Applying Latent Variable Models to Estimate Cumulative Exposure Burden to Chemical Mixtures and Identify Latent Exposure Subgroups: A Critical Review and Future Directions

Shelley H. Liu (), Yitong Chen, Jordan R. Kuiper, Emily Ho, Jessie P. Buckley and Leah Feuerstahler
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Shelley H. Liu: Icahn School of Medicine at Mount Sinai
Yitong Chen: Icahn School of Medicine at Mount Sinai
Jordan R. Kuiper: The George Washington University Milken Institute School of Public Health
Emily Ho: Northwestern University
Jessie P. Buckley: John Hopkins Bloomberg School of Public Health
Leah Feuerstahler: Fordham University

Statistics in Biosciences, 2024, vol. 16, issue 2, No 9, 482-502

Abstract: Abstract Environmental mixtures, which reflect joint exposure to multiple environmental agents, are a major focus of environmental health and risk assessment research. Advancements in latent variable modeling and psychometrics can be used to address contemporary questions in environmental mixtures research. In particular, latent variable models can quantify an individual’s cumulative exposure burden to mixtures and identify hidden subpopulations with distinct exposure patterns. Here, we first provide a review of measurement approaches from the psychometrics field, including structural equation modeling and latent class/profile analysis, and discuss their prior environmental epidemiologic applications. Then, we discuss additional, underutilized opportunities to leverage the strengths of psychometric approaches. This includes using item response theory to create a common scale for comparing exposure burden scores across studies; facilitating data harmonization through the use of anchors. We also discuss studying fairness or appropriateness of measurement models to quantify exposure burden across diverse populations, through the use of mixture item response theory and through evaluation of measurement invariance and differential item functioning. Multi-dimensional models to quantify correlated exposure burden sub-scores, and methods to adjust for imprecision of chemical exposure data, are also discussed. We show that there is great potential to address pressing environmental epidemiology and exposure science questions using latent variable methods.

Keywords: Latent variable models; Psychometrics; Item response theory; Environmental epidemiology; Exposure mixtures; Environmental health (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12561-023-09410-9

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