Development and Practical Application of A Mathematical Model for Predicting the Risk of Dental Diseases, Taking into Account Professional Factors
Keywords:
Occupational Hazards, Dental Caries, Periodontal Disease, Predictive Mathematical Model, Local Immunity, Textile Industry WorkersAbstract
Industrial workers face heightened risks of dental diseases due to prolonged exposure to harmful occupational factors like chemical reagents, high humidity, and dust. Standard dental diagnostics often fail to account for specific occupational hazards and localized immune statuses, limiting early risk identification and effective disease prediction. A clinical and laboratory study was conducted from 2022 to 2024 on 139 textile workers (dyeing shop, n=70; spinning shop, n=69) and 50 unexposed controls. Comprehensive dental status was evaluated using KPU, KPI, CPITN, and OHI-S indices. Local immunity was monitored via s-IgA and lysozyme activity, alongside biochemical and radiographic tracking. The parameters were integrated to construct a predictive mathematical risk model. Significant dental deterioration strongly correlated with work experience (p < 0.001). Gray-brown plaque affected up to 81.3% of workers, and deep periodontal pockets (>=6) were recorded in up to 49.2% of the dyeing group. Long-term occupational exposure initiated a severe progressive decline in local immunity, notably dropping average lysozyme activity to 34.50% in the dyeing shop. A predictive mathematical model was successfully developed, achieving a 91% diagnostic accuracy, 88% sensitivity, and 90% specificity in stratifying patient risk. Utilizing this targeted predictive framework and tailored preventive strategies significantly mitigates workplace-driven dental pathology, successfully reducing inflammatory exacerbations by 47.3% and normalizing local oral immunity.
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