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

Author

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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
    Department of Surgical Sciences, Uppsala University, 75237 Uppsala, 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
    Department of Health Sciences, Karlstad University, 65188 Karlstad, Sweden)

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.

Suggested Citation

  • Chris Gennings & Katherine Svensson & Alicja Wolk & Christian Lindh & Hannu Kiviranta & Carl-Gustaf Bornehag, 2022. "Using Metrics of a Mixture Effect and Nutrition from an Observational Study for Consideration towards Causal Inference," IJERPH, MDPI, vol. 19(4), pages 1-15, February.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2273-:d:751423
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    References listed on IDEAS

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    1. Erika Garcia & Robert Urman & Kiros Berhane & Rob McConnell & Frank Gilliland, 2019. "Effects of policy-driven hypothetical air pollutant interventions on childhood asthma incidence in southern California," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(32), pages 15883-15888, August.
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