Bayesian G-Computation for Estimating Impacts of Interventions on Exposure Mixtures: Demonstration With Metals From Coal-Fired Power Plants and Birth Weight

Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia

BACKGROUND

Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts.

OBJECTIVES

Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach.

METHODS

BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined short-term single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (NO 2 ), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups.

RESULTS

Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include NO 2 (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included NO 2 ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants].

DISCUSSION

Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. https://doi.org/10.1289/EHP12969.

Exposure to metal mixtures and adverse pregnancy and birth outcomes: A systematic review

BACKGROUND

Prenatal exposure to metal mixtures is associated with adverse pregnancy and birth outcomes like low birth weight, preterm birth, and small for gestational age. However, prior studies have used individual metal analysis, lacking real-life exposure scenarios.

OBJECTIVES

This systematic review aims to evaluate the strength and consistency of the association between metal mixtures and pregnancy and birth outcomes, identify research gaps, and inform future studies and policies in this area.

METHODS

The review adhered to the updated Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) checklist, along with the guidelines for conducting systematic reviews and meta-analyses of observational studies of etiology (COSMOS-E). Our data collection involved searching the PubMed, MEDLINE, and SCOPUS databases. We utilized inclusion criteria to identify relevant studies. These chosen studies underwent thorough screening and data extraction procedures. Methodological quality evaluations were conducted using the NOS framework for cohort and case-control studies, and the AXIS tool for cross-sectional studies.

RESULTS

The review included 34 epidemiological studies, half of which focused on birth weight, and the others investigated neonate size, preterm birth, small for gestational age, miscarriage, and placental characteristics. The findings revealed significant associations between metal mixtures (including mercury (Hg), nickel (Ni), arsenic (As), cadmium (Cd), manganese (Mn), cobalt (Co), lead (Pb), zinc (Zn), barium (Ba), cesium (Cs), copper (Cu), selenium (Se), and chromium (Cr)) and adverse pregnancy and birth outcomes, demonstrating diverse effects and potential interactions.

CONCLUSION

In conclusion, this review consistently establishes connections between metal exposure during pregnancy and adverse consequences for birth weight, gestational age, and other vital birth-related metrics. This review further demonstrates the need to apply mixture methods with caution but also shows that they can be superior to traditional approaches. Further research is warranted to deeper understand the underlying mechanisms and to develop effective strategies for mitigating the potential risks associated with metal mixture exposure during pregnancy.

"We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

Exposure to phthalate metabolites, bisphenol A, and psychosocial stress mixtures and pregnancy outcomes in the Atlanta African American maternal-child cohort

BACKGROUND

Consumer products are common sources of exposure for phthalates and bisphenol A (BPA), which disrupt the endocrine system. Psychosocial stressors have been shown to amplify the toxic effects of endocrine disruptors but, information is limited among African Americans (AAs), who experience the highest rates of adverse pregnancy outcomes and are often exposed to the highest levels of chemical and non-chemical stressors. We examined the association between an exposure mixture of phthalate metabolites, BPA, and psychosocial stressors with gestational age at delivery and birthweight for gestational age z-scores in pregnant AA women.

STUDY DESIGN

Participants were enrolled in the Atlanta African American Maternal-Child Cohort (N = 247). Concentrations of eight phthalate metabolites and BPA were measured in urine samples collected at up to two timepoints during pregnancy (8-14 weeks gestation and 20-32 weeks gestation) and were averaged. Psychosocial stressors were measured using self-reported, validated questionnaires that assessed experiences of discrimination, gendered racial stress, depression, and anxiety. Linear regression was used to estimate individual associations between stress exposures (chemical and psychosocial) and birth outcomes. We leveraged quantile g-computation was used to examine joint effects of chemical and stress exposures on gestational age at delivery (in weeks) and birthweight for gestational age z-scores.

RESULTS

A simultaneous increase in all phthalate metabolites and BPA was associated with a moderate reduction in birthweight z-scores (mean change per quartile increase = -0.22, 95% CI = -0.45, 0.0). The association between our exposure mixture and birthweight z-scores became stronger when including psychosocial stressors as additional exposures (mean change per quantile increase = -0.35, 95% CI = -0.61, -0.08). Overall, we found null associations between exposure to chemical and non-chemical stressors with gestational age at delivery.

CONCLUSIONS

In a prospective cohort of AA mother-newborn dyads, we observed that increased prenatal exposure to phthalates, BPA, and psychosocial stressors were associated with adverse pregnancy outcomes.

Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials: A Systematic Review

Importance

Observational (nonexperimental) studies that aim to emulate a randomized trial (ie, the target trial) are increasingly informing medical and policy decision-making, but it is unclear how these studies are reported in the literature. Consistent reporting is essential for quality appraisal, evidence synthesis, and translation of evidence to policy and practice.

Objective

To assess the reporting of observational studies that explicitly aimed to emulate a target trial.

Evidence Review

We searched Medline, Embase, PsycINFO, and Web of Science for observational studies published between March 2012 and October 2022 that explicitly aimed to emulate a target trial of a health or medical intervention. Two reviewers double-screened and -extracted data on study characteristics, key predefined components of the target trial protocol and its emulation (eligibility criteria, treatment strategies, treatment assignment, outcome[s], follow-up, causal contrast[s], and analysis plan), and other items related to the target trial emulation.

Findings

A total of 200 studies that explicitly aimed to emulate a target trial were included. These studies included 26 subfields of medicine, and 168 (84%) were published from January 2020 to October 2022. The aim to emulate a target trial was explicit in 70 study titles (35%). Forty-three studies (22%) reported use of a published reporting guideline (eg, Strengthening the Reporting of Observational Studies in Epidemiology). Eighty-five studies (43%) did not describe all key items of how the target trial was emulated and 113 (57%) did not describe the protocol of the target trial and its emulation.

Conclusion and Relevance

In this systematic review of 200 studies that explicitly aimed to emulate a target trial, reporting of how the target trial was emulated was inconsistent. A reporting guideline for studies explicitly aiming to emulate a target trial may improve the reporting of the target trial protocols and other aspects of these emulation attempts.

Mixtures of Metals and Micronutrients in Early Pregnancy and Cognition in Early and Mid-Childhood: Findings from the Project Viva Cohort

BACKGROUND

The developing fetal brain is sensitive to many environmental exposures. However, the independent and joint effects of prenatal exposure to metals and micronutrients on child cognition are not well understood.

OBJECTIVES

Our aim was to evaluate associations of first-trimester (∼ 10 wk) maternal erythrocyte concentrations of mixtures of nonessential and essential metals and micronutrients with early (∼ 3 y) and mid-childhood (∼ 8 y) cognitive test scores in Project Viva, a prebirth cohort in Boston, Massachusetts, USA.

METHODS

We measured concentrations of five essential metals (Cu, Mg, Mn, Se, Zn) and two micronutrients (vitamin B12 and folate), together termed the "nutrient mixture," as well as six nonessential metals (As, Ba, Cd, Cs, Hg, Pb), together termed the "neurotoxic mixture," in first-trimester (∼ 10 wk) maternal erythrocytes (metals) or plasma (micronutrients). We assessed visual-motor function and receptive vocabulary in early childhood (∼ 3 y), and visual-motor function, visual memory, and fluid and crystallized intelligence in mid-childhood (∼ 8 y). We employed adjusted quantile g-computation and linear regression to estimate mixture and individual component associations, respectively.

RESULTS

Analyses included 900 mother-child pairs (74% college graduates; 52% male children). In mixture analyses, a quartile increase in the nutrient mixture was associated with a mean difference in early childhood receptive vocabulary score of 1.58 points [95% confidence interval (CI): 0.06, 3.10], driven by Zn and Se. A quartile increase in the neurotoxic mixture was associated with a mean difference in mid-childhood visual-motor score of - 3.01 points (95% CI: - 5.55 , - 0.47 ), driven by Ba and Cs. Linear regressions supported quantile g-computation findings for mixture component contributions.

DISCUSSION

Maternal circulating concentrations of several essential (Zn and Se) and nonessential (Ba and Cs) metals were associated with some domains of child cognition. In this folate-replete cohort, first-trimester circulating concentrations of known neurotoxic metals, such as Pb, were not associated with child cognition. https://doi.org/10.1289/EHP12016.

Sulfur dioxide reduction at coal-fired power plants in North Carolina and associations with preterm birth among surrounding residents

Coal-fired power plants (CFPP) are major contributors of air pollution, including the majority of anthropogenic sulfur dioxide (SO2) emissions, which have been associated with preterm birth (PTB). To address a 2002 North Carolina (NC) policy, 14 of the largest NC CFPPs either installed desulfurization equipment (scrubbers) or retired coal units, resulting in substantial reductions of SO2 air emissions. We investigated whether SO2 air emission reduction strategies at CFPPs in NC were associated with changes in prevalence of PTB in nearby communities.

Methods

We used US EPA Air Markets Program Data to track SO2 emissions and determine the implementation dates of intervention at CFPPs and geocoded 2003-2015 NC singleton live births. We conducted a difference-in-difference analysis to estimate change in PTB associated with change in SO2 reduction strategies for populations living 0-<4 and 4-<10 miles from CFPPs pre- and postintervention, with a comparison of those living 10-<15 miles from CFPPs.

Results

With the spatial-temporal exposure restrictions applied, 42,231 and 41,218 births were within 15 miles of CFPP-scrubbers and CFPP-retired groups, respectively. For residents within 4-<10 miles from a CFPP, we estimated that the absolute prevalence of PTB decreased by -1.5% [95% confidence interval (CI): -2.6, -0.4] associated with scrubber installation and -0.5% (95% CI: -1.6, 0.6) associated with the retirement of coal units at CFPPs. Our findings were imprecise and generally null-to-positive among those living within 0-<4 miles regardless of the intervention type.

Conclusions

Results suggest a reduction of PTB among residents 4-<10 miles of the CFPPs that installed scrubbers.

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.

Using Parametric g-Computation to Estimate the Effect of Long-Term Exposure to Air Pollution on Mortality Risk and Simulate the Benefits of Hypothetical Policies: The Canadian Community Health Survey Cohort (2005 to 2015)

BACKGROUND

Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter (PM2.5)] on mortality even at relatively low levels. However, methodological challenges remain to consider potential regulatory intervention's complexity and provide actionable evidence on the predicted benefits of interventions. We propose the parametric g-computation as an alternative analytical approach to such challenges.

METHOD

We applied the parametric g-computation to estimate the cumulative risks of nonaccidental death under different hypothetical intervention strategies targeting long-term exposure to PM2.5 in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored the benefits of hypothetical intervention strategies compared with the natural course that a) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold (8.8 μg/m3, 7.04 μg/m3, 5 μg/m3, and 4 μg/m3), and b) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average PM2.5 concentration with 1-y lag at the postal code of respondents' annual mailing addresses as their long-term exposure to PM2.5. We considered baseline and time-varying confounders, including demographics, behavior characteristics, income level, and neighborhood socioeconomic status. We also included the R syntax for reproducibility and replication.

RESULTS

All hypothetical intervention strategies explored led to lower 11-y cumulative mortality risks than the estimated value under the natural course without intervention, with the smallest reduction of 0.20 per 1,000 participants (95% CI: 0.06, 0.34) under the threshold of 8.8 μg/m3, and the largest reduction of 3.40 per 1,000 participants (95% CI: -0.23, 7.03) under the relative reduction of 10% per interval. The reductions in cumulative risk, or numbers of deaths that would have been prevented if the intervention was employed instead of maintaining the status quo, increased over time but flattened toward the end of the follow-up period. Estimates among those ≥65 years of age were greater with a similar pattern. Our estimates were robust to different model specifications.

DISCUSSION

We found evidence that any intervention further reducing the long-term exposure to PM2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM2.5. The parametric g-computation used in this study provides flexibilities in simulating real-world interventions, accommodates time-varying exposure and confounders, and estimates adjusted survival curves with clearer interpretation and more information than a single hazard ratio, making it a valuable analytical alternative in air pollution epidemiological research. https://doi.org/10.1289/EHP11095.

Metabolic Signatures of Youth Exposure to Mixtures of Per- and Polyfluoroalkyl Substances: A Multi-Cohort Study

BACKGROUND

Exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous and has been associated with an increased risk of several cardiometabolic diseases. However, the metabolic pathways linking PFAS exposure and human disease are unclear.

OBJECTIVE

We examined associations of PFAS mixtures with alterations in metabolic pathways in independent cohorts of adolescents and young adults.

METHODS

Three hundred twelve overweight/obese adolescents from the Study of Latino Adolescents at Risk (SOLAR) and 137 young adults from the Southern California Children's Health Study (CHS) were included in the analysis. Plasma PFAS and the metabolome were determined using liquid-chromatography/high-resolution mass spectrometry. A metabolome-wide association study was performed on log-transformed metabolites using Bayesian regression with a g-prior specification and g-computation for modeling exposure mixtures to estimate the impact of exposure to a mixture of six ubiquitous PFAS (PFOS, PFHxS, PFHpS, PFOA, PFNA, and PFDA). Pathway enrichment analysis was performed using Mummichog and Gene Set Enrichment Analysis. Significance across cohorts was determined using weighted Z -tests.

RESULTS

In the SOLAR and CHS cohorts, PFAS exposure was associated with alterations in tyrosine metabolism (meta-analysis p = 0.00002 ) and de novo fatty acid biosynthesis (p = 0.03 ), among others. For example, when increasing all PFAS in the mixture from low (∼ 30 th percentile) to high (∼ 70 th percentile), thyroxine (T4), a thyroid hormone related to tyrosine metabolism, increased by 0.72 standard deviations (SDs; equivalent to a standardized mean difference) in the SOLAR cohort (95% Bayesian credible interval (BCI): 0.00, 1.20) and 1.60 SD in the CHS cohort (95% BCI: 0.39, 2.80). Similarly, when going from low to high PFAS exposure, arachidonic acid increased by 0.81 SD in the SOLAR cohort (95% BCI: 0.37, 1.30) and 0.67 SD in the CHS cohort (95% BCI: 0.00, 1.50). In general, no individual PFAS appeared to drive the observed associations.

DISCUSSION

Exposure to PFAS is associated with alterations in amino acid metabolism and lipid metabolism in adolescents and young adults. https://doi.org/10.1289/EHP11372.

Associations Between Prenatal Urinary Biomarkers of Phthalate Exposure and Preterm Birth: A Pooled Study of 16 US Cohorts

Importance

Phthalate exposure is widespread among pregnant women and may be a risk factor for preterm birth.

Objective

To investigate the prospective association between urinary biomarkers of phthalates in pregnancy and preterm birth among individuals living in the US.

Design, Setting, and Participants

Individual-level data were pooled from 16 preconception and pregnancy studies conducted in the US. Pregnant individuals who delivered between 1983 and 2018 and provided 1 or more urine samples during pregnancy were included.

Exposures

Urinary phthalate metabolites were quantified as biomarkers of phthalate exposure. Concentrations of 11 phthalate metabolites were standardized for urine dilution and mean repeated measurements across pregnancy were calculated.

Main Outcomes and Measures

Logistic regression models were used to examine the association between each phthalate metabolite with the odds of preterm birth, defined as less than 37 weeks of gestation at delivery (n = 539). Models pooled data using fixed effects and adjusted for maternal age, race and ethnicity, education, and prepregnancy body mass index. The association between the overall mixture of phthalate metabolites and preterm birth was also examined with logistic regression. G-computation, which requires certain assumptions to be considered causal, was used to estimate the association with hypothetical interventions to reduce the mixture concentrations on preterm birth.

Results

The final analytic sample included 6045 participants (mean [SD] age, 29.1 [6.1] years). Overall, 802 individuals (13.3%) were Black, 2323 (38.4%) were Hispanic/Latina, 2576 (42.6%) were White, and 328 (5.4%) had other race and ethnicity (including American Indian/Alaskan Native, Native Hawaiian, >1 racial identity, or reported as other). Most phthalate metabolites were detected in more than 96% of participants. Higher odds of preterm birth, ranging from 12% to 16%, were observed in association with an interquartile range increase in urinary concentrations of mono-n-butyl phthalate (odds ratio [OR], 1.12 [95% CI, 0.98-1.27]), mono-isobutyl phthalate (OR, 1.16 [95% CI, 1.00-1.34]), mono(2-ethyl-5-carboxypentyl) phthalate (OR, 1.16 [95% CI, 1.00-1.34]), and mono(3-carboxypropyl) phthalate (OR, 1.14 [95% CI, 1.01-1.29]). Among approximately 90 preterm births per 1000 live births in this study population, hypothetical interventions to reduce the mixture of phthalate metabolite levels by 10%, 30%, and 50% were estimated to prevent 1.8 (95% CI, 0.5-3.1), 5.9 (95% CI, 1.7-9.9), and 11.1 (95% CI, 3.6-18.3) preterm births, respectively.

Conclusions and Relevance

Results from this large US study population suggest that phthalate exposure during pregnancy may be a preventable risk factor for preterm delivery.

Invited Commentary: The Promise and Pitfalls of Causal Inference With Multivariate Environmental Exposures

The accompanying article by Keil et al. (Am J Epidemiol. 2021;190(12):2647-2657) deploys Bayesian g-computation to investigate the causal effect of 6 airborne metal exposures linked to power-plant emissions on birth weight. In so doing, it articulates the potential value of framing the analysis of environmental mixtures as an explicit contrast between exposure distributions that might arise in response to a well-defined intervention-here, the decommissioning of coal plants. Framing the mixture analysis as that of an approximate "target trial" is an important approach that deserves incorporation into the already rich literature on the analysis of environmental mixtures. However, its deployment in the power plant example highlights challenges that can arise when the target trial is at odds with the exposure distribution observed in the data, a discordance that seems particularly difficult in studies of environmental mixtures. Bayesian methodology such as model averaging and informative priors can help, but they are ultimately limited for overcoming this salient challenge.