Extending the Distributed Lag Model framework to handle chemical mixtures

Abstract

Distributed Lag Models (DLMs) are used in environmental health studies to analyze the time-delayed effect of an exposure on an outcome of interest. Given the increasing need for analytical tools for evaluation of the effects of exposure to multi-pollutant mixtures, this study attempts to extend the classical DLM framework to accommodate and evaluate multiple longitudinally observed exposures. We introduce 2 techniques for quantifying the time-varying mixture effect of multiple exposures on an outcome of interest. Lagged WQS, the first technique, is based on Weighted Quantile Sum (WQS) regression, a penalized regression method that estimates mixture effects using a weighted index. We also introduce Tree-based DLMs, a nonparametric alternative for assessment of lagged mixture effects. This technique is based on the Random Forest (RF) algorithm, a nonparametric, tree-based estimation technique that has shown excellent performance in a wide variety of domains. In a simulation study, we tested the feasibility of these techniques and evaluated their performance in comparison to standard methodology. Both methods exhibited relatively robust performance, accurately capturing pre-defined non-linear functional relationships in different simulation settings. Further, we applied these techniques to data on perinatal exposure to environmental metal toxicants, with the goal of evaluating the effects of exposure on neurodevelopment. Our methods identified critical neurodevelopmental windows showing significant sensitivity to metal mixtures.

Associations between maternal weekly air pollutant exposures and low birth weight: a distributed lag non-linear model

“…However, monthly potentially relevant exposure windows are still relatively broad range of time periods. In addition, we only explored the effect of an exposure at a single gestational month and exposures during other months could not be adjusted for, which might introduce bias because exposures of adjacent gestational months are generally highly correlated [33,34].…”

“…To address these knowledge gaps, we used air pollutants, temperature, and birth data in urban areas of Jinan, China from 2013-2016 attempting to identify a potentially relevant exposure window for maternal PM 2.5 , NO 2 , and SO 2 exposure on TLBW and we mainly focused on weekly-specific associations. To accomplish this, we used flexible statistical models based on distributed lag non-linear models (DLNM), which could be used to study the effect of an exposure at a certain time point while adjusting for all the past (lagged) values of that exposure [34,35].…”

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

“…This is particularly important when assessing exposure to environmental chemicals with short half-lives. As the field of environmental health moves forward, more complex models are needed to integrate exposures across multiple lifestages, assessment methods and cohorts [ 31 , 32 ]. The recent NIH effort to integrate data across cohorts as part of the Environmental Influences on Child Health Outcomes (ECHO) program will require exposure assessment standardization techniques, such as the methods presented in this study.…”

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

“…Pal and Mitra [16] implement autoregressive DLMs to analyze the relationship between diesel and soybean prices in the USA. Bello et al [17] employ DLMs for modelling the longitudinal effects of chemicals to figure out the time-delayed effect of an exposure on an outcome. Huang et al [18] investigate the association between cardiovascular mortality and temperature using nonlinear DLMs.…”