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Description of exposure profiles for seven environmental chemicals in a US population using recursive partition mixture modeling (RPMM)


Biomonitoring studies have shown that humans are exposed to numerous environmental chemicals. Previous work provides limited insights into the dynamic relationship between different chemicals within a population. The objective of this study is to develop an analytical method identifying exposure profiles of seven common environmental chemicals and determine how exposure profiles differ by sociodemographic groups and National Health and Nutrition Examination Survey (NHANES) 2003–2012 cycle year. We used recursive partition mixture modeling (RPMM) to define classes of the population with similar exposure profiles of lead, cadmium, 2,4-dichlorophenol, 2,5-dichlorophenol, bisphenol A (BPA), triclosan, and benzophenone-3 in individuals aged ≥6 years. Additionally, quasibinomial logistic regression was used to examine the association between each class and selected demographic characteristics. Eight exposure profiles were identified. Individuals who clustered together and had the highest chemical exposures were more likely to be older, to be Non-Hispanic Black (NHB) or Other Hispanic (OH), more likely to live below the poverty line, more likely to be male, and more likely to have participated in the earlier NHANES cycle (2003–2004). The developed method described the dynamic relationship between chemicals and shows that this relationship is different for subpopulations based on their sociodemographic characteristics.

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The authors would like to thank Dr. Robert Tanguay for his input on study design. Additionally, we would like to that Dr Andy Houseman for his insights into applying this clustering modeling framework to NHANES data.

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Correspondence to Jennifer Przybyla.

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The authors declare that they have no conflict of interest.

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Przybyla, J., Kile, M. & Smit, E. Description of exposure profiles for seven environmental chemicals in a US population using recursive partition mixture modeling (RPMM). J Expo Sci Environ Epidemiol 29, 61–70 (2019).

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  • Environmental mixtures
  • recursive partition mixture modeling
  • RPMM

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