Critical window variable selection: estimating the impact of air pollution on very preterm birth

Racial disparities in maternal exposure to ambient air pollution during pregnancy and prevalence of congenital heart defects

Air pollution may be a potential cause of congenital heart defects (CHDs), but racial disparities in this association are unexplored. We conducted a statewide population-based cohort study using North Carolina birth data from 2003 to 2015 (n = 1 225 285) to investigate the relationship between air pollution and CHDs (specifically pulmonary valve atresia/stenosis, tetralogy of Fallot [TOF], and atrioventricular septal defect [AVSD]). Maternal exposure to particulate matter ≤ 2.5 μm in diameter (PM2.5) and ozone during weeks 3 to 9 of pregnancy were estimated using the Environmental Protection Agency's Downscaler Model. Single- and co-pollutant log-binomial models were created for the entire population and stratified by race to investigate disparities. Positive associations between PM2.5 and CHDs were observed. An increasing concentration-response association was found for PM2.5 and TOF in adjusted, co-pollutant models (quartile 4 prevalence ratio: 1.46; 95% CI, 1.06-2.03). Differences in the effect of PM2.5 on CHD prevalence were seen in some models stratified by race, although clear exposure-prevalence gradients were not evident. Positive associations were also seen in adjusted, co-pollutant models of ozone and AVSD. Study results suggest that prenatal PM2.5 and ozone exposure may increase the prevalence of certain CHDs. A consistent pattern of differences in association by race/ethnicity was not apparent. This article is part of a Special Collection on Environmental Epidemiology.

LOW-RANK LONGITUDINAL FACTOR REGRESSION WITH APPLICATION TO CHEMICAL MIXTURES

Developmental epidemiology commonly focuses on assessing the association between multiple early life exposures and childhood health. Statistical analyses of data from such studies focus on inferring the contributions of individual exposures, while also characterizing time-varying and interacting effects. Such inferences are made more challenging by correlations among exposures, nonlinearity, and the curse of dimensionality. Motivated by studying the effects of prenatal bisphenol A (BPA) and phthalate exposures on glucose metabolism in adolescence using data from the ELEMENT study, we propose a low-rank longitudinal factor regression (LowFR) model for tractable inference on flexible longitudinal exposure effects. LowFR handles highly-correlated exposures using a Bayesian dynamic factor model, which is fit jointly with a health outcome via a novel factor regression approach. The model collapses on simpler and intuitive submodels when appropriate, while expanding to allow considerable flexibility in time-varying and interaction effects when supported by the data. After demonstrating LowFR's effectiveness in simulations, we use it to analyze the ELEMENT data and find that diethyl and dibutyl phthalate metabolite levels in trimesters 1 and 2 are associated with altered glucose metabolism in adolescence.

High ambient temperature in pregnancy and risk of childhood acute lymphoblastic leukaemia: an observational study

BACKGROUND

High ambient temperature is increasingly common due to climate change and is associated with risk of adverse pregnancy outcomes. Acute lymphoblastic leukaemia is the most common malignancy in children, the incidence is increasing, and in the USA disproportionately affects Latino children. We aimed to investigate the potential association between high ambient temperature in pregnancy and risk of childhood acute lymphoblastic leukaemia.

METHODS

We used data from California birth records (children born from Jan 1, 1982, to Dec 31, 2015) and California Cancer Registry (those diagnosed with childhood cancer in California from Jan 1, 1988, to Dec 31, 2015) to identify acute lymphoblastic leukaemia cases diagnosed in infants and children aged 14 years and younger and controls matched by sex, race, ethnicity, and date of last menstrual period. Ambient temperatures were estimated on a 1-km grid. The association between ambient temperature and acute lymphoblastic leukaemia was evaluated per gestational week, restricted to May-September, adjusting for confounders. Bayesian meta-regression was applied to identify critical exposure windows. For sensitivity analyses, we evaluated a 90-day pre-pregnancy period (assuming no direct effect before pregnancy), adjusted for relative humidity and particulate matter less than 2·5 microns in aerodynamic diameter, and constructed an alternatively matched dataset for exposure contrast by seasonality.

FINDINGS

6849 cases of childhood acute lymphoblastic leukaemia were identified and, of these, 6258 had sufficient data for study inclusion. We also included 307 579 matched controls. Most of the study population were male (174 693 [55·7%] of the 313 837 included in the study) and of Latino ethnicity (174 906 [55·7%]). The peak association between ambient temperature and risk of acute lymphoblastic leukaemia was observed in gestational week 8, where a 5°C increase was associated with an odds ratio of 1·07 (95% CI 1·04-1·11). A slightly larger effect was seen among Latino children (OR 1·09 [95% CI 1·04-1·14]) than non-Latino White children (OR 1·05 [1·00-1·11]). The sensitivity analyses supported the results of the main analysis.

INTERPRETATION

Our findings suggest an association between high ambient temperature in early pregnancy and risk of childhood acute lymphoblastic leukaemia. Further replication and investigation of mechanistic pathways might inform mitigation strategies.

FUNDING

Yale Center on Climate Change and Health, The National Center for Advancing Translational Science, National Institutes of Health.

Association of longitudinal pet ownership with wheezing in 3-year-old children using the distributed lag model: the Japan Environment and Children's Study

BACKGROUND

Time-varying exposures like pet ownership pose challenges for identifying critical windows due to multicollinearity when modeled simultaneously. The Distributed Lag Model (DLM) estimates critical windows for time-varying exposures, which are mainly continuous variables. However, applying complex functions such as high-order splines and nonlinear functions within DLMs may not be suitable for situations with limited time points or binary exposure, such as in questionnaire surveys.

OBJECTIVES

(1) We examined the estimation performance of a simple DLM with fractional polynomial function for time-varying binary exposures through simulation experiments. (2) We evaluated the impact of pet ownership on childhood wheezing onset and estimate critical windows.

METHODS

(1) We compared logistic regression including time-varying exposure in separate models, in one model simultaneously, and using DLM. For evaluation, we employed bias, empirical standard error (EmpSE), and mean squared error (MSE). (2) The Japan Environment and Children's Study (JECS) is a prospective birth cohort study of approximately 100,000 parent-child pairs, registered across Japan from 2011 to 2014. We applied DLM to the JECS data up to age 3. The estimated odds ratios (OR) were considered to be within critical windows when they were significant at the 5% level.

RESULTS

(1) DLM and the separate model exhibited lower bias compared to the simultaneously model. Additionally, both DLM and the simultaneously model demonstrated lower EmpSEs than the separate model. In all scenarios, DLM had lower MSEs than the other methods. Specifically, where critical windows is clearly present and exposure correlation is high, DLM showed MSEs about 1/2 to 1/200 of those of other models. (2) Application of DLM to the JECS data showed that, unlike other models, a significant exposure effect was observed only between the ages of 0 and 6 months. During that periods, the highest ORs were 1.07 (95% confidence interval, 1.01 to 1.14) , observed between the ages of 2 and 5 months.

CONCLUSIONS

(1) A simple DLM improves the accuracy of exposure effect and critical windows estimation. (2) 0-6 months may be the critical windows for the effect of pet ownership on the wheezing onset at 3 years.

Association between risk of preterm birth and long-term and short-term exposure to ambient carbon monoxide during pregnancy in chongqing, China: a study from 2016-2020

BACKGROUND

Preterm birth (PTB) is an important predictor of perinatal morbidity and mortality. Previous researches have reported a correlation between air pollution and an increased risk of preterm birth. However, the specific relationship between short-term and long-term exposure to carbon monoxide (CO) and preterm birth remains less explored.

METHODS

A population-based study was conducted among 515,498 pregnant women in Chongqing, China, to assess short-term and long-term effects of CO on preterm and very preterm births. Generalized additive models (GAM) were applied to evaluate short-term effects, and exposure-response correlation curves were plotted after adjusting for confounding factors. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using COX proportional hazard models to estimate the long-term effect.

RESULTS

The daily incidence of preterm and very preterm birth was 5.99% and 0.41%, respectively. A positive association between a 100 µg/m³ increase in CO and PTB was observed at lag 0-3 days and 12-21 days, with a maximum relative risk (RR) of 1.021(95%CI: 1.001-1.043). The exposure-response curves (lag 0 day) revealed a rapid increase in PTB due to CO. Regarding long-term exposure, positive associations were found between a 100 µg/m3 CO increase for each trimester(Model 2 for trimester 1: HR = 1.054, 95%CI: 1.048-1.060; Model 2 for trimester 2: HR = 1.066, 95%CI: 1.060-1.073; Model 2 for trimester 3: HR = 1.007, 95%CI: 1.001-1.013; Model 2 for entire pregnancy: HR = 1.080, 95%CI: 1.073-1.088) and higher HRs of very preterm birth. Multiplicative interactions between air pollution and CO on the risk of preterm and very preterm birth were detected (P- interaction<0.05).

CONCLUSIONS

Our findings suggest that short-term exposure to low levels of CO may have protective effects against preterm birth, while long-term exposure to low concentrations of CO may reduce the risk of both preterm and very preterm birth. Moreover, our study indicated that very preterm birth is more susceptible to the influence of long-term exposure to CO during pregnancy, with acute CO exposure exhibiting a greater impact on preterm birth. It is imperative for pregnant women to minimize exposure to ambient air pollutants.

Multiple exposure distributed lag models with variable selection

Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado.

Associations between weekly gestational exposure of fine particulate matter, ozone, and nitrogen dioxide and preterm birth in a North Carolina Birth Cohort, 2003-2015

Background

Preterm birth (PTB; <37 weeks completed gestation) is associated with exposure to air pollution, though variability in association magnitude and direction across exposure windows exists. We evaluated associations between weekly gestational exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) with PTB in a North Carolina Birth Cohort from 2003 to 2015 (N = 1,367,517).

Methods

Daily average PM2.5 and daily 8-hour maximum NO2 concentration estimates were obtained from a hybrid ensemble model with a spatial resolution of 1 km2. Daily 8-hour maximum census tract-level concentration estimates for O3 were obtained from the EPA's Fused Air Quality Surface Using Downscaling model. Air pollutant concentrations were linked by census tract to residential address at delivery and averaged across each week of pregnancy. Modified Poisson regression models with robust errors were used to estimate risk differences (RD [95% confidence intervals (CI)]) for an interquartile range increase in pollutants per 10,000 births, adjusted for potential confounders.

Results

Associations were similar in magnitude across weeks. We observed positive associations for PM2.5 and O3 exposures, but generally null associations with NO2. RDs ranged from 15 (95% CI = 11, 18) to 32 (27, 37) per 10,000 births for PM2.5; from -7 (-14, -1) to 0 (-5, 4) for NO2; and from 4 (1, 7) to 13 (10, 16) for O3.

Conclusion

Our results show that increased PM2.5 exposure is associated with an increased risk of PTB across gestational weeks, and these associations persist in multipollutant models with NO2 and/or O3.

Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures

Studying time-dependent exposure mixtures has gained increasing attentions in environmental health research. When a scalar outcome is of interest, distributed lag (DL) models have been employed to characterize the exposures effects distributed over time on the mean of final outcome. However, there is a methodological gap on investigating time-dependent exposure mixtures with different quantiles of outcome. In this paper, we introduce semiparametric partial-linear single-index (PLSI) DL quantile regression, which can describe the DL effects of time-dependent exposure mixtures on different quantiles of outcome and identify susceptible periods of exposures. We consider two time-dependent exposure settings: discrete and functional, when exposures are measured in a small number of time points and at dense time grids, respectively. Spline techniques are used to approximate the nonparametric DL function and single-index link function, and a profile estimation algorithm is proposed. Through extensive simulations, we demonstrate the performance and value of our proposed models and inference procedures. We further apply the proposed methods to study the effects of maternal exposures to ambient air pollutants of fine particulate and nitrogen dioxide on birth weight in New York University Children's Health and Environment Study (NYU CHES).

High Ambient Temperature in Pregnancy and Risk of Childhood Acute Lymphoblastic Leukemia

Background

High ambient temperature is increasingly common due to climate change and is associated with risk of adverse pregnancy outcomes. Acute lymphoblastic leukemia (ALL) is the most common malignancy in children, the incidence is increasing, and in the United States it disproportionately affects Latino children. We aimed to investigate the potential association between high ambient temperature in pregnancy and risk of childhood ALL.

Methods

We used data from California birth records (1982-2015) and California Cancer Registry (1988-2015) to identify ALL cases diagnosed <14 years and 50 times as many controls matched by sex, race/ethnicity, and date of last menstrual period. Ambient temperatures were estimated on a 1-km grid. Association between ambient temperature and ALL was evaluated per gestational week, restricted to May-September, adjusting for confounders. Bayesian meta-regression was applied to identify critical exposure windows. For sensitivity analyses, we evaluated a 90-day pre-pregnancy period (assuming no direct effect before pregnancy) and constructed an alternatively matched dataset for exposure contrast by seasonality.

Findings

Our study included 6,258 ALL cases and 307,579 controls. The peak association between ambient temperature and risk of ALL was observed in gestational week 8, where a 5 °C increase was associated with an odds ratio of 1.09 (95% confidence interval 1.04-1.14) and 1.05 (95% confidence interval 1.00-1.11) among Latino and non-Latino White children, respectively. The sensitivity analyses supported this.

Interpretation

Our findings suggest an association between high ambient temperature in early pregnancy and risk of childhood ALL. Further replication and investigation of mechanistic pathways may inform mitigation strategies.

The impact of correlated exposures and missing data on multiple informant models used to identify critical exposure windows

There has been heightened interest in identifying critical windows of exposure for adverse health outcomes; that is, time points during which exposures have the greatest impact on a person's health. Multiple informant models implemented using generalized estimating equations (MIM GEEs) have been applied to address this research question because they enable statistical comparisons of differences in associations across exposure windows. As interest rises in using MIMs, the feasibility and appropriateness of their application under settings of correlated exposures and partially missing exposure measurements requires further examination. We evaluated the impact of correlation between exposure measurements and missing exposure data on the power and differences in association estimated by the MIM GEE and an inverse probability weighted extension to account for informatively missing exposures. We assessed these operating characteristics under a variety of correlation structures, sample sizes, and missing data mechanisms considering various exposure-outcome scenarios. We showed that applying MIM GEEs maintains higher power when there is a single critical window of exposure and exposure measures are not highly correlated, but may result in low power and bias under other settings. We applied these methods to a study of pregnant women living with HIV to explore differences in association between trimester-specific viral load and infant neurodevelopment.

The short-term effects of air pollution exposure on preterm births in Chongqing, China: 2015-2020

Accumulating evidence suggested that the risk of preterm births (PTBs) following prenatal exposure to air pollution was inconclusive. The aim of this study is to investigate the relationship between air pollution exposure in the days before delivery and PTB and assess the threshold effect of short-term prenatal exposure to air pollution on PTB. This study collected data including meteorological factors, air pollutants, and information in Birth Certificate System from 9 districts during 2015-2020 in Chongqing, China. Generalized additive models (GAMs) with the distributed lag non-linear models were conducted to assess the acute impact of air pollutants on the daily counts of PTB, after controlling for potential confounding factors. We observed that PM2.5 was related to increased occurrence of PTB on lag 0-3 and lag 10-21 days, with the strongest on the first day (RR = 1.017, 95%CI: 1.000-1.034) and then decreasing. The thresholds of PM2.5 for lag 1-7 and 1-30 days were 100 μg/m3 and 50 μg/m3, respectively. The lag effect of PM10 on PTB was very similar to that of PM2.5. In addition, the lagged and cumulative exposure of SO2 and NO2 was also associated with the increased risk of PTB. The lag relative risk and cumulative relative risk of CO exposure were the strongest, with a maximum RR at lag 0 (RR = 1.044, 95%CI: 1.018, 1.069). Importantly, the exposure-response curve of CO showed that RR increased rapidly when the concentration exceeded 1000 μg/m3. This study indicated significant associations between air pollution and PTB. The relative risk decreases with day lag, while the cumulative effect increases. Thus, pregnant women should understand the risk of air pollution and try to avoid high concentration exposure.

Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs

Maternal exposure to environmental chemicals during pregnancy can alter birth and children's health outcomes. Research seeks to identify critical windows, time periods when exposures can change future health outcomes, and estimate the exposure-response relationship. Existing statistical approaches focus on estimation of the association between maternal exposure to a single environmental chemical observed at high temporal resolution (e.g., weekly throughout pregnancy) and children's health outcomes. Extending to multiple chemicals observed at high temporal resolution poses a dimensionality problem and statistical methods are lacking. We propose a regression tree-based model for mixtures of exposures observed at high temporal resolution. The proposed approach uses an additive ensemble of tree pairs that defines structured main effects and interactions between time-resolved predictors and performs variable selection to select out of the model predictors not correlated with the outcome. In simulation, we show that the tree-based approach performs better than existing methods for a single exposure and can accurately estimate critical windows in the exposure-response relation for mixtures. We apply our method to estimate the relationship between five exposures measured weekly throughout pregnancy and birth weight in a Denver, Colorado, birth cohort. We identified critical windows during which fine particulate matter, sulfur dioxide, and temperature are negatively associated with birth weight and an interaction between fine particulate matter and temperature. Software is made available in the R package dlmtree.

CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk in gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.

Prenatal exposure to ambient air pollution and adverse birth outcomes: An umbrella review of 36 systematic reviews and meta-analyses

Multiple systematic reviews and meta-analyses linked prenatal exposure to ambient air pollutants to adverse birth outcomes with mixed findings, including results indicating positive, negative, and null associations across the pregnancy periods. The objective of this study was to systematically summarise systematic reviews and meta-analyses on air pollutants and birth outcomes to assess the overall epidemiological evidence. Systematic reviews with/without meta-analyses on the association between air pollutants (NO2, CO, O3, SO2, PM2.5, and PM10) and birth outcomes (preterm birth; stillbirth; spontaneous abortion; birth weight; low birth weight, LBW; small-for-gestational-age) up to March 30, 2022 were included. We searched PubMed, CINAHL, Scopus, Medline, Embase, and the Web of Science Core Collection, systematic reviews repositories, grey literature databases, internet search engines, and references of included studies. The consistency in the directions of the effect estimates was classified as more consistent positive or negative, less consistent positive or negative, unclear, and consistently null. Next, the confidence in the direction was rated as either convincing, probable, limited-suggestive, or limited non-conclusive evidence. Final synthesis included 36 systematic reviews (21 with and 15 without meta-analyses) that contained 295 distinct primary studies. PM2.5 showed more consistent positive associations than other pollutants. The positive exposure-outcome associations based on the entire pregnancy period were more consistent than trimester-specific exposure averages. For whole pregnancy exposure, a more consistent positive association was found for PM2.5 and birth weight reductions, particulate matter and spontaneous abortion, and SO2 and LBW. Other exposure-outcome associations mostly showed less consistent positive associations and few unclear directions of associations. Almost all associations showed probable evidence. The available evidence indicates plausible causal effects of criteria air pollutants on birth outcomes. To strengthen the evidence, more high-quality studies are required, particularly from understudied settings, such as low-and-middle-income countries. However, the current evidence may warrant the adoption of the precautionary principle.

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.

Spatially and Temporally Resolved Ambient PM2.5 in Relation to Preterm Birth

Growing evidence suggests that maternal exposure to ambient fine particulate matter (PM_2.5) during pregnancy is associated with preterm birth; however, few studies have examined critical windows of exposure, which can help elucidate underlying biologic mechanisms and inform public health messaging for limiting exposure. Participants included 891 mother-newborn pairs enrolled in a U.S.-based pregnancy cohort study. Daily residential PM_2.5 concentrations at a 1 × 1 km² resolution were estimated using a satellite-based hybrid model. Gestational age at birth was abstracted from electronic medical records and preterm birth (PTB) was defined as <37 completed weeks of gestation. We used Critical Window Variable Selection to examine weekly PM_2.5 exposure in relation to the odds of PTB and examined sex-specific associations using stratified models. The mean ± standard deviation PM_2.5 level averaged across pregnancy was 8.13 ± 1.10 µg/m³. PM_2.5 exposure was not associated with an increased odds of PTB during any gestational week. In sex-stratified models, we observed a marginal increase in the odds of PTB with exposure occurring during gestational week 16 among female infants only. This study does not provide strong evidence supporting an association between weekly exposure to PM_2.5 and preterm birth.

Weekly-specific ambient fine particular matter exposures before and during pregnancy were associated with risks of small for gestational age and large for gestational age: results from Project ELEFANT

BACKGROUND

Investigations on the potential effects of ambient fine particulate matter (PM2.5) on large for gestational age (LGA) are limited. Furthermore, no study has explored weekly-specific susceptible exposure windows for small for gestational age (SGA) and LGA. This study evaluated the associations of exposure to ambient PM2.5 over the preconception and entire-pregnancy periods with risks of SGA and LGA, as well as explored critical weekly-specific exposure windows.

METHODS

10 916 singleton pregnant women with 24-42 completed gestational weeks from the Project Environmental and LifEstyle FActors iN metabolic health throughout life-course Trajectories between 2014 and 2016 were included in this study. Distributed lag models (DLMs) incorporated in Cox proportional-hazards models were applied to explore the associations of maternal exposure to weekly ambient PM2.5 throughout 12 weeks before pregnancy and pregnancy periods with risks of SGA and LGA after controlling for potential confounders.

RESULTS

For a 10-μg/m3 increase in maternal exposure to PM2.5, positive associations with SGA were observed during the 1st to 9th preconceptional weeks and the 1st to 2nd gestational weeks (P<0.05), with the strongest association in the 5th preconceptional week [hazard ratio (HR), 1.06; 95% confidential interval (CI), 1.03-1.09]. For LGA, positive associations were observed during the 1st to 12th preconceptional weeks and the 1st to 5th gestational weeks (P<0.05), with the strongest association in the 7th preconceptional week (HR, 1.10; 95% CI, 1.08-1.12).

CONCLUSIONS

Exposure to high-level ambient PM2.5 is associated with increased risks of both SGA and LGA, and the most susceptible exposure windows are the preconception and early-pregnancy periods.

The Dynamics of Infectious Diseases Associated With Injection Drug Use in Lawrence and Lowell, Massachusetts

Background

There are a wide variety of infectious complications of injection drug use. Understanding the trajectory of these complications might inform the development of an early warning system for human immunodeficiency virus (HIV) outbreaks that occur regularly among people who inject drugs (PWID).

Methods

A distributed lag Poisson regression model in the Bayesian setting was used to examine temporal patterns in the incidence of injection-associated infectious diseases and their association with HIV cases in Lawrence and Lowell, Massachusetts between 2005 and 2018.

Results

Current-month HIV counts are associated with fatal overdoses approximately 8 months prior, cases of infective endocarditis 10 months prior, and cases of skin and soft tissue infections and incision and drainage procedures associated with these infections 12 months prior.

Conclusions

Collecting data on these other complications associated with injection drug use by public health departments may be important to consider because these complications may serve as input to a sentinel system to trigger early intervention and avert potential outbreaks of HIV.

SPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION

We introduce spatial (DLfuse) and spatiotemporal (DLfuseST) distributed lag data fusion methods for predicting point-level ambient air pollution concentrations, using, as input, gridded average pollution estimates from a deterministic numerical air quality model. The methods incorporate predictive information from grid cells surrounding the prediction location of interest and are shown to collapse to existing downscaling approaches when this information adds no benefit. The spatial lagged parameters are allowed to vary spatially/spatiotemporally to accommodate the setting where surrounding geographic information is useful in one area/time but not in another. We apply the new methods to predict ambient concentrations of eight-hour maximum ozone and 24-hour average PM2.5 at unobserved spatial locations and times, and compare the predictions with those from several state-of-the-art data fusion approaches. Results show that DLfuse and DLfuseST often provide improved model fit and predictive accuracy when the lagged information is shown to be beneficial. Code to apply the methods is available in the R package DLfuse.

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.

A spatially varying distributed lag model with application to an air pollution and term low birth weight study

Distributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the possibility of spatial variability in the lagged health effect parameters that may result from exposure characteristics and/or residual confounding. We develop a spatially varying Gaussian process model for critical windows called 'SpGPCW' and use it to investigate geographic variability in the association between term low birth weight and average weekly concentrations of ozone and PM_2:5 during pregnancy by using birth records from North Carolina. SpGPCW is designed to accommodate areal level spatial correlation between lagged health effect parameters and temporal smoothness in risk estimation across pregnancy. Through simulation and a real data application, we show that the consequences of ignoring spatial variability in the lagged health effect parameters include less reliable inference for the parameters and diminished ability to identify true critical window sets, and we investigate the use of existing Bayesian model comparison techniques as tools for determining the presence of spatial variability. We find that exposure to PM_2:5 is associated with elevated term low birth weight risk in selected weeks and counties and that ignoring spatial variability results in null associations during these periods. An R package (SpGPCW) has been developed to implement the new method.