Quantile regression analysis of the socioeconomic inequalities in air pollution and birth weight

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.

Estimating birthweight reduction attributable to maternal ozone exposure in low- and middle-income countries

The effect of O3 on birthweight in low- and middle-income countries (LMICs) remains unknown. A multicenter epidemiological study was conducted to evaluate the association between maternal peak-season O3 exposure and birthweight, using 697,148 singleton newborns obtained in 54 LMICs between 2003 and 2019. We estimated the birthweight reduction attributable to peak-season O3 exposure in 123 LMICs based on a nonlinear exposure-response function (ERF). With every 10-part per billion increment in O3 concentration, we found a reduction in birthweight of 19.9 g [95% confidence interval (CI): 14.8 to 24.9 g]. The nonlinear ERF had a monotonic decreasing curve, and no safe O3 exposure threshold was identified. The mean reduction in birthweight reduction attributable to O3 across the 123 LMICs was 43.8 g (95% CI: 30.5 to 54.3 g) in 2019. The reduction in O3-related birthweight was greatest in countries in South Asia, the Middle East, and North Africa. Effective O3 pollution control policies have the potential to substantially improve infant health.

Joint effects of prenatal exposure to air pollution and pregnancy-related anxiety on birth weight: A prospective birth cohort study in Ma'anshan, China

BACKGROUND

A growing number of studies have shown that prenatal exposure to chemical and non-chemical stressors has effects on fetal growth. The co-exposure of both better reflects real-life exposure patterns. However, no studies have included air pollutants and pregnancy-related anxiety (PrA) as mixtures in the analysis.

METHOD

Using the birth cohort study method, 576 mother-child pairs were included in the Ma'anshan Maternal and Child Health Hospital. Evaluate the exposure levels of six air pollutants during pregnancy using inverse distance weighting (IDW) based on the pregnant woman's residential address and air pollution data from monitoring stations. Prenatal anxiety levels were assessed using the PrA Questionnaire. Generalized linear regression (GLR), quantile g-computation (QgC) and bayesian kernel machine regression (BKMR) were used to assess the independent or combined effects of air pollutants and PrA on birth weight for gestational age z-score (BWz).

RESULT

The results of GLR indicate that the correlation between the six air pollutants and PrA with BWz varies depending on the different stages of pregnancy and pollutants. The QgC shows that during trimester 1, when air pollutants and PrA are considered as a whole exposure, an increase of one quartile is significantly negatively correlated with BWz. The BKMR similarly indicates that during trimester 1, the combined exposure of air pollutants and PrA is moderately correlated with a decrease in BWz.

CONCLUSION

Using the method of analyzing mixed exposures, we found that during pregnancy, the combined exposure of air pollutants and PrA, particularly during trimester 1, is associated with BWz decrease. This supports the view that prenatal exposure to chemical and non-chemical stressors has an impact on fetal growth.

Prenatal PM2.5 exposure increases the risk of adverse pregnancy outcomes: evidence from meta-analysis of cohort studies

Adverse pregnancy outcomes (APOs) are a significant cause of fetal death. A wide range of maternal psychological, social, and environmental factors may contribute to these outcomes. Mounting epidemiological studies have indicated that PM2.5 may result in these unfavorable consequences. Previously published meta-analyses have been updated and extended. Cohort studies were searched from three databases (up to July 24, 2023), and their quality was assessed by Newcastle-Ottawa Scale (NOS). Publication bias was examined by Egger's test and funnel plot. Despite a large number of studies showing similar results, the inconsistencies between these findings require careful generalization before concluding. This meta-analysis included 67 cohort studies from 20 countries, and the findings revealed that maternal PM2.5 exposure and five APOs were correlated significantly throughout pregnancy: preterm birth (PTB) (RR = 1.05; 95% CI: 1.03, 1.07); low birth weight (LBW) (RR = 1.02; 95% CI: 1.01, 1.04); small for gestational age (SGA) (RR = 1.03; 95% CI: 1.01, 1.04); stillbirth (RR = 1.24; 95% CI: 1.06, 1.45); and change in birthweight (weight change = -6.82 g; 95% CI: -11.39, -2.25). A positive association was found between APOs and PM2.5 exposure in this meta-analysis, and the degree of increased risk of APOs varied due to different gestation periods. Therefore, it is necessary to protect pregnant women at specific times.

Gestational Glucose Intolerance and Birth Weight-Related Complications

OBJECTIVE

To evaluate the risks of large-for-gestational-age birth weight (LGA) and birth weight-related complications in pregnant individuals with gestational glucose intolerance, an abnormal screening glucose loading test result without meeting gestational diabetes mellitus (GDM) criteria.

METHODS

In a retrospective cohort study of 46,989 individuals with singleton pregnancies who delivered after 28 weeks of gestation, those with glucose loading test results less than 140 mg/dL were classified as having normal glucose tolerance. Those with glucose loading test results of 140 mg/dL or higher and fewer than two abnormal values on a 3-hour 100-g oral glucose tolerance test (OGTT) were classified as having gestational glucose intolerance. Those with two or more abnormal OGTT values were classified as having GDM. We hypothesized that gestational glucose intolerance would be associated with higher odds of LGA (birth weight greater than the 90th percentile for gestational age and sex). We used generalized estimating equations to examine the odds of LGA in pregnant individuals with gestational glucose intolerance compared with those with normal glucose tolerance, after adjustment for age, body mass index, parity, health insurance, race and ethnicity, and marital status. In addition, we investigated differences in birth weight-related adverse pregnancy outcomes.

RESULTS

Large for gestational age was present in 7.8% of 39,685 pregnant individuals with normal glucose tolerance, 9.5% of 4,155 pregnant individuals with gestational glucose intolerance and normal OGTT, 14.5% of 1,438 pregnant individuals with gestational glucose intolerance and one abnormal OGTT value, and 16.0% of 1,711 pregnant individuals with GDM. The adjusted odds of LGA were higher in pregnant individuals with gestational glucose intolerance than in those with normal glucose tolerance overall (adjusted odds ratio [aOR] 1.35, 95% CI 1.23-1.49, P <.001). When compared separately with pregnant individuals with normal glucose tolerance, those with either gestational glucose intolerance subtype had higher adjusted LGA odds (gestational glucose intolerance with normal OGTT aOR 1.21, 95% CI 1.08-1.35, P <.001; gestational glucose intolerance with one abnormal OGTT value aOR 1.77, 95% CI 1.52-2.08, P <.001). The odds of birth weight-related adverse outcomes (including cesarean delivery, severe perineal lacerations, and shoulder dystocia or clavicular fracture) were higher in pregnant individuals with gestational glucose intolerance with one abnormal OGTT value than in those with normal glucose tolerance.

CONCLUSION

Gestational glucose intolerance in pregnancy is associated with birth weight-related adverse pregnancy outcomes. Glucose lowering should be investigated as a strategy for lowering the risk of these outcomes in this group.

Before the first breath: why ambient air pollution and climate change should matter to neonatal-perinatal providers

Common outdoor air pollutants present threats to fetal and neonatal health, placing neonatal-perinatal clinical specialists in an important role for harm reduction through patient counseling and advocacy. Climate change is intertwined with air pollution and influences air quality. There is increasing evidence demonstrating the unique vulnerability in the development of adverse health consequences from exposures during the preconception, prenatal, and early postnatal periods, as well as promising indications that policies aimed at addressing these toxicants have improved birth outcomes. Advocacy by neonatal-perinatal providers articulating the potential impact of pollutants on newborns and mothers is essential to promoting improvements in air quality and reducing exposures. The goal of this review is to update neonatal-perinatal clinical specialists on the key ambient air pollutants of concern, their sources and health effects, and to outline strategies for protecting patients and communities from documented adverse health consequences.

Combined exposure to PM2.5 and PM10 in reductions of physiological development among preterm birth: a retrospective study from 2014 to 2017 in China

Background

Preterm birth (PTB) has been linked with ambient particulate matter (PM) exposure. However, data are limited between physiological development of PTB and PM exposure.

Methods

Trimester and season-specific PM exposure including PM2.5 and PM10 was collected from Jiaxing between January 2014 and December 2017. Information about parents and 3,054 PTB (gestational age < 37 weeks) outcomes such as weight (g), head circumference (cm), chest circumference (cm), height (cm) and Apgar 5 score were obtained from birth records. We used generalized linear models to assess the relationship between PTB physiological developmental indices and PM2.5, PM10 and their combined exposures. A binary logistic regression model was performed to assess the association between exposures and low birth weight (LBW, < 2,500 g).

Results

Results showed that there were 75.5% of low birth weight (LBW) infants in PTB. Decreased PM2.5 and PM10 levels were found in Jiaxing from 2014 to 2017, with a higher PM10 level than PM2.5 each year. During the entire pregnancy, the highest median concentration of PM2.5 and PM10 was in winter (61.65 ± 0.24 vs. 91.65 ± 0.29 μg/m3) followed by autumn, spring and summer, with statistical differences in trimester-specific stages. After adjusting for several potential factors, we found a 10 μg/m3 increase in joint exposure of PM2.5 and PM10 during the entire pregnancy associated with reduced 0.02 week (95%CI: -0.05, -0.01) in gestational age, 7.9 g (95%CI: -13.71, -2.28) in birth weight, 0.8 cm in height (95%CI: -0.16, -0.02), 0.05 cm (95%CI: -0.08, - 0.01) in head circumference, and 0.3 (95%CI: -0.04, -0.02) in Apgar 5 score, except for the chest circumference. Trimester-specific exposure of PM2.5 and PM10 sometimes showed an opposite effect on Additionally, PM2.5 (OR = 1.37, 95%CI: 1.11, 1.68) was correlated with LBW.

Conclusion

Findings in this study suggest a combined impact of fine particulate matter exposure on neonatal development, which adds to the current understanding of PTB risk and health.

Factors affecting Hemoglobin A1c in the longitudinal study of the Iranian population using mixed quantile regression

Diabetes, a major non-communicable disease, presents challenges for healthcare systems worldwide. Traditional regression models focus on mean effects, but factors can impact the entire distribution of responses over time. Linear mixed quantile regression models (LQMMs) address this issue. A study involving 2791 diabetic patients in Iran explored the relationship between Hemoglobin A1c (HbA1c) levels and factors such as age, sex, body mass index (BMI), disease duration, cholesterol, triglycerides, ischemic heart disease, and treatments (insulin, oral anti-diabetic drugs, and combination). LQMM analysis examined the association between HbA1c and the explanatory variables. Associations between cholesterol, triglycerides, ischemic heart disease (IHD), insulin, oral anti-diabetic drugs (OADs), a combination of OADs and insulin, and HbA1c levels exhibited varying degrees of correlation across all quantiles (p < 0.05), demonstrating a positive effect. While BMI did not display significant effects in the lower quantiles (p > 0.05), it was found to be significant in the higher quantiles (p < 0.05). The impact of disease duration differed between the low and high quantiles (specifically at the quantiles of 5, 50, and 75; p < 0.05). Age was discovered to have an association with HbA1c in the higher quantiles (specifically at the quantiles of 50, 75, and 95; p < 0.05). The findings reveal important associations and shed light on how these relationships may vary across different quantiles and over time. These insights can serve as guidance for devising effective strategies to manage and monitor HbA1c levels.

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

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

A nationwide study of maternal exposure to ambient ozone and term birth weight in the United States

BACKGROUND

Maternal exposure to ozone (O₃) may cause systemic inflammation and oxidative stress and contribute to fetal growth restriction. We sought to estimate the association between maternal exposure to O₃ and term birth weight and term small for gestational age (SGA) in the United States (US).

METHODS

We conducted a nationwide study including 2,179,040 live term singleton births that occurred across 453 populous counties in the contiguous US in 2002. Daily county-level concentrations of O₃ data were estimated using a Bayesian fusion model. We used linear regression to estimate the association between O₃ exposure and term birth weight and logistic regression to estimate the association between O₃ exposure and term SGA during each trimester of the pregnancy and the entire pregnancy after adjusting for maternal characteristics, infant sex, season of conception, ambient temperature, county poverty rate, and census region. We additionally used distributed lag models to identify the critical exposure windows by estimating the monthly and weekly associations.

RESULTS

A 10 parts per billion (ppb) increase in O₃ over the entire pregnancy was associated with a lower term birth weight (-7.6 g; 95 % CI: -8.8 g, -6.4 g) and increased risk of SGA (odds ratio = 1.030; 95 % CI: 1.020, 1.040). The identified critical exposure windows were the 13th- 25th and 32nd -37th gestational weeks for term birth weight and 13th- 25th for term SGA. We found the association was more pronounced among mothers who were non-Hispanic Black, unmarried, or had lower education level.

CONCLUSIONS

Among US singleton term births, maternal exposure to O₃ was associated with lower rates of fetal growth, and the 13th- 25th gestational weeks were the identified critical exposure windows.

Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina

Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler ("downscaler") model, which provides spatial predictions of daily concentrations of fine particulate matter (PM2.5) and ozone (O3) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM2.5 and lower levels of O3. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM2.5 and O3 between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM2.5 and O3, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PEPM2.5 and PEO3) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM2.5 and O3 levels decreased and O3 predictions became more reliable. However, the predictive uncertainty for PM2.5 has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM2.5. In contrast, racial isolation was associated with a greater decline in the reliability of PM2.5 predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O3 within the same time frame.

Maternal exposure to ambient PM2.5 and term birth weight: A systematic review and meta-analysis of effect estimates

Effect estimates of prenatal exposure to ambient PM_2.5 on change in grams (β) of birth weight among term births (≥37 weeks of gestation; term birth weight, TBW) vary widely across studies. We present the first systematic review and meta-analysis of evidence regarding these associations. Sixty-two studies met the eligibility criteria for this review, and 31 studies were included in the meta-analysis. Random-effects meta-analysis was used to assess the quantitative relationships. Subgroup analyses were performed to gain insight into heterogeneity derived from exposure assessment methods (grouped by land use regression [LUR]-models, aerosol optical depth [AOD]-based models, interpolation/dispersion/Bayesian models, and data from monitoring stations), study regions, and concentrations of PM_2.5 exposure. The overall pooled estimate involving 23,925,941 newborns showed that TBW was negatively associated with PM_2.5 exposure (per 10 μg/m³ increment) during the entire pregnancy (β = -16.54 g), but with high heterogeneity (I² = 95.6%). The effect estimate in the LUR-models subgroup (β = -16.77 g) was the closest to the overall estimate and with less heterogeneity (I² = 18.3%) than in the other subgroups of AOD-based models (β = -41.58 g; I² = 95.6%), interpolation/dispersion models (β = -10.78 g; I² = 86.6%), and data from monitoring stations (β = -11.53 g; I² = 97.3%). Even PM_2.5 exposure levels of lower than 10 μg/m³ (the WHO air quality guideline value) had adverse effects on TBW. The LUR-models subgroup was the only subgroup that obtained similar significant of negative associations during the three trimesters as the overall trimester-specific analyses. In conclusion, TBW was negatively associated with maternal PM_2.5 exposures during the entire pregnancy and each trimester. More studies based on relatively standardized exposure assessment methods need to be conducted to further understand the precise susceptible exposure time windows and potential mechanisms.

Association between ambient air pollution and perceived stress in pregnant women

Air pollution may influence prenatal maternal stress, but research evidence is scarce. Using data from a prospective cohort study conducted on pregnant women (n = 2153), we explored the association between air pollution and perceived stress, which was assessed using the 14-item Perceived Stress Scale (PSS), among pregnant women. Average exposures to particulate matter with an aerodynamic diameter of < 2.5 µm (PM_2.5) or < 10 µm (PM₁₀), nitrogen dioxide (NO₂), and ozone (O₃) for each trimester and the entire pregnancy were estimated at maternal residential addresses using land-use regression models. Linear regression models were applied to estimate associations between PSS scores and exposures to each air pollutant. After adjustment for potential confounders, interquartile-range (IQR) increases in whole pregnancy exposures to PM_2.5, PM₁₀, and O₃ in the third trimester were associated with 0.37 (95% confidence interval [CI] 0.01, 0.74), 0.54 (95% CI 0.11, 0.97), and 0.30 (95% CI 0.07, 0.54) point increases in prenatal PSS scores, respectively. Furthermore, these associations were more evident in women with child-bearing age and a lower level of education. Also, the association between PSS scores and PM₁₀ was stronger in the spring. Our findings support the relationship between air pollution and prenatal maternal stress.

Gestational exposures to outdoor air pollutants in relation to low birth weight: A retrospective observational study

Findings for impacts of outdoor air pollutants on birth outcomes were controversial. We performed a retrospective observational study in 2527 preschoolers of Shanghai, China and investigated associations of duration-averaged concentrations of outdoor sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter with an aerodynamic diameter ≤ 10 μm (PM₁₀) in different months and trimesters of gestation, with preterm birth (PB), low birth weight (LBW), term low birth weight (T-LBW), and small for gestational age (SGA). Daily concentrations of outdoor air pollutants were collected in each residence-located district. Parents reported health information. In the multivariate logistic regression analyses, exposures to outdoor NO₂ were consistently associated with the higher odds of LBW and T-LBW. These associations were generally stronger for early months than for later months of the gestation. Adjusted odds ratios generally were larger in multi-pollutant model than in single-pollutant model. Exposure to NO₂ in the first month of the gestation was significantly associated with T-LBW (adjusted OR, 95%CI: 1.91, 1.02-3.58 for increment of interquartile range (18.5 μg/m³); p-value = 0.044) in multi-pollutant model. This association was stronger in girls, renters, and children whose mothers ≥30 years-old, with household dampness-related exposures, and with parental smoking during pregnancy. Our results indicate that exposure to NO₂ during gestation perhaps is a risk factor for LBW and T-LBW, and effects of NO₂ exposures could be greater during early periods than during later periods of gestation.