattern opposite to what occurs in response to acute pregnancy stress, with strongest effects on head circumference and birth length and weakest on skinfolds. We speculate that developmental sensitivities are reversed for stable, long‐term nutritional cues that reflect average local environments. These findings are relevant to public health and life‐history theory as further evidence of developmental influences on health and resource allocation across the life course.
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Abstract Objectives The maternal environment during gestation influences offspring health at birth and throughout the life course. Recent research has demonstrated that endogenous immune processes such as dysregulated inflammation adversely impact birth outcomes, increasing the risk for preterm birth and restricted fetal growth. Prior analyses examining this association suggest a relationship between maternal C‐reactive protein (CRP), a summary measure of inflammation, and offspring anthropometric outcomes. This study investigates pro‐ and anti‐inflammatory cytokines, and their ratio, to gain deeper insight into the regulation of inflammation during pregnancy.
Methods IL6, IL10, TNFɑ, and CRP were quantified in dried blood spots collected in the early third trimester (mean = 29.9 weeks) of 407 pregnancies in Metropolitan Cebu, Philippines. Relationships between these immune markers and offspring anthropometrics (birth weight, length, head circumference, and sum of skinfold thicknesses) were evaluated using multivariate regression analyses. Ratios of pro‐ to anti‐inflammatory cytokines were generated.
Results Higher maternal IL6 relative to IL10 was associated with reduced offspring weight and length at birth. Individual cytokines did not predict birth outcomes.
Conclusions Consistent with the idea that the relative balance of cytokines with pro‐ and anti‐inflammatory effects is a key regulator of inflammation in pregnancy, the IL6:IL10 ratio, but neither cytokine on its own, predicted offspring birth outcomes. Our findings suggest that prior reports of association between CRP and fetal growth may reflect, in part, the balance between pro‐ and anti‐inflammatory cytokines, and that the gestational environment is significantly shaped by cytokine imbalance.
