Validity of caries risk assessment programmes in preschool children

Abstract

Objectives: Various programmes have been developed for caries risk assessment (CRA). Nevertheless, scientific evidence on their validity is lacking. This study aimed to compare the validity of 4 CRA programmes (CAT, CAMBRA, Cariogram, and NUS-CRA) in predicting early childhood caries.

Methods: A total of 544 children aged 3 years underwent oral examination and biological tests (saliva flow rate, salivary buffering capacity and abundance of cariogenic bacteria mutans Streptococci and Lactobacilli). Their parents completed a questionnaire. Children's caries risk was predicted using the 4 study programmes without biological tests (screening mode) and with biological tests (comprehensive mode). After 12 months, caries increment in 485 (89%) children was recorded and compared with the baseline risk predictions.

Results: Reasoning-based programmes (CAT and CAMBRA screening) had high sensitivity (≥ 93.8%) but low specificity (≤ 43.6%) in predicting caries in children. CAMBRA comprehensive assessment reached a better balance (sensitivity/specificity of 83.7%/62.9%). Algorithm-based programmes (Cariogram and NUS-CRA) generated better predictions. The sensitivity/specificity of NUS-CRA screening and comprehensive models were 73.6%/84.7% and 78.1%/85.3%, respectively, higher than those of the Cariogram screening (62.9%/77.9%) and comprehensive assessment (64.6%/78.5%). NUS-CRA comprehensive model met the criteria for a useful CRA tool (sensitivity+specificity ≥ 160%), while its screening model approached that target.

Conclusions: Our results supported algorithm-based approach of caries risk modelling and the usefulness of NUS-CRA in identifying children susceptible to caries.

Clinical significance: This prospective study provided evidence for practitioners to select tools for assessing children's caries risk, so that prevention measures can be tailored and treatment plan can be optimised.

Keywords: Caries risk assessment; Early childhood caries; Multifactorial modelling; Prospective study; Sensitivity; Specificity.