A Comprehensive Review on Social Inequalities and Pregnancy Outcome-Identification of Relevant Pathways and Mechanisms

Scientific literature tends to support the idea that the pregnancy and health status of fetuses and newborns can be affected by maternal, parental, and contextual characteristics. In addition, a growing body of evidence reports that social determinants, measured at individual and/or aggregated level(s), play a crucial role in fetal and newborn health. Numerous studies have found social factors (including maternal age and education, marital status, pregnancy intention, and socioeconomic status) to be linked to poor birth outcomes. Several have also suggested that beyond individual and contextual social characteristics, living environment and conditions (or "neighborhood") emerge as important determinants in health inequalities, particularly for pregnant women. Using a comprehensive review, we present a conceptual framework based on the work of both the Commission on Social Determinants of Health and the World Health Organization (WHO), aimed at describing the various pathways through which social characteristics can affect both pregnancy and fetal health, with a focus on the structural social determinants (such as socioeconomic and political context) that influence social position, as well as on intermediary determinants. We also suggest that social position may influence more specific intermediary health determinants; individuals may, on the basis of their social position, experience differences in environmental exposure and vulnerability to health-compromising living conditions. Our model highlights the fact that adverse birth outcomes, which inevitably lead to health inequity, may, in turn, affect the individual social position. In order to address both the inequalities that begin in utero and the disparities observed at birth, it is important for interventions to target various unhealthy behaviors and psychosocial conditions in early pregnancy. Health policy must, then, support: (i) midwifery availability and accessibility and (ii) enhanced multidisciplinary support for deprived pregnant women.

Susceptibility of individuals with chronic obstructive pulmonary disease to respiratory inflammation associated with short-term exposure to ambient air pollution: A panel study in Beijing

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide. There is no clear evidence of whether COPD patients are more susceptible to respiratory inflammation associated with short-term exposure to air pollutants than those without COPD.

OBJECTIVES

This study directly compared air pollutant-associated respiratory inflammation between COPD patients and healthy controls.

METHODS

This study is based on the COPDB panel study (COPD in Beijing). Fractional exhaled nitric oxide (FeNO) was repeatedly measured in 53 COPD patients and 82 healthy controls at up to four clinical visits. Concentrations of carbon monoxide (CO), nitrogen monoxide, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), fine particulate matter (PM_2.5), black carbon (BC), ultrafine particles (UFPs), and accumulated-mode particles (Acc) were monitored continuously at a fixed-site monitoring station. Linear mixed-effects models were used to compare the associations between ln-transformed FeNO and average 1-23 h concentrations of air pollutants before the clinical visits.

RESULTS

FeNO was positively associated with interquartile range (IQR) increases in average concentrations of CO, NO₂, SO₂, BC, UFPs, and Acc in all participants, with the strongest associations in different time-windows (range from 6.6% for average 1 h NO₂ exposure to 32.1% for average 7 h SO₂ exposure). Associations between FeNO and average 13-23 h PM_2.5 exposure differed significantly according to COPD status. Increases in FeNO associated with average 1-2 h NO exposure were significant in COPD patients (range 8.9-10.2%), while the associations were nonsignificant in healthy controls. Associations between FeNO and average 1-23 h CO and SO₂ exposure tended to be higher in COPD patients than in healthy controls, although the differences were not significant. UFPs-associated respiratory inflammation was robust in both subgroups.

CONCLUSIONS

COPD patients are more susceptible to respiratory inflammation following PM_2.5, NO, CO, and SO₂ exposure than individuals without COPD.

Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study

Predicting PM_2.5 concentrations at a fine spatial and temporal resolution (i.e., neighborhood, hourly) is challenging. Recent growth in low cost sensor networks is providing increased spatial coverage of air quality data that can be used to supplement data provided by monitors of regulatory agencies. We developed an hourly, 500 × 500 m gridded PM_2.5 model that integrates PurpleAir low-cost air sensor network data for Los Angeles County. We developed a quality control scheme for PurpleAir data. We included spatially and temporally varying predictors in a random forest model with random oversampling of high concentrations to predict PM_2.5. The model achieved high prediction accuracy (10-fold cross-validation (CV) R² = 0.93, root mean squared error (RMSE) = 3.23 μg/m³; spatial CV R² = 0.88, spatial RMSE = 4.33 μg/m³; temporal CV R² = 0.90, temporal RMSE = 3.85 μg/m³). Our model was able to predict spatial and diurnal patterns in PM_2.5 on typical weekdays and weekends, as well as non-typical days, such as holidays and wildfire days. The model allows for far more precise estimates of PM_2.5 than existing methods based on few sensors. Taking advantage of low-cost PM_2.5 sensors, our hourly random forest model predictions can be combined with time-activity diaries in future studies, enabling geographically and temporally fine exposure estimation for specific population groups in studies of acute air pollution health effects and studies of environmental justice issues.

Adverse Birth Outcomes Related to NO2 and PM Exposure: European Systematic Review and Meta-Analysis

There is a growing number of international studies on the association between ambient air pollution and adverse pregnancy outcomes, and this systematic review and meta-analysis has been conducted focusing on European countries, to assess the crucial public health issue of this suspected association on this geographical area. A systematic literature search (based on Preferred Reporting Items for Systematic reviews and Meta-Analyses, PRISMA, guidelines) has been performed on all European epidemiological studies published up until 1 April 2020, on the association between maternal exposure during pregnancy to nitrogen dioxide (NO₂) or particular matter (PM) and the risk of adverse birth outcomes, including: low birth weight (LBW) and preterm birth (PTB). Fourteen articles were included in the systematic review and nine of them were included in the meta-analysis. Our meta-analysis was conducted for 2 combinations of NO₂ exposure related to birth weight and PTB. Our systematic review revealed that risk of LBW increases with the increase of air pollution exposure (including PM₁₀, PM_2.5 and NO₂) during the whole pregnancy. Our meta-analysis found that birth weight decreases with NO₂ increase (pooled beta = −13.63, 95% confidence interval (CI) (−28.03, 0.77)) and the risk of PTB increase for 10 µg/m3 increase in NO₂ (pooled odds ratio (OR) = 1.07, 95% CI (0.90, 1.28)). However, the results were not statistically significant. Our finding support the main international results, suggesting that increased air pollution exposure during pregnancy might contribute to adverse birth outcomes, especially LBW. This body of evidence has limitations that impede the formulation of firm conclusions. Further studies, well-focused on European countries, are called to resolve the limitations which could affect the strength of association such as: the exposure assessment, the critical windows of exposure during pregnancy, and the definition of adverse birth outcomes. This analysis of limitations of the current body of research could be used as a baseline for further studies and may serve as basis for reflection for research agenda improvements.

Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging

Ambient fine particulate matter (PM_2.5) plays an important role in cardiovascular- and respiratory-related death. Empirical statistical models have been widely applied to estimate ambient PM_2.5 concentrations with correlated variables. However, empirical statistical models ignore the nonlinear relationship between PM_2.5 and covariates and assume that residuals are independent and identically distributed random variables. Here, a hybrid approach, which integrates random forest (RF) model and spatiotemporal kriging, is proposed to estimate the daily PM_2.5 concentration. The proposed RF-based spatiotemporal kriging (RFSTK) model effectively captures nonlinear interactions among different predictors and accounts for the detailed spatiotemporal dependence of the PM_2.5 concentration. The RFSTK model performs well in predicting the daily PM_2.5 concentration. The 10-fold overall cross-validation R² value is 0.881, the mean absolute error (MAE) is 6.89 μg/m³ and the root-mean-square error (RMSE) is 11.48 μg/m³, indicating better performance than the original RF model (R² = 0.848, MAE = 7.88 μg/m³ and RMSE = 13.26 μg/m³). The spatiotemporal prediction of the PM_2.5 concentration shows that approximately 90.04% of China had a daily exposure to PM_2.5 in 2018 that was below the nation's air quality standard of 75 μg/m³. The proposed hybrid method is entirely general and can be applied to map the ambient PM_2.5 concentration over a large spatiotemporal domain.

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.

Impacts of snow and cloud covers on satellite-derived PM2.5 levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM2.5 and made full- coverage PM2.5 predictions by considering these impacts. To estimate missing AOD values, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The "out-of-bag" R2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R2 of 0.82. In the gap-filling models, the snow fraction was of higher significance to the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM2.5 levels with high resolutions and complete coverage.

Kerosene lighting contributes to household air pollution in rural Uganda

The literature on the contribution of kerosene lighting to indoor air particulate concentrations is sparse. In rural Uganda, kitchens are almost universally located outside the main home, and kerosene is often used for lighting. In this study, we obtained longitudinal measures of particulate matter 2.5 microns or smaller in size (PM_2.5 ) from living rooms and kitchens of 88 households in rural Uganda. Linear mixed-effects models with a random intercept for household were used to test the hypotheses that primary reported lighting source and kitchen location (indoor vs outdoor) are associated with PM_2.5 levels. During initial testing, households reported using the following sources of lighting: open-wick kerosene (19.3%), hurricane kerosene (45.5%), battery-powered (33.0%), and solar (1.1%) lamps. During follow-up testing, these proportions changed to 29.5%, 35.2%, 18.2%, and 9.1%, respectively. Average ambient, living room, and kitchen PM_2.5 levels were 20.2, 35.2, and 270.0 μg/m³ . Living rooms using open-wick kerosene lamps had the highest PM_2.5 levels (55.3 μg/m³ ) compared to those using solar lighting (19.4 μg/m³ ; open wick vs solar, P=.01); 27.6% of homes using open-wick kerosene lamps met World Health Organization indoor air quality standards compared to 75.0% in homes using solar lighting.

Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach

To estimate PM_2.5 concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM_2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross-validation (CV) R² value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 μg/m³, respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM_2.5 measurements increase CV R² by ∼0.02 and ∼0.06, respectively, indicating their significant contributions to prediction accuracy. A pair of different variable importance measures both indicate that the convolutional layer for nearby PM_2.5 measurements and AOD values are among the most-important predictor variables for the training process.

Prenatal Ambient Air Pollution, Placental Mitochondrial DNA Content, and Birth Weight in the INMA (Spain) and ENVIRONAGE (Belgium) Birth Cohorts

BACKGROUND

Mitochondria are sensitive to environmental toxicants due to their lack of repair capacity. Changes in mitochondrial DNA (mtDNA) content may represent a biologically relevant intermediate outcome in mechanisms linking air pollution and fetal growth restriction.

OBJECTIVE

We investigated whether placental mtDNA content is a possible mediator of the association between prenatal nitrogen dioxide (NO2) exposure and birth weight.

METHODS

We used data from two independent European cohorts: INMA (n = 376; Spain) and ENVIRONAGE (n = 550; Belgium). Relative placental mtDNA content was determined as the ratio of two mitochondrial genes (MT-ND1 and MTF3212/R3319) to two control genes (RPLP0 and ACTB). Effect estimates for individual cohorts and the pooled data set were calculated using multiple linear regression and mixed models. We also performed a mediation analysis.

RESULTS

Pooled estimates indicated that a 10-μg/m3 increment in average NO2 exposure during pregnancy was associated with a 4.9% decrease in placental mtDNA content (95% CI: -9.3, -0.3%) and a 48-g decrease (95% CI: -87, -9 g) in birth weight. However, the association with birth weight was significant for INMA (-66 g; 95% CI: -111, -23 g) but not for ENVIRONAGE (-20 g; 95% CI: -101, 62 g). Placental mtDNA content was associated with significantly higher mean birth weight (pooled analysis, interquartile range increase: 140 g; 95% CI: 43, 237 g). Mediation analysis estimates, which were derived for the INMA cohort only, suggested that 10% (95% CI: 6.6, 13.0 g) of the association between prenatal NO2 and birth weight was mediated by changes in placental mtDNA content.

CONCLUSION

Our results suggest that mtDNA content can be one of the potential mediators of the association between prenatal air pollution exposure and birth weight.

CITATION

Clemente DB, Casas M, Vilahur N, Begiristain H, Bustamante M, Carsin AE, Fernández MF, Fierens F, Gyselaers W, Iñiguez C, Janssen BG, Lefebvre W, Llop S, Olea N, Pedersen M, Pieters N, Santa Marina L, Souto A, Tardón A, Vanpoucke C, Vrijheid M, Sunyer J, Nawrot TS. 2016. Prenatal ambient air pollution, placental mitochondrial DNA content, and birth weight in the INMA (Spain) and ENVIRONAGE (Belgium) birth cohorts. Environ Health Perspect 124:659-665; http://dx.doi.org/10.1289/ehp.1408981.

Fine particulate matter in acute exacerbation of COPD

Chronic obstructive pulmonary disease (COPD) is a common airway disorder. In particular, acute exacerbations of COPD (AECOPD) can significantly reduce pulmonary function. The majority of AECOPD episodes are attributed to infections, although environmental stress also plays a role. Increasing urbanization and associated air pollution, especially in developing countries, have been shown to contribute to COPD pathogenesis. Elevated levels of particulate matter (PM) in polluted air are strongly correlated with the onset and development of various respiratory diseases. In this review, we have conducted an extensive literature search of recent studies of the role of PM_2.5 (fine PM) in AECOPD. PM_2.5 leads to AECOPD via inflammation, oxidative stress (OS), immune dysfunction, and altered airway epithelial structure and microbiome. Reducing PM_2.5 levels is a viable approach to lower AECOPD incidence, attenuate COPD progression and decrease the associated healthcare burden.