Neighborhood characteristics as confounders and effect modifiers for the association between air pollution exposure and subjective cognitive functioning

Li, Zhenjiang; Christensen, Grace M.; Lah, James J.; Marcus, Michele; Russell, Armistead G.; Ebelt, Stefanie; Waller, Lance A.; Hüls, Anke

doi:10.1016/j.envres.2022.113221

Cited by 12 publications

(22 citation statements)

References 51 publications

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“…To the best of our knowledge, this paper is the first to use cutting edge environmental mixture methods to study air pollution and nSES characteristics in relation to cognitive decline. Previous studies of the effect of air pollution on cognitive decline have treated nSES as a confounder or effect modifier, not a co-exposure ( Ailshire et al, 2017 ; Ailshire and Crimmins, 2014 ; Bowe et al, 2019 ; Cullen et al, 2018 ; Li et al, 2022 ). Utilizing multiple mixtures method approaches such as, LASSO, SOM, BKMR, and quantile-based G-computation supports a comprehensive examination of the relationship between the exposure mixtures and outcomes ( Taylor et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…As most of the 16 nSES variables are highly correlated ( Figure S2B ), principal components (PCs) were derived from nSES characteristics that account for 80% of the total variance in nSES. The uncorrelated nSES variables were then included as confounding variables in the association analyses, following the work in Li et al, 2022 . For the assessment of joint effects, air pollution and nSES were considered as co-exposures.…”

Section: Methodsmentioning

confidence: 99%

“…Despite the importance and relevance of the concept of environmental injustice for cognitive health, few epidemiologic studies on air pollution and cognition have considered nSES as a confounder ( Ailshire and Crimmins, 2014 ; Bowe et al, 2019 ; Cullen et al, 2018 ; Li et al, 2022 ) or effect modifier ( Ailshire et al, 2017 ; Li et al, 2022 ). One study treated nSES as an effect modifier and found the association between PM 2.5 and cognitive errors was stronger in older adults living in high stress neighborhoods ( Ailshire et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…One study treated nSES as an effect modifier and found the association between PM 2.5 and cognitive errors was stronger in older adults living in high stress neighborhoods ( Ailshire et al, 2017 ). Another study found that nSES was both a confounder and an effect modifier of the association of air pollution and cognitive decline ( Li et al, 2022 ). These studies show that it is imperative to include socioeconomic context of participants in studies of air pollution and cognitive decline.…”

Section: Introductionmentioning

confidence: 99%

“…One methodologic challenge of analyses related to environmental injustice is the high correlation between environmental and social stressors. Previous studies have evaluated interactions and effect modifications of air pollutants and nSES by including interaction terms or conducting stratified linear regression models ( Ailshire et al, 2017 ; Li et al, 2022 ). Modeling environmental exposures, including pollutants and socioeconomic contexts of the population, as a mixture is a necessary next step to describe the associations between environmental pollutants and health effects and understand their joint and potentially synergistic effects ( Carlin et al, 2013 ; Taylor et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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The complex relationship of air pollution and neighborhood socioeconomic status and their association with cognitive decline

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Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model

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Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one‐dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi‐center case‐control study of non‐Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.

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Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low‐rank kriging multiple membership model

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et al. 2022

Statistics in Medicine

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The exposome is an ideal in public health research that posits that individuals experience risk for adverse health outcomes from a wide variety of sources over their lifecourse. There have been increases in data collection in the various components of the exposome, but novel statistical methods are needed that capture multiple dimensions of risk at once. We introduce a Bayesian index low-rank kriging (LRK) multiple membership model (MMM) to simultaneously estimate the health effects of one or more groups of exposures, the relative importance of exposure components, and cumulative spatial risk over time using residential histories. The model employs an MMM to consider all residential locations for subjects weighted by duration and LRK to increase computational efficiency.We demonstrate the performance of the Bayesian index LRK-MMM through a simulation study, showing that the model accurately and consistently estimates the health effects of one or several group indices and has high power to identify a region of elevated spatial risk due to unmeasured environmental exposures. Finally, we apply our model to data from a multicenter case-control study of non-Hodgkin lymphoma (NHL), finding a significant positive association between one index of pesticides and risk for NHL in Iowa. Additionally, we find an area of significantly elevated spatial risk for NHL in Los Angeles. In conclusion, our Bayesian index LRK-MMM represents a step forward toward bringing the ideals of the exposome into practice for environmental risk analyzes.

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Neighborhood characteristics as confounders and effect modifiers for the association between air pollution exposure and subjective cognitive functioning

Cited by 12 publications

References 51 publications

The complex relationship of air pollution and neighborhood socioeconomic status and their association with cognitive decline

The complex relationship of air pollution and neighborhood socioeconomic status and their association with cognitive decline

Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model

Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low‐rank kriging multiple membership model

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