Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County

Health effects of air pollutant mixtures (volatile organic compounds, particulate matter, sulfur and nitrogen oxides) - a review of the literature

The health risks associated with individual air pollutant exposures have been studied and documented, but in real-life, the population is exposed to a multitude of different substances, designated as mixtures. A body of literature on air pollutants indicated that the next step in air pollution research is investigating pollutant mixtures and their potential impacts on health, as a risk assessment of individual air pollutants may actually underestimate the overall risks. This review aims to synthesize the health effects related to air pollutant mixtures containing selected pollutants such as: volatile organic compounds, particulate matter, sulfur and nitrogen oxides. For this review, the PubMed database was used to search for articles published within the last decade, and we included studies assessing the associations between air pollutant mixtures and health effects. The literature search was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A number of 110 studies were included in the review from which data on pollutant mixtures, health effects, methods used, and primary results were extracted. Our review emphasized that there are a relatively small number of studies addressing the health effects of air pollutants as mixtures and there is a gap in knowledge regarding the health effects associated with these mixtures. Studying the health effects of air pollutant mixtures is challenging due to the complexity of components that mixtures may contain, and the possible interactions these different components may have.

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Analysis of gasoline-related pollutant exposures and risks in California between 1996 and 2014

BACKGROUND

Gasoline-powered vehicles and equipment are an important source of air pollution in California. Many gasoline-related pollutants pose significant health concerns. The California Air Resources Board strictly regulates the state's gasoline formulation and vehicle emissions.

OBJECTIVE

To investigate exposure trends for gasoline-related air pollutants between 1996 and 2014, capturing the period before and after the removal of methyl t-butyl ether (MTBE).

METHODS

We identified gasoline-related chemicals with known or suspected health concerns and adequate ambient air monitoring data. Average exposures to the general public were estimated from 1996 to 2014 in five major air basins and statewide. We determined the fractions of exposures attributable to gasoline use and evaluated cancer and non-cancer risks for chemicals with available cancer potencies and health reference values.

RESULTS

We found that average gasoline-attributable cancer risks for the general California population from the most highly emitted carcinogens (acetaldehyde, benzene, 1,3-butadiene, and formaldehyde) declined by over 80% between 1996 and 2014. This decline occurred despite roughly constant statewide gasoline sales, an increase in vehicle miles traveled, and an approximately 10% increase in vehicle registrations over this same period. Naphthalene, measured as a volatile organic compound (VOC), was the most abundant gasoline-related polycyclic aromatic hydrocarbon (PAH). From 1996 to 2014, gasoline-attributable cancer risks for naphthalene were estimated to drop approximately threefold in the South Coast Air Basin. Exposures to gasoline-related chemicals associated with non-cancer health effects, such as chronic respiratory toxicity or neurotoxicity, were generally below levels of concern. The exception was acrolein, with gasoline-related exposures in 2014 estimated to be high enough to pose risks for respiratory toxicity.

IMPACT STATEMENT

Our historical analysis demonstrated the success of California's regulatory efforts to reduce gasoline-related air pollutant exposures and risks to the general public. New efforts are focused on addressing gasoline-related and other air pollution in heavily impacted communities affected by multiple environmental and social stressors.

Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA

Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017-2019), and the last five years (2014-2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health.

Preterm Birth and Metal Mixture Exposure among Pregnant Women from the Navajo Birth Cohort Study

BACKGROUND

Preterm birth (PTB), defined as birth before 37 wk gestation, is associated with hypertension, diabetes, inadequate prenatal care, unemployment or poverty, and metal exposure. Indigenous individuals are more likely to have maternal risk factors associated with PTB compared with other populations in the United States; however, the role of environmental metals on PTB among pregnant Indigenous women remains uncertain. Previous research identified associations between PTB and individual metals, but there is limited investigation on metal mixtures and this birth outcome.

OBJECTIVES

We used a mixtures analysis framework to investigate the association between metal mixtures and PTB among pregnant Indigenous women from the Navajo Birth Cohort Study (NBCS).

METHODS

Maternal urine and blood samples were collected at the time of study enrollment and analyzed for metals by inductively coupled plasma dynamic reaction cell mass spectrometry. Bayesian Profile Regression was used to identify subgroups (clusters) of individuals with similar patterns of coexposure and to model association with PTB.

RESULTS

Results indicated six subgroups of maternal participants with distinct exposure profiles, including one group with low exposure to all metals and one group with total arsenic, cadmium, lead, and uranium concentrations exceeding representative concentrations calculated from the National Health and Nutrition Examination Survey (NHANES). Compared with the reference group (i.e., the lowest exposure subgroup), the subgroup with the highest overall exposure had a relative risk of PTB of 2.9 times (95% credible interval: 1.1, 6.1). Exposures in this subgroup were also higher overall than NHANES median values for women 14-45 years of age.

DISCUSSION

Given the wide range of exposures and elevated PTB risk for the most exposed subgroups in a relatively small study, follow-up investigation is recommended to evaluate associations between metal mixture profiles and other birth outcomes and to test hypothesized mechanisms of action for PTB and oxidative stress caused by environmental metals. https://doi.org/10.1289/EHP10361.

AQI prediction using layer recurrent neural network model: a new approach

The air quality index (AQI) prediction is important to evaluate the effects of air pollutants on human health. The airborne pollutants have been a major threat in Delhi both in the past and coming years. The air quality index is a figure, based on the cumulative effect of major air pollutant concentrations, used by Government agencies, for air quality assessment. Thus, the main aim of the present study is to predict the daily AQI one year in advance through three different neural network models (FF-NN, CF-NN and LR-NN) for the year 2020 and compare them. The models were trained using AQI values of previous year (2019). In addition to main air pollutants like PM10/PM2.5, O3, SO2, NOx, CO and NH3, the non-criteria pollutants and meteorological data were also included as input parameter in this study. The model performances were assessed using statistical analysis. The key air pollutants contributing to high level of daily AQI were found to be PM2.5/PM10, CO and NO2. The root mean square error (RMSE) values of 31.86 and 28.03 were obtained for the FF-NN and CF-NN models respectively whereas the LR-NN model has the minimum RMSE value of 26.79. LR-NN algorithm predicted the AQI values very closely to the actual values in almost all the seasons of the year. The LR-NN performance was also found to be the best in post-monsoon season i.e., October and November (maximum R2 = 0.94) with respect to other seasons. The study would aid air pollution control authorities to predict AQI more precisely and adopt suitable pollution control measures. Further research studies are recommended to compare the performance of LR-NN model with statistical, numerical and computational models for accurate air quality assessment.

Mammographic density in the environs of multiple industrial sources

BACKGROUND

Mammographic density (MD), defined as the percentage of dense fibroglandular tissue in the breast, is a modifiable marker of the risk of developing breast cancer. Our objective was to evaluate the effect of residential proximity to an increasing number of industrial sources in MD.

METHODS

A cross-sectional study was conducted on 1225 premenopausal women participating in the DDM-Madrid study. We calculated distances between women's houses and industries. The association between MD and proximity to an increasing number of industrial facilities and industrial clusters was explored using multiple linear regression models.

RESULTS

We found a positive linear trend between MD and proximity to an increasing number of industrial sources for all industries, at distances of 1.5 km (p-trend = 0.055) and 2 km (p-trend = 0.083). Moreover, 62 specific industrial clusters were analyzed, highlighting the significant associations found between MD and proximity to the following 6 industrial clusters: cluster 10 and women living at ≤1.5 km (β = 10.78, 95 % confidence interval (95%CI) = 1.59; 19.97) and at ≤2 km (β = 7.96, 95%CI = 0.21; 15.70); cluster 18 and women residing at ≤3 km (β = 8.48, 95%CI = 0.01; 16.96); cluster 19 and women living at ≤3 km (β = 15.72, 95%CI = 1.96; 29.49); cluster 20 and women living at ≤3 km (β = 16.95, 95%CI = 2.90; 31.00); cluster 48 and women residing at ≤3 km (β = 15.86, 95%CI = 3.95; 27.77); and cluster 52 and women living at ≤2.5 km (β = 11.09, 95%CI = 0.12; 22.05). These clusters include the following industrial activities: surface treatment of metals/plastic, surface treatment using organic solvents, production/processing of metals, recycling of animal waste, hazardous waste, urban waste-water treatment plants, inorganic chemical industry, cement and lime, galvanization, and food/beverage sector.

CONCLUSIONS

Our results suggest that women living in the proximity to an increasing number of industrial sources and those near certain types of industrial clusters have higher MD.

Characterization of the short-term temporal variability of road dust chemical mixtures and meteorological profiles in a near-road urban site in British Columbia

Implications: Non-tailpipe emissions driven by springtime road dust in northern latitude communities is increasing in importance for air pollution control and improving our understanding of the health effects of chemical mixtures from particulate matter exposure. High-volume samples from a near-road site indicated that days affected by springtime road dust are substantively different from other days with respect to particulate matter mixture composition and meteorological drivers. The high load of trace elements in PM₁₀ on high road dust days has important implications for the acute toxicity of inhaled air and subsequent health effects. The complex relationships between road dust and weather identified in this study may facilitate further research on the health effects of chemical mixtures related to road dust while also highlighting potential changes in this unique form of air pollution as the climate changes.

Trimester-specific ambient PM2.5 exposures and risk of intellectual disability in Utah

Prenatal fine particulate matter (PM2.5) exposure is an understudied risk factor for neurodevelopmental outcomes, including intellectual disability (ID). Associations among prenatal exposures and neurodevelopmental outcomes may vary depending on the timing of exposure. Limited numbers of studies examining PM2.5 and neurodevelopmental outcomes have considered exposures occurring during the preconception period. To address these gaps, we conducted a case-control study of children born in Utah between 2002 and 2008 (n = 1032). Cases were identified using methods developed by the Centers for Disease Control and Prevention's Autism and Developmental Disabilities Monitoring Network and matched with controls on birth year, sex, and birth county. We estimated the daily average PM2.5 concentration during a period spanning 12 weeks before the estimated conception date, as well as during each of the three trimesters at the maternal residential address listed on the child's birth certificate. In a multivariable model, the third (OR: 2.119, CI: 1.123-3.998, p = .021) and fourth (OR: 2.631, CI: 1.750-3.956, p < .001) quartiles for preconception average PM2.5 demonstrated significantly increased risk of ID relative to the first quartile. Second quartile preconception exposure was also associated with increased risk, though it did not reach significance (OR: 1.385, CI: 0.979-1.959, p = .07). The fourth quartile of first trimester average PM2.5 was positive and significant (OR: 2.278, CI: 1.522-3.411, p < .001); the third quartile was positive, but not significant (OR: 1.159, CI: 0.870-1.544, p = .312). Quartiles of second and third trimester were not associated with higher risk of ID. These findings from Utah, which were robust to a variety of sensitivity analyses, provide initial evidence that preconception and prenatal PM2.5 exposure may be associated with ID. Future studies are needed across other geographic locations and populations.

Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy

Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid -19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.

Dirichlet process mixture models for regression discontinuity designs

The regression discontinuity design is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold for a continuous variable. The regression discontinuity design assumes that subjects with measurements within a bandwidth around the threshold belong to a common population, so that the threshold can be seen as a randomising device assigning treatment to those falling just above the threshold and withholding it from those who fall below. Bandwidth selection represents a compelling decision for the regression discontinuity design analysis as results may be highly sensitive to its choice. A few methods to select the optimal bandwidth, mainly from the econometric literature, have been proposed. However, their use in practice is limited. We propose a methodology that, tackling the problem from an applied point of view, considers units' exchangeability, that is, their similarity with respect to measured covariates, as the main criteria to select subjects for the analysis, irrespectively of their distance from the threshold. We cluster the sample using a Dirichlet process mixture model to identify balanced and homogeneous clusters. Our proposal exploits the posterior similarity matrix, which contains the pairwise probabilities that two observations are allocated to the same cluster in the Markov chain Monte Carlo sample. Thus we include in the regression discontinuity design analysis only those clusters for which we have stronger evidence of exchangeability. We illustrate the validity of our methodology with both a simulated experiment and a motivating example on the effect of statins on cholesterol levels.

Childhood exposures to environmental chemicals and neurodevelopmental outcomes in congenital heart disease

BACKGROUND

Children with congenital heart defects have an increased risk of neurodevelopmental disability. The impact of environmental chemical exposures during daily life on neurodevelopmental outcomes in toddlers with congenital heart defects is unknown.

METHODS

This prospective study investigated the impacts of early childhood exposure to mixtures of environmental chemicals on neurodevelopmental outcomes after cardiac surgery. Outcomes were assessed at 18 months of age using The Bayley Scales of Infant and Toddler Development-III. Urinary concentrations of exposure biomarkers of pesticides, phenols, parabens, and phthalates, and blood levels of lead, mercury, and nicotine were measured at the same time point. Bayesian profile regression and weighted quantile sum regression were utilized to assess associations between mixtures of biomarkers and neurodevelopmental scores.

RESULTS

One-hundred and forty infants were enrolled, and 110 (79%) returned at 18 months of age. Six biomarker exposure clusters were identified from the Bayesian profile regression analysis; and the pattern was driven by 15 of the 30 biomarkers, most notably 13 phthalate biomarkers. Children in the highest exposure cluster had significantly lower adjusted language scores by -9.41 points (95%CI: -17.2, -1.7) and adjusted motor scores by -4.9 points (-9.5, -0.4) compared to the lowest exposure. Weighted quantile sum regression modeling for the overall exposure-response relationship showed a significantly lower adjusted motor score (β = -2.8 points [2.5th and 97.5th percentile: -6.0, -0.6]). The weighted quantile sum regression index weights for several phthalates, one paraben, and one phenol suggest their relevance for poorer neurodevelopmental outcomes.

CONCLUSIONS

Like other children, infants with congenital heart defects are exposed to complex mixtures of environmental chemicals in daily life. Higher exposure biomarker concentrations were associated with significantly worse performance for language and motor skills in this population.

Towards healthy school neighbourhoods: A baseline analysis in Greater London

Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400 m (5-10 min walking distance) of schools in Greater London. We modelled joint exposure to air pollution (NO₂ and PM_2.5), access to public greenspace, food environment, and road safety for 2,929 schools, employing a Bayesian non-parametric approach based on the Dirichlet Process Mixture modelling. We identified 12 latent clusters of schools with similar exposure profiles and observed some spatial clustering patterns. Socioeconomic and ethnicity disparities were manifested with respect to exposure profiles. Specifically, three clusters (containing 645 schools) showed the highest joint exposure to air pollution, poor food environment, and unsafe roads and were characterized with high deprivation. The neighbourhood of the most deprived cluster of schools had a median of 2.5 ha greenspace, 29.0 µg/m³ of NO₂, 19.3 µg/m³ of PM_2.5, 20 fast food retailers, and five child pedestrian crashes over a three-year period. The neighbourhood of the least deprived cluster of schools had a median of 21.8 ha greenspace, 15.6 µg/m³ of NO₂, 15.1 µg/m³ of PM_2.5, 2 fast food retailers, and one child pedestrian crash over a three-year period. To have a school-level understanding of exposure levels, we then benchmarked schools based on the probability of exceeding the median exposure to various features of interest. Our study accounts for multiple exposures, enabling us to highlight spatial distribution of exposure profile clusters, and to identify predominant exposure to urban environment features for each cluster of schools. Our findings can help relevant stakeholders, such as schools and public health authorities, to compare schools based on their exposure levels, prioritize interventions, and design local policies that target the schools most in need.

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.

Differences in Rates of Low Birth Weight among Prefectures in Japan: An Ecological Study Using Government Statistics Data

The differences in the rates and trends of the overall low birth weight and term low birth weight in recent years are unknown for the Japanese prefectures. In this ecological study, we revealed the rates for each prefecture and investigated the factors affecting the regional differences in these outcomes. Aggregated vital statistics data from 2007 to 2019 were obtained from the Ministry of Health, Labour, and Welfare in Japan. The association between the outcomes and the variables, including the infants’ birth characteristics, medical characteristics, and socioeconomic characteristics of the prefectures, were analyzed. An analysis of repeated-measures data was conducted using the data from 2013 and 2018 for each prefecture. The trend for the rates of overall low birth weight and term low birth weight over the years differed among the prefectures. Moreover, the proportions of multiple births and lean (body mass index <18.5 kg/m2) and obese (body mass index ≥25.0 kg/m2) women had a statistically significant positive association with both the overall low birth weight rate and the term low birth weight rate among the prefectures. It was suggested that to resolve the difference in these outcomes among the prefectures, being obese or underweight needs to be addressed in mothers.

Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes

Air pollution is a well-known contributor to asthma. Air toxics are hazardous air pollutants that cause or may cause serious health effects. Although individual air toxics have been associated with asthma, only a limited number of studies have specifically examined combinations of air toxics associated with the disease. We geocoded air toxic levels from the US National Air Toxics Assessment (NATA) to residential locations for participants of our AiRway in Asthma (ARIA) study. We then applied Data-driven ExposurE Profile extraction (DEEP), a machine learning-based method, to discover combinations of early-life air toxics associated with current use of daily asthma controller medication, lifetime emergency department visit for asthma, and lifetime overnight hospitalization for asthma. We discovered 20 multi-air toxic combinations and 18 single air toxics associated with at least 1 outcome. The multi-air toxic combinations included those containing acrylic acid, ethylidene dichloride, and hydroquinone, and they were significantly associated with asthma outcomes. Several air toxic members of the combinations would not have been identified by single air toxic analyses, supporting the use of machine learning-based methods designed to detect combinatorial effects. Our findings provide knowledge about air toxic combinations associated with childhood asthma.

A time series analysis of the ecologic relationship between acute and intermediate PM2.5 exposure duration on neonatal intensive care unit admissions in Florida

Admissions of newborn infants into Neonatal Intensive Care Units (NICU) has increased in the US over the last decade yet the role of environmental exposures as a risk factor for NICU admissions is under studied. Our study aims to determine the ecologic association between acute and intermediate ambient PM2.5 exposure durations and rates of NICU admissions, and to explore whether this association differs by area-level social stressors and meteorological factors. We conducted an ecologic time-series analysis of singleton neonates (N = 1,027,797) born in Florida hospitals between December 26, 2011 to April 30, 2019. We used electronic medical records (EMRs) in the OneFlorida Data Trust and included infants with a ZIP code in a Metropolitan Statistical Areas (MSA) and excluded extreme preterm births (<24wks gestation). The study outcome is the number of daily NICU admission at 28 days old or younger for each ZIP code in the study area. The exposures of interest are average same day, 1- and 2-day lags, and 1-3 weeks ambient PM2.5 concentration at the ZIP code-level estimated using inverse distance weighting (IDW) for each day of the study period. We used a zero-inflated Poisson regression mixed effects models to estimate adjusted associations between acute and intermediate PM2.5 exposure durations and NICU admissions rates. NICU admissions rates increased over time during the study period. Ambient 7-day average PM2.5 concentrations was significantly associated with incidence of NICU admissions, with an interquartile range (IQR = 2.37 μg/m³) increase associated with a 1.4% (95% CI: 0.4%, 2.4%) higher adjusted incidence of daily NICU admissions. No other exposure duration metrics showed a significant association with daily NICU admission rates. The magnitude of the association between PM2.5 7-day average concentrations with NICU admissions was significantly (p < 0.05) higher among ZIP codes with higher proportions of non-Hispanic Blacks, ZIP codes with household incomes in the lowest quartile, and on days with higher relative humidity. Our data shows a positive relationship between acute (7-day average) PM2.5 concentrations and daily NICU admissions in Metropolitan Statistical Areas of Florida. The observed associations were stronger in socioeconomically disadvantaged areas, areas with higher proportions with non-Hispanic Blacks, and on days with higher relative humidity. Further research is warranted to study other air pollutants and multipollutant effects and identify health conditions that are driving these associations with NICU admissions.

Multiple air pollutant exposure and lung cancer in Tehran, Iran

Lung cancer is the most rapidly increasing malignancy worldwide with an estimated 2.1 million cancer cases in the latest, 2018 World Health Organization (WHO) report. The objective of this study was to investigate the association of air pollution and lung cancer, in Tehran, Iran. Residential area information of the latest registered lung cancer cases that were diagnosed between 2014 and 2016 (N = 1,850) were inquired from the population-based cancer registry of Tehran. Long-term average exposure to PM₁₀, SO₂, NO, NO₂, NO_X, benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene (BTEX), and BTEX in 22 districts of Tehran were estimated using land use regression models. Latent profile analysis (LPA) was used to generate multi-pollutant exposure profiles. Negative binomial regression analysis was used to examine the association between air pollutants and lung cancer incidence. The districts with higher concentrations for all pollutants were mostly in downtown and around the railway station. Districts with a higher concentration for NOx (IRR = 1.05, for each 10 unit increase in air pollutant), benzene (IRR = 3.86), toluene (IRR = 1.50), ethylbenzene (IRR = 5.16), p-xylene (IRR = 9.41), o-xylene (IRR = 7.93), m-xylene (IRR = 2.63) and TBTEX (IRR = 1.21) were significantly associated with higher lung cancer incidence. Districts with a higher multiple air-pollution profile were also associated with more lung cancer incidence (IRR = 1.01). Our study shows a positive association between air pollution and lung cancer incidence. This association was stronger for, respectively, p-xylene, o-xylene, ethylbenzene, benzene, m-xylene and toluene.

Air pollution and pregnancy outcomes based on exposure evaluation using a land use regression model: A systematic review

This review systematically assessed those studies investigating the association between air pollution and birth outcomes using land use regression (LUR) models for exposure assessment. Fifty-four studies were identified which were published between 2007 and 2019. Most of these were conducted in America, Spain and Canada, while only five were conducted in China. One hundred and ninety-seven LUR models were developed for different pollutants. The main pollutants that these studies assessed were NO₂ and PM_2.5, and the main pregnancy outcomes investigated were preterm birth (PTB), small for gestational age (SGA) and birth weight. Studies consistently found that NO₂ exposure during pregnancy was associated with reduced fetal growth and development. The effect of NO₂ on other adverse pregnancy outcomes is unclear. In addition, it was found that increased PM_2.5 (aerodynamic equivalent diameter ≤ 2.5 um) exposure during pregnancy reduced birth weight. The effect of PM_2.5 on other adverse pregnancy outcomes is also unclear. The relationship between other pollutants and adverse pregnancy outcomes is uncertain based on the existing research. Exposure assessment with LUR modeling has been widely used in Europe and North America, but used less in China. Future studies are recommended to use LUR modeling for individual exposure evaluation in China to better characterize the relationship between air pollution and adverse pregnancy outcomes. In addition, further research is required given that a lot of the associations looked at in the review were inconclusive.

Combined Impacts of Prenatal Environmental Exposures and Psychosocial Stress on Offspring Health: Air Pollution and Metals

PURPOSE OF REVIEW

Pregnant women and their offspring are vulnerable to the adverse effects of environmental and psychosocial stressors, individually and in combination. Here, we review the literature on how air pollution and metal exposures may interact with structural and individual-level stressors (including poverty and stressful life events) to impact perinatal and child outcomes.

RECENT FINDINGS

The adverse associations between air pollution and metal exposures and adverse infant and child health outcomes are often exacerbated by co-exposure to psychosocial stressors. Although studies vary by geography, study population, pollutants, stressors, and outcomes considered, the effects of environmental exposures and psychosocial stressors on early health outcomes are sometimes stronger when considered in combination than individually. Environmental and psychosocial stressors are often examined separately, even though their co-occurrence is widespread. The evidence that combined associations are often stronger raises critical issues around environmental justice and protection of vulnerable populations.

Prenatal Exposure to Multiple Air Pollutants, Mediating Molecular Mechanisms, and Shifts in Birthweight

Mechanisms underlying adverse birth and later in life health effects from exposure to air pollution during the prenatal period have not been not fully elucidated, especially in the context of mixtures. We assessed the effects of prenatal exposure to mixtures of air pollutants of particulate matter (PM), PM2.5, PM10, nitrogen oxides, NO2, NOx, ultrafine particles (UFP), and oxidative potential (OP) of PM2.5 on infant birthweight in four European birth cohorts and the mechanistic underpinnings through cross-omics of metabolites and inflammatory proteins. The association between mixtures of air pollutants and birthweight z-scores (standardized for gestational age) was assessed for three different mixture models, using Bayesian machine kernel regression (BKMR). We determined the direct effect for PM2.5, PM10, NO2, and mediation by cross-omic signatures (identified using sparse partial least-squares regression) using causal mediation BKMR models. There was a negative association with birthweight z-scores and exposure to mixtures of air pollutants, where up to -0.21 or approximately a 96 g decrease in birthweight, comparing the 75th percentile to the median level of exposure to the air pollutant mixture could occur. Shifts in birthweight z-scores from prenatal exposure to PM2.5, PM10, and NO2 were mediated by molecular mechanisms, represented by cross-omics scores. Interleukin-17 and epidermal growth factor were identified as important inflammatory responses underlyingair pollution-associated shifts in birthweight. Our results signify that by identifying mechanisms through which mixtures of air pollutants operate, the causality of air pollution-associated shifts in birthweight is better supported, substantiating the need for reducing exposure in vulnerable populations.

Predicting differential improvements in annual pollutant concentrations and exposures for regulatory policy assessment

Over the past decade, researchers and policy-makers have become increasingly interested in regulatory and policy interventions to reduce air pollution concentrations and improve human health. Studies have typically relied on relatively sparse environmental monitoring data that lack the spatial resolution to assess small-area improvements in air quality and health. Few studies have integrated multiple types of measures of an air pollutant into one single modeling framework that combines spatially- and temporally-rich monitoring data. In this paper, we investigated the differential effects of California emissions reduction plan on reducing air pollution between those living in the goods movement corridors (GMC) that are within 500 m of major highways that serve as truck routes to those farther away or adjacent to routes that prohibit trucks. A mixed effects Deletion/Substitution/Addition (D/S/A) machine learning algorithm was developed to model annual pollutant concentrations of nitrogen dioxide (NO₂) by taking repeated measures into consideration and by integrating multiple types of NO₂ measurements, including those through government regulatory and research-oriented saturation monitoring into a single modeling framework. Difference-in-difference analysis was conducted to identify whether those living in GMC demonstrated statistically larger reductions in air pollution exposure. The mixed effects D/S/A machine learning modeling result indicated that GMC had 2 ppb greater reductions in NO₂ concentrations from pre- to post-policy period than far away areas. The difference-in-difference analysis demonstrated that the subjects living in GMC experienced statistically significant greater reductions in NO₂ exposure than those living in the far away areas. This study contributes to scientific knowledge by providing empirical evidence that improvements in air quality via the emissions reductions plan policies impacted traffic-related air pollutant concentrations and associated exposures most among low-income Californians with chronic conditions living in GMC. The identified differences in pollutant reductions across different location domains may be applicable to other states or other countries if similar policies are enacted.

Rothwell J, Shah S, El Fatouhi D, Romana Mancini F, Boutron-Ruault MC, Fagherazzi G. Profiles of Polyphenol Intake and Type 2 Diabetes Risk in 60,586 Women Followed for 20 Years: Results from the E3N Cohort Study

Most studies on dietary polyphenol intake and type 2 diabetes (T2D) risk have focused on total or specific subclasses of polyphenols. Since polyphenols are often consumed simultaneously, the joint effect of an intake of multiple subclasses should be explored. We aimed to identify profiles of the dietary polyphenol subclasses intake associated with T2D. A total of 60,586 women from the Etude Epidémiologique auprès de femmes de l'Education Nationale (E3N) cohort study were followed for 20 years between 1993 and 2014. T2D cases were identified and validated. The individual energy-adjusted daily intakes of 15 subclasses of polyphenols were estimated at baseline using a food frequency questionnaire and the PhenolExplorer database. We used Bayesian profile regression to perform the clustering of the covariates by identifying exposure profiles of polyphenol intakes and, simultaneously, link these to T2D risk by using multivariable Cox regression models. We validated 2740 incident T2D cases during follow-up, and identified 15 distinct clusters with different intake profiles and T2D risk. When compared to the largest cluster (n = 6298 women), higher risks of T2D were observed in three of those clusters, which were composed of women with low or medium intakes of anthocyanins, dihydroflavonols, catechins, flavonols, hydroxybenzoic acids, lignans, and stilbenes. One cluster (n = 4243), characterized by higher intakes of these polyphenol subclasses, exhibited lower T2D risk when compared to the reference cluster. These results highlight the importance of a varied diet of polyphenol-rich foods such as nuts, fruits, and vegetables to prevent T2D risk.

Clustering method for censored and collinear survival data

In this paper we propose a Dirichlet process mixture model for censored survival data with covariates. This model is suitable in two scenarios. First, this method can be used to identify clusters determined by both the censored survival data and the predictors. Second, this method is suitable for highly correlated predictors, in cases when the usual survival models cannot be implemented because they would be unstable due to multicollinearity. The Dirichlet process mixture model links a response vector to covariate data through cluster membership and in this paper this model is extended for mixtures of Weibull distributions, which can be used to model survival times and also allow for censoring. We propose two variants of this model, one with a shape parameter common to all clusters (referred to as a global parameter) for the Weibull distributions and one with a cluster-specific shape parameter. The first satisfies the proportional hazard assumption, while the latter is very flexible, as it has the advantage of allowing estimation of the survival curve whether or not the proportional hazards assumption is satisfied. We present a simulation study and, to demonstrate the applicability of the method in practice, a real application to sleep surveys in older women from The Australian Longitudinal Study on Women’s Health. The method developed in the paper is available in the R package PReMiuM.

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.

Household air pollution profiles associated with persistent childhood cough in urban Uganda

BACKGROUND

Most household air pollution (HAP) interventions in developing countries of sub-Saharan Africa have focused on a single source, such as replacing polluting cooking sources with cleaner burning cooking stoves. Such interventions, however, have resulted in insufficient reductions in HAP levels and respiratory health risks in children. In this study we determined how multiple HAP combustion sources and exposure-mitigation factors in the home environment influence child respiratory health alone and in combination.

METHODS

We carried out a case-control study to determine associations between multiple indicators of HAP and persistent cough among children (<15 years of age) seeking care at three primary-care clinics in Kampala, Uganda. HAP indicators included self-report of combustion sources inside the home (e.g., stove type, fuel type, and smoking); housing characteristics and cooking practices that mitigate HAP exposure (e.g., use of windows, location of cooking, location of children during cooking) and perceptions of neighborhood air quality. To explore joint associations between indicators of HAP, we applied a Bayesian clustering technique (Bayesian profile regression) to identify HAP indicator profiles most strongly associated with persistent cough in children.

RESULTS

Most HAP indicators demonstrated significant positive bivariate associations with persistent cough among children, including fuel-type (kerosene), the number of hours burning solid fuels, use of polluting fuels (kerosene or candles) for lighting the home, tobacco smoking indoors, cooking indoors, cooking with children indoors, lack of windows in the cooking area, and not opening windows while cooking. Bayesian cluster analysis revealed 11 clusters of HAP indicator profiles. Compared to a reference cluster that was representative of the underlying study population cough prevalence, three clusters with profiles characterized by highly adverse HAP indicators resulted in ORs of 1.72 (95% credible interval: 1.15, 2.60), 4.74 (2.88, 8.0), and 8.6 (3.9, 23.9). Conversely, at least two clusters of HAP indicator-profiles were protective compared to the reference cluster, despite the fact that these protective HAP indicator profiles used solid fuels for cooking in combination with an unimproved stove (cooking was performed predominantly outdoors in these protective clusters).

CONCLUSIONS

In addition to cooking fuel and type of cook stove, multiple HAP indicators were strongly associated with persistent cough in children. Bayesian profile regression revealed that the combination of HAP sources and HAP exposure-mitigating factors was driving risk of adverse cough associations in children, rather than any single HAP source at the home.

Modeling tails for collinear data with outliers in the English Longitudinal Study of Ageing: Quantile profile regression

Research has shown that high blood glucose levels are important predictors of incident diabetes. However, they are also strongly associated with other cardiometabolic risk factors such as high blood pressure, adiposity, and cholesterol, which are also highly correlated with one another. The aim of this analysis was to ascertain how these highly correlated cardiometabolic risk factors might be associated with high levels of blood glucose in older adults aged 50 or older from wave 2 of the English Longitudinal Study of Ageing (ELSA). Due to the high collinearity of predictor variables and our interest in extreme values of blood glucose we proposed a new method, called quantile profile regression, to answer this question. Profile regression, a Bayesian nonparametric model for clustering responses and covariates simultaneously, is a powerful tool to model the relationship between a response variable and covariates, but the standard approach of using a mixture of Gaussian distributions for the response model will not identify the underlying clusters correctly, particularly with outliers in the data or heavy tail distribution of the response. Therefore, we propose quantile profile regression to model the response variable with an asymmetric Laplace distribution, allowing us to model more accurately clusters that are asymmetric and predict more accurately for extreme values of the response variable and/or outliers. Our new method performs more accurately in simulations when compared to Normal profile regression approach as well as robustly when outliers are present in the data. We conclude with an analysis of the ELSA.

Accounting for measurement error to assess the effect of air pollution on omic signals

Studies on the effects of air pollution and more generally environmental exposures on health require measurements of pollutants, which are affected by measurement error. This is a cause of bias in the estimation of parameters relevant to the study and can lead to inaccurate conclusions when evaluating associations among pollutants, disease risk and biomarkers. Although the presence of measurement error in such studies has been recognized as a potential problem, it is rarely considered in applications and practical solutions are still lacking. In this work, we formulate Bayesian measurement error models and apply them to study the link between air pollution and omic signals. The data we use stem from the "Oxford Street II Study", a randomized crossover trial in which 60 volunteers walked for two hours in a traffic-free area (Hyde Park) and in a busy shopping street (Oxford Street) of London. Metabolomic measurements were made in each individual as well as air pollution measurements, in order to investigate the association between short-term exposure to traffic related air pollution and perturbation of metabolic pathways. We implemented error-corrected models in a classical framework and used the flexibility of Bayesian hierarchical models to account for dependencies among omic signals, as well as among different pollutants. Models were implemented using traditional Markov Chain Monte Carlo (MCMC) simulative methods as well as integrated Laplace approximation. The inclusion of a classical measurement error term resulted in variable estimates of the association between omic signals and traffic related air pollution measurements, where the direction of the bias was not predictable a priori. The models were successful in including and accounting for different correlation structures, both among omic signals and among different pollutant exposures. In general, more associations were identified when the correlation among omics and among pollutants were modeled, and their number increased when a measurement error term was additionally included in the multivariate models (particularly for the associations between metabolomics and NO2).

Risk of particulate matter on birth outcomes in relation to maternal socio-economic factors: a systematic review

A growing number of studies provide evidence of an association between exposure to maternal air pollution during pregnancy and adverse birth outcomes including low birth weight and preterm birth. Prevention of these health effects of air pollution is critical to reducing the adverse infant outcomes, which can have impacts throughout the life course. However, there is no consensus on whether the association between air pollution exposure and birth outcomes varies by maternal risk factors including demographic characteristics and socio-economic status. Such information is vital to understand potential environmental health disparities. Our search found 859 unique studies, of which 45 studies met our inclusion criteria (Jan. 2000- July. 2019). We systematically reviewed the 45 identified epidemiologic studies and summarized the results on effect modifications by maternal race/ethnicity, educational attainment, income, and area-level socio-economic status. We considered adverse birth outcomes of preterm birth, low birth weight, small for gestational age (SGA), and stillbirth. Suggestive evidence of higher risk of particulate matter in infants of African-American/black mothers than infants of other women was found for preterm birth and low birth weight. We found weak evidence that particulate matter risk was higher for infants of mothers with lower educational attainment for preterm birth and low birth weight. Due to the small study numbers, we were unable to conclude whether effect modification is present for income, occupation, and area-level socio-economic status, and additional research is needed. Furthermore, adverse birth outcomes such as SGA and stillbirth need more study to understand potential environmental justice issues regarding the impact of particulate matter exposure during pregnancy on birth outcomes.

Spatial analysis of drug poisoning deaths in the American west: A comparison study using profile regression to adjust for collinearity and spatial correlation

BACKGROUND

The USA has seen dramatic increases in drug poisoning deaths (DPD) recently. State-level rates have responded to federal and state initiatives, yet the counties with the highest rates are stable. Spatial analysis enables investigators to identify the highest risk counties and most important risk factors, although results are often confounded by spatial autocorrelation and multicollinearity.

METHODS

Profile regression (PR) is an integrated method for cluster and regression analysis, which adjusts for spatial-autocorrelation and multi-collinearity.

RESULTS

With PR, three clusters were identified in the Western USA with most of NM, NV and UT and several counties in AZ, CO, ID and WY being high-risk. Cluster analysis in a previous study only identified high-risk counties in northern CA, NM and NV. Elevation, suicide and LDS population were positively, and population density was negatively linked with DPD for PR and standard regression (SR) showing differences between the mountain west and coastal areas. Complex relationships between DPD and several variables were identified by PR which was not possible with SR.

CONCLUSIONS

Statistically principled methods like PR are needed for appropriate identification of the highest risk counties and important risk factors given the complex relationships with DPD. Funding for prevention, education and medical services should be targeted at rural, mountain communities in the west which have high %LDS and suicide rates. Counties with high %poverty and %Hispanic were also at high-risk. Individual-level studies are needed to confirm important risk factors in high-risk counties.

Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes

BACKGROUND

Adverse birth outcomes (ABO) such as prematurity and small for gestational age confer a high risk of mortality and morbidity. ABO have been linked to air pollution; however, relationships with mixtures of industrial emissions are poorly understood. The exploration of relationships between ABO and mixtures is complex when hundreds of chemicals are analyzed simultaneously, requiring the use of novel approaches.

OBJECTIVE

We aimed to generate robust hypotheses spatially linking mixtures and the occurrence of ABO using a spatial data mining algorithm and subsequent geographical and statistical analysis. The spatial data mining approach aimed to reduce data dimensionality and efficiently identify spatial associations between multiple chemicals and ABO.

METHODS

We discovered co-location patterns of mixtures and ABO in Alberta, Canada (2006-2012). An ad-hoc spatial data mining algorithm allowed the extraction of primary co-location patterns of 136 chemicals released into the air by 6279 industrial facilities (National Pollutant Release Inventory), wind-patterns from 182 stations, and 333,247 singleton live births at the maternal postal code at delivery (Alberta Perinatal Health Program), from which we identified cases of preterm birth, small for gestational age, and low birth weight at term. We selected secondary patterns using a lift ratio metric from ABO and non-ABO impacted by the same mixture. The relevance of the secondary patterns was estimated using logistic models (adjusted by socioeconomic status and ABO-related maternal factors) and a geographic-based assignment of maternal exposure to the mixtures as calculated by kernel density.

RESULTS

From 136 chemicals and three ABO, spatial data mining identified 1700 primary patterns from which five secondary patterns of three-chemical mixtures, including particulate matter, methyl-ethyl-ketone, xylene, carbon monoxide, 2-butoxyethanol, and n-butyl alcohol, were subsequently analyzed. The significance of the associations (odds ratio > 1) between the five mixtures and ABO provided statistical support for a new set of hypotheses.

CONCLUSION

This study demonstrated that, in complex research settings, spatial data mining followed by pattern selection and geographic and statistical analyses can catalyze future research on associations between air pollutant mixtures and adverse birth outcomes.

Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes

In this paper, we propose a stepwise forward selection algorithm for detecting the effects of a set of correlated exposures and their interactions on a health outcome of interest when the underlying relationship could potentially be nonlinear. Though the proposed method is very general, our application in this paper remains to be on analysis of multiple pollutants and their interactions. Simultaneous exposure to multiple environmental pollutants could affect human health in a multitude of complex ways. For understanding the health effects of multiple environmental exposures, it is often important to identify and estimate complex interactions among exposures. However, this issue becomes analytically challenging in the presence of potential nonlinearity in the outcome-exposure response surface and a set of correlated exposures. Through simulation studies and analyses of test datasets that were simulated as a part of a data challenge in multipollutant modeling organized by the National Institute of Environmental Health Sciences (http://www.niehs.nih.gov/about/events/pastmtg/2015/statistical/), we illustrate the advantages of our proposed method in comparison with existing alternative approaches. A particular strength of our method is that it demonstrates very low false positives across empirical studies. Our method is also used to analyze a dataset that was released from the Health Outcomes and Measurement of the Environment Study as a benchmark beta-tester dataset as a part of the same workshop.

Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression

PURPOSE OF REVIEW

The inter-correlated nature of exposure-based risk factors in environmental health studies makes it a challenge to determine their combined effect on health outcomes. As such, there has been much research of late regarding the development and utilization of methods in the field of multi-pollutant modeling. However, much of this work has focused on issues related to variable selection in a regression context, with the goal of identifying which exposures are the "bad actors" most responsible for affecting the health outcome of interest. However, the question addressed by these approaches does not necessarily represent the only or most important questions of interest in a multi-pollutant modeling context, where researchers may be interested in health effects from co-exposure patterns and in identifying subpopulations associated with patterns defined by different levels of constituent exposures.

RECENT FINDINGS

One approach to analyzing multi-pollutant data is to use a method known as Bayesian profile regression, which aids in identifying susceptible subpopulations associated with exposure mixtures defined by different levels of each exposure. Identification of exposure-level patterns that correspond to a location may provide a starting point for policy-based exposure reduction. Also, in a spatial context, identification of locations with the most health-relevant exposure-mixture profiles might provide further policy relevant information. In this brief report, we review and describe an approach that can be used to identify exposures in subpopulations or locations known as Bayesian profile regression. An example is provided in which we examine associations between air pollutants, an indicator of healthy food retailer availability, and indicators of poverty in Los Angeles County. A general tread suggesting that vulnerable individuals are more highly exposed and have limited access to healthy food retailers is observed, though the associations are complex and non-linear.

Personal care product use as a predictor of urinary concentrations of certain phthalates, parabens, and phenols in the HERMOSA study

Use of personal care products, such as makeup, soaps, and sunscreen, may expose adolescent girls to potential endocrine disruptors, including phthalates, parabens, and other phenols. We evaluated the relationship between recent self-reported personal care product use and concentrations for urinary metabolites of phthalates, parabens, triclosan, and benzophenone-3 (BP-3) in 100 Latina adolescents. Girls who reported using makeup every day vs. rarely/never had higher urinary concentrations of monoethyl phthalate (MEP) (102.2 ng/mL vs. 52.4 ng/mL, P-value: 0.04), methyl paraben (MP) (120.5 ng/mL vs. 13.4 ng/mL, P-value < 0.01), and propyl paraben (PP) (60.4 ng/mL vs. 2.9 ng/mL, P-value < 0.01). Girls who reported recent use of specific makeup products, including foundation, blush, and mascara, had higher urinary concentrations of MEP, mono-n-butyl phthalate (MBP), MP, and PP. Use of Colgate Total toothpaste was associated with 86.7% higher urinary triclosan concentrations. Use of sunscreen was associated with 57.8% higher urinary concentrations of BP-3. Our findings suggest that personal care product use is associated with higher exposure to certain phthalates, parabens, and other phenols in urine. This may be especially relevant in adolescent girls who have high use of personal care products during a period of important reproductive development.

Associations between multipollutant day types and select cardiorespiratory outcomes in Columbia, South Carolina, 2002 to 2013

Background

Health studies of air pollution are increasingly aiming to study associations between air pollutant mixtures and health.

Objective

Estimate associations between observed combinations of ambient air pollutants and select cardiorespiratory outcomes in Columbia, SC during 2002 to 2013.

Methods

We estimate associations using a two-stage approach. First, we identified a collection of observed pollutant combinations, which we define as multipollutant day types (MDTs), by applying a self-organizing map (SOM) to daily measures of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter ≤ 2.5 microns (PM_2.5). Then, overdispersed Poisson time-series models were used to estimate associations between MDTs and each outcome using a 'clean' MDT referent and controlling for long-term, seasonal, and day-of-the-week trends and meteorology. Outcomes included daily emergency department visits for asthma and upper respiratory infection (URI), and hospital admissions for congestive heart failure (CHF) and ischemic heart disease (IHD).

Results

We found that a number of MDTs were significantly and positively associated (point estimates ranged from~2-5%) with cardiorespiratory outcomes in Columbia when compared to days with low pollution. Estimated associations revealed that outcomes for asthma, URIs, and IHD increased 2-4% on warm, dry days experiencing elevated levels of O₃ and PM_2.5. We also found that cooler days with higher NO₂ pollution associated with increased asthma, CHF, and IHD outcomes (2-5%).

Conclusion

Our analysis continues support for using self-organizing maps to develop multipollutant exposure metrics and further illustrates how such metrics can be applied to explore associations between pertinent pollutant combinations and health.

Associations between maternal residential proximity to air emissions from industrial facilities and low birth weight in Texas, USA

BACKGROUND

Most previous studies examining associations between maternal exposures to air pollutants during pregnancy and low birth weight (LBW) in offspring focused on criteria air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO, and Pb). The relationship between non-criteria air pollutants and LBW is understudied and requires greater coverage.

OBJECTIVES

This study investigated associations between maternal residential exposure to industrial air pollutants during pregnancy and LBW in offspring.

METHODS

This study used a case-control study design that included 94,106 term LBW cases and 376,424 controls. It covered 78 air pollutants common to both the Toxics Release Inventory (TRI) and ground air quality monitoring databases in Texas during 1996-2008. A modified version of the Emission Weighted Proximity Model (EWPM), calibrated with ground monitoring data, was used to estimate maternal residential exposure to industrial air pollutants during pregnancy. Binary logistic regression analyses were performed to calculate odds ratios (ORs) reflecting the associations of maternal exposure to industrial air pollutants and LBW in offspring, adjusted for child's sex, gestational weeks, maternal age, education, race/ethnicity, marital status, prenatal care, tobacco use during pregnancy, public health region of maternal residence, and year of birth. In addition, the Bonferroni correction for multiple comparisons was applied to the results of logistic regression analysis.

RESULTS

Relative to the non-exposed reference group, maternal residential exposure to benzene (adjusted odds ratio (aOR) 1.06, 95% confidence interval (CI) 1.04, 1.08), benzo(g,h,i)perylene (aOR 1.04, 95% CI 1.02, 1.07), cumene (aOR 1.05, 95% CI 1.03, 1.07), cyclohexane (aOR 1.04, 95% CI 1.02, 1.07), dichloromethane (aOR 1.04, 95% CI 1.03, 1.07), ethylbenzene (aOR 1.05, 95% CI 1.03, 1.06), ethylene (aOR 1.06, 95% CI 1.03, 1.09), mercury (aOR 1.04, 95% CI 1.02, 1.07), naphthalene (aOR 1.03, 95% CI 1.01, 1.05), n-hexane (aOR 1.06, 95% CI 1.04, 1.08), propylene (aOR 1.06, 95% CI 1.03, 1.10), styrene (aOR 1.06, 95% CI 1.04, 1.08), toluene (aOR 1.05, 95% CI 1.03, 1.07), and zinc (fume or dust) (aOR 1.10, 95% CI 1.06, 1.13) was found to have significantly higher odds of LBW in offspring. When the estimated exposures were categorized into four different groups (zero, low, medium, and high) in the analysis, eleven of the fourteen air pollutants, with the exception of benzo(g,h,i)perylene, ethylene, and propylene, remained as significant risk factors.

CONCLUSIONS

Results indicate that maternal residential proximity to industrial facilities emitting any of the fourteen pollutants identified by this study during pregnancy may be associated with LBW in offspring. With the exception of benzene, ethylbenzene, toluene, and zinc, the rest of the fourteen air pollutants are identified as LBW risk factors for the first time by this study. Further epidemiological, biological, and toxicological studies are suggested to verify the findings from this study.

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.

Industrial air pollution and low birth weight: a case-control study in Texas, USA

Many studies have investigated associations between maternal residential exposures to air pollutants and low birth weight (LBW) in offspring. However, most studies focused on the criteria air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO, and Pb), and only a few studies examined the potential impact of other air pollutants on LBW. This study investigated associations between maternal residential exposure to industrial air emissions of 449 toxics release inventory (TRI) chemicals and LBW in offspring using a case-control study design based on a large dataset consisting of 94,106 LBW cases and 376,424 controls in Texas from 1996 to 2008. Maternal residential exposure to chemicals was estimated using a modified version of the emission-weighted proximity model (EWPM). The model takes into account reported quantities of annual air emission from industrial facilities and the distances between the locations of industrial facilities and maternal residence locations. Binary logistic regression was used to compute odds ratios measuring the association between maternal exposure to different TRI chemicals and LBW in offspring. Odds ratios were adjusted for child's sex, birth year, gestational length, maternal age, education, race/ethnicity, and public health region of maternal residence. Among the ten chemicals selected for a complete analysis, maternal residential exposures to five TRI chemicals were positively associated with LBW in offspring. These five chemicals include acetamide (adjusted odds ratio [aOR] 2.29, 95% confidence interval [CI] 1.24, 4.20), p-phenylenediamine (aOR 1.63, 95% CI 1.18, 2.25), 2,2-dichloro-1,1,1-trifluoroethane (aOR 1.41, 95% CI 1.20, 1.66), tributyltin methacrylate (aOR 1.20, 95% CI 1.06, 1.36), and 1,1,1-trichloroethane (aOR 1.11, 95% CI 1.03, 1.20). These findings suggest that maternal residential proximity to industrial air emissions of some chemicals during pregnancy may be associated with LBW in offspring.

Prenatal exposure to air pollution and intergenerational economic mobility: Evidence from U.S. county birth cohorts

New estimates reveal intergenerational economic mobility varies substantially across U.S. counties. The potential role of local environmental health exposures in structuring mobility outcomes has been thus far unexamined, despite mounting evidence that early life exposure to environmental pollutants has lasting impacts for individual human capital development and labor market performance. This study aims to fill this gap by estimating the impact of exposure to air pollution in the birth year on the average intergenerational mobility outcomes of children from low-income families as measured in adulthood. We do so by linking measures of intergenerational economic mobility for U.S. county-cohorts born between 1980 and 1986 to the county average concentration of total suspended particulates (TSP) in the birth year. We then estimate multivariate linear regression models that adjust for birth-cohort fixed effects, county-fixed effects and time-varying county-level covariates to address potential confounding. We find higher levels of TSP in birth year is associated with less upward economic mobility for children from low-income families: a one standard deviation increase in TSP levels is associated with a 0.14 point reduction in average income percentile ranking as measured in adulthood. Notably, we find no association for children from high income families. Our findings indicate early life exposure to air pollution may reduce the prospects children from low-income families will achieve upward economic mobility and suggest variation in environmental quality may help explain observed variation in mobility outcomes.

Spatial clustering of metal and metalloid mixtures in unregulated water sources on the Navajo Nation - Arizona, New Mexico, and Utah, USA

Contaminant mixtures are identified regularly in public and private drinking water supplies throughout the United States; however, the complex and often correlated nature of mixtures makes identification of relevant combinations challenging. This study employed a Bayesian clustering method to identify subgroups of water sources with similar metal and metalloid profiles. Additionally, a spatial scan statistic assessed spatial clustering of these subgroups and a human health metric was applied to investigate potential for human toxicity. These methods were applied to a dataset comprised of metal and metalloid measurements from unregulated water sources located on the Navajo Nation, in the southwest United States. Results indicated distinct subgroups of water sources with similar contaminant profiles and that some of these subgroups were spatially clustered. Several profiles had metal and metalloid concentrations that may have potential for human toxicity including arsenic, uranium, lead, manganese, and selenium. This approach may be useful for identifying mixtures in water sources, spatially evaluating the clusters, and help inform toxicological research investigating mixtures.

The gains in life expectancy by ambient PM2.5 pollution reductions in localities in Nigeria

Global burden of disease estimates reveal that people in Nigeria are living shorter lifespan than the regional or global average life expectancy. Ambient air pollution is a top risk factor responsible for the reduced longevity. But, the magnitude of the loss or the gains in longevity accruing from the pollution reductions, which are capable of driving mitigation interventions in Nigeria, remain unknown. Thus, we estimate the loss, and the gains in longevity resulting from ambient PM_2.5 pollution reductions at the local sub-national level using life table approach. Surface average PM_2.5 concentration datasets covering Nigeria with spatial resolution of ∼1 km were obtained from the global gridded concentration fields, and combined with ∼1 km gridded population of the world (GPWv4), and global administrative unit layers (GAUL) for territorial boundaries classification. We estimate the loss or gains in longevity using population-weighted average pollution level and baseline mortality data for cardiopulmonary disease and lung cancer in adults ≥25 years and for respiratory infection in children under 5. As at 2015, there are six "highly polluted", thirty "polluted" and one "moderately polluted" States in Nigeria. People residing in these States lose ∼3.8-4.0, 3.0-3.6 and 2.7 years of life expectancy, respectively, due to the pollution exposure. But, assuming interventions achieve global air quality guideline of 10 μg/m³, longevity would increase by 2.6-2.9, 1.9-2.5 and 1.6 years for people in the State-categories, respectively. The longevity gains are indeed high, but to achieve them, mitigation interventions should target emission sources having the highest population exposures.

Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty

The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects.

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children

Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was -1.19 points (95% CI -1.94, -0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other populations, exposures and outcomes to generate hypotheses within high-dimensional exposure data.

Asian Americans and disproportionate exposure to carcinogenic hazardous air pollutants: A national study

Studies have demonstrated disparate exposures to carcinogenic hazardous air pollutants (HAPs) in neighborhoods with high densities of Black and Hispanic residents in the US. Asians are the fastest growing racial/ethnic group in the US, yet they have been underemphasized in previous studies of environmental health and injustice. This cross-sectional study investigated possible disparities in residential exposure to carcinogenic HAPs among Asian Americans, including Asian American subgroups in the US (including all 50 states and the District of Columbia, n = 71,208 US census tracts) using National Air Toxics Assessment and US Census data. In an unadjusted analysis, Chinese and Korean Americans experience the highest mean cancer risks from HAPs, followed by Blacks. The aggregated Asian category ranks just below Blacks and above Hispanics, in terms of carcinogenic HAP risk. Multivariate models adjusting for socioeconomic status, population density, urban location, and geographic clustering show that an increase in proportion of Asian residents in census tracts is associated with significantly greater cancer risk from HAPs. Neighborhoods with higher proportions (as opposed to lower proportions) of Chinese, Korean, and South Asian residents have significantly greater cancer risk burdens relative to Whites. Tracts with higher concentrations of Asians speaking a non-English language and Asians that are US-born have significantly greater cancer risk burdens. Asian Americans experience substantial residential exposure to carcinogenic HAPs in US census tracts and in the US more generally.

Association between Pesticide Profiles Used on Agricultural Fields near Maternal Residences during Pregnancy and IQ at Age 7 Years

We previously showed that potential prenatal exposure to agricultural pesticides was associated with adverse neurodevelopmental outcomes in children, yet the effects of joint exposure to multiple pesticides is poorly understood. In this paper, we investigate associations between the joint distribution of agricultural use patterns of multiple pesticides (denoted as "pesticide profiles") applied near maternal residences during pregnancy and Full-Scale Intelligence Quotient (FSIQ) at 7 years of age. Among a cohort of children residing in California's Salinas Valley, we used Pesticide Use Report (PUR) data to characterize potential exposure from use within 1 km of maternal residences during pregnancy for 15 potentially neurotoxic pesticides from five different chemical classes. We used Bayesian profile regression (BPR) to examine associations between clustered pesticide profiles and deficits in childhood FSIQ. BPR identified eight distinct clusters of prenatal pesticide profiles. Two of the pesticide profile clusters exhibited some of the highest cumulative pesticide use levels and were associated with deficits in adjusted FSIQ of -6.9 (95% credible interval: -11.3, -2.2) and -6.4 (95% credible interval: -13.1, 0.49), respectively, when compared with the pesticide profile cluster that showed the lowest level of pesticides use. Although maternal residence during pregnancy near high agricultural use of multiple neurotoxic pesticides was associated with FSIQ deficit, the magnitude of the associations showed potential for sub-additive effects. Epidemiologic analysis of pesticides and their potential health effects can benefit from a multi-pollutant approach to analysis.

The health burden and economic costs averted by ambient PM2.5 pollution reductions in Nagpur, India

National estimates of the health and economic burdens of exposure to ambient fine particulate matter (PM_2.5) in India reveal substantial impacts. This information, often lacking at the local level, can justify and drive mitigation interventions. Here, we assess the health and economic gains resulting from attainment of WHO guidelines for PM_2.5 concentrations - including interim target 2 (IT-2), interim target 3 (IT-3), and the WHO air quality guideline (AQG) - in Nagpur district to inform policy decision making for mitigation. We conducted a detailed assessment of concentrations of PM_2.5 in 9 areas, covering urban, peri-urban and rural environments, from February 2013 to June 2014. We used a combination of hazard and survival analyses based on the life table method to calculate attributed annual number of premature deaths and disability-adjusted life years (DALYs) for five health outcomes linked to PM_2.5 exposure: acute lower respiratory infection for children <5years, ischemic heart disease, chronic obstructive pulmonary disease, stroke and lung cancer in adults ≥25years. We used GBD 2013 data on deaths and DALYs for these diseases. We calculated averted deaths, DALYs and economic loss resulting from planned reductions in average PM_2.5 concentration from current level to IT-2, IT-3 and AQG by the years 2023, 2033 and 2043, respectively. The economic cost for premature mortality was estimated as the product of attributed deaths and value of statistical life for India, while morbidity was assumed to be 10% of the mortality cost. The annual average PM_2.5 concentration in Nagpur district is 34±17μgm^-3 and results in 3.3 (95% confidence interval [CI]: 2.6, 4.2) thousand premature deaths and 91 (95% CI: 68, 116) thousand DALYs in 2013 with economic loss of USD 2.2 (95% CI: 1.7, 2.8) billion in that year. It is estimated that interventions that achieve IT-2, IT-3 and AQG by 2023, 2033 and 2043, would avert, respectively, 15, 30 and 36%, of the attributed health and economic loss in those years, translating into an impressively large health and economic gain. To achieve this, we recommend an exposure-integrated source reduction approach.

Spatial Determinants of Ebola Virus Disease Risk for the West African Epidemic

INTRODUCTION

Although many studies have investigated the probability of Ebola virus disease (EVD) outbreaks while other studies have simulated the size and speed of EVD outbreaks, few have investigated the environmental and population-level predictors of Ebola transmission once an outbreak is underway. Identifying strong predictors of transmission could help guide and target limited public health resources during an EVD outbreak. We examined several environmental and population-level demographic predictors of EVD risk from the West African epidemic.

METHODS

We obtained district-level estimates from the World Health Organization EVD case data, demographic indicators obtained from the Demographic and Health surveys, and satellite-derived temperature, rainfall, and land cover estimates. A Bayesian hierarchical Poisson model was used to estimate EVD risk and to evaluate the spatial variability explained by the selected predictors.

RESULTS

 We found that districts had greater risk of EVD with increasing proportion of households not possessing a radio (RR 2.79, 0.90-8.78; RR 4.23, 1.16-15.93), increasing rainfall (RR 2.18; 0.66-7.20; 5.34, 1.20-23.90), and urban land cover (RR 4.87, 1.56-15.40; RR 5.74, 1.68-19.67).

DISCUSSION

 The finding of radio ownership and reduced EVD transmission risk suggests that the use of radio messaging for control and prevention purposes may have been crucial in reducing the EVD transmission risk in certain districts, although this association requires further study. Future research should examine the etiologic relationships between the identified risk factors and human-to-human transmission of EVD with a focus on factors related to population mobility and healthcare accessibility, which are critical features of epidemic propagation and control.

Associations of gestational and early life exposures to ambient air pollution with childhood respiratory diseases in Shanghai, China: A retrospective cohort study

BACKGROUND

Associations of ambient air pollutants with respiratory health are inconsistent.

OBJECTIVES

We analyzed the associations of gestational and early life exposures to air pollutants with doctor-diagnosed asthma, allergic rhinitis, and pneumonia in children.

METHODS

We selected 3358 preschool children who did not alter residences after birth from a cross-sectional study in 2011-2012 in Shanghai, China. Parents reported children's respiratory health history, home environment, and family lifestyle behaviors. We collected daily concentrations of sulphur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter with an aerodynamic diameter ≤10μm (PM10) during the child's total lifetime (2006-2012) for each district where the children lived. We analyzed the associations using logistic regression models.

RESULTS

After adjusting for covariates and the other studied pollutants, we found that exposure to NO2 (increment of 20μg/m(3)) during the first year of life was significantly associated with asthma [odds ratio (OR)=1.77; 95% confidence interval (CI): 1.29-2.43] and allergic rhinitis (OR=1.67; 95% CI: 1.07-2.61). Exposure to NO2 during gestation, the first two and three years, and over total lifetimewas all consistently associated with increased odds of allergic rhinitis. Quartiles of NO2 concentration during different exposure periods showed a slight dose-response relationship with the studied diseases. These diseases had significant associations with pollutant mixtures that included NO2, but had no significant association with exposures to SO2 and PM10 individually or in mixtures.

CONCLUSIONS

Gestational and early life exposures to ambient NO2 are risk factors for childhood respiratory diseases.