Date of Award
January 2022
Document Type
Thesis
Degree Name
Master of Public Health (MPH)
Department
School of Public Health
First Advisor
Vasilis Vasiliou
Abstract
Current literature suggests PFAS carbon chain length is a predicative variable of toxicity. If so, statistical modeling may be used to predict toxicity, thus improving efficiency of PFAS regulations. Data containing clinical chemistry health outcomes of rats exposed to various PFAS compound obtained from the National Toxicology Program 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 13 of 15 health outcomes showed significant differences in mean values. Results of simple linear regressions were statistically significant in five of 15 health outcomes. After predicative modeling generated a theoretical dataset, unpaired t-tests comparing results of an actual dataset, indicating no significant differences among the mean values of two of five health outcomes. Therefore, predictive statistical modeling may be used to predict health outcomes.
Recommended Citation
Bellia, Giselle Rita Maria, "Regulation Of Pfas Chemicals Using Predictive Statistical Modeling" (2022). Public Health Theses. 2132.
https://elischolar.library.yale.edu/ysphtdl/2132
Comments
This thesis is restricted to Yale network users only. This thesis is permanently embargoed from public release.