Extending the Distributed Lag Model framework to handle chemical mixtures

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.

Prenatal Household Air Pollution Exposure and Childhood Blood Pressure in Rural Ghana

BACKGROUND

The association between prenatal household air pollution (HAP) exposure and childhood blood pressure (BP) is unknown.

OBJECTIVE

Within the Ghana Randomized Air Pollution and Health Study (GRAPHS) we examined time-varying associations between a) maternal prenatal and b) first-year-of-life HAP exposure with BP at 4 years of age and, separately, whether a stove intervention delivered prenatally and continued through the first year of life could improve BP at 4 years of age.

METHODS

GRAPHS was a cluster-randomized cookstove intervention trial wherein n = 1,414 pregnant women were randomized to one of two stove interventions: a) a liquefied petroleum gas (LPG) stove or improved biomass stove, or b) control (open fire cooking). Maternal HAP exposure over pregnancy and child HAP exposure over the first year of life was quantified by repeated carbon monoxide (CO) measurements; a subset of women (n = 368 ) also performed one prenatal and one postnatal personal fine particulate matter (PM 2.5 ) measurement. Systolic and diastolic BP (SBP and DBP) were measured in n = 667 4-y-old children along with their PM 2.5 exposure (n = 692 ). We examined the effect of the intervention on resting BP z -scores. We also employed reverse distributed lag models to examine time-varying associations between a) maternal prenatal and b) first-year-of-life HAP exposure and resting BP z -scores. Among those with PM 2.5 measures, we examined associations between PM 2.5 and resting BP z -scores. Sex-specific effects were considered.

RESULTS

Intention-to-treat analyses identified that DBP z -score at 4 years of age was lower among children born in the LPG arm (LPG β = - 0.20 ; 95% CI: - 0.36 , - 0.03 ) as compared with those in the control arm, and females were most susceptible to the intervention. Higher CO exposure in late gestation was associated with higher SBP and DBP z -score at 4 years of age, whereas higher late-first-year-of-life CO exposure was associated with higher DBP z -score. In the subset with PM 2.5 measurements, higher maternal postnatal PM 2.5 exposure was associated with higher SBP z -scores.

DISCUSSION

These findings suggest that prenatal and first-year-of-life HAP exposure are associated with child BP and support the need for reductions in exposure to HAP, with interventions such as cleaner cooking beginning in pregnancy. https://doi.org/10.1289/EHP13225.

Multiple exposure distributed lag models with variable selection

Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado.

Statistical Approaches to Study Exposome-Health Associations in the Context of Repeated Exposure Data: A Simulation Study

The exposome concept aims to consider all environmental stressors simultaneously. The dimension of the data and the correlation that may exist between exposures lead to various statistical challenges. Some methodological studies have provided insight regarding the efficiency of specific modeling approaches in the context of exposome data assessed once for each subject. However, few studies have considered the situation in which environmental exposures are assessed repeatedly. Here, we conduct a simulation study to compare the performance of statistical approaches to assess exposome-health associations in the context of multiple exposure variables. Different scenarios were tested, assuming different types and numbers of exposure-outcome causal relationships. An application study using real data collected within the INMA mother-child cohort (Spain) is also presented. In the simulation experiment, assessed methods showed varying performance across scenarios, making it challenging to recommend a one-size-fits-all strategy. Generally, methods such as sparse partial least-squares and the deletion-substitution-addition algorithm tended to outperform the other tested methods (ExWAS, Elastic-Net, DLNM, or sNPLS). Notably, as the number of true predictors increased, the performance of all methods declined. The absence of a clearly superior approach underscores the additional challenges posed by repeated exposome data, such as the presence of more complex correlation structures and interdependencies between variables, and highlights that careful consideration is essential when selecting the appropriate statistical method. In this regard, we provide recommendations based on the expected scenario. Given the heightened risk of reporting false positive or negative associations when applying these techniques to repeated exposome data, we advise interpreting the results with caution, particularly in compromised contexts such as those with a limited sample size.

Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures

Studying time-dependent exposure mixtures has gained increasing attentions in environmental health research. When a scalar outcome is of interest, distributed lag (DL) models have been employed to characterize the exposures effects distributed over time on the mean of final outcome. However, there is a methodological gap on investigating time-dependent exposure mixtures with different quantiles of outcome. In this paper, we introduce semiparametric partial-linear single-index (PLSI) DL quantile regression, which can describe the DL effects of time-dependent exposure mixtures on different quantiles of outcome and identify susceptible periods of exposures. We consider two time-dependent exposure settings: discrete and functional, when exposures are measured in a small number of time points and at dense time grids, respectively. Spline techniques are used to approximate the nonparametric DL function and single-index link function, and a profile estimation algorithm is proposed. Through extensive simulations, we demonstrate the performance and value of our proposed models and inference procedures. We further apply the proposed methods to study the effects of maternal exposures to ambient air pollutants of fine particulate and nitrogen dioxide on birth weight in New York University Children's Health and Environment Study (NYU CHES).

Association of air pollution and COVID-19 in India

The COVID-19 pandemic has affected the world, leading to significant morbidity and mortality. Various meteorological parameters are considered essential for the viability and transmission of the virus. Multiple reports from various parts of the world suggest a correlation between the disease spread and air pollution severity. This study was carried out to identify the relationship between meteorological parameters, air pollution, and COVID-19 in New Delhi, one of the worst-affected states in India. We studied air pollution and meteorological parameters in New Delhi, India. We obtained data about COVID-19 occurrence, meteorological parameters, and air pollution indicators from various sources from April 1, 2020, until November 12, 2020. We performed correlational analysis and employed autoregressive distributed lag models to identify the relationship between COVID-19 cases, air pollution and meteorological parameters. We found a significant impact of particulate matter (PM) 2.5, PM10, and meteorological parameters on COVID-19. There was a significant positive correlation between daily COVID-19 cases and COVID-19-related deaths with PM2.5 and PM10 levels. Increasing temperature and wind speed were associated with a reduction in the number of cases, while increasing humidity was associated with increased cases. This study demonstrated a significant association between PM2.5 and PM10 and daily COVID-19 cases and COVID-19-related mortality. This knowledge will likely help us prepare well for the future and implement air pollution control measures for other airborne disease epidemics.

The impact of correlated exposures and missing data on multiple informant models used to identify critical exposure windows

There has been heightened interest in identifying critical windows of exposure for adverse health outcomes; that is, time points during which exposures have the greatest impact on a person's health. Multiple informant models implemented using generalized estimating equations (MIM GEEs) have been applied to address this research question because they enable statistical comparisons of differences in associations across exposure windows. As interest rises in using MIMs, the feasibility and appropriateness of their application under settings of correlated exposures and partially missing exposure measurements requires further examination. We evaluated the impact of correlation between exposure measurements and missing exposure data on the power and differences in association estimated by the MIM GEE and an inverse probability weighted extension to account for informatively missing exposures. We assessed these operating characteristics under a variety of correlation structures, sample sizes, and missing data mechanisms considering various exposure-outcome scenarios. We showed that applying MIM GEEs maintains higher power when there is a single critical window of exposure and exposure measures are not highly correlated, but may result in low power and bias under other settings. We applied these methods to a study of pregnant women living with HIV to explore differences in association between trimester-specific viral load and infant neurodevelopment.

NICU-based stress response and preterm infant neurobehavior: exploring the critical windows for exposure

BACKGROUND

Exposure to maternal stress in utero negatively impacts cognitive and behavioral outcomes of children born at term. The neonatal intensive care unit (NICU) can be stressful for preterm infants during a developmental period corresponding to the third trimester of gestation. It is unknown whether stress in the NICU contributes to adverse neurodevelopment among NICU graduates. The aim was to examine the association between salivary cortisol and early neurodevelopment in preterm infants.

METHODS

We examined the association between cortisol levels during the NICU hospitalization and subsequent performance on the NICU Network Neurobehavioral Scales (NNNS), estimating time-specific associations and considering sex differences.

RESULTS

Eight hundred and forty salivary cortisol levels were measured from 139 infants. Average cortisol levels were inversely associated with NNNS Regulation scores for both male and female infants (β = -0.19; 95% CI: -0.44, -0.02). Critical developmental windows based on postmenstrual age were identified, with cortisol measured <30 weeks PMA positively associated with Habituation and Lethargy scores (β = 0.63-1.04). Critical developmental windows based on chronological age were identified, with cortisol measured in the first week of life inversely associated with Attention score (β = -1.01 for females; -0.93 for males).

CONCLUSIONS

Stress in the NICU at specific developmental time points may impact early preterm infant neurodevelopment.

IMPACT

Stress in the neonatal intensive care unit can impact the neurodevelopmental trajectory of premature infants. The impact of stress is different at different points in development. The impact of stress is sexually dimorphic.

Association between short-term exposure to ambient PM1 and PM2.5 and forced vital capacity in Chinese children and adolescents

This study aims to examine the association between short-term exposure to ambient PM₁, PM_1-2.5, and PM_2.5 and forced vital capacity (FVC). Population data were obtained from a school-based cross-sectional survey in Shandong in 2014. Distributed lag non-linear models were used to examine the association between exposure to PM₁, PM_1-2.5, and PM_2.5 and FVC at the day of FVC measurement and the previous 6 days (lag 0 to 6 days). A total of 35,334 students aged 9 to 18 years were included in the study, and the mean exposure concentrations of ambient PM₁, PM_1-2.5, and PM_2.5 for them were 47.4 (standard deviation [SD] = 21.3) μg/m³, 32.8 (SD = 32.2) μg/m³, and 80.1 (SD = 47.7) μg/m³, respectively. An inter-quartile range (IQR, 24 μg/m³) increment in exposure to PM₁ was significantly associated with a lower FVC at lag 0 and lag 1 day (β =  - 80 mL, 95% CI =  - 119, - 42, and β =  - 37 mL, 95% CI =  - 59, - 16, respectively), and an IQR (54 μg/m³) increment in exposure to PM_2.5 was significantly associated with a lower FVC at lag 0 and lag 1 day (β =  - 57 mL, 95% CI =  - 89, - 18, and β =  - 34 mL, 95% CI =  - 56, - 12, respectively) after adjustment for gender, age, body mass index category, residence, month of the survey, intake of eggs, intake of milk, physical activity, and screen time. No significant associations were observed for PM_1-2.5. The inverse associations of PM₁ and PM_2.5 with FVC were larger in males, younger children, those overweight or obese, and those with insufficient physical activity levels. Short-term exposure to ambient PM₁ and PM_2.5 was associated with decreased FVC, and PM₁ may be the primary fraction of PM_2.5 causing the adverse pulmonary effects. Our findings emphasize the need to address ambient PM, especially PM₁, pollution for affecting pulmonary health in children and adolescents.

CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk in gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.

KERNEL MACHINE AND DISTRIBUTED LAG MODELS FOR ASSESSING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL MIXTURES IN CHILDREN'S HEALTH STUDIES

Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy have focused on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing data on chemical mixtures that are observed at a high temporal resolution. As motivation, we analyze the association between exposure to four ambient air pollutants observed weekly throughout gestation and birth weight in a Boston-area prospective birth cohort. To explore patterns in the data, we first apply methods for analyzing data on (1) a single chemical observed at high temporal resolution, and (2) a mixture measured at a single point in time. We highlight the shortcomings of these approaches for temporally-resolved data on exposure to chemical mixtures. Second, we propose a novel method, a Bayesian kernel machine regression distributed lag model (BKMR-DLM), that simultaneously accounts for nonlinear associations and interactions among time-varying measures of exposure to mixtures. BKMR-DLM uses a functional weight for each exposure that parameterizes the window of susceptibility corresponding to that exposure within a kernel machine framework that captures non-linear and interaction effects of the multivariate exposure on the outcome. In a simulation study, we show that the proposed method can better estimate the exposure-response function and, in high signal settings, can identify critical windows in time during which exposure has an increased association with the outcome. Applying the proposed method to the Boston birth cohort data, we find evidence of a negative association between organic carbon and birth weight and that nitrate modifies the organic carbon, elemental carbon, and sulfate exposure-response functions.

Prenatal Diet as a Modifier of Environmental Risk Factors for Autism and Related Neurodevelopmental Outcomes

PURPOSE OF REVIEW

Environmental chemicals and toxins have been associated with increased risk of impaired neurodevelopment and specific conditions like autism spectrum disorder (ASD). Prenatal diet is an individually modifiable factor that may alter associations with such environmental factors. The purpose of this review is to summarize studies examining prenatal dietary factors as potential modifiers of the relationship between environmental exposures and ASD or related neurodevelopmental outcomes.

RECENT FINDINGS

Twelve studies were identified; five examined ASD diagnosis or ASD-related traits as the outcome (age at assessment range: 2-5 years) while the remainder addressed associations with neurodevelopmental scores (age at assessment range: 6 months to 6 years). Most studies focused on folic acid, prenatal vitamins, or omega-3 fatty acids as potentially beneficial effect modifiers. Environmental risk factors examined included air pollutants, endocrine disrupting chemicals, pesticides, and heavy metals. Most studies took place in North America. In 10/12 studies, the prenatal dietary factor under study was identified as a significant modifier, generally attenuating the association between the environmental exposure and ASD or neurodevelopment. Prenatal diet may be a promising target to mitigate adverse effects of environmental exposures on neurodevelopmental outcomes. Further research focused on joint effects is needed that encompasses a broader variety of dietary factors, guided by our understanding of mechanisms linking environmental exposures with neurodevelopment. Future studies should also aim to include diverse populations, utilize advanced methods to optimize detection of novel joint effects, incorporate consideration of timing, and consider both synergistic and antagonistic potential of diet.

Prenatal trace elements mixture is associated with learning deficits on a behavioral acquisition task among young children

Children are exposed to many trace elements throughout their development. Given their ubiquity and potential to have effects on children's neurodevelopment, these exposures are a public health concern. This study sought to identify trace element mixture-associated deficits in learning behavior using operant testing in a prospective cohort. We included 322 participants aged 6-7 years recruited in Mexico City with complete data on prenatal trace elements measurements (third trimester blood lead and manganese levels, and & urine cadmium and arsenic levels), demographic covariates, and the Incremental Repeated Acquisition (IRA), an associative learning task. Weighted quantile sum (WQS) regression models were used to estimate the joint association of the mixture of all four trace elements and IRA performance. Performance was adversely impacted by the mixture, with different elements relating to different aspects of task performance suggesting that prenatal exposure to trace element mixtures yields a broad dysregulation of learning behavior.

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

Effects of gestational exposures to chemical mixtures on birth weight using Bayesian factor analysis in the Health Outcome and Measures of Environment (HOME) Study

Studying the effects of gestational exposures to chemical mixtures on infant birth weight is inconclusive due to several challenges. One of the challenges is which statistical methods to rely on. Bayesian factor analysis (BFA), which has not been utilized for chemical mixtures, has advantages in variance reduction and model interpretation.

METHODS

We analyzed data from a cohort of 384 pregnant women and their newborns using urinary biomarkers of phthalates, phenols, and organophosphate pesticides (OPs) and serum biomarkers of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), perfluoroalkyl substances (PFAS), and organochlorine pesticides (OCPs). We examined the association between exposure to chemical mixtures and birth weight using BFA and compared with multiple linear regression (MLR) and Bayesian kernel regression models (BKMR).

RESULTS

For BFA, a 10-fold increase in the concentrations of PCB and PFAS mixtures was associated with an 81 g (95% confidence intervals [CI] = -132 to -31 g) and 57 g (95% CI = -105 to -10 g) reduction in birth weight, respectively. BKMR results confirmed the direction of effect. However, the 95% credible intervals all contained the null. For single-pollutant MLR, a 10-fold increases in the concentrations of multiple chemicals were associated with reduced birth weight, yet the 95% CI all contained the null. Variance inflation from MLR was apparent for models that adjusted for copollutants, resulting in less precise confidence intervals.

CONCLUSION

We demonstrated the merits of BFA on mixture analysis in terms of precision and interpretation compared with MLR and BKMR. We also identified the association between exposure to PCBs and PFAS and lower birth weight.

Human Health Exposure Analysis Resource (HHEAR): A model for incorporating the exposome into health studies

BACKGROUND

Characterizing the complexity of environmental exposures in relation to human health is critical to advancing our understanding of health and disease throughout the life span. Extant cohort studies open the door for such investigations more rapidly and inexpensively than launching new cohort studies and the Human Health Exposure Analysis Resource (HHEAR) provides a resource for implementing life-stage exposure studies within existing study populations. Primary challenges to incorporation of environmental exposure assessment in health studies include: (1) lack of widespread knowledge of biospecimen and environmental sampling and storage requirements for environmental exposure assessment among investigators; (2) lack of availability of and access to laboratories capable of analyzing multiple environmental exposures throughout the life-course; and (3) studies lacking sufficient power to assess associations across life-stages. HHEAR includes a consortium of researchers with expertise in laboratory analyses, statistics and logistics to overcome these limitations and enable inclusion of exposomics in human health studies.

OBJECTIVE

This manuscript describes the structure and strengths of implementing the harmonized HHEAR resource model, and our approaches to addressing challenges. We describe how HHEAR incorporates analyses of biospecimens and environmental samples and human health studies across the life span - serving as a model for incorporating environmental exposures into national and international research. We also present program successes to date.

DISCUSSION

HHEAR provides a full-service laboratory and data analysis exposure assessment resource, linking scientific, life span, and toxicological consultation with both laboratory and data analysis expertise. HHEAR services are provided without cost but require NIH, NCI, NHLBI, or ECHO funding of the original cohort; internal HHEAR scientific review and approval of a brief application; and adherence to data sharing and publication policies. We describe the benefits of HHEAR's structure, collaborative framework and coordination across project investigators, analytical laboratories, biostatisticians and bioinformatics specialists; quality assurance/quality control (QA/QC) including integrated sample management; and tools that have been developed to support the research (exposure information pages, ontology, new analytical methods, common QA/QC approach across laboratories, etc.). This foundation supports HHEAR's inclusion of new laboratory and statistical analysis methods and studies that are enhanced by including targeted analysis of specific exposures and untargeted analysis of chemicals associated with phenotypic endpoints in biological and environmental samples.

CONCLUSION

HHEAR is an interdisciplinary team of toxicologists, epidemiologists, laboratory scientists, and data scientists across multiple institutions to address broad and complex questions that benefit from integrated laboratory and data analyses. HHEAR's processes, features, and tools include all life stages and analysis of biospecimens and environmental samples. They are available to the wider scientific community to augment studies by adding state of the art environmental analyses to be linked to human health outcomes.

Prenatal air pollution exposure and neurodevelopment: A review and blueprint for a harmonized approach within ECHO

BACKGROUND

Air pollution exposure is ubiquitous with demonstrated effects on morbidity and mortality. A growing literature suggests that prenatal air pollution exposure impacts neurodevelopment. We posit that the Environmental influences on Child Health Outcomes (ECHO) program will provide unique opportunities to fill critical knowledge gaps given the wide spatial and temporal variability of ECHO participants.

OBJECTIVES

We briefly describe current methods for air pollution exposure assessment, summarize existing studies of air pollution and neurodevelopment, and synthesize this information as a basis for recommendations, or a blueprint, for evaluating air pollution effects on neurodevelopmental outcomes in ECHO.

METHODS

We review peer-reviewed literature on prenatal air pollution exposure and neurodevelopmental outcomes, including autism spectrum disorder, attention deficit hyperactivity disorder, intelligence, general cognition, mood, and imaging measures. ECHO meta-data were compiled and evaluated to assess frequency of neurodevelopmental assessments and prenatal and infancy residential address locations. Cohort recruitment locations and enrollment years were summarized to examine potential spatial and temporal variation present in ECHO.

DISCUSSION

While the literature provides compelling evidence that prenatal air pollution affects neurodevelopment, limitations in spatial and temporal exposure variation exist for current published studies. As >90% of the ECHO cohorts have collected a prenatal or infancy address, application of advanced geographic information systems-based models for common air pollutant exposures may be ideal to address limitations of published research.

CONCLUSIONS

In ECHO we have the opportunity to pioneer unifying exposure assessment and evaluate effects across multiple periods of development and neurodevelopmental outcomes, setting the standard for evaluation of prenatal air pollution exposures with the goal of improving children's health.

Metal mixtures and neurodevelopment: recent findings and emerging principles

Children are constantly exposed to a wide range of environmental factors including essential and non-essential metals. In recent years, the mixtures paradigm has emerged to foster the examination of combined effects that emerge from exposures to multiple elements. In this review, we summarized recent literature studying the relationship between prenatal and childhood metal mixtures with neurodevelopmental outcomes. Our review highlights two basic principles to emerge from this approach. First, recent findings emphasize that the effect of a given exposure is contextual and may be dependent on past or concurrent metal exposures. Second, the timing of exposures is equally critical to the mixture composition in determining neurodevelopmental effects. Our discussion emphasizes how these principles may apply to future exposure-related neurodevelopmental studies.

Chemical mixtures and neurobehavior: a review of epidemiologic findings and future directions

Background Epidemiological studies have historically focused on single toxicants, or toxic chemicals, and neurodevelopment, even though the interactions of chemicals and nutrients may result in additive, synergistic, antagonistic, or potentiating effects on neurological endpoints. Investigating the impact of environmentally-relevant chemical mixtures, including heavy metals and endocrine disrupting chemicals (EDCs), is more reflective of human exposures and may result in more refined environmental policies to protect the public. Objective In this review, we provide a summary of epidemiological studies that have analyzed chemical mixtures of heavy metals and EDCs and neurobehavior utilizing multi-chemical models, including frequentist and Bayesian methods. Content Studies investigating chemicals and neurobehavior have the opportunity to not only examine the impact of chemical mixtures, but they can also identify chemicals from a mixture that may play a key role in neurotoxicity, investigate interactive effects, estimate non-linear dose response, and identify potential windows of susceptibility. The examination of neurobehavioral domains is particularly challenging given that traits emerge and change over time and subclinical nuances of neurobehavior are often unrecognized. To date, only a handful of epidemiological studies examining neurodevelopment have utilized multi-pollutant models in the investigation of heavy metals and EDCs. However, these studies were successful in identifying contaminants of importance from the exposure mixtures. Summary and Outlook Investigators are encouraged to broaden their focus to include more environmentally relevant mixtures of chemicals using advanced statistical approaches, particularly to aid in identifying potential mechanisms underlying associations.

Performance of variable and function selection methods for estimating the nonlinear health effects of correlated chemical mixtures: A simulation study

Statistical methods for identifying harmful chemicals in a correlated mixture often assume linearity in exposure-response relationships. Nonmonotonic relationships are increasingly recognized (eg, for endocrine-disrupting chemicals); however, the impact of nonmonotonicity on exposure selection has not been evaluated. In a simulation study, we assessed the performance of Bayesian kernel machine regression (BKMR), Bayesian additive regression trees (BART), Bayesian structured additive regression with spike-slab priors (BSTARSS), generalized additive models with double penalty (GAMDP) and thin plate shrinkage smoothers (GAMTS), multivariate adaptive regression splines (MARS), and lasso penalized regression. We simulated realistic exposure data based on pregnancy exposure to 17 phthalates and phenols in the US National Health and Nutrition Examination Survey using a multivariate copula. We simulated data sets of size N = 250 and compared methods across 32 scenarios, varying by model size and sparsity, signal-to-noise ratio, correlation structure, and exposure-response relationship shapes. We compared methods in terms of their sensitivity, specificity, and estimation accuracy. In most scenarios, BKMR, BSTARSS, GAMDP, and GAMTS achieved moderate to high sensitivity (0.52-0.98) and specificity (0.21-0.99). BART and MARS achieved high specificity (≥0.90), but low sensitivity in low signal-to-noise ratio scenarios (0.20-0.51). Lasso was highly sensitive (0.71-0.99), except for quadratic relationships (≤0.27). Penalized regression methods that assume linearity, such as lasso, may not be suitable for studies of environmental chemicals hypothesized to have nonmonotonic relationships with outcomes. Instead, BKMR, BSTARSS, GAMDP, and GAMTS are attractive methods for flexibly estimating the shapes of exposure-response relationships and selecting among correlated exposures.

Lagged WQS regression for mixtures with many components

The developmental timing of exposures to toxic chemicals or combinations of chemicals may be as important as the dosage itself. This concept is called "critical windows of exposure." The time boundaries of such windows can be detected if exposure data are collected repeatedly in short time intervals. The development of tooth-matrix biomarkers which provide prenatal and postnatal exposure measures in repeated intervals can provide such data. Using teeth, we use reverse distributed lagged models (DLMs) to incorporate weekly prenatal and postnatal measures of exposures to estimate time-varying associations with developmental effects. The analysis of such data using lagged weighted quantile sum (WQS) regression as an extension to reverse DLMs for complex mixtures was first proposed by Bello et al. This prior algorithm was not operationally generalizable to large numbers of components (say, more than five or six). We propose a revised algorithm that may be useful for larger mixtures by combining time-specific WQS(t) indices in a reverse DLM. We demonstrate the new algorithm using tooth data in association with a neurodevelopmental score and in simulated data from 3 cases wherein different components of a mixture have time varying associations and in the case where none have associations. The new algorithm correctly detects the simulated associations when the number of samples within the time-specific analyses is moderate to large.

Early life metal dysregulation in amyotrophic lateral sclerosis

OBJECTIVE

Deficiencies and excess of essential elements and toxic metals are implicated in amyotrophic lateral sclerosis (ALS), but the age when metal dysregulation appears remains unknown. This study aims to determine whether metal uptake is dysregulated during childhood in individuals eventually diagnosed with ALS.

METHODS

Laser ablation-inductively coupled plasma-mass spectrometry was used to obtain time series data of metal uptake using biomarkers in teeth from autopsies or dental extractions of ALS (n = 36) and control (n = 31) participants. Covariate data included sex, smoking, occupational exposures, and ALS family history. Case-control differences were identified in temporal profiles of metal uptake for individual metals using distributed lag models. Weighted quantile sum (WQS) regression was used for metals mixture analyses. Similar analyses were performed on an ALS mouse model to further verify the relevance of dysregulation of metals in ALS.

RESULTS

Metal levels were higher in cases than in controls: 1.49 times for chromium (1.11-1.82; at 15 years), 1.82 times for manganese (1.34-2.46; at birth), 1.65 times for nickel (1.22-2.01; at 8 years), 2.46 times for tin (1.65-3.30; at 2 years), and 2.46 times for zinc (1.49-3.67; at 6 years). Co-exposure to 11 elements indicated that childhood metal dysregulation was associated with ALS. The mixture contribution of metals to disease outcome was likewise apparent in tooth biomarkers of an ALS mouse model, and differences in metal distribution were evident in ALS mouse brains compared to brains from littermate controls.

INTERPRETATION

Overall, our study reveals direct evidence that altered metal uptake during specific early life time windows is associated with adult-onset ALS.

Environmental mixtures and children's health: identifying appropriate statistical approaches

PURPOSE OF REVIEW

Biomonitoring studies have shown that children are constantly exposed to complex patterns of chemical and nonchemical exposures. Here, we briefly summarize the rationale for studying multiple exposures, also called mixture, in relation to child health and key statistical approaches that can be used. We discuss advantages over traditional methods, limitations and appropriateness of the context.

RECENT FINDINGS

New approaches allow pediatric researchers to answer increasingly complex questions related to environmental mixtures. We present methods to identify the most relevant exposures among a high-multitude of variables, via shrinkage and variable selection techniques, and identify the overall mixture effect, via Weighted Quantile Sum and Bayesian Kernel Machine regressions. We then describe novel extensions that handle high-dimensional exposure data and allow identification of critical exposure windows.

SUMMARY

Recent advances in statistics and machine learning enable researchers to identify important mixture components, estimate joint mixture effects and pinpoint critical windows of exposure. Despite many advantages over single chemical approaches, measurement error and biases may be amplified in mixtures research, requiring careful study planning and design. Future research requires increased collaboration between epidemiologists, statisticians and data scientists, and further integration with causal inference methods.

A spatially varying distributed lag model with application to an air pollution and term low birth weight study

Distributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the possibility of spatial variability in the lagged health effect parameters that may result from exposure characteristics and/or residual confounding. We develop a spatially varying Gaussian process model for critical windows called 'SpGPCW' and use it to investigate geographic variability in the association between term low birth weight and average weekly concentrations of ozone and PM_2:5 during pregnancy by using birth records from North Carolina. SpGPCW is designed to accommodate areal level spatial correlation between lagged health effect parameters and temporal smoothness in risk estimation across pregnancy. Through simulation and a real data application, we show that the consequences of ignoring spatial variability in the lagged health effect parameters include less reliable inference for the parameters and diminished ability to identify true critical window sets, and we investigate the use of existing Bayesian model comparison techniques as tools for determining the presence of spatial variability. We find that exposure to PM_2:5 is associated with elevated term low birth weight risk in selected weeks and counties and that ignoring spatial variability results in null associations during these periods. An R package (SpGPCW) has been developed to implement the new method.

dLagM: An R package for distributed lag models and ARDL bounds testing

In this article, we introduce the R package dLagM for the implementation of distributed lag models and autoregressive distributed lag (ARDL) bounds testing to explore the short and long-run relationships between dependent and independent time series. Distributed lag models constitute a large class of time series regression models including the ARDL models used for cointegration analysis. The dLagM package provides a user-friendly and flexible environment for the implementation of the finite linear, polynomial, Koyck, and ARDL models and ARDL bounds cointegration test. Particularly, in this article, a new search algorithm to specify the orders of ARDL bounds testing is proposed and implemented by the dLagM package. Main features and input/output structures of the dLagM package and use of the proposed algorithm are illustrated over the datasets included in the package. Features of dLagM package are benchmarked with some mainstream software used to implement distributed lag models and ARDLs.

Repeated holdout validation for weighted quantile sum regression

Weighted Quantile Sum (WQS) regression is a method commonly used in environmental epidemiology to assess the impact of chemical mixtures in relation to a health outcome of interest. Data are partitioned into a single training and test set to reduce sample-specific chemical weights. However, in typical epidemiology sample sizes, this may produce unstable chemical weights and WQS index estimates, and investigators may resort to training and testing on the same data. To solve this problem, we propose repeated holdout validation whereby data are randomly partitioned 100 times, producing a distribution of validated results. Taking the mean as the final estimate, confidence estimates may also be calculated for inference. Further, this method helps characterize the variability in chemical weights, aiding in the identification of chemicals of concern. This is important since it may direct future research into specific chemicals.

Using data from 718 mother-child pairs in the Swedish Environmental Longitudinal, Mother and Child, Asthma and Allergy (SELMA) study, we assessed the association between prenatal exposure to 26 endocrine disrupting chemicals and child Intelligence Quotient (IQ). Results using a single partition were unstable, varying by random seed. The WQS index estimate was significant when all data was used (e.g. no partition) (β = −2.2 CI = −3.43, −0.98), but attenuated and nonsignificant using repeated holdout validation (β = −0.82 CI = −2.11, 0.45). When implementing WQS in epidemiologic studies with limited sample sizes, repeated holdout validation is a viable alternative to using a single, or no partitioning. Repeated holdout can both stabilize results and help characterize the uncertainty in identifying chemicals of concern, while maintaining some of the the rigor of holdout validation.

•

Repeated holdout validation improves the stability of WQS estimates in finite study samples

•

Uncertainty in identifying toxic chemicals of concern is acknowledged and characterized

Time-varying associations between prenatal metal mixtures and rapid visual processing in children

BACKGROUND

Humans are exposed to mixtures of chemicals across their lifetimes, a concept sometimes called the "exposome." Mixtures likely have temporal "critical windows" of susceptibility like single agents and measuring them repeatedly might help to define such windows. Common approaches to evaluate the effects of chemical mixtures have focused on their effects at a single time point. Our goal is to expand upon these previous techniques and examine the time-varying critical windows for metal mixtures on subsequent neurobehavior in children.

METHODS

We propose two methods, joint weighted quantile sum regression (JWQS) and meta-weighted quantile sum regression (MWQS), to estimate the effects of chemical mixtures measured across multiple time points, while providing data on their critical windows of exposure. We compare the performance of both methods using simulations. We also applied both techniques to assess second and third trimester metal mixture effects in predicting performance in the Rapid Visual Processing (RVP) task from the Cambridge Neuropsychological Test Automated Battery (CANTAB) assessed at 6-9 years in children who are part of the PROGRESS (Programming Research in Obesity, GRowth, Environment and Social Stressors) longitudinal cohort study. The metals, arsenic, cadmium (Cd), cesium, chromium, lead (Pb) and antimony (Sb) were selected based on their toxicological profile.

RESULTS

In simulations, JWQS and MWQS had over 80% accuracy in classifying exposures as either strongly or weakly contributing to an association. In real data, both JWQS and MWQS consistently found that Pb and Cd exposure jointly predicted longer latency in the RVP and that second trimester exposure better predicted the results than the third trimester. Additionally, both JWQS and MWQS highlighted the strong association Cd and Sb had with lower accuracy in the RVP and that third trimester exposure was a better predictor than second trimester exposure.

CONCLUSIONS

Our results indicate that metal mixtures effects vary across time, have distinct critical windows and that both JWQS and MWQS can determine longitudinal mixture effects including the cumulative contribution of each exposure and critical windows of effect.

Environmental exposure to low-level lead (Pb) co-occurring with other neurotoxicants in early life and neurodevelopment of children

Lead (Pb) is a worldwide environmental contaminant that even at low levels influences brain development and affects neurobehavior later in life; nevertheless it is only a small fraction of the neurotoxicant (NT) exposome. Exposure to environmental Pb concurrent with other NT substances is often the norm, but their joint effects are challenging to study during early life. The aim of this review is to integrate studies of Pb-containing NT mixtures during the early life and neurodevelopment outcomes of children. The Pb-containing NT mixtures that have been most studied involve other metals (Mn, Al, Hg, Cd), metalloids (As), halogen (F), and organo-halogen pollutants. Co-occurring Pb-associated exposures during pregnancy and lactation depend on the environmental sources and the metabolism and half-life of the specific NT contaminant; but offspring neurobehavioral outcomes are also influenced by social stressors. Nevertheless, Pb-associated effects from prenatal exposure portend a continued burden on measurable neurodevelopment; they thus favor increased neurological health issues, decrements in neurobehavioral tests and reductions in the quality of life. Neurobehavioral test outcomes measured in the first 1000 days showed Pb-associated negative outcomes were frequently noticed in infants (<6 months). In older (preschool and school) children studies showed more variations in NT mixtures, children's age, and sensitivity and/or specificity of neurobehavioral tests; these variations and choice of statistical model (individual NT stressor or collective effect of mixture) may explain inconsistencies. Multiple exposures to NT mixtures in children diagnosed with 'autism spectrum disorders' (ASD) and 'attention deficit and hyperactivity disorders' (ADHD), strongly suggest a Pb-associated effect. Mixture potency (number or associated NT components and respective concentrations) and time (duration and developmental stage) of exposure often showed a measurable impact on neurodevelopment; however, net effects, reversibility and/or predictability of delays are insufficiently studied and need urgent attention. Nevertheless, neurodevelopment delays can be prevented and/or attenuated if public health policies are implemented to protect the unborn and the young child.

An Important Need to Monitor from an Early Age the Neurotoxins in the Blood or by an Equivalent Biomarker

An overwhelming amount of evidence now suggests that some people are becoming overloaded with neurotoxins. This is mainly from changes in their living environment and style, coupled with the fact that all people are different and display a broad distribution of genetic susceptibilities. It is important for individuals to know where they lie concerning their ability to either reject or retain toxins. Everyone is contaminated with a certain baseline of toxins that are alien to the body, namely aluminum, arsenic, lead, and mercury. Major societal changes have modified their intake, such as vaccines in enhanced inoculation procedures and the addition of sushi into diets, coupled with the ever-present lead, arsenic, and traces of manganese. It is now apparent that no single toxin is responsible for the current neurological epidemics, but rather a collaborative interaction with possible synergistic components. Selenium, although also a neurotoxin if in an excessive amount, is always present and is generally more present than other toxins. It performs as the body’s natural chelator. However, it is possible that the formation rates of active selenium proteins may become overburdened by other toxins. Every person is different and it now appears imperative that the medical profession establish an individual’s neurotoxicity baseline. Moreover, young women should certainly establish their baselines long before pregnancy in order to identify possible risk factors.

Statistical Approaches for Investigating Periods of Susceptibility in Children's Environmental Health Research

PURPOSE OF REVIEW

Children's environmental health researchers are increasingly interested in identifying time intervals during which individuals are most susceptible to adverse impacts of environmental exposures. We review recent advances in methods for assessing susceptible periods.

RECENT FINDINGS

We identified three general classes of modeling approaches aimed at identifying susceptible periods in children's environmental health research: multiple informant models, distributed lag models, and Bayesian approaches. Benefits over traditional regression modeling include the ability to formally test period effect differences, to incorporate highly time-resolved exposure data, or to address correlation among exposure periods or exposure mixtures. Several statistical approaches exist for investigating periods of susceptibility. Assessment of susceptible periods would be advanced by additional basic biological research, further development of statistical methods to assess susceptibility to complex exposure mixtures, validation studies evaluating model assumptions, replication studies in different populations, and consideration of susceptible periods from before conception to disease onset.

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures

The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.

Application of improved approach to evaluate a community intervention to reduce exposure of young children living in farmworker households to organophosphate pesticides

The take-home pathway is a significant source of organophosphate pesticide exposure for young children (3-5 years old) living with an adult farmworker. This avoidable exposure pathway is an important target for intervention. We selected 24 agricultural communities in the Yakima Valley of Washington State and randomly assigned them to receive an educational intervention (n = 12) to reduce children's pesticide exposure or usual care (n = 12). We assessed exposure to pesticides in nearly 200 adults and children during the pre and post-intervention periods by measuring metabolites in urine. We compared pre- and post-intervention exposures by expressing the child's pesticide metabolite concentration as a fraction of the adult's concentration living in the same household, because the amount of pesticides applied during the collection periods varied. Exposures in our community were consistently higher, sometimes above the 95^th percentile of the exposures reported by the National Health and Nutrition Examination Survey (NHANES). While intervention and control communities demonstrated a reduction in the ratio of child to adult exposure, this reduction was more pronounced in intervention communities (2.7-fold, p < 0.001 compared to 1.7-fold, p = 0.052 for intervention and control, respectively). By examining the child/adult biomarker ratio, we demonstrated that our community-based intervention was effective in reducing pesticide exposure to children in agricultural communities.

Statistical Methodology in Studies of Prenatal Exposure to Mixtures of Endocrine-Disrupting Chemicals: A Review of Existing Approaches and New Alternatives

BACKGROUND

Prenatal exposures to endocrine-disrupting chemicals (EDCs) during critical developmental windows have been implicated in the etiologies of a wide array of adverse perinatal and pediatric outcomes. Epidemiological studies have concentrated on the health effects of individual chemicals, despite the understanding that EDCs act together via common mechanisms, that pregnant women are exposed to multiple EDCs simultaneously, and that substantial toxicological evidence of adverse developmental effects has been documented. There is a move toward multipollutant models in environmental epidemiology; however, there is no current consensus on appropriate statistical methods.

OBJECTIVES

We aimed to review the statistical methods used in these studies, to identify additional applicable methods, and to determine the strengths and weaknesses of each method for addressing the salient statistical and epidemiological challenges.

METHODS

We searched Embase, MEDLINE, and Web of Science for epidemiological studies of endocrine-sensitive outcomes in the children of mothers exposed to EDC mixtures during pregnancy and identified alternative statistical methods from the wider literature.

DISCUSSION

We identified 74 studies and analyzed the methods used to estimate mixture health effects, identify important mixture components, account for nonmonotonicity in exposure–response relationships, assess interactions, and identify windows of exposure susceptibility. We identified both frequentist and Bayesian methods that are robust to multicollinearity, performing shrinkage, variable selection, dimension reduction, statistical learning, or smoothing, including methods that were not used by the studies included in our review.

CONCLUSIONS

Compelling motivation exists for analyzing EDCs as mixtures, yet many studies make simplifying assumptions about EDC additivity, relative potency, and linearity, or overlook the potential for bias due to asymmetries in chemical persistence. We discuss the potential impacts of these choices and suggest alternative methods to improve analyses of prenatal exposure to EDC mixtures. https://doi.org/10.1289/EHP2207.

Dentine biomarkers of prenatal and early childhood exposure to manganese, zinc and lead and childhood behavior

BACKGROUND

Metal exposure alters neurodevelopmental outcomes; little is known about critical windows of susceptibility when exposure exerts the strongest effect.

OBJECTIVE

To examine associations between dentine biomarkers of manganese (Mn), zinc (Zn) and lead (Pb) and later childhood behaviors.

METHODS

Subjects enrolled in a longitudinal birth cohort study in Mexico City provided naturally shed deciduous teeth. We estimated weekly prenatal and postnatal dentine Mn, Zn and Pb concentrations in teeth using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and measured behavior at ages 8-11 years of age using the Behavior Assessment System for Children, 2nd edition (BASC-2). We used distributed lag models and lagged weighted quantile sum regression to identify the role of individual and combined dentine biomarkers of Mn, Zn and Pb on behavioral outcomes controlling for maternal education and gestational age.

RESULTS

Among the 133 subjects included in this study, prenatal and early postnatal dentine Mn appeared protective against childhood behavioral problems, specifically hyperactivity and attention. Postnatal dentine Mn was associated with increased reporting of internalizing problems, specifically anxiety. At 6 months, a 1-unit increase (unit = 1 SD of log concentration) in Mn was associated with a 0.18-unit (unit = 1 SD of BASC-2 score) increase in internalizing symptoms score and a 0.25-unit increase in anxiety. Postnatal Pb was associated with increasing anxiety symptoms; at 12 months, a 1-unit increase in Pb was associated with a 0.4 unit increase in anxiety symptoms. When examined as a metal mixture, we observed two potential windows of susceptibility to increased anxiety symptoms: the first window (0-8 months) appeared driven by Mn, the second window (8-12 months) was driven by the metal mixture and dominated by Pb. A 1-unit increase in the mixture index was associated with a 0.7-unit increase in SD of anxiety symptoms.

CONCLUSIONS

Childhood behaviors may demonstrate postnatal windows of susceptibility to individual and mixed metal concentrations measured in deciduous teeth. Prenatal dentine Mn may be protective, while excessive early postnatal Mn may increase risk for adverse behaviors. In combination, higher concentrations of Mn, Zn and Pb may have an adverse impact on behavior.

Assessing exposure to metals using biomonitoring: Achievements and challenges experienced through surveys in low- and middle-income countries

In this narrative account based on a keynote presentation on exposure biomonitoring of metals in low- and middle-income countries (LMIC), we first briefly address practical issues that have arisen from our experience during the conduct of various surveys in LMIC. These have included the statistical handling of multiple pollutants in the same subject, the problem of correctly adjusting for urinary flow in spot samples of urine, and the possible external contamination of samples when doing field surveys in challenging environments. We then review and present selected results from surveys conducted in the mining area of Katanga in the Democratic Republic of Congo (DR Congo), where we documented high urinary levels of cobalt and other trace metals (arsenic, uranium) in people living close (<3 km) to mining or smelting operations (Banza et al., 2009). Consumption of contaminated foodstuffs (maize, legumes, fish) and, especially among children, dust ingestion proved to be the main sources of exposure to cobalt (Cheyns et al., 2014). Urinary biomonitoring studies among artisanal workers involved in mining cobalt, craftsmen working malachite, and workers processing gold ore revealed high to extremely high values of cobalt (largely exceeding the Biological Exposure Index of 15 μg/L), as well as other trace metals such as uranium, manganese, lead or mercury, depending on the type of jobs. This abundant biomonitoring data has been valuable to argue for improved enforcement of legislation to protect workers and citizens against the hazards posed by the mining activities in the area. Epidemiological studies have been undertaken and are ongoing to assess the human health impact of this pollution.

Environmental exposure mixtures: questions and methods to address them

PURPOSE OF THIS REVIEW

This review provides a summary of statistical approaches that researchers can use to study environmental exposure mixtures. Two primary considerations are the form of the research question and the statistical tools best suited to address that question. Because the choice of statistical tools is not rigid, we make recommendations about when each tool may be most useful.

RECENT FINDINGS

When dimensionality is relatively low, some statistical tools yield easily interpretable estimates of effect (e.g., risk ratio, odds ratio) or intervention impacts. When dimensionality increases, it is often necessary to compromise this interpretablity in favor of identifying interesting statistical signals from noise; this requires applying statistical tools that are oriented more heavily towards dimension reduction via shrinkage and/or variable selection.

SUMMARY

The study of complex exposure mixtures has prompted development of novel statistical methods. We suggest that further validation work would aid practicing researchers in choosing among existing and emerging statistical tools for studying exposure mixtures.

[Exposure Assessment in Japan Environment and Children's Study]

In this review, we present an initial plan for exposure assessment in the Japan Environment and Children's Study (JECS) by focusing on a biomonitoring technique and discuss the challenges encountered when using the biomonitoring technique for exposure measurements. JECS registered 103,099 pregnant mothers and has been following children born to them. Various biological samples were collected from mothers during pregnancy (blood and urine), at birth (blood and hair) and at check-up one month after birth (breast milk). Samples were also collected from children at birth (cord blood) and at check-up one month after birth (hair and blood spot). Those samples will be used to assess maternal and foetal exposures to chemical substances. Measurement reliability, i.e., intraclass correlation coefficient (ICC), and attenuation bias related to low ICCs should be taken into consideration when using the biomonitoring results. Along with the biomonitoring technique, simulation models, pharmacokinetic (PK) models and exposomics techniques are under development in JECS. New analytical techniques include deciduous teeth measurements and -omics analyses. In particular, PK models and sensor technologies are one of the most important methodologies for future JECS exposure analyses. Statistical methods for examining the effects of intercorrelated multiple exposures as well as nondetection data should also be explored.

Modeling the health effects of time-varying complex environmental mixtures: Mean field variational Bayes for lagged kernel machine regression

There is substantial interest in assessing how exposure to environmental mixtures, such as chemical mixtures, affect child health. Researchers are also interested in identifying critical time windows of susceptibility to these complex mixtures. A recently developed method, called lagged kernel machine regression (LKMR), simultaneously accounts for these research questions by estimating effects of time-varying mixture exposures, and identifying their critical exposure windows. However, LKMR inference using Markov chain Monte Carlo methods (MCMC-LKMR) is computationally burdensome and time intensive for large datasets, limiting its applicability. Therefore, we develop a mean field variational Bayesian inference procedure for lagged kernel machine regression (MFVB-LKMR). The procedure achieves computational efficiency and reasonable accuracy as compared with the corresponding MCMC estimation method. Updating parameters using MFVB may only take minutes, while the equivalent MCMC method may take many hours or several days. We apply MFVB-LKMR to PROGRESS, a prospective cohort study in Mexico. Results from a subset of PROGRESS using MFVB-LKMR provide evidence of significant positive association between second trimester cobalt levels and z-scored birthweight. This positive association is heightened by cesium exposure. MFVB-LKMR is a promising approach for computationally efficient analysis of environmental health datasets, to identify critical windows of exposure to complex mixtures.

Evaluating effects of prenatal exposure to phthalate mixtures on birth weight: A comparison of three statistical approaches

OBJECTIVES

We applied three statistical approaches for evaluating associations between prenatal urinary concentrations of a mixture of phthalate metabolites and birth weight.

METHODS

We included 300 women who provided 732 urine samples during pregnancy and delivered a singleton infant. We measured urinary concentrations of metabolites of di(2-ethylhexyl)-phthalate, di-isobutyl-, di-n-butyl-, butylbenzyl-, and diethyl phthalates. We applied 1) linear regressions; 2) classification methods [principal component analysis (PCA) and structural equation models (SEM)]; and 3) Bayesian kernel machine regression (BKMR), to evaluate associations between phthalate metabolite mixtures and birth weight adjusting for potential confounders. Data were presented as mean differences (95% CI) in birth weight (grams) as each phthalate increased from the 10th to the 90th percentile.

RESULTS

When analyzing individual phthalate metabolites using linear regressions, each metabolite demonstrated a modest inverse association with birth weight [from -93 (-206, 21) to -49 (-164, 65)]. When simultaneously including all metabolites in a multivariable model, inflation of the estimates and standard errors were noted. PCA identified two principal components, both inversely associated with birth weight [-23 (-68, 22), -27 (-71, 17), respectively]. These inverse associations were confirmed when applying SEM. BKMR further identified that monoethyl and mono(2-ethylhexyl) phthalate and phthalate concentrations were linearly related to lower birth weight [-51(-164, 63) and -122 (-311, 67), respectively], and suggested no evidence of interaction between metabolites.

CONCLUSIONS

While none of the methods produced significant results, we demonstrated the potential issues arising using linear regression models in the context of correlated exposures. Among the other selected approaches, classification techniques identified common sources of exposures with implications for interventions, while BKMR further identified specific contributions of individual metabolites.

The child health exposure analysis resource as a vehicle to measure environment in the environmental influences on child health outcomes program

PURPOSE OF REVIEW

Demonstrate the role of environment as a predictor of child health.

RECENT FINDINGS

The children's health exposure analysis resource (CHEAR) assists the Environmental influences on child health outcomes (ECHO) program in understanding the time sensitive and dynamic nature of perinatal and childhood environment on developmental trajectories by providing a central infrastructure for the analysis of biological samples from the ECHO cohort awards. CHEAR will assist ECHO cohorts in defining the critical or sensitive period for effects associated with environmental exposures. Effective incorporation of these principles into multiple existing cohorts requires extensive multidisciplinary expertise, creativity, and flexibility. The pursuit of life course - informed research within the CHEAR/ECHO structure represents a shift in focus from single exposure inquiries to one that addresses multiple environmental risk factors linked through shared vulnerabilities. CHEAR provides ECHO both targeted analyses of inorganic and organic toxicants, nutrients, and social-stress markers and untargeted analyses to assess the exposome and discovery of exposure-outcome relationships.

SUMMARY

Utilization of CHEAR as a single site for characterization of environmental exposures within the ECHO cohorts will not only support the investigation of the influence of environment on children's health but also support the harmonization of data across the disparate cohorts that comprise ECHO.