Development and application of a prediction model for dental caries

Abstract

The development and validation of a caries prediction model comprising 13 sociodemographic and dental examination variables on Grade 1 and Grade 5 children in the National Preventive Dentistry Demonstration Program are described. The objective was to derive a method of predicting children at high risk to caries early in order that preventive measures might be undertaken. True high risk children were defined in two ways: highest 25% of children based on their 4-yr DMFS increment, and their total DMFS score at the end of the study. In both cases, children predicted to be at high risk were defined as the 25% with the highest discriminant score. Discriminant function and logistic regression analyses were used to determine the extent to which the 13 variables collectively discriminated between true high risk and non-high risk children so defined. Sensitivity was approximately 0.50 and specificity around 0.82, using the 4-yr increment as the criterion for defining true high risk, and approximately 0.64 and 0.88, respectively, using the final DMFS score for defining true high risk.

The application of caries risk assessment in minimum intervention dentistry

“…A number of authors have published prediction models using generalized linear or logistic regression analysis to support their final explanatory model for caries presence. [6][7][8][9][10][11] Relative risk of disease occurrence or absence over time is often quantified using a surrogate measure such as an odds ratio (OR). 5,[12][13][14][15] On the other hand, a statistical strength of association using sensitivity and specificity measures is often used to report an incremental change in caries over time.…”

A Study on Assessing the Chance of Dental Caries Level using Early Childhood Oral Health Impact Scale in 5-yearold Children in Kandy District, Sri Lanka

“…[13][14][15] However, most of these caries prediction attempts are not noninvasive but minimally invasive and individual level studies. 13,20 The caries prediction model that was generated from our preliminary study is a totally noninvasive population level study as reported by others.…”

“…13,20 The caries prediction model that was generated from our preliminary study is a totally noninvasive population level study as reported by others. 14,15 Further, several recent reviews 13,21 have highlighted the importance of having minimally invasive dentistry caries management strategies that should be incorporated to estimate caries activity at each visit. These reviews 13,21,22 have also highlighted the current lack of such methods validated for reliable and precise estimation of caries activity.…”

“…As a result, there are a number of research papers published on the use of caries risk assessment tools as prediction instruments to identify the relative occurrence or absence of caries in the setup of minimally invasive clinical management. [11][12][13] Further, Abernathy et al 14 used the discriminate function and logistic regression analysis to predict the caries risk and showed that both statistical methods are equal in specificity and sensitivity. However, it further says that the logistic regression is a relatively new technique in this area of application, i.e., not limited by requiring predictor variable to be normally distributed.…”

“…However, it further says that the logistic regression is a relatively new technique in this area of application, i.e., not limited by requiring predictor variable to be normally distributed. 14,15 Thus, our aim of the preliminary study was to evaluate the level of oral disease, especially dental caries, and to assess the chance of the dental caries status with the score of the ECOHIS in a group of 5+ years Sri Lankan schoolchildren using multivariable logistic regression.…”