Co-exposure confounding and amplification of bias—an exploration with practical interpretation and perspectives

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ID: 322807
2026
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Abstract
Epidemiologists increasingly estimate associations between mixtures of correlated co-exposures and health outcomes. We propose an exploratory approach to help differentiate co-exposure confounding from co-exposure amplification of bias (CAB) for studies considering correlated co-exposures using a case example and simulations based on observed relationships. Our example uses associations observed in published studies between co-occurring per- and polyfluoroalkyl substance (PFAS) biomarkers and tetanus antibody concentrations but could apply to any context where statistical modeling is performed with and without inclusion of correlated co-exposures. Using directed acyclic graphs (DAGs), to define an assumed causal structure, and simulations of three PFAS predicting antibody titers, we show that if researchers can assume one component in the exposure mixture is correlated with the other components, but does not cause the outcome, then CAB can be detected by testing whether the estimate for the non-causal component in a multi-pollutant model is different from zero. In our simulation, the estimate for the non-causal component was zero (95%CI, -0.22, 0.25) when CAB was absent and -0.25 (95%CI, -0.48, 0.00) when present. These results illustrate a possible method to identify CAB and select statistical models that may produce less biased effect estimates if our assumed DAG is correct.
Reference Key
openalex_W7171552800 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Krista Y Christensen, Michael Leung, Elizabeth G. Radke, J Michael Wright, Paige A. Bommarito, Thomas F. Bateson, Marc G. Weisskopf
Journal american journal of epidemiology
Year 2026
DOI
10.1093/aje/kwag183
URL
https://doi.org/10.1093/aje/kwag183
Keywords Keywords not found

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