摩洛哥牙医使用牙科汞合金的汞中毒风险:潜类回归法

Pub Date : 2023-01-01 DOI:10.3233/JRS-210052
Nourdine Attiya, Rkia Fattahi, Mohamed-Yassine Amarouch, Ahmed El-Haidani, Samir El Jaafari, Younes Filali-Zegzouti
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引用次数: 0

摘要

背景:使用牙科汞合金的牙医长期接触低剂量的元素汞。这种全身性毒物的复杂毒性动力学导致临床表型的多态性和多变性。在这种情况下,需要采用适当的统计方法来突出职业汞暴露对牙医健康的潜在不利影响:本研究旨在根据摩洛哥自由职业牙医人群的职业汞暴露情况,分析自我报告的主观症状的分布情况,这些症状通常与慢性汞中毒有关:为了实现既定目标,我们采用了三步潜类回归法。首先进行潜类分析,根据研究对象宣称的症状对其进行分组。然后,根据牙医的后验概率将其归入已定义的潜类。最后,进行逻辑回归,以确定与潜类成员资格相关的预测因素:结果:最终得到的模型显示了可接受的校准和区分度。其解释表明,汞合金使用频率的增加与属于高风险潜类的几率显著增加有关:结论:本研究是开发根据职业汞暴露预测临床特征的诊断模型的第一步。
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Mercurial risk from dental amalgam use in a population of Moroccan dentists: A latent class regression approach.

Background: 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.

Objective: 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.

Methods: 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 the latent classes' membership.

Results: 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.

Conclusions: 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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