Hyper-localized measures of air pollution and risk of preterm birth in Oakland and San Jose, California

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

BACKGROUND

Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.

METHODS

We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.

RESULTS

Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.

CONCLUSION

Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

Scalable Multipollutant Exposure Assessment Using Routine Mobile Monitoring Platforms

INTRODUCTION

The absence of spatially resolved air pollution measurements remains a major gap in health studies of air pollution, especially in disadvantaged communities in the United States and lower-income countries. Many urban air pollutants vary over short spatial scales, owing to unevenly distributed emissions sources, rapid dilution away from sources, and physicochemical transformations. Primary air pollutants from traffic have especially sharp spatial gradients, which lead to disparate effects on human health for populations who live near air pollution sources, with important consequences for environmental justice. Conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize these heterogeneous human exposures and localized pollution hotspots. In this study, we assessed the potential for repeated mobile air quality measurements to provide a scalable approach to developing high-resolution pollution exposure estimates. We assessed the utility and validity of mobile monitoring as an exposure assessment technique, compared the insights from this measurement approach against other widely accepted methods, and investigated the potential for mobile monitoring to be scaled up in the United States and low- and middle-income countries.

METHODS

Our study had five key analysis modules (M1- M5). The core approach of the study revolved around repeated mobile monitoring to develop time-stable estimates of central-tendency air pollution exposures at high spatial resolution. All mobile monitoring campaigns in California were completed prior to beginning this study. In analysis M1, we conducted an intensive summerlong sampling campaign in West Oakland, California. In M2, we explored the dynamics of ultrafine particles (UFPs) in the San Francisco Bay Area. In analysis M3, we scaled up our multipollutant mobile monitoring approach to 13 different neighborhoods with ~450,000 inhabitants to evaluate within- and between-neighborhood heterogeneity. In M4, we evaluated the coupling of mobile monitoring with land use regression models to estimate intraurban variation. Finally, in M5, we reproduced our mobile monitoring approach in a pilot study in Bangalore, India.

RESULTS

For M1, we found a moderate-to-high concordance in the time-averaged spatial patterns between mobile and fixed-site observations of black carbon (BC) in West Oakland. The dense fixed-site monitor network added substantial insight about spatial patterns and local hotspots. For M2, a seasonal divergence in the relationship between UFPs and other traffic-related air pollutants was evident from both approaches. In M3, we found distinct spatial distribution of exposures across the Bay Area for primary and secondary air pollutants. We found substantially unequal exposures by race and ethnicity, mostly driven by between-neighborhood concentration differences. In M4, we demonstrated that empirical modeling via land use regression could dramatically reduce the data requirements for building high-resolution air quality maps. In M5, we developed exposure maps of BC and UFPs in a Bangalore neighborhood and demonstrated that the measurement technique worked successfully.

CONCLUSIONS

We demonstrated that mobile monitoring can produce insights about air pollution exposure that are externally validated against multiple other analysis approaches, while adding complementary information about spatial patterns and exposure heterogeneity and inequity that is not readily obtained with other methods.

Climate change as a threat multiplier to environmental reproductive justice

Legacies of racial capitalism and colonialism drive present day racial disparities in perinatal health outcomes. Climate change amplifies existing social inequalities associated with environmental exposures and reproductive health, of which BIPOC (Black, Indigenous, and people of color) communities bear a disproportionate burden. Through case studies, this article summarizes three examples of climate justice issues with reproductive healthcare outcomes: traffic related air pollution exposure, chemical exposures in personal care products and plastics, and natural disaster frequency. We advocate for incorporation of climate justice and environmental health impact into medical school curriculum, increased prenatal screening for environmental toxins, and physician engagement with local environmental issues.

Methods in Public Health Environmental Justice Research: a Scoping Review from 2018 to 2021

PURPOSE OF REVIEW

The volume of public health environmental justice (EJ) research produced by academic institutions increased through 2022. However, the methods used for evaluating EJ in exposure science and epidemiologic studies have not been catalogued. Here, we completed a scoping review of EJ studies published in 19 environmental science and epidemiologic journals from 2018 to 2021 to summarize research types, frameworks, and methods.

RECENT FINDINGS

We identified 402 articles that included populations with health disparities as a part of EJ research question and met other inclusion criteria. Most studies (60%) evaluated EJ questions related to socioeconomic status (SES) or race/ethnicity. EJ studies took place in 69 countries, led by the US (n = 246 [61%]). Only 50% of studies explicitly described a theoretical EJ framework in the background, methods, or discussion and just 10% explicitly stated a framework in all three sections. Among exposure studies, the most common area-level exposure was air pollution (40%), whereas chemicals predominated personal exposure studies (35%). Overall, the most common method used for exposure-only EJ analyses was main effect regression modeling (50%); for epidemiologic studies the most common method was effect modification (58%), where an analysis evaluated a health disparity variable as an effect modifier. Based on the results of this scoping review, current methods in public health EJ studies could be bolstered by integrating expertise from other fields (e.g., sociology), conducting community-based participatory research and intervention studies, and using more rigorous, theory-based, and solution-oriented statistical research methods.

Toxicity and health effects of ultrafine particles: Towards an understanding of the relative impacts of different transport modes

Exposure to particulate matter (PM) has been associated with a wide range of adverse health effects, but it is still unclear how particles from various transport modes differ in terms of toxicity and associations with different human health outcomes. This literature review aims to summarize toxicological and epidemiological studies of the effect of ultrafine particles (UFPs), also called nanoparticles (NPs, <100 nm), from different transport modes with a focus on vehicle exhaust (particularly comparing diesel and biodiesel) and non-exhaust as well as particles from shipping (harbor), aviation (airport) and rail (mainly subway/underground). The review includes both particles collected in laboratory tests and the field (intense traffic environments or collected close to harbor, airport, and in subway). In addition, epidemiological studies on UFPs are reviewed with special attention to studies aimed at distinguishing the effects of different transport modes. Results from toxicological studies indicate that both fossil and biodiesel NPs show toxic effects. Several in vivo studies show that inhalation of NPs collected in traffic environments not only impacts the lung, but also triggers cardiovascular effects as well as negative impacts on the brain, although few studies compared NPs from different sources. Few studies were found on aviation (airport) NPs, but the available results suggest similar toxic effects as traffic-related particles. There is still little data related to the toxic effects linked to several sources (shipping, road and tire wear, subway NPs), but in vitro results highlighted the role of metals in the toxicity of subway and brake wear particles. Finally, the epidemiological studies emphasized the current limited knowledge of the health impacts of source-specific UFPs related to different transport modes. This review discusses the necessity of future research for a better understanding of the relative potencies of NPs from different transport modes and their use in health risk assessment.

Estimating Causal Effects of Interventions on Early-life Environmental Exposures Using Observational Data

PURPOSE OF REVIEW

We discuss how epidemiologic studies have used observational data to estimate the effects of potential interventions on early-life environmental exposures. We summarize the value of posing questions about interventions, how a group of techniques known as "g-methods" can provide advantages for estimating intervention effects, and how investigators have grappled with the strong assumptions required for causal inference.

RECENT FINDINGS

We identified nine studies that estimated health effects of hypothetical interventions on early-life environmental exposures. Of these, six examined air pollution. Interventions evaluated by these studies included setting exposure levels at a specific value, shifting exposure distributions, and limiting exposure levels to less than a threshold value. Only one study linked exposure contrasts to a specific intervention on an exposure source, however. There is growing interest in estimating intervention effects of early-life environmental exposures, in part because intervention effects are directly related to possible public health actions. Future studies can build on existing work by linking research questions to specific hypothetical interventions that could reduce exposure levels. We discuss how framing questions around interventions can help overcome some of the barriers to causal inference and how advances related to machine learning may strengthen studies by sidestepping the overly restrictive assumptions of parametric regression models. By leveraging advancements in causal inference and exposure science, an intervention framework for environmental epidemiology can guide actionable solutions to improve children's environmental health.

Maternal atmospheric particulate matter exposure and risk of adverse pregnancy outcomes: A meta-analysis of cohort studies

Ambient air particulate exposure not only capable of elevating the risks of adverse pregnancy outcomes, but also has profound implications for human health, but the results are discrepant. This meta-analysis aimed to provide higher grade evidence on the impacts of air particulate on specific pregnancy outcomes. A total of 81 eligible cohort studies were included in this meta-analysis, of which the outcomes included preterm birth (PTB), moderate PTB, very PTB, extreme PTB, term low birth weight (TLBW), term birth weight (TBW), stillbirth (SB) and small for gestational age (SGA). The results showed that every 10 μg/m³ increase of PM_2.5 exposure associated with 2.7%-9.3% increase of PTB risk in entire pregnancy, 2nd and 3rd trimesters; 10.5%-19.3% increase of very PTB risk in entire pregnancy, 1st and 2nd trimesters; 8.3% and 10.1% increase of TLBW and SGA risk in entire pregnancy; 25.6% and 10.1% increase of SB in entire pregnancy and 3rd trimester; and -13.274 g and -4.916 g reduce of TBW during entire pregnancy and 2nd trimester, respectively. Every 10 μg/m³ increase of PM₁₀ exposure associated with 12.1% and 2.6% increase of PTB risk in entire pregnancy and 3rd trimester; 48.9% and 5.0% increase of moderate PTB risk in entire pregnancy and 2nd trimester; 14.4% and 10.3% increase of very PTB risk in 1st and 3rd trimesters; 2.9% increase of extremely PTB risk in 2nd trimester; 1.5%-3.8% and 2.9%-3.7% increase of TLBW and SGA risk in entire pregnancy, 1st and 2nd trimesters; 7.0% increase of SB risk in 3rd trimesters; and -4.537 g and -5.263 g reduce of TBW in 1st and 2nd trimesters, respectively. High mean annual PM concentrations were associated with more extreme adverse pregnancy outcomes (PTBs, SGA and SB), while low mean annual PM concentrations were associated with decreased TBW and increased risk of TLBW.

Adverse Effects of Black Carbon (BC) Exposure during Pregnancy on Maternal and Fetal Health: A Contemporary Review

Black carbon (BC) is a major component of ambient particulate matter (PM), one of the six Environmental Protection Agency (EPA) Criteria air pollutants. The majority of research on the adverse effects of BC exposure so far has been focused on respiratory and cardiovascular systems in children. Few studies have also explored whether prenatal BC exposure affects the fetus, the placenta and/or the course of pregnancy itself. Thus, this contemporary review seeks to elucidate state-of-the-art research on this understudied topic. Epidemiological studies have shown a correlation between BC and a variety of adverse effects on fetal health, including low birth weight for gestational age and increased risk of preterm birth, as well as cardiometabolic and respiratory system complications following maternal exposure during pregnancy. There is epidemiological evidence suggesting that BC exposure increases the risk of gestational diabetes mellitus, as well as other maternal health issues, such as pregnancy loss, all of which need to be more thoroughly investigated. Adverse placental effects from BC exposure include inflammatory responses, interference with placental iodine uptake, and expression of DNA repair and tumor suppressor genes. Taking into account the differences in BC exposure around the world, as well as interracial disparities and the need to better understand the underlying mechanisms of the health effects associated with prenatal exposure, toxicological research examining the effects of early life exposure to BC is needed.

Prenatal exposure to fine particulate matter and the risk of spontaneous preterm birth: A population-based cohort study of twins

Background

Studies in singletons have suggested that prenatal exposure to fine particulate matter (PM_2.5) and some of its chemical components is associated with an increased risk of preterm birth (PTB). However, no study has been conducted in twins.

Purpose

To examine the associations of maternal exposure to total PM_2.5 mass and its carbonaceous components with PTB in twin pregnancies.

Methods

A total of 1,515 pairs of twins and their mothers were enrolled from a previous twin birth cohort that had been conducted at the Shanghai First Maternity and Infant Hospital School of Medicine of Tongji University in China. Participants who had iatrogenic PTBs were excluded. Maternal exposure to total PM_2.5 mass and two carbonaceous components, namely, organic carbon (OC) and black carbon (BC), was estimated by a satellite-based model. The associations between PM_2.5 exposure and the risk of spontaneous PTB were evaluated by logistic regression analysis.

Results

This study found that exposure to total PM_2.5 mass and OC during the second trimester of pregnancy was significantly associated with an increased risk of spontaneous PTB. An interquartile range (IQR) increase in total PM_2.5 mass and OC exposure during the second trimester was associated with 48% (OR = 1.48, 95% CI, 1.06, 2.05) and 50% (OR = 1.50, 95% CI, 1.00, 2.25) increases in the odds of PTB, respectively. However, no significant association was found between BC exposure during any exposure window and the risk of PTB.

Conclusion

The findings suggest that exposure to ambient air pollution with fine particles may be a risk factor for spontaneous PTB in twin pregnancies. The middle stage of pregnancy seems to be a critical window for the impacts of PM_2.5 exposure on PTB in twin pregnancies.

The type of previous abortion modifies the association between air pollution and the risk of preterm birth

BACKGROUND

Air pollution and previous abortion have been reported to be related to preterm birth (PTB). But rare study examined the effect of air pollution on PTB risk among mothers with previous abortion.

OBJECTIVE

To estimate the effect of air pollution on PTB and the potential effect modification of previous abortion on such an association in rural part of Henan province (China).

METHOD

Based on National Free Preconception Health Examination Project (NFPHEP), information from the medical records of 57,337 mothers with previous abortion were obtained. An inverse distance-weighted model was used to estimate exposure levels of air pollutants. The effect of air pollution on the risk of PTB was estimated with a multiple logistic regression model. Stratified and interaction analyses were undertaken to explore the potential effect modification of previous abortion on this association.

RESULTS

The risk of PTB was positively associated with exposure to levels of nitrogen dioxide (NO₂; OR: 1.03; 95%CI: 1.02-1.04)], and sulfur dioxide (SO₂; 1.04; 1.02-1.07), and negatively associated with ozone (O₃) exposure (0.97; 0.97-0.98) during the entire pregnancy. Besides, we observed a positive effect of carbon monoxide (CO) exposure during the third trimester of pregnancy on PTB (1.14; 1.01-1.29). The type of previous abortion could modify the effect of air pollution on the PTB risk (P-_interaction < 0.05). Compared with mothers with previous induced abortion, mothers with previous spontaneous abortion carried a higher risk of PTB induced by NO₂, CO, and O₃.

CONCLUSIONS

The risk of PTB was positively associated with levels of NO₂, SO₂ and CO, and negatively associated with the O₃ level. The types of previous abortion could modify the effect of air pollution on PTB. Mothers who had an abortion previously, especially spontaneous abortion, should avoid exposure to air pollution to improve their pregnancy outcome.