Multifactorial modelling for caries prediction in Jordanian university students

Area of Science:

• Oral health research
• Dental epidemiology
• Public health dentistry

Background:

• Dental caries is a multifactorial disease.
• Predicting caries risk is crucial for targeted interventions.
• Understanding risk factors in university populations is important.

Purpose of the Study:

• To develop a caries experience prediction model for Jordanian university students.
• To identify key explanatory risk factors for dental caries.
• To assess the predictive accuracy of various oral health parameters.

Main Methods:

• A random sample of 180 university students participated.
• Data collected included salivary parameters, microbial counts, plaque, oral hygiene, and sugar intake.
• Statistical analyses involved correlation, multiple regression, discriminant analysis, and logistic regression.

Main Results:

• Sugar-containing snack intake showed the highest correlation (0.43) with DMFS (decayed, missing, and filled surfaces).
• The multiple regression model had low predictive power (R2 = 0.38).
• Logistic regression correctly identified 76% of subjects, with 80% sensitivity and 75% specificity.

Conclusions:

• The complex etiology of dental caries necessitates further research.
• Current evidence supports the implementation of established preventive measures for this demographic.
• Predictive models can aid in identifying individuals at higher risk for caries.