Using Metrics of a Mixture Effect and Nutrition from an Observational Study for Consideration towards Causal Inference

Chris Gennings, Katherine Svensson, Alicja Wolk, Christian Lindh, Hannu Kiviranta and Carl-Gustaf Bornehag
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Chris Gennings: Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Katherine Svensson: Department of Health Sciences, Karlstad University, 65188 Karlstad, Sweden
Alicja Wolk: Institute of Environmental Medicine, Karolinska Institutet, 17177 Stockholm, Sweden
Christian Lindh: Division of Occupational and Environmental Medicine, Lund University, 22381 Lund, Sweden
Hannu Kiviranta: National Institute for Health and Welfare, FI-00271 Helsinki, Finland
Carl-Gustaf Bornehag: Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

IJERPH, 2022, vol. 19, issue 4, 1-15

Abstract: Environmental exposures to a myriad of chemicals are associated with adverse health effects in humans, while good nutrition is associated with improved health. Single chemical in vivo and in vitro studies demonstrate causal links between the chemicals and outcomes, but such studies do not represent human exposure to environmental mixtures. One way of summarizing the effect of the joint action of chemical mixtures is through an empirically weighted index using weighted quantile sum (WQS) regression. My Nutrition Index (MNI) is a metric of overall dietary nutrition based on guideline values, including for pregnant women. Our objective is to demonstrate the use of an index as a metric for more causally linking human exposure to health outcomes using observational data. We use both a WQS index of 26 endocrine-disrupting chemicals (EDCs) and MNI using data from the SELMA pregnancy cohort to conduct causal inference using g-computation with counterfactuals for assumed either reduced prenatal EDC exposures or improved prenatal nutrition. Reducing the EDC exposure using the WQS index as a metric or improving dietary nutrition using MNI as a metric, the counterfactuals in a causal inference with one SD change indicate significant improvement in cognitive function. Evaluation of such a strategy may support decision makers for risk management of EDCs and individual choices for improving dietary nutrition.

Keywords: WQS regression; endocrine disruptors; nutritional status; g-computation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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