Associations between combined exposure to environmental hazards and social stressors at the neighborhood level and individual perinatal outcomes in the ECHO-wide cohort

Martenies, Sheena E.; Zhang, Mingyu; Corrigan, Anne E.; Kvit, Anton; Shields, Timothy; Wheaton, William; Bastain, Theresa M.; Breton, Carrie V.; Dabelea, Dana; Habre, Rima; Magzamen, Sheryl; Padula, Amy; Him, Deana Around; Camargo, Carlos A.; Cowell, Whitney; Croen, Lisa; Deoni, Sean; Everson, Todd M.; Hartert, Tina; Hipwell, Alison E.; McEvoy, Cindy T.; Morello‐Frosch, Rachel; O’Connor, Thomas G.; Petriello, Michael C.; Sathyanarayana, Sheela; Stanford, Joseph B.; Woodruff, Tracey J.; Wright, Rosalind J.; Kress, Amii M.

doi:10.1016/j.healthplace.2022.102858

Cited by 12 publications

(15 citation statements)

References 92 publications

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“…We randomly generated 1000 birth dates and constructed 37 weeks of exposure data for each birth from observed ambient

{\mathrm{PM}}_{2.5}

data. For each birth, we simulated modifier value

{m}_i

from a continuous uniform distribution on

\left(0,1\right)

because the modifier of interest in this paper and other motivating papers is a bounded index (Martenies, Zhang, et al, 2022; Niu et al, 2022).…”

Section: Simulationsmentioning

confidence: 99%

“…Another approach is using time‐averaged measures of exposure (e.g., pregnancy average exposure) modified by a continuous factor. Martenies, Zhang, et al (2022) use a neighborhood‐level score modeled after the California EnviroScreen score as a continuous modifier but fit a linear regression model using annual average exposure. The aggregation in this approach prevents identifying windows of susceptibility during pregnancy.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, several studies have used an index aggregating multiple neighborhood factors as a candidate modifying factor to study the effect of air pollution on health. Most of these studies considered averaged exposure (Martenies, Zhang, et al, 2022), while others have used DLMs for repeated measures of exposure stratified by a dichotomized version of a composite index (Niu et al, 2022). We consider the Colorado EnviroScreen health and social factors score, which is a measure of neighborhood‐level health and social vulnerabilities in Colorado, as a modifier of the association between ambient air pollution and birth weight (Colorado Department of Public Health and Environment (CDPHE), 2022; Colorado EnviroScreen Tool Team, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability

Demateis,

Keller,

Rojas‐Rueda

et al. 2024

Environmetrics

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Maternal exposure to air pollution during pregnancy has a substantial public health impact. Epidemiological evidence supports an association between maternal exposure to air pollution and low birth weight. A popular method to estimate this association while identifying windows of susceptibility is a distributed lag model (DLM), which regresses an outcome onto exposure history observed at multiple time points. However, the standard DLM framework does not allow for modification of the association between repeated measures of exposure and the outcome. We propose a distributed lag interaction model that allows modification of the exposure‐time‐response associations across individuals by including an interaction between a continuous modifying variable and the exposure history. Our model framework is an extension of a standard DLM that uses a cross‐basis, or bi‐dimensional function space, to simultaneously describe both the modification of the exposure‐response relationship and the temporal structure of the exposure data. Through simulations, we showed that our model with penalization out‐performs a standard DLM when the true exposure‐time‐response associations vary by a continuous variable. Using a Colorado, USA birth cohort, we estimated the association between birth weight and ambient fine particulate matter air pollution modified by an area‐level metric of health and social adversities from Colorado EnviroScreen.

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“…We randomly generated 1000 birth dates and constructed 37 weeks of exposure data for each birth from observed ambient

{\mathrm{PM}}_{2.5}

data. For each birth, we simulated modifier value

{m}_i

from a continuous uniform distribution on

\left(0,1\right)

because the modifier of interest in this paper and other motivating papers is a bounded index (Martenies, Zhang, et al, 2022; Niu et al, 2022).…”

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability

Demateis,

Keller,

Rojas‐Rueda

et al. 2024

Environmetrics

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show abstract

“…The neighborhood-level exposures to environmental and social stressors study uses the extant data from 35 cohorts. Study inclusion criteria have been described previously Martenies and others (2022). The primary outcome of interest in this study was the gestational age at birth.…”

Section: Neighborhood-level Exposures To Environmental and Social Str...mentioning

confidence: 99%

Missing data interpolation in integrative multi-cohort analysis with disparate covariate information

Смирнова¹,

Zhong²,

Alsaadawi³

et al. 2022

Preprint

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Integrative analysis of datasets generated by multiple cohorts is a widely-used approach for increasing sample size, precision of population estimators, and generalizability of analysis results in epidemiological studies. However, often each individual cohort dataset does not have all variables of interest for an integrative analysis collected as a part of an original study. Such cohort-level missingness poses methodological challenges to the integrative analysis since missing variables have traditionally: (1) been removed from the data for complete case analysis; or (2) been completed by missing data interpolation techniques using data with the same covariate distribution from other studies. In most integrative-analysis studies, neither approach is optimal as it leads to either loosing the majority of study covariates or challenges in specifying the cohorts following the same distributions. We propose a novel approach to identify the studies with same distributions that could be used for completing the cohort-level missing information. Our methodology relies on (1) identifying sub-groups of cohorts with similar covariate distributions using cohort identity random forest prediction models followed by clustering; and then (2) applying a recursive pairwise distribution test for high dimensional data to these sub-groups. Extensive simulation studies show that cohorts with the same distribution are correctly grouped together in almost all simulation settings. Our methods' application to two ECHO-wide Cohort Studies reveals that the cohorts grouped together reflect the similarities in study design. The methods are implemented in R software package relate.

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“…Recently, several methodological approaches have been proposed to address the independent and joint contribution of environmental exposures and social factors to health outcomes (Martenies, Hoskovec, et al., 2022 ; Martenies, Zhang, et al., 2022 ; Martenies et al., 2023 ). Identification of relevant social or environmental factors associated with disease outcomes are an important pathway to identify effective intervention and mediation strategies to improve health.…”

Section: Introductionmentioning

confidence: 99%

Cumulative Exposures to Environmental and Socioeconomic Risk Factors in Milwaukee County, Wisconsin

Kodros,

Carter,

Oke

et al. 2024

GeoHealth

Self Cite

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The environmental justice literature demonstrates consistently that low‐income and minority communities are disproportionately exposed to environmental hazards. In this case study, we examined cumulative multipollutant, multidomain, and multimatrix environmental exposures in Milwaukee County, Wisconsin for the year 2015. We identified spatial hot spots in Milwaukee County both individually (using local Moran's I) and through clusters (using K‐means clustering) across a profile of environmental pollutants that span regulatory domains and matrices of exposure, as well as socioeconomic indicators. The cluster with the highest exposures within the urban area was largely characterized by low socioeconomic status and an overrepresentation of the Non‐Hispanic Black population relative to the county as a whole. In this cluster, average pollutant concentrations were equivalent to the 78th percentile in county‐level blood lead levels, 67th percentile in county‐level NO2, 79th percentile in county‐level CO, and 78th percentile in county‐level air toxics. Simultaneously, this cluster had an average equivalent to the 62nd percentile in county‐level unemployment, 70th percentile in county‐level population rate lacking a high school diploma, 73rd percentile in county‐level poverty rate, and 28th percentile in county‐level median household income. The spatial patterns of pollutant exposure and SES indicators suggested that these disparities were not random but were instead structured by socioeconomic and racial factors. Our case study, which combines environmental pollutant exposures, sociodemographic data, and clustering analysis, provides a roadmap to identify and target overburdened communities for interventions that reduce environmental exposures and consequently improve public health.

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Associations between combined exposure to environmental hazards and social stressors at the neighborhood level and individual perinatal outcomes in the ECHO-wide cohort

Cited by 12 publications

References 92 publications

Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability

Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability

Missing data interpolation in integrative multi-cohort analysis with disparate covariate information

Cumulative Exposures to Environmental and Socioeconomic Risk Factors in Milwaukee County, Wisconsin

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