Caries Risk Prediction Models in a Medical Health Care Setting.

Area of Science:

• Pediatric Dentistry
• Public Health
• Epidemiology

Background:

• Tooth decay in primary teeth is a global health issue, particularly in young children.
• Existing caries risk assessment (CRA) models often rely on dental visits, which are infrequent in early childhood.
• There is a need for CRA models applicable in primary care settings.

Purpose of the Study:

• To construct and evaluate risk prediction models for caries in 2- and 3-year-old children.
• To utilize information readily available to medical practitioners for caries prediction.
• To improve early identification of children at risk for tooth decay.

Main Methods:

• Data from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) mother-offspring cohort were used.
• Multivariable logistic regression and receiver operating characteristic (ROC) analyses were employed.
• Missing data were handled using multiple imputation by chained equations.

Main Results:

• Caries rates at 2 and 3 years were 17.8% and 42.9%, respectively.
• Models demonstrated good predictive performance with Area Under the Curve (AUC) values ranging from 0.71 to 0.91.
• Inclusion of anterior caries significantly improved prediction accuracy for year 3 models.

Conclusions:

• Medical CRA models integrating oral and general health data show promise for screening caries-susceptible infants and toddlers.
• Training medical professionals to "lift the lip" can aid in early detection of anterior caries lesions.
• Predictors identified include ethnicity, prenatal smoke exposure, allergies, maternal health, and maternal oral hygiene practices.