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Abstract
Current literature suggests PFAS carbon chain length may be a predictive variable of toxicity. If so, statistical modeling may be used to help predict toxicity, thus improving the efficiency of PFAS regulation development. Data were analyzed using one-way ANOVAs, Tukey’s HSD post hoc tests, and simple linear regressions. A dataset was predicted using modeling from this data. Analysis indicated that 11 of 15 health outcomes showed significant differences in mean values. Two of 15 health outcomes were analyzed using simple linear regressions, with statistically significant results. After predictive modeling generated a theoretical dataset, unpaired t-tests comparing the results of an actual dataset indicated no significant differences among the mean values of the two health outcomes. Therefore, predictive statistical modeling may be used to predict health outcomes for PFAS exposure.
Acknowledgments
The authors gratefully thank the Yale School of Public Health, Department of Environmental Health Sciences for the resources and mentorship provided by the facility, faculty, and students. The authors graciously thank the National Toxicology Program for the open access data they have provided, as well as for the continued commitment to toxicology and public health.
Disclosure statement
Robert A. Bilott, J.D., reports serving as counsel for various plaintiffs in on-going litigation involving exposure and damage from PFAS. No other declarations of interest have been reported.
Data availability statement
The data that support the findings of this study are available in the National Toxicology Program at [https://cebs.niehs.nih.gov/cebs/publication/TOX-96] and [https://cebs.niehs.nih.gov/cebs/publication/TOX-97].