Time-to-event analysis of fine particle air pollution and preterm birth: results from North Carolina, 2001-2005

Using Parametric g-Computation for Time-to-Event Data and Distributed Lag Models to Identify Critical Exposure Windows for Preterm Birth: An Illustrative Example Using PM2.5 in a Retrospective Birth Cohort Based in Eastern Massachusetts (2011-2016)

BACKGROUND

Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped.

OBJECTIVES

We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach "g-survival-DLM" and illustrate its use examining the association between PM 2.5 during pregnancy and the risk of preterm birth (PTB).

METHODS

We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM 2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM 2.5 was taken from a 1 -km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax.

RESULTS

There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM 2.5 concentration was relatively stable across pregnancy at ∼ 7 μ g / m 3 . We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by - 0.009 (95% confidence interval: - 0.034 , 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20.

DISCUSSION

We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 )] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.

Prenatal exposure to PM2.5 components and the risk of different types of preterm birth and the mediating effect of pregnancy complications: a cohort study

OBJECTIVES

This study aims to reveal the single and mixed associations of PM2.5 and its components with very, moderately, and late preterm births and to explore the potential mediating role of pregnancy complications in PM2.5-induced preterm birth.

STUDY DESIGN

This was a retrospective cohort study.

METHODS

We enrolled 168,852 mothers and matched the concentrations of PM2.5 and its five components (OM, SO42-, BC, NO3-, and NH4+) based on their geographical location. Next, we used generalized linear models, quantile g-computation, and mediation analysis to evaluate the associations of PM2.5 and its components with very, moderately, and late preterm births and the mediating role of pregnancy complications.

RESULTS

Prenatal exposure to PM2.5 and its components was associated with preterm birth, and the association was strongest in the third trimester. Preterm birth was associated with co-exposure to a mixture of PM2.5 components in the third trimester, and the contributions of NO3-, NH4+, and BC to the risk of preterm birth were positive. Meanwhile, pregnancy complications mediated PM2.5-induced preterm birth. Moreover, very and moderately preterm births were associated with PM2.5 and its components in the second and third trimesters, and very and late preterm births were associated with co-exposure to a mixture of PM2.5 components in the third trimester.

CONCLUSIONS

Later exposure to PM2.5 during pregnancy will cause earlier preterm birth. Targeted and positive interventions for anthropogenic sources of specific PM2.5 components and pregnancy complications are helpful for preterm birth prevention.

A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth

Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure 3 days prior to delivery. The newly developed methods are available in the R package KDExp.

Prenatal Ambient Air Pollutant Mixture Exposure and Early School-age Lung Function

Research linking prenatal ambient air pollution with childhood lung function has largely considered one pollutant at a time. Real-life exposure is to mixtures of pollutants and their chemical components; not considering joint effects/effect modification by co-exposures contributes to misleading results.

Methods

Analyses included 198 mother-child dyads recruited from two hospitals and affiliated community health centers in Boston, Massachusetts, USA. Daily prenatal pollutant exposures were estimated using satellite-based hybrid chemical-transport models, including nitrogen dioxide(NO2), ozone(O3), and fine particle constituents (elemental carbon [EC], organic carbon [OC], nitrate [NO3 -], sulfate [SO4 2-], and ammonium [NH4 +]). Spirometry was performed at age 6.99 ± 0.89 years; forced expiratory volume in 1s (FEV1), forced vital capacity (FVC), and forced mid-expiratory flow (FEF25-75) z-scores accounted for age, sex, height, and race/ethnicity. We examined associations between weekly-averaged prenatal pollution mixture levels and outcomes using Bayesian Kernel Machine Regression-Distributed Lag Models (BKMR-DLMs) to identify susceptibility windows for each component and estimate a potentially complex mixture exposure-response relationship including nonlinear effects and interactions among exposures. We also performed linear regression models using time-weighted-mixture component levels derived by BKMR-DLMs adjusting for maternal age, education, perinatal smoking, and temperature.

Results

Most mothers were Hispanic (63%) or Black (21%) with ≤12 years of education (67%). BKMR-DLMs identified a significant effect for O3 exposure at 18-22 weeks gestation predicting lower FEV1/FVC. Linear regression identified significant associations for O3, NH4 +, and OC with decreased FEV1/FVC, FEV1, and FEF25-75, respectively. There was no evidence of interactions among pollutants.

Conclusions

In this multi-pollutant model, prenatal O3, OC, and NH4 + were most strongly associated with reduced early childhood lung function.

Indoor and outdoor air pollution and couple fecundability: a systematic review

BACKGROUND

Air pollution is both a sensory blight and a threat to human health. Inhaled environmental pollutants can be naturally occurring or human-made, and include traffic-related air pollution (TRAP), ozone, particulate matter (PM) and volatile organic compounds, among other substances, including those from secondhand smoking. Studies of air pollution on reproductive and endocrine systems have reported associations of TRAP, secondhand smoke (SHS), organic solvents and biomass fueled-cooking with adverse birth outcomes. While some evidence suggests that air pollution contributes to infertility, the extant literature is mixed, and varying effects of pollutants have been reported.

OBJECTIVE AND RATIONALE

Although some reviews have studied the association between common outdoor air pollutants and time to pregnancy (TTP), there are no comprehensive reviews that also include exposure to indoor inhaled pollutants, such as airborne occupational toxicants and SHS. The current systematic review summarizes the strength of evidence for associations of outdoor air pollution, SHS and indoor inhaled air pollution with couple fecundability and identifies gaps and limitations in the literature to inform policy decisions and future research.

SEARCH METHODS

We performed an electronic search of six databases for original research articles in English published since 1990 on TTP or fecundability and a number of chemicals in the context of air pollution, inhalation and aerosolization. Standardized forms for screening, data extraction and study quality were developed using DistillerSR software and completed in duplicate. We used the Newcastle-Ottawa Scale to assess risk of bias and devised additional quality metrics based on specific methodological features of both air pollution and fecundability studies.

OUTCOMES

The search returned 5200 articles, 4994 of which were excluded at the level of title and abstract screening. After full-text screening, 35 papers remained for data extraction and synthesis. An additional 3 papers were identified independently that fit criteria, and 5 papers involving multiple routes of exposure were removed, yielding 33 articles from 28 studies for analysis. There were 8 papers that examined outdoor air quality, while 6 papers examined SHS exposure and 19 papers examined indoor air quality. The results indicated an association between outdoor air pollution and reduced fecundability, including TRAP and specifically nitrogen oxides and PM with a diameter of ≤2.5 µm, as well as exposure to SHS and formaldehyde. However, exposure windows differed greatly between studies as did the method of exposure assessment. There was little evidence that exposure to volatile solvents is associated with reduced fecundability.

WIDER IMPLICATIONS

The evidence suggests that exposure to outdoor air pollutants, SHS and some occupational inhaled pollutants may reduce fecundability. Future studies of SHS should use indoor air monitors and biomarkers to improve exposure assessment. Air monitors that capture real-time exposure can provide valuable insight about the role of indoor air pollution and are helpful in assessing the short-term acute effects of pollutants on TTP.

Where Is Air Quality Improving, and Who Benefits? A Study of PM2.5 and Ozone Over 15 Years

In the United States, concentrations of criteria air pollutants have declined in recent decades. Questions remain regarding whether improvements in air quality are equitably distributed across subpopulations. We assessed spatial variability and temporal trends in concentrations of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) and ozone (O3) across North Carolina from 2002-2016, and associations with community characteristics. Estimated daily PM2.5 and O3 concentrations at 2010 Census tracts were obtained from the Fused Air Quality Surface Using Downscaling archive and averaged to create tract-level annual PM2.5 and O3 estimates. We calculated tract-level measures of: racial isolation of non-Hispanic Black individuals, educational isolation of non-college educated individuals, the neighborhood deprivation index (NDI), and percentage of the population in urban areas. We fitted hierarchical Bayesian space-time models to estimate baseline concentrations of and time trends in PM2.5 and O3 for each tract, accounting for spatial between-tract correlation. Concentrations of PM2.5 and O3 declined by 6.4 μg/m3 and 13.5 ppb, respectively. Tracts with lower educational isolation and higher urbanicity had higher PM2.5 and more pronounced declines in PM2.5. Racial isolation was associated with higher PM2.5 but not with the rate of decline in PM2.5. Despite declines in pollutant concentrations, over time, disparities in exposure increased for racially and educationally isolated communities.

Exposure to Ambient Air Pollutant PM10 in the Second Trimester of Pregnancy Is Associated with Preterm Birth: A Birth-Based Health Information Cohort Study

Objectives

We evaluated the effects of exposure to high concentrations of particulate matter (PM)10 on preterm birth (PTB) and identified a critical concentration of PM10 that could lead to PTB via a birth-based health information cohort study.

Methods

We conducted a birth-based cohort study consisting of nonanomalous singleton births at 22-42 weeks. PTB was defined as babies born alive before 37 weeks of pregnancy. Pregnancy period exposure averages were estimated for PM10 based on the China National Environmental Monitoring Centre (CNEMC). Pregnant women who lived within 50 km of the monitor station were recruited into this study. Logistic regression analyses were performed to determine the association between PTB and exposure to PM10 at different pregnancy periods with adjustment for confounding factors.

Results

The relative frequency of PTB was 8.7% in the study cohort of 5,291 singleton live births. A total of 1137 women had a high level of PM10 exposure (≥60 μg/m3) in the second trimester of pregnancy. The average concentrations of PM10 in the first, second, and third trimesters of pregnancy and throughout pregnancy were 53.8 μg/m3, 54.2 μg/m3, 55.6 μg/m3, and 54.3 μg/m3, respectively. The generalized additive model (GAM) analysis showed that there was a nonlinear correlation between PM10 and PTB in the second trimester of pregnancy (P < 0.001). The adjusted odds ratio between PTB and low concentration PM10 exposure (PM10 < 60 μg/m3) in the second trimester of pregnancy was 1.01 (95% CI 0.95-1.05). However, high PM10 exposure (PM10 ≥ 60 μg/m3) in the second trimester of pregnancy had an increased PTB risk even after adjustment for coexisting risk factors with an adjusted odds ratio of 1.78 (95% CI 1.69-1.87), and the incidence of PTB increased with an increase in PM10 exposure.

Conclusions

Our research discovered that exposure to high levels of PM10 increases the risk of PTB and the second trimester is the most vulnerable gestational period to ambient air pollution exposure. PM10 concentrations more than 60 μg/m3 are detrimental to pregnant women in their second trimester. This study has implications for health informatics-oriented healthcare decision support systems.

KERNEL MACHINE AND DISTRIBUTED LAG MODELS FOR ASSESSING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL MIXTURES IN CHILDREN'S HEALTH STUDIES

Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy have focused on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing data on chemical mixtures that are observed at a high temporal resolution. As motivation, we analyze the association between exposure to four ambient air pollutants observed weekly throughout gestation and birth weight in a Boston-area prospective birth cohort. To explore patterns in the data, we first apply methods for analyzing data on (1) a single chemical observed at high temporal resolution, and (2) a mixture measured at a single point in time. We highlight the shortcomings of these approaches for temporally-resolved data on exposure to chemical mixtures. Second, we propose a novel method, a Bayesian kernel machine regression distributed lag model (BKMR-DLM), that simultaneously accounts for nonlinear associations and interactions among time-varying measures of exposure to mixtures. BKMR-DLM uses a functional weight for each exposure that parameterizes the window of susceptibility corresponding to that exposure within a kernel machine framework that captures non-linear and interaction effects of the multivariate exposure on the outcome. In a simulation study, we show that the proposed method can better estimate the exposure-response function and, in high signal settings, can identify critical windows in time during which exposure has an increased association with the outcome. Applying the proposed method to the Boston birth cohort data, we find evidence of a negative association between organic carbon and birth weight and that nitrate modifies the organic carbon, elemental carbon, and sulfate exposure-response functions.

Both parents matter: a national-scale analysis of parental race/ethnicity, disparities in prenatal PM2.5 exposures and related impacts on birth outcomes

BACKGROUND

Most U.S. studies that report racial/ethnic disparities in increased risk of low birth weight associated with air pollution exposures have been conducted in California or northeastern states and/or urban areas, limiting generalizability of study results. Few of these studies have examined maternal racial/ethnic groups other than Non-Hispanic Black, non-Hispanic White and Hispanic, nor have they included paternal race. We aimed to examine the independent effects of PM_2.5 on birth weight among a nationally representative sample of U.S. singleton infants and how both maternal and paternal race/ethnicity modify relationships between prenatal PM_2.5 exposures and birth outcomes.

METHODS

We used data from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B), a longitudinal nationally representative cohort of 10,700 U.S. children born in 2001, which we linked to U.S.EPA's Community Multi-scale Air Quality (CMAQ)-derived predicted daily PM2.5 concentrations at the centroid of each Census Bureau Zip Code Tabulation Area (ZCTA) for maternal residences. We examined relationships between term birthweight (TBW)_, term low birthweight rate (TLBW) and gestational PM_2.5 pollutant using multivariate regression models. Effect modification of air pollution exposures on birth outcomes by maternal and paternal race was evaluated using stratified models. All analyses were conducted with sample weights to provide national-scale estimates.

RESULTS

The majority of mothers were White (61%). Fourteen percent of mothers identified as Black, 21% as Hispanic, 3% Asian American and Pacific Islander (AAPI) and 1% American Indian and Alaskan Native (AIAN). Fathers were also racially/ethnically diverse with 55% identified as White Non-Hispanic, 10% as Black Non-Hispanic, 19% as Hispanic, 3% as AAPI and 1% as AIAN. Results from the chi-square and ANOVA tests of significance for racial/ethnic differences indicate disparities in prenatal exposures and birth outcomes by both maternal and paternal race/ethnicity. Prenatal PM_2.5 was associated with reduced birthweights during second and third trimester and over the entire gestational period in adjusted regression models, although results did not reach statistical significance. In models stratified by maternal race and paternal race, one unit increase in PM_2.5 was statistically significantly associated with lower birthweights among AAPI mothers, -5.6 g (95% CI:-10.3, -1.0 g) and AAPI fathers, -7.6 g (95% CI: -13.1, -2.1 g) during 3^rd trimester and among births where father's race was not reported, -14.2 g (95% CI: -24.0, -4.4 g).

CONCLUSIONS

These data suggest that paternal characteristics should be used, in addition to maternal characteristics, to describe the risks of adverse birth outcomes. Additionally, our study suggests that serious consideration should be given to investigating environmental and social mechanisms, such as air pollution exposures, as potential contributors to disparities in birth outcomes among AAPI populations.

Association between maternal exposure to PM10 and risk of anorectal atresia/stenosis in offspring: a population-based case-control study in Liaoning Province, China

The potential association between maternal exposure to PM₁₀ ranging from 3 months prior to conception to the end of the early trimester and the risk of anorectal atresia/stenosis in offspring has not been established. Thus, we determined the association between maternal exposure to PM₁₀ and risk of anorectal atresia/stenosis in offspring in this study. We recruited 713 patients including 480 male and 233 female with anorectal atresia/stenosis and 7950 randomly selected healthy offspring from the Maternal and Child Health Certificate Registry of Liaoning Province and delivered between 1 January 2010 and 31 December 2015. Monthly PM₁₀ concentrations were retrieved from the Environment Protection Bureau of each city in Liaoning Province. We established a multivariable logistic regression model to calculate the adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Maternal exposure to PM₁₀ was associated with an increased risk for anorectal atresia/stenosis in offspring during the 3 months prior to conception (per 10 μg/m³ increment: OR = 1.15, 95% CI = 1.03-1.20; per SD [27 μg/m³] increment: OR = 1.33, 95% CI = 1.09-1.63) and the first trimester (per 10 μg/m³ increment: OR = 1.08, 95% CI = 1.00-1.17; per SD [28 μg/m³] increment: OR = 1.26, 95% CI = 1.01-1.57). Evaluation of the association with a shorter exposure window (1 month) revealed a positive association between anorectal atresia/stenosis and PM₁₀ from the 3rd month prior to pregnancy to each month of the 1st trimester. Maternal exposure to PM₁₀ 3 months prior to conception and during the 1st trimester was associated with an increased risk of anorectal atresia/stenosis in the offspring. Future perspective cohort studies are needed to confirm our findings.

Extreme heat, preterm birth, and stillbirth: A global analysis across 14 lower-middle income countries

Stillbirths and complications from preterm birth are two of the leading causes of neonatal deaths across the globe. Lower- to middle-income countries (LMICs) are experiencing some of the highest rates of these adverse birth outcomes. Research has suggested that environmental determinants, such as extreme heat, can increase the risk of preterm birth and stillbirth. Under climate change, extreme heat events have become more severe and frequent and are occurring in differential seasonal patterns. Little is known about how extreme heat affects the risk of preterm birth and stillbirth in LMICs. Thus, it is imperative to examine how exposure to extreme heat affects adverse birth outcomes in regions with some of the highest rates of preterm and stillbirths. Most of the evidence linking extreme heat and adverse birth outcomes has been generated from high-income countries (HICs) notably because measuring temperature in LMICs has proven challenging due to the scarcity of ground monitors. The paucity of health data has been an additional obstacle to study this relationship in LMICs. In this study, globally gridded meteorological data was linked with spatially and temporally resolved Demographic and Health Surveys (DHS) data on adverse birth outcomes. A global analysis of 14 LMICs was conducted per a pooled time-stratified case-crossover design with distributed-lag nonlinear models to ascertain the relationship between acute exposure to extreme heat and PTB and stillbirths. We notably found that experiencing higher maximum temperatures and smaller diurnal temperature range during the last week before birth increased the risk of preterm birth and stillbirth. This study is the first global assessment of extreme heat events and adverse birth outcomes and builds the evidence base for LMICs.

Long-term impact of ambient air pollution on preterm birth in Xuzhou, China: a time series study

Accumulating evidence witnesses the negative influence of air pollution on human health, but the relationship between air pollution and premature babies has been inconsistent. In this study, the association between weekly average concentration of air pollutants and preterm birth (PTB) was conducted in Xuzhou, a heavy industry city, in China. We constructed a distributed lag non-linear model (DLNM), an ecological study, to access the associations between ambient air pollutants and PTB in this study. Totally, 5408 premature babies were included, and the weekly average levels of PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO were 61.24, 110.21, 22.55, 40.55, 104.45, and 1.04 mg/m³, respectively. We found that PM_2.5, PM₁₀, SO₂, and NO₂ significantly increased the risk of PTB, and the susceptibility windows of these contaminants were the second trimester and third trimester (from 12 to 29 weeks). Every 10 μg/m³ increase of PM_2.5, PM₁₀, SO₂, and NO₂, the greatest relative risk (RR) values and 95% confidence interval (CI) on PTB were 1.0075 [95% CI, 1.0019-1.0131], 1.0053 [95% CI, 1.0014-1.0092], 1.0203 [95% CI, 1.0030-1.0379], and 1.0170 [95% CI, 1.0052-1.0289] in lag 16th, 18th, 19th, and 20th gestational weeks, respectively. No significant influence of O₃ and CO were found on preterm birth. Subgroup analysis showed that the risk of premature delivery was higher for younger pregnant women and in warm season. This finding shows that prenatal exposure to ambient air pollutants is associated with preterm birth, and there existed an exposure window period.

Reducing Prenatal Exposure to Toxic Environmental Agents: ACOG Committee Opinion, Number 832

ABSTRACT

There is emerging evidence that links exposure to toxic environmental agents and adverse reproductive and developmental health outcomes. Toxic exposures related to reproductive and developmental health primarily have been associated with infertility and miscarriage, obstetric outcomes such as preterm birth and low birth weight, neurodevelopmental delay such as autism and attention deficit hyperactivity disorder, and adult and childhood cancer. Although there is substantial overlap in the type of exposure and the associated health outcomes, for the purposes of this document, exposures generally can be grouped into the following categories: toxic chemicals, air pollution, and climate change-related exposures. Obstetric care clinicians do not need to be experts in environmental health science to provide useful information to patients and refer patients to appropriate specialists, if needed, when a hazardous exposure is identified. It is important for obstetrician-gynecologists and other obstetric care clinicians to become knowledgeable about toxic environmental exposures that are endemic to their specific geographic areas, such as local water safety advisories (eg, lead-contaminated water), local air quality levels, and patients' proximity to power plants and fracking sites. Although exposure to toxic environmental agents is widespread across populations, many environmental factors that are harmful to reproductive health disproportionately affect underserved populations and are subsumed in issues of environmental justice. Clinical encounters offer an opportunity to screen and counsel patients during the prepregnancy and prenatal periods-particularly individuals most disproportionately affected-about opportunities to reduce toxic environmental health exposures. This Committee Opinion is revised to integrate more recent literature regarding reducing prepregnancy and prenatal toxic environmental exposures.

Air Pollution and Risk of Placental Abruption: A Study of Births in New York City, 2008-2014

We evaluated the associations of exposure to fine particulate matter (particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) at concentrations of <12 μg/m3, 12-14 μg/m3, and ≥15 μg/m3) and nitrogen dioxide (at concentrations of <26 parts per billion (ppb), 26-29 ppb, and ≥30 ppb) with placental abruption in a prospective cohort study of 685,908 pregnancies in New York, New York (2008-2014). In copollutant analyses, these associations were examined using distributed-lag nonlinear models based on Cox models. The prevalence of abruption was 0.9% (n = 6,025). Compared with a PM2.5 concentration less than 12 μg/m3, women exposed to PM2.5 levels of ≥15 μg/m3 in the third trimester had a higher rate of abruption (hazard ratio (HR) = 1.68, 95% confidence interval (CI): 1.41, 2.00). Compared with a nitrogen dioxide concentration less than 26 ppb, women exposed to nitrogen dioxide levels of 26-29 ppb (HR = 1.11, 95% CI: 1.02, 1.20) and ≥30 ppb (HR = 1.06, 95% CI: 0.96, 1.24) in the first trimester had higher rates of abruption. Compared with both PM2.5 and nitrogen dioxide levels less than the 95th percentile in the third trimester, rates of abruption were increased with both PM2.5 and nitrogen dioxide ≥95th percentile (HR = 1.44, 95% CI: 1.15, 1.80) and PM2.5 ≥95th percentile and nitrogen dioxide <95th percentile (HR = 1.43 95% CI: 1.23, 1.66). Increased levels of PM2.5 exposure in the third trimester and nitrogen dioxide exposure in the first trimester are associated with elevated rates of placental abruption, suggesting that these exposures may be important triggers of premature placental separation through different pathways.

Maternal exposure to ambient SO2 and risk of polydactyly and syndactyly: a population-based case-control study in Liaoning Province, China

Evidence links exposure to maternal sulfur dioxide (SO₂) and the risk of limb defects have been inconsistent. To investigate associations between SO₂ exposure during preconception and the first trimester and risks of polydactyly and syndactyly. The study population was acquired from the Maternal and Child Health Certificate Registry of Liaoning Province between 2010 to 2015, and consisted of 2605 polydactyly, 595 syndactyly cases, and 7950 controls. Ambient air pollutants levels were retrieved from air quality monitoring stations. We used multivariable logistic regression model to assess the adjusted odds ratios (ORs) and 95% confidence intervals (CIs). We found that exposure to increased SO₂ concentrations was associated with polydactyly during both the 3 months preconception (OR_{Q4 vs. Q1} = 3.76; 95% CI 2.61, 5.42; per 10 μg/m³ increment: OR = 1.07; 95% CI 1.04, 1.10) and the first trimester (OR_{Q4 vs. Q1} = 2.03; 95% CI 1.41, 2.92; per 10 μg/m³ increment: OR = 1.07; 95% CI 1.03, 1.11). However, we only observed increased risk for syndactyly in the analysis of high vs. low quartiles (three months preconception: OR_{Q4 vs. Q1} = 3.72; 95% CI 2.05, 6.75; first trimester: OR_{Q4 vs. Q1} = 1.98; 95% CI 1.11, 3.51). Most results of analyses based on single-month exposure window generally showed similar positive associations. Additionally, these findings were broadly consistent across subgroups and sensitivity analyses. Maternal SO₂ exposure increase the risk of polydactyly and syndactyly.

Treed distributed lag nonlinear models

In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort.

Prenatal particulate air pollution exposure and expression of the miR-17/92 cluster in cord blood: Findings from the ENVIRONAGE birth cohort

BACKGROUND

Air pollution exposure during pregnancy is an important environmental health issue. Epigenetics mediate the effects of prenatal exposure and could increase disease predisposition in later life. The oncogenic miR-17/92 cluster is involved in normal development and disease.

OBJECTIVES

Here, for the first time the potential prenatal effects of particulate matter with a diameter<2.5 μm (PM_2.5) exposure on expression of the miR-17/92 cluster in cord blood are explored.

METHODS

In 370 mother-newborn pairs from the ENVIRONAGE birth cohort, expression of three members of the miR-17/92 cluster was measured in cord blood by qRT-PCR. Expression of C-MYC and CDKN1A, a cluster activator and a target gene, respectively, was also analyzed. Multivariable linear regression models were used to associate the relative m(i)RNA expression with prenatal PM_2.5 exposure.

RESULTS

PM_2.5 exposure averaged (10^th-90^th percentile) 11.7 (9.0-14.4) µg/m³ over the entire pregnancy. In cord blood, miR-17 and miR-20a showed a -45.0% (95%CI: -55.9 to -31.4, p < 0.0001) and a -33.7% (95%CI: -46.9 to -17.2, p = 0.0003), decrease in expression in association with first trimester PM_2.5 exposure, and a -32.5% (95%CI: -45.6 to -16.3, p = 0.0004) and -23.3% (95%CI: -38.1 to -4.8, p = 0.02), respectively, decrease in expression in association with PM_2.5 exposure during the entire pregnancy. In association with third trimester PM_2.5 exposure, a reduction of -25.8% (95%CI: -40.2 to -8.0, p = 0.007) and -14.2% (95%CI: -27.7 to 1.9, p = 0.08), for miR-20a and miR-92a expression, respectively, was identified. Only miR-92a expression (-15.7%, 95%CI: -27.3 to -2.4, p = 0.02) was associated with PM_2.5 exposure during the last month of pregnancy. C-MYC expression was downregulated in cord blood in association with prenatal PM_2.5 exposure during the first trimester and the entire pregnancy, in the adjusted model.

DISCUSSION

Lower expression levels of the miR-17/92 cluster in cord blood in association with increased prenatal PM_2.5 exposure were observed. Whether this oncogenic microRNA cluster plays a role in trans-placental carcinogenesis remains to be elucidated.

Semiparametric estimation of cross-covariance functions for multivariate random fields

The prevalence of spatially referenced multivariate data has impelled researchers to develop procedures for joint modeling of multiple spatial processes. This ordinarily involves modeling marginal and cross-process dependence for any arbitrary pair of locations using a multivariate spatial covariance function. However, building a flexible multivariate spatial covariance function that is nonnegative definite is challenging. Here, we propose a semiparametric approach for multivariate spatial covariance function estimation with approximate Matérn marginals and highly flexible cross-covariance functions via their spectral representations. The flexibility in our cross-covariance function arises due to B-spline-based specification of the underlying coherence functions, which in turn allows us to capture nontrivial cross-spectral features. We then develop a likelihood-based estimation procedure and perform multiple simulation studies to demonstrate the performance of our method, especially on the coherence function estimation. Finally, we analyze particulate matter concentrations (PM_2.5 ) and wind speed data over the West-North-Central climatic region of the United States, where we illustrate that our proposed method outperforms the commonly used full bivariate Matérn model and the linear model of coregionalization for spatial prediction.

Urban-rural disparity in the relationship between ambient air pollution and preterm birth

BACKGROUND

Some studies have reported that air pollution exposure can have adverse effects on pregnancy outcomes. However, the disparity between urban and rural areas in the risk of preterm birth (PTB) has yet to be elucidated. Considering geographic contexts as homogeneous or ignoring urban-rural differences cannot accurately reveal the disparities in the health effects of air pollution under different geographic contexts. The aims of this study were to examine the disparities in the risks of PTB in three different regions and five urban-rural types and to investigate the extent to which fine particulate matter (PM2.5) exposure during the entire pregnancy can explain the variations.

METHODS

We collected data on 429,865 singleton newborns born in 2014 in Hubei Province, China, and divided Hubei Province into three regions. Spatial correlation methods were employed to measure the associations between the rate of PTB and air pollution using average annual indexes for the entire province and regions. A series of multilevel logistic models were conducted to examine disparities in the risks of PTB with decreases in urbanity and the effects of air pollution exposure on the occurrence of preterm births.

RESULTS

The PM2.5 concentration was significantly different across the regions. The eastern region had the most wide-ranged and serious level of pollution, whereas the levels in the middle and western regions weakened. The odds of PTB and air pollution exhibited a positive spatial correlation for the entire province and in the east (BiMoran's I = 0.106 and 0.697, respectively). Significant urban-rural disparities in the risks of PTB were noted in the east and middle regions, and the mean PM2.5 exposure during the entire pregnancy was positively associated with PTB risk. However, in the west, the results showed weak differences in the risks of PTB among the five urban-rural types and an insignificant effect of PM2.5 exposure. The direction of the effect of district/county-level income on PTB varied by region.

CONCLUSIONS

This study finds that air pollution exposure and PTB have significant and positive spatial relationships in areas with a serious air pollution burden. The risks of PTB in three regions of Hubei Province follow the same W-shaped pattern as urbanity decreases and rurality increases. High levels of air pollution exposure may be an important disadvantage for urban pregnant women in this setting.

Association of Air Pollution and Heat Exposure With Preterm Birth, Low Birth Weight, and Stillbirth in the US: A Systematic Review

IMPORTANCE

Knowledge of whether serious adverse pregnancy outcomes are associated with increasingly widespread effects of climate change in the US would be crucial for the obstetrical medical community and for women and families across the country.

OBJECTIVE

To investigate prenatal exposure to fine particulate matter (PM2.5), ozone, and heat, and the association of these factors with preterm birth, low birth weight, and stillbirth.

EVIDENCE REVIEW

This systematic review involved a comprehensive search for primary literature in Cochrane Library, Cochrane Collaboration Registry of Controlled Trials, PubMed, ClinicalTrials.gov website, and MEDLINE. Qualifying primary research studies included human participants in US populations that were published in English between January 1, 2007, and April 30, 2019. Included articles analyzed the associations between air pollutants or heat and obstetrical outcomes. Comparative observational cohort studies and cross-sectional studies with comparators were included, without minimum sample size. Additional articles found through reference review were also considered. Articles analyzing other obstetrical outcomes, non-US populations, and reviews were excluded. Two reviewers independently determined study eligibility. The Arskey and O'Malley scoping review framework was used. Data extraction was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.

FINDINGS

Of the 1851 articles identified, 68 met the inclusion criteria. Overall, 32 798 152 births were analyzed, with a mean (SD) of 565 485 (783 278) births per study. A total of 57 studies (48 of 58 [84%] on air pollutants; 9 of 10 [90%] on heat) showed a significant association of air pollutant and heat exposure with birth outcomes. Positive associations were found across all US geographic regions. Exposure to PM2.5 or ozone was associated with increased risk of preterm birth in 19 of 24 studies (79%) and low birth weight in 25 of 29 studies (86%). The subpopulations at highest risk were persons with asthma and minority groups, especially black mothers. Accurate comparisons of risk were limited by differences in study design, exposure measurement, population demographics, and seasonality.

CONCLUSIONS AND RELEVANCE

This review suggests that increasingly common environmental exposures exacerbated by climate change are significantly associated with serious adverse pregnancy outcomes across the US.

Short-term prenatal exposure to ambient air pollution and risk of preterm birth - A population-based cohort study in Finland

BACKGROUND

Previous studies have provided evidence that prenatal exposure to low-level air pollution increases the risk of preterm birth (PTB), but the findings of the effects of short-term exposure have been inconclusive. Moreover, there is little knowledge on potential synergistic effects of different combinations of air pollutants.

OBJECTIVES

To assess independent and joint effects of prenatal exposure to air pollutants during the week prior to the delivery on the risk of PTB.

METHODS

The study population included 2568 members of the Espoo Cohort Study, living in the City of Espoo, Finland, born between 1984 and 1990. We assessed individual-level prenatal exposure to ambient air pollutants of interest based on maternal residential addresses, while taking into account their residential mobility. We used both regional-to-city-scale dispersion modelling and land-use regression-based method to estimates the pollutant concentrations. We contrasted the risk of PTB in the highest quartile (Q₄) of exposure to the lower exposure quartiles (Q₁-Q₃) during the specific periods of pregnancy. We applied Poisson regression analysis to estimate the adjusted risk ratios (RRs) with their 95% confidence intervals (CI), adjusting for season of birth, maternal age, sex of the baby, family's socioeconomic status, maternal smoking, and exposure to environmental tobacco smoke during pregnancy, single parenthood, and exposure to other air pollutants (this in multi-pollutant models).

RESULTS

The risk of PTB was related to exposures to PM_2.5, PM₁₀ and NO₂ during the week prior to the delivery with adjusted RRs of 1.67 (95%CI: 1.14, 2.46), 1.60 (95% CI: 1.09, 2.34) and 1.65 (95% CI: 1.14, 2.37), from three-pollutant models respectively. There were no significant joint effects for these different air pollutants (during the week prior to the delivery).

CONCLUSION

Our results provide evidence that exposure to fairly low-level air pollution may trigger PTB, but synergistic effects of different pollutants are not likely.

Ambient air pollution and markers of fetal growth: A retrospective population-based cohort study of 2.57 million term singleton births in China

BACKGROUNDS

Evidence is scarce on the relation between maternal exposure to ambient air pollution during pregnancy and fetal growth in developing countries. Moreover, the current evidence is inconsistent. We aimed to investigate the association of trimester-specific exposure to air pollution with risk of being born small for gestational age (SGA) and birth weight-markers of fetal growth-among Chinese term births.

METHODS

This retrospective population-based cohort study consisted of 2,567,457 singleton term live-births from January 1, 2014 to December 31, 2017 across 123 Chinese districts and counties. Personal exposure to ambient air pollutants including carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), particulate matter with aerodynamic diameter < 2.5 μm (PM_2.5), and PM₁₀ was assigned using the inverse distance weighting spatial interpolation algorithm. Generalized estimating equations (GEE) logistic regression models were performed to estimate the associations between trimester-specific exposure to air pollution and risk of SGA or low birth weight (LBW), and GEE linear regression to examine the associations between the exposure and term birth weight, adjusting for maternal demographics, maternal cigarette smoking status during pregnancy, mode of delivery, gravidity, gestational age, year and month of conception, neonate's sex, and meteorological factors. Stratified and sensitivity analyses were also performed.

RESULTS

When mother exposed to ambient air pollutants over the entire pregnancy, per IQR increment (0.122 mg/m³) in ambient CO concentrations was associated with higher risk of SGA (odds ratio (OR) = 1.04, 95% confidence interval (CI): 1.02, 1.05) and reduced birth weight among term births (-5.95 g, 95% CI: -8.01, -3.89). This association was also pronounced in the second and third trimesters. Term birth weight was negatively associated with per IQR increase of O₃ (-3.52 g, 95% CI: -6.23, -0.81), PM_2.5 (-5.93 g, 95% CI: -8.36, -3.49) and PM₁₀ (-7.78 g, 95% CI: -10.41, -5.16) during the entire pregnancy, respectively. No significant association was detected between maternal exposure to air pollutants and term LBW. Effect estimates of heterogeneity suggested that maternal age and infant sex modified the impact of air pollution on birth weight.

CONCLUSIONS

The findings suggest that maternal exposure to air pollution during pregnancy is adversely affecting fetal growth. Further studies are warranted to integrate these findings and take clinical or public health interventions in pregnancy.

A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration

A typical challenge in air pollution epidemiology is to perform detailed exposure assessment for individuals for which health data are available. To address this problem, in the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at fine spatial and temporal scales (daily, usually) with complete coverage. However, it is not clear how much the predicted exposures yielded by the various methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance. Using PM2.5 in year 2011 over the continental U.S. as a case study, we generate national maps of ambient PM2.5 concentration using: (i) ordinary least squares and inverse distance weighting; (ii) kriging; (iii) statistical downscaling models, that is, spatial statistical models that use the information contained in air quality model outputs; (iv) land use regression, that is, linear regression modeling approaches that leverage the information in Geographical Information System (GIS) covariates; and (v) machine learning methods, such as neural networks, random forests and support vector regression. We examine the various methods' predictive performance via cross-validation using Root Mean Squared Error, Mean Absolute Deviation, Pearson correlation, and Mean Spatial Pearson Correlation. Additionally, we evaluated whether factors such as, season, urbanicty, and levels of PM2.5 concentration (low, medium or high) affected the performance of the different methods. Overall, statistical methods that explicitly modeled the spatial correlation, e.g. universal kriging and the downscaler model, outperform all the other exposure assessment approaches regardless of season, urbanicity and PM2.5 concentration level. We posit that the better predictive performance of spatial statistical models over machine learning methods is due to the fact that they explicitly account for spatial dependence, thus borrowing information from neighboring observations. In light of our findings, we suggest that future exposure assessment methods for regional PM2.5 incorporate information from neighboring sites when deriving predictions at unsampled locations or attempt to account for spatial dependence.

Association between maternal exposure to ambient PM10 and neural tube defects: A case-control study in Liaoning Province, China

BACKGROUND

The potential association between maternal exposure to ambient PM₁₀ and offspring neural tube defects (NTDs) is a contentious issue. This study aims to evaluate the aforementioned association at a provincial level in China.

METHODS

A total of 2736 cases and 7950 randomly selected healthy infants included in the Maternal and Child Health Certificate Registry of Liaoning Province and delivered between 2010 and 2015 were recruited. Daily PM₁₀ concentrations were obtained from all available monitoring stations located within the study area. A multivariable logistic regression model was established to calculate the adjusted odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

PM₁₀ exposure was significantly associated with the risk of NTDs during the three months preconception (per 10 μg/m³ increment: OR = 1.10, 95% CI 1.07-1.14; per standard deviation [27 μg/m³] increment: OR = 1.29, 95% CI 1.18-1.41; highest tertile: OR = 1.61, 95% CI 1.37-1.91) and the first trimester (highest tertile: OR = 1.57, 95% CI: 1.31-1.87). When focusing on shorter exposure windows, similar associations were observed for PM₁₀ exposure from the third month before pregnancy to the third month after pregnancy. Sensitivity analyses using propensity score matching yielded consistent findings.

CONCLUSIONS

Maternal exposure to PM₁₀ is positively associated with NTDs during the critical period of fetal neural tube development. However, due to the limitation of the exposure assessment as well as potential residual confounding, further research is warranted to confirm our findings.

Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles

People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally-sized fixed-location network. This modeling framework has important real-world implications in understanding citizens' personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies.

Maternal ambient air pollution exposure with spatial-temporal variations and preterm birth risk assessment during 2013-2017 in Zhejiang Province, China

Preterm birth (PTB) can give rise to significant neonatal morbidity and mortality, as well as children's long-term health defects. Many studies have illustrated the associations between ambient air pollution exposure during gestational periods and PTB risks, but most of them only focused on one single air pollutant, such as PM_2.5. In this population-based environmental-epidemiology study, we recruited 6275 pregnant mothers in Zhejiang Province, China, and evaluated their gestational exposures to various air pollutants during 2013-2017. Time-to-event logistic regressions were performed to estimate risk associations after adjusting all confounders, and Quasi-AQI model and PCA-GLM analysis were applied to resolve the collinearity issues in multi-pollutant regression models. It was found that gestational exposure to ambient air pollutants was significantly associated with the occurrence of PTB, and SO₂ was the largest contributor with a proportion of 29.4%. Three new variables, prime factor (a combination of PM_2.5, PM₁₀, SO₂, and NO₂), carbon factor (CO), and ozone factor (O₃), were generated by PCA integration, contributing 63.4%, 17.1%, and 19.5% to PTB risks, respectively. The first and third trimester was the most crucial exposure window, suggesting the pregnant mothers better to avoid severe air pollution exposures during these sensitive periods.

A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation

Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) has been linked to various adverse health outcomes. PM_2.5 arises from both natural and anthropogenic sources, and PM_2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM_2.5 measurements, potentially limiting the accuracy of PM_2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM_2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM_2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM_2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R². The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.

Modeling Wildland Fire-Specific PM2.5 Concentrations for Uncertainty-Aware Health Impact Assessments

Wildland fire is a major emission source of fine particulate matter (PM_2.5), which has serious adverse health effects. Most fire-related health studies have estimated human exposures to PM_2.5 using ground observations, which have limited spatial/temporal coverage and could not separate PM_2.5 emanating from wildland fires from other sources. The Community Multiscale Air Quality (CMAQ) model has the potential to fill the gaps left by ground observations and estimate wildland fire-specific PM_2.5 concentrations, although the issues around systematic bias in CMAQ models remain to be resolved. To address these problems, we developed a two-step calibration strategy under the consideration of prediction uncertainties. In a case study of the eastern U.S. in 2014, we evaluated the calibration performance using three cross-validation methods, which consistently indicated that the prediction accuracy was improved with an R² of 0.47-0.64. In a health impact study based on the wildland fire-specific PM_2.5 predictions, we identified regions with excess respiratory hospital admissions due to wildland fire events and quantified the estimation uncertainty propagated from multiple components in health impact function. We concluded that the proposed calibration strategy could provide reliable wildland fire-specific PM_2.5 predictions and health burden estimates to support policy development for reducing fire-related risks.

Characteristics of cohort studies of long-term exposure to PM2.5: a systematic review

This study systematically reviewed all the cohort studies investigating the relationship between long-term exposure to PM_2.5 and any health outcome until February 2018. We searched ISI Web of Knowledge, Pubmed, and Scopus databases for peer-reviewed journal research articles published in English. We only extracted the results of the single-pollutant main analysis of each study, excluding the effect modifications and sensitivity analyses. Out of the initial 9523 articles, 203 articles were ultimately included for analysis. Based on the different characteristics of studies such as study design, outcome, exposure assessment method, and statistical model, we calculated the number and relative frequency of analyses with statistically significant and insignificant results. Most of the studies were prospective (84.8%), assessed both genders (66.5%), and focused on a specific age range (86.8%). Most of the articles (78.1%) had used modeling techniques for exposure assessment of cohorts' participants. Among the total of 317 health outcomes, the most investigated outcomes include mortality due to cardiovascular disease (6.19%), all causes (5.48%), lung cancer (4.00%), ischemic heart disease (3.50%), and non-accidental causes (3.50%). The percentage of analyses with statistically significant results were higher among studies that used prospective design, mortality as the outcome, fixed stations as exposure assessment method, hazard ratio as risk measure, and no covariate adjustment. We can somehow conclude that the choice of right characteristics for cohort studies can make a difference in their results.

Ambient Fine Particulate Matter and Preterm Birth in California: Identification of Critical Exposure Windows

Exposure to ambient fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5)) during pregnancy is associated with preterm birth (PTB), a leading cause of infant morbidity and mortality. Results from studies attempting to identify etiologically relevant exposure periods of vulnerability have been inconsistent, possibly because of failure to consider the time-to-event nature of the outcome and lagged exposure effects of PM2.5. In this study, we aimed to identify critical exposure windows for weekly PM2.5 exposure and PTB in California using California birth cohort data from 2005-2010. Associations were assessed using distributed-lag Cox proportional hazards models. We assessed effect-measure modification by race/ethnicity by calculating the weekly relative excess risk due to interaction. For a 10-μg/m3 increase in PM2.5 exposure over the entire period of gestation, PTB risk increased by 11% (hazard ratio = 1.11, 95% confidence interval: 1.09, 1.14). Gestational weeks 17-24 and 36 were associated with increased vulnerability to PM2.5 exposure. We find that non-Hispanic black mothers may be more susceptible to effects of PM2.5 exposure than non-Hispanic white mothers, particularly at the end of pregnancy. These findings extend our knowledge about the existence of specific exposure periods during pregnancy that have the greatest impact on preterm birth.

Longitudinal Effect of Ambient Air Pollution and Pollen Exposure on Asthma Control: The Patient-Reported Outcomes Measurement Information System (PROMIS) Pediatric Asthma Study

OBJECTIVE

Although exposure to air pollution and pollen is associated with asthma exacerbation and increased health care use, longitudinal effects of fine particulate matter 2.5 (PM_2.5), ozone (O₃), and pollen exposure on asthma control status in pediatric patients are understudied. This study investigated effects of exposure to PM_2.5, O₃, and pollen on asthma control status among pediatric patients with asthma.

METHODS

A total of 229 dyads of pediatric patients with asthma and their parents were followed for 15 months. The Asthma Control and Communication Instrument was used to measure asthma control, which was reported weekly by parents during a 26-week period. PM_2.5 and O₃ data were collected from the US Environmental Protection Agency Air Quality System. Pollen data were obtained from Intercontinental Marketing Services Health. Mean air pollutant and pollen exposures within 7 days before the reporting of asthma control were used to estimate weekly exposures for each participant. Linear mixed-effects models were performed to test associations of PM_2.5, O₃, and pollen exposure with asthma control status. Sensitivity analyses were performed to evaluate the robustness of findings by different exposure monitoring days per week and distances between monitoring sites and participants' residences.

RESULTS

Elevated PM_2.5 concentration and pollen severity were associated with poorer asthma control status (P < .05), yet elevated O₃ concentration was marginally associated with better asthma control (P < .1).

CONCLUSIONS

Poorer asthma control status was associated with elevated PM_2.5 and pollen severity. Reducing harmful outdoor environmental ambient exposure may improve asthma outcomes in children and adolescents.

Environmental Exposures in Reproductive Health

Our genetic makeup and environment interact. Evidence has emerged demonstrating preconception and prenatal exposure to toxic agents have a profound effect on reproductive health. We cannot change our genetics, but we can change our environment. Health providers can protect pregnancies from harmful exposures. Pregnancy is the most critical time-window for human development, when any toxic exposure can cause lasting damage to brain development. Reproductive care professionals can provide useful information to patients and refer patients to appropriate specialists when hazardous exposure is identified. Clinical experience and expertise in communicating risks of treatment are transferable to environmental health.

Review of Acellular Assays of Ambient Particulate Matter Oxidative Potential: Methods and Relationships with Composition, Sources, and Health Effects

Oxidative stress is a potential mechanism of action for particulate matter (PM) toxicity and can occur when the body's antioxidant capacity cannot counteract or detoxify harmful effects of reactive oxygen species (ROS) due to an excess presence of ROS. ROS are introduced to the body via inhalation of PM with these species present on and/or within the particles (particle-bound ROS) and/or through catalytic generation of ROS in vivo after inhaling redox-active PM species (oxidative potential, OP). The recent development of acellular OP measurement techniques has led to a surge in research across the globe. In this review, particle-bound ROS techniques are discussed briefly while OP measurements are the focus due to an increasing number of epidemiologic studies using OP measurements showing associations with adverse health effects in some studies. The most common OP measurement techniques, including the dithiothreitol assay, glutathione assay, and ascorbic acid assay, are discussed along with evidence for utility of OP measurements in epidemiologic studies and PM characteristics that drive different responses between assay types (such as species composition, emission source, and photochemistry). Overall, most OP assays respond to metals like copper than can be found in emission sources like vehicles. Some OP assays respond to organics, especially photochemically aged organics, from sources like biomass burning. Select OP measurements have significant associations with certain cardiorespiratory end points, such as asthma, congestive heart disease, and lung cancer. In fact, multiple studies have found that exposure to OP measured using the dithiothreitol and glutathione assays drives higher risk ratios for certain cardiorespiratory outcomes than PM mass, suggesting OP measurements may be integrating the health-relevant fraction of PM and will be useful tools for future health analyses. The compositional impacts, including species and emission sources, on OP could have serious implications for health-relevant PM exposure. Though more work is needed, OP assays show promise for health studies as they integrate the impacts of PM species and properties on catalytic redox reactions into one measurement, and current work highlights the importance of metals, organic carbon, vehicles, and biomass burning emissions to PM exposures that could impact health.

Traffic-related Air Pollution and Pregnancy Loss

BACKGROUND

Traffic-related air pollution has been linked to multiple adverse pregnancy outcomes. However, few studies have examined pregnancy loss, targeting losses identified by hospital records, a large limitation as it does not capture events not reported to the medical system.

METHODS

We used a novel variation of the time-series design to determine the association, and identify the critical window of vulnerability, between week-to-week traffic-related air pollution and conceptions resulting in live births, using nitrogen dioxide (NO2) as a traffic emissions tracer. We used information from all live births recorded at Beth Israel Deaconess Medical Center in Boston, MA (2000-2013) and all live births in Tel Aviv District, Israel (2010-2013).

RESULTS

In Boston (68,969 live births), the strongest association was during the 15th week of gestation; for every 10 ppb of NO2 increase during that week, we observed a lower rate of live births (rate ratio [RR] = 0.87; 95% confidence interval [CI], 0.78, 0.97), using live birth-identified conceptions to infer pregnancy losses. In the Tel Aviv District (95,053 live births), the strongest estimate was during the 16th gestational week gestation (RR = 0.82; 95% CI, 0.76, 0.90 per 10 ppb of NO2).

CONCLUSIONS

Using weekly conceptions ending in live birth rather than identified pregnancy losses, we comprehensively analyzed the relationship between air pollution and all pregnancy loss throughout gestation. The observed results, with remarkable similarity in two independent locations, suggest that higher traffic-related air pollution levels are associated with pregnancy loss, with strongest estimates between the 10th and 20th gestational weeks.

Acute and chronic effects of ambient fine particulate matter on preterm births in Beijing, China: A time-series model

Accumulating evidence suggests that short- and long-term exposure to ambient fine particulate matter ≤ 2.5 μm (PM_2.5) during pregnancy is associated with preterm births, yet the results are inconsistent, and the shape of the exposure-response curve is unclear, partially due to the limited studies conducted in areas with high air pollution. Our study evaluated the association between ambient PM_2.5 concentration and preterm births in Beijing, China. Daily preterm birth data were collected from a hospital in Beijing during 2006 to 2013; a time-series of daily PM_2.5 concentrations during the same period is assembled with measured data at three monitoring sites in Beijing. An extension of the Poisson regression and a time-series model were applied to simultaneously estimate the acute and chronic effects of exposure to PM_2.5, with mutual adjustment for short- and long-term exposure as well as for confounders. During the study period, the PM_2.5 concentration was 70.4 ± 60.6 μg/m³ and was found to be associated with an increased risk of preterm birth. In the study cohort, a 0.52% (95% confidence interval, CI: 0.09%, 0.96%) and 3.13% (95%CI: 1.92%, 4.35%) increase in preterm births was estimated for each 10-μg/m³ increase in short- and long-term exposure, respectively. This association was significantly modified by season (p < 0.05). With mutual adjustments for short- and long-term exposure, a more robust association (3.16%, 95% CI: 1.95%, 4.39%; per 10-μg/m³ increment in PM_2.5) was observed for chronic effects. The exposure-response relationships for both short- and long-term exposure were linear, without a threshold, over the relatively low exposure range and flattened out at higher concentration levels. The maximum effect for long-term exposure to PM_2.5 (33.6%) was much greater than that for short-term exposure (19.9%). These findings indicate that air quality improvements over a long period could yield significant health benefits.

Exposure to ambient PM2.5 during pregnancy and preterm birth in metropolitan areas of the state of Georgia

A number of studies has pointed to air pollution as an additional factor that could be associated with preterm birth. We assessed in this study the association between exposure to PM_2.5 in ambient air during pregnancy and preterm birth in metropolitan areas of the state of Georgia, where the rate of preterm birth has been among the highest in the nation over the years. Birth data were obtained from the National Center for Health Statistics natality dataset. The study population consisted of 53,094 singleton live births between January 1 and December 31, 2004 in nine metropolitan counties of Georgia. Preterm birth was defined as birth, which occurs before 37 weeks of gestation. County-level daily air quality index (AQI) data obtained from the US Environmental Protection Agency (EPA) was used to estimate individual exposure levels of PM_2.5 for each study participant based on the county of residence for the duration of the pregnancy. A multivariate logistic regression analysis was conducted to assess the association, adjusting for potential confounders. Of the infants whose mothers resided in the nine metropolitan counties of Georgia, 4543 (8.6%) were born preterm. A higher rate of preterm birth (9.8%) was observed in infants whose mothers were exposed to ambient PM_2.5 with AQI values > 50 than the ones with AQI ≤ 50 (EPA standard for good air quality conditions). Mothers with exposure to PM_2.5 at average AQI values greater than 50 during the entire pregnancy were at increased risk of preterm birth (odds ratio 1.15; 95% CI 1.07, 1.25), after adjusting for sex of infant, mother's age, race/ethnicity, education, marital status, prenatal care, cigarette smoking, alcohol consumption, and season of conception. The study provides more evidence on the role of PM_2.5 in preterm birth. Reducing exposure to ambient particulate matter, especially in urban areas, for pregnant women would be necessary to improve the health of infants.

Impacts of gestational age uncertainty in estimating associations between preterm birth and ambient air pollution

Supplemental Digital Content is available in the text.

Background:

Previous epidemiologic studies utilizing birth records have shown heterogeneous associations between air pollution exposure during pregnancy and the risk of preterm birth (PTB, gestational age <37 weeks). Uncertainty in gestational age at birth may contribute to this heterogeneity.

Methods:

We first examined disagreement between clinical and last menstrual period-based (LMP) determination of PTB from individual-level birth certificate data for the 20-county Atlanta metropolitan area during 2002 to 2006. We then estimated associations between five trimester-averaged pollutant exposures and PTB, defined using various methods based on the clinical or LMP gestational age. Finally, using a multiple imputation approach, we incorporated uncertainty in gestational age to quantify the impact of this variability on associations between pollutant exposures and PTB.

Results:

Odds ratios (OR) were most elevated when a more stringent definition of PTB was used. For example, defining PTB only when LMP and clinical diagnoses agree yielded an OR of 1.09 (95% confidence interval [CI] = 1.04, 1.14) per interquartile range increase in first trimester carbon monoxide exposure versus an OR of 1.04 (95% CI = 1.01, 1.08) when PTB was defined as either an LMP or clinical diagnosis. Accounting for outcome uncertainty resulted in wider CIs—between 7.4% and 43.8% wider than those assuming the PTB outcome is without error.

Conclusions:

Despite discrepancies in PTB derived using either the clinical or LMP gestational age estimates, our analyses demonstrated robust positive associations between PTB and ambient air pollution exposures even when gestational age uncertainty is present.

Identifying windows of susceptibility for maternal exposure to ambient air pollution and preterm birth

Maternal exposure to ambient air pollution has been associated with preterm birth (PTB), however, entire pregnancy or trimester-specific associations were generally reported, which may not sufficiently identify windows of susceptibility. Using birth registry data from Guangzhou, a megacity of southern China (population ~14.5 million), including 469,975 singleton live births between January 2015 and July 2017, we assessed the association between weekly air pollution exposure and PTB in a retrospective cohort study. Daily average concentrations of PM_2.5, PM₁₀, NO₂, SO₂, and O₃ from 11 monitoring stations were used to estimate district-specific exposures for each participant based on their district residency during pregnancy. Distributed lag models (DLMs) incorporating Cox proportional hazard models were applied to estimate the association between weekly maternal exposure to air pollutant and PTB risk (as a time-to-event outcome), after controlling for temperature, seasonality, and individual-level covariates. We also considered moderate PTB (32-36 gestational weeks) and very PTB (28-31 gestational weeks) as outcomes of interest. Hazard ratios (HRs) and 95% confidential intervals (95% CIs) were calculated for an interquartile range (IQR) increase in air pollutants during the study period. An IQR increase in PM_2.5 exposure during the 20th to 28th gestational weeks (27.0 μg/m³) was significantly associated with PTB risk, with the strongest effect in the 25th week (HR = 1.034, 95% CI:1.010-1.059). The significant exposure windows were the 19th-28th weeks for PM₁₀, the 18th-31st weeks for NO₂, and the 23rd-31st weeks for O₃, respectively. The strongest associations were observed in the 25th week for PM₁₀ (IQR = 37.0 μg/m³; HR = 1.048, 95% CI:1.034-1.062), the 26th week for NO₂ (IQR = 29.0 μg/m³; HR = 1.060, 95% CI:1.028-1.094), and in the 28th week for O₃ (IQR = 90.0 μg/m³; HR = 1.063, 95% CI:1.046-1.081). Similar patterns were observed for moderate PTB (32-36 gestational weeks) and very PTB (28-31 gestational weeks) for PM_2.5, PM₁₀, NO₂ exposure, but the effects were greater for very PTB. We did not observe any association between pregnancy SO₂ exposure and the risk of PTB. Our results suggest that middle to late pregnancy is the most susceptible air pollution exposure window for air pollution and PTB among women in Guangzhou, China.

Ambient air pollution and pregnancy outcomes: A comprehensive review and identification of environmental public health challenges

There is a growing number of studies on the association between ambient air pollution and adverse pregnancy outcomes, but their results have been inconsistent. Consequently, a comprehensive review of this research area is needed. There was a wide variability in studied pregnancy outcomes, observed gestational windows of exposure, observed ambient air pollutants, applied exposure assessment methods and statistical analysis methods Gestational duration, preterm birth, (low) birth weight, and small for gestational age/intrauterine growth restriction were most commonly investigated pregnancy outcomes. Gestational windows of exposure typically included were whole pregnancy period, 1st, 2nd, 3rd trimester, first and last gestational months. Preterm birth was the outcome most extensively studied across various gestational windows, especially at the beginning and at the end of pregnancy. Particulate matter, nitrogen dioxide, ozone, and carbon monoxide were the most commonly used markers of ambient air pollution. Continuous monitoring data were frequently combined with spatially more precisely modelled estimates of exposure. Exposure to particulate matter and ozone over the entire pregnancy was significantly associated with higher risk for preterm birth: the pooled effect estimates were 1.09 (1.03-1.16) per 10 μg/m³ increase in particulate matter with an aerodynamic diameter of 10 µm or less (PM₁₀)_,1.24 (1.08-1.41) per 10 μg/m³ increase in particulate matter with an aerodynamic diameter of 2.5 µm or less (PM_2.5), and 1.03 (1.01-1.04) per 10 ppb increase in ozone. For pregnancy outcomes other than PTB, ranges of observed effect estimates were reported due to smaller number of studies included in each gestational window of exposure. Further research is needed to link the routine pregnancy outcome data with spatially and temporally resolved ambient air pollution data, while adjusting for commonly defined confounders. Methods for assessing exposure to mixtures of pollutants, indoor air pollution exposure, and various other environmental exposures, need to be developed.

Using a Clustering Approach to Investigate Socio-Environmental Inequality in Preterm Birth-A Study Conducted at Fine Spatial Scale in Paris (France)

Background & Objectives: Today, to support public policies aiming to tackle environmental and health inequality, identification and monitoring of the spatial pattern of adverse birth outcomes are crucial. Spatial identification of the more vulnerable population to air pollution may orient health interventions. In this context, the objective of this study is to investigate the geographical distribution of the risk of preterm birth (PTB, gestational age ≤36 weeks) at the census block level in in city of Paris, France. We also aimed to assess the implication of neighborhood characteristics including air pollution and socio-economic deprivation. Material & Methods: Newborn health data are available from the first birth certificate registered by the Maternal and Child Care department of Paris. All PTB from January 2008 to December 2011 were geocoded at the mother residential census block. Each census block was assigned a socioeconomic deprivation level and annual average ambient concentrations of NO₂. A spatial clustering approach was used to investigate the spatial distribution of PTB. Results: Our results highlight that PTB is non-randomly spatially distributed, with a cluster of high risk in the northeastern area of Paris (RR = 1.15; p = 0.06). After adjustment for socio-economic deprivation and NO₂ concentrations, this cluster becomes not statistically significant or shifts suggesting that these characteristics explain the spatial distribution of PTB; further, their combination shows an interaction in comparison with SES or NO₂ levels alone. Conclusions: Our results may inform the decision makers about the areas where public health efforts should be strengthened to tackle the risk of PTB and to choose the most appropriate and specific community-oriented health interventions.

Associations between birth outcomes and maternal PM2.5 exposure in Shanghai: A comparison of three exposure assessment approaches

BACKGROUND

Few studies have estimated effects of maternal PM_2.5 exposure on birth outcomes in China due to the lack of historical air pollution data.

OBJECTIVES

We estimated the associations between maternal PM_2.5 exposure and birth outcomes using gap-filled satellite estimates in Shanghai, China.

METHODS

We obtained birth registration records of 132,783 singleton live births during 2011-2014 in Shanghai. PM_2.5 exposures were assessed from satellite-derived estimates or central-site measurements. Linear and logistic regressions were used to estimate associations with term birth weight and term low birth weight (LBW), respectively. Logistic and discrete-time survival models were used to estimate associations with preterm birth. Effect modification by maternal age and parental education levels was investigated.

RESULTS

A 10 μg/m³ increase in gap-filled satellite-based whole-pregnancy PM_2.5 exposure was associated with a -12.85 g (95% CI: -18.44, -7.27) change in term birth weight, increased risk of preterm birth (OR 1.27, 95% CI: 1.20, 1.36), and increased risk of term LBW (OR 1.22, 95% CI: 1.06, 1.41). Sensitivity analyses during 2013-2014, when ground PM_2.5 measurements were available, showed that the health associations using gap-filled satellite PM_2.5 concentrations were higher than those obtained using satellite PM_2.5 concentrations without accounting for missingness. The health associations using gap-filled satellite PM_2.5 had similar magnitudes to those using central-site measurements, but with narrower confidence intervals.

CONCLUSIONS

The magnitude of associations between maternal PM_2.5 exposure and adverse birth outcomes in Shanghai was higher than previous findings. One reason could be reduced exposure error of the gap-filled high-resolution satellite PM_2.5 estimates.

Maternal exposure to ambient PM10 during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models

Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10μm in aerodynamic diameter (PM₁₀) on CHDs are inconsistent. We used two machine learning models (i.e., random forest (RF) and gradient boosting (GB)) to investigate the non-linear effects of PM₁₀ exposure during the critical time window, weeks 3-8 in pregnancy, on risk of CHDs. From 2009 through 2012, we carried out a population-based birth cohort study on 39,053 live-born infants in Beijing. RF and GB models were used to calculate odds ratios for CHDs associated with increase in PM₁₀ exposure, adjusting for maternal and perinatal characteristics. Maternal exposure to PM₁₀ was identified as the primary risk factor for CHDs in all machine learning models. We observed a clear non-linear effect of maternal exposure to PM₁₀ on CHDs risk. Compared to 40μgm^-3, the following odds ratios resulted: 1) 92μgm^-3 [RF: 1.16 (95% CI: 1.06, 1.28); GB: 1.26 (95% CI: 1.17, 1.35)]; 2) 111μgm^-3 [RF: 1.04 (95% CI: 0.96, 1.14); GB: 1.04 (95% CI: 0.99, 1.08)]; 3) 124μgm^-3 [RF: 1.01 (95% CI: 0.94, 1.10); GB: 0.98 (95% CI: 0.93, 1.02)]; 4) 190μgm^-3 [RF: 1.29 (95% CI: 1.14, 1.44); GB: 1.71 (95% CI: 1.04, 2.17)]. Overall, both machine models showed an association between maternal exposure to ambient PM₁₀ and CHDs in Beijing, highlighting the need for non-linear methods to investigate dose-response relationships.

Fine Particulate Air Pollution and Adverse Birth Outcomes: Effect Modification by Regional Nonvolatile Oxidative Potential

BACKGROUND

Prenatal exposure to fine particulate matter air pollution with aerodynamic diameter ≤2.5 μm (PM_2.5) has been associated with preterm delivery and low birth weight (LBW), but few studies have examined possible effect modification by oxidative potential.

OBJECTIVES

The aim of this study was to evaluate if regional differences in the oxidative potential of PM_2.5 modify the relationship between PM_2.5 and adverse birth outcomes.

METHODS

A retrospective cohort study was conducted using 196,171 singleton births that occurred in 31 cities in the province of Ontario, Canada, from 2006 to 2012. Daily air pollution data were collected from ground monitors, and city-level PM_2.5 oxidative potential was measured. We used random-effects meta-analysis to combine the estimates of effect from regression models across cities on preterm birth, term LBW, and term birth weight and used meta-regression to evaluate the modifying effect of PM_2.5 oxidative potential.

RESULTS

An interquartile increase (2.6 μg/m³) in first-trimester PM_2.5 was positively associated with term LBW among women in the highest quartile of glutathione (GSH)-related oxidative potential [odds ratio (OR)=1.28; 95% confidence interval (CI): 1.10, 1.48], but not the lowest quartile (OR=0.99; 95% CI: 0.87, 1.14; p-interaction=0.03). PM_2.5 on the day of delivery also was associated with preterm birth among women in the highest quartile of GSH-related oxidative potential [hazard ratio (HR)=1.02; 95% CI: 1.01, 1.04], but not the lowest quartile [HR=0.97; 95% CI: 0.95, 1.00; p-interaction=0.04]. Between-city differences in ascorbate (AA)-related oxidative potential did not significantly modify associations with PM_2.5.

CONCLUSIONS

Between-city differences in GSH-related oxidative potential may modify the impact of PM_2.5 on the risk of term LBW and preterm birth. https://doi.org/10.1289/EHP2535.

Ambient air quality and spatio-temporal patterns of cardiovascular emergency department visits

BACKGROUND

Air pollutants have been associated with various adverse health effects, including increased rates of hospital admissions and emergency room visits. Although numerous time-series studies and case-crossover studies have estimated associations between day-to-day variation in pollutant levels and mortality/morbidity records, studies on geographic variations in emergency department use and the spatial effects in their associations with air pollution exposure are rare.

METHODS

We focused on the elderly who visited emergency room for cardiovascular related disease (CVD) in 2011. Using spatially and temporally resolved multi-pollutant exposures, we investigated the effect of short-term exposures to ambient air pollution on emergency department utilization. We developed two statistical models with and without spatial random effects within a hierarchical Bayesian framework to capture the spatial heterogeneity and spatial autocorrelation remaining in emergency department utilization.

RESULTS

Although the cardiovascular effect of spatially homogeneous pollutants, such as PM2.5 and ozone, was unchanged, we found the cardiovascular effect of NO[Formula: see text] was pronounced after accounting for the spatially correlated structure in emergency department utilization. We also identified areas with high ED utilization for CVD among the elderly and assessed the uncertainty associated with risk estimates.

CONCLUSIONS

We assessed the short-term effect of multi-pollutants on cardiovascular risk of the elderly and demonstrated the use of community multiscale air quality model-derived spatially and temporally resolved multi-pollutant exposures to an epidemiological study. Our results indicate that NO[Formula: see text] was significantly associated with the elevated ED utilization for CVD among the elderly.

Exposure to firework chemicals from production factories in pregnant women and risk of preterm birth occurrence in Liuyang, China

In the production of fireworks, various pollutants including particles of metals and organic compounds are released into the environment. Although the adverse effects of these air pollutants are known, the impact on pregnant women residing in this area remains to be determined. The aim of this study was to examine the association between maternal exposure to fireworks production chemicals and frequency of preterm birth in Liuyang, China. Maternal exposure to fireworks production was estimated at the residential district level and assessed using factory density, which was defined as the number of fireworks factories per 1000 residents in each district. The association of maternal exposure to particulates released from fireworks production plants with frequency of preterm birth was determined using data obtained from a cohort study conducted in Liuyang, China. Data were analyzed utilizing linear regression and logistic regression. There was no significant association between factory density and spontaneous preterm or medically induced preterm birth. Unexpectedly, pregnant women residing in areas with higher density of fireworks factories were at a reduced risk of preterm premature rupture of membranes (PPROM). Data demonstrated that residential density of fireworks factories appeared to be negatively correlated with preterm birth rate as evidenced by PPROM. At present, it is difficult to reconcile the inverse relationship between firework chemical exposure and frequency of preterm births as ambient particulate inhalation is known to adversely affect preterm birth occurrence.

The Role of Body, Mind, and Environment in Preterm Birth: Mind the Gap

Preterm birth continues to be a problem affecting low-, middle-, and high-income countries, with rates increasing in some areas despite ongoing efforts to reduce the incidence. This emphasizes the need for more effective interventions, particularly if we aim to achieve the broad health targets of the Sustainable Development Goals (SDGs) by 2030. The current focus on medically-oriented interventions such as reducing nonmedically-indicated induction of labor, cesarean birth, and multiple embryo transfers associated with assisted reproductive technologies, as well as the application of cervical cerclage and use of progesterone therapy, though important, are likely only partial solutions to the complex phenomenon of preterm birth. Preterm birth has multiple etiologies. The biologic mechanisms involved in preterm labor and how it may be triggered are not well understood. There is growing evidence to suggest some of these triggers may also be related to stress and environmental conditions. In this review, we focus on evidence concerning psychosocial (mind) and environmental factors (environment) as well as briefly review the evidence related to maternal and fetal factors (body) associated with the risk of preterm birth, with reference to some of the SDGs. We also assess emerging evidence regarding the interaction of the body, mind, and environment in relation to preterm birth, currently a gap in our knowledge, and how these interactions could impact clinical practice. Findings suggest that multidisciplinary expertise and approaches will be needed to develop effective interventions to address the complex etiologies of preterm birth, as opposed to single-risk-factor mitigation. Clinicians and researchers will play key roles in identifying many of these risk factors and shaping interventions that address this complex issue. Addressing the interlinkages between body, mind, and environment through the integration of research and clinical practice is critical to reducing the risk of preterm birth and contributing to the achievement of the SDGs.

Time-Variant Genetic Effects as a Cause for Preterm Birth: Insights from a Population of Maternal Cousins in Sweden

Preterm delivery (PTD) is the leading cause of neonatal mortality worldwide, yet its etiology remains largely unexplained. We propose that the genetic factors controlling this trait could act in a nonuniform manner during pregnancy, with each factor having a unique “window of sensitivity.” We test this hypothesis by modeling the distribution of gestational ages (GAs) observed in maternal cousins from the Swedish Medical Birth Register (MBR) (n = 35,541 pairs). The models were built using a time-to-event framework, with simulated genetic factors that increase the hazard of birth either uniformly across the pregnancy (constant effect) or only in particular windows (varying effect). By including various combinations of these factors, we obtained four models that were then optimized and compared. Best fit to the clinical data was observed when most of the factors had time-variant effects, independently of the number of loci simulated. Finally, power simulations were performed to assess the ability to discover varying-effect loci by usual methods for genome-wide association testing. We believe that the tools and concepts presented here should prove useful for the design of future studies of PTD and provide new insights into the genetic architecture determining human GA.

Analysis of short-term ozone and PM2.5 measurements: Characteristics and relationships for air sensor messaging

Air quality sensors are becoming increasingly available to the general public, providing individuals and communities with information on fine-scale, local air quality in increments as short as 1 min. Current health studies do not support linking 1-min exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8 hr; 24 hr). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 min to 1 hr), and the longer term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings. This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hr average (ozone) and 24-hr average (PM_2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hr averages (ozone) and 24-hr averages (PM_2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI.

IMPLICATIONS

Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality.

Environmental chemicals and preterm birth: Biological mechanisms and the state of the science

PURPOSE OF REVIEW

Preterm birth is a significant worldwide health problem of uncertain origins. The extant body of literature examining environmental contaminant exposures in relation to preterm birth is extensive but results remain ambiguous for most organic pollutants, metals and metalloids, and air pollutants. In the present review we examine recent epidemiologic studies investigating these associations, and identify recent advances and the state of the science. Additionally, we highlight biological mechanisms of action in the pathway between chemical exposures and preterm birth, including inflammation, oxidative stress, and endocrine disruption, that deserve more attention in this context.

RECENT FINDINGS

Important advances have been made in the study of the environment and preterm birth, particularly in regard to exposure assessment methods, exploration of effect modification by co-morbidities and exposures, and in identification of windows of vulnerability during gestation. There is strong evidence for an association between maternal exposure to some persistent pesticides, lead, and fine particulate matter, but data on other contaminants is sparse and only suggestive trends can be noted with the current data.

SUMMARY

Beyond replicating current findings, further work must be done to improve understanding of mechanisms underlying the associations observed between environmental chemical exposures and preterm birth. By examining windows of vulnerability, disaggregating preterm birth by phenotypes, and measuring biomarkers of mechanistic pathways in these epidemiologic studies we can improve our ability to detect associations with exposure, provide additional evidence for causality in an observational setting, and identify opportunities for intervention.