Persistent organic pollutant exposure contributes to Black/White differences in leukocyte telomere length in the National Health and Nutrition Examination Survey

Roberts, Elizabeth S.; Boss, Jonathan; Mukherjee, Bhramar; Salerno, Stephen; Zota, Ami R.; Needham, Belinda L.

doi:10.1038/s41598-022-24316-0

Cited by 5 publications

(2 citation statements)

References 80 publications

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“…Social vulnerability has also shown disparities in diseases associated with pesticide exposure, including obesity and diabetes [35], due to differences in food availability in more disadvantaged areas [36]; long telomere length (LTL), a bio-marker for cellular aging, is higher in non-Hispanic Black Americans than non-Hispanic White Americans [37], Parkinson's disease [38], childhood cancer [39], and birth outcomes [40] [41]. It is important to identify the contiguous US counties with disproportionately high pesticide application and social vulnerability due to disparities that exist among vulnerable populations in pesticide exposure and health outcomes.…”

Section: Introductionmentioning

confidence: 99%

Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States

Taiba,

Beseler,

Kolok

et al. 2024

Preprint

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Background In the contiguous United States, environmental justice burdens and disparities in pesticide exposure are prevalent among racial and ethnic minorities and low socioeconomic groups. Identifying the counties with high pesticide exposure and social vulnerability is essential to mitigating risk. Methods We created an index for pesticides commonly used in the contiguous US states from 1992 to 2019, as well as a social vulnerability index. We identified the US counties with elevated pesticide exposure and elevated social vulnerability. The USGS Pesticide National Synthesis Project quantified pesticide exposures at a county scale for frequently applied pesticides from 1992 to 2019 in 3069 contiguous US counties. We retrieved social vulnerability data from five-year estimates (2015–2019) of the American Community Survey (ACS) for selected variables: race, income, and educational attainment, and created a social vulnerability index. We implemented the pesticide index and social vulnerability index using a principal component analysis (PCA) approach. We used an Intergovernmental Panel on Climate Change ICCP risk-based approach to identify the counties with both high pesticide exposure and social vulnerability. Results One hundred and forty-three US counties had high pesticide use and social vulnerability. Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania had significantly higher proportions of these high pesticide application and social vulnerability counties than any other state. In conclusion, disparities in pesticide exposure and associated health outcomes due to social vulnerability are widespread across the contiguous US counties in both rural and urban communities. Conclusions Our study will inform regulatory bodies about areas with both high pesticide exposure and social vulnerability areas, as well as facilitate regulatory and public health decisions.

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Section: Introductionmentioning

confidence: 99%

Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States

Taiba,

Beseler,

Kolok

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, studies that involve examining the mediating effect of multiple health behaviors have the potential for residual correlation (Schuit et al, 2002;Laaksonen et al, 2002) as do studies examining the mediating effect of multiple environmental exposures (e.g. Kim et al (2019), Bellavia et al (2019), Roberts et al (2022)). Note that in our paper, the term "residual correlation" represents the remaining dependence of the mediators beyond what can be explained by the covariates and group label in the mediator model(s).…”

Section: Introductionmentioning

confidence: 99%

Computational Simulation of Staggered 3-D Riblets for Skin Friction Drag Reduction

Smith

Yagle

McClure

2023

AIAA SCITECH 2023 Forum

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A causal decomposition analysis allows researchers to determine whether the difference in a health outcome between two groups can be attributed to a difference in each group's distribution of one or more modifiable mediator variables. With this knowledge, researchers and policymakers can focus on designing interventions that target these mediator variables. Existing methods for causal decomposition analysis either focus on one mediator variable or assume that each mediator variable is conditionally independent given the group label and the mediator-outcome confounders. In this paper, we propose a flexible causal decomposition analysis method that can accommodate multiple correlated and interacting mediator variables, which are frequently seen in studies of health behaviors and studies of environmental pollutants. We extend a Monte Carlo-based causal decomposition analysis method to this setting by using a multivariate mediator model that can accommodate any combination of binary and continuous mediator variables. Furthermore, we state the causal assumptions needed to identify both joint and path-specific decomposition effects through each mediator variable. To illustrate the reduction in bias and confidence interval width of the decomposition effects under our proposed method, we perform a simulation study. We also apply our approach to examine whether differences in smoking status and dietary inflammation score explain any of the Black-White differences in incident diabetes using data from a national cohort study.

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Path‐specific causal decomposition analysis with multiple correlated mediator variables

Smith,

McClure,

Long

2024

Statistics in Medicine

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A causal decomposition analysis allows researchers to determine whether the difference in a health outcome between two groups can be attributed to a difference in each group's distribution of one or more modifiable mediator variables. With this knowledge, researchers and policymakers can focus on designing interventions that target these mediator variables. Existing methods for causal decomposition analysis either focus on one mediator variable or assume that each mediator variable is conditionally independent given the group label and the mediator‐outcome confounders. In this article, we propose a flexible causal decomposition analysis method that can accommodate multiple correlated and interacting mediator variables, which are frequently seen in studies of health behaviors and studies of environmental pollutants. We extend a Monte Carlo‐based causal decomposition analysis method to this setting by using a multivariate mediator model that can accommodate any combination of binary and continuous mediator variables. Furthermore, we state the causal assumptions needed to identify both joint and path‐specific decomposition effects through each mediator variable. To illustrate the reduction in bias and confidence interval width of the decomposition effects under our proposed method, we perform a simulation study. We also apply our approach to examine whether differences in smoking status and dietary inflammation score explain any of the Black‐White differences in incident diabetes using data from a national cohort study.

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Persistent organic pollutant exposure contributes to Black/White differences in leukocyte telomere length in the National Health and Nutrition Examination Survey

Cited by 5 publications

References 80 publications

Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States

Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States

Computational Simulation of Staggered 3-D Riblets for Skin Friction Drag Reduction

Path‐specific causal decomposition analysis with multiple correlated mediator variables

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