Abstract

Dentists using dental amalgam are chronically exposed to low doses of elemental mercury. The complex toxico-kinetics of this systemic toxicant results in polymorphic and variable clinical phenotypes. In this context, adapted statistical methods are required to highlight potential adverse effects of occupational mercury exposure on dentists' health. The present study aims to analyze the distribution of self-reported subjective symptoms, commonly associated with chronic mercury poisoning, according to occupational mercury exposure in a population of Moroccan liberal dentists. In order to achieve the defined objectives, a three-step latent class regression was fitted. First a latent class analysis was performed to cluster the studied population according to their declared symptoms. Dentists were then classified in the defined latent classes based on their posterior probabilities. Finally, a logistic regression is fitted to identify predictors associated with latent classes' membership. The final obtained model showed acceptable calibration and discrimination. Its interpretation revealed that the increase of the frequency of amalgam use was associated with significant higher odds of belonging to the high risk latent class. The present study represents an initial step towards the development of diagnosis model that predict clinical profiles according to occupational mercury exposure.

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