Modelling the validity of periodontal disease screening questions in a nondental clinical setting

Objective: Periodontal examinations are time-consuming and potentially uncomfortable for recipients. We modelled if self-reported questions alone, or combined with objective evidence of periodontal bone loss observable from radiographs, are accurate predictors of periodontitis. Methods: Self-reported data from the Australian National Survey of Adult Oral Heath 2004-06 were compared with clinical periodontal examinations to assess the validity of 8 periodontitis screening questions in predicting moderate/severe periodontitis. To model alveolar bone loss, a proxy variable simulating radiographic clinical attachment level (rCAL) was created. Three multivariable binary logistic regression models were constructed: responses to 8 screening questions alone (Model 1), screening questions combined with 5 classic periodontitis risk indicators (age, sex, smoking status, country of birth, and diabetes status) (Model 2), and the addition of rCAL (Model 3). Predictive validity was determined via sensitivity (Se) and specificity (Sp) scores and graphically represented using area under the receiver operator characteristic curves (AUROC). Results: Data from 3630 participants periodontally examined determined that 32.4% exhibited periodontitis. Periodontitis risk indicators were all significantly associated with periodontitis case status. Six of 8 screening questions (Model 1) were weak periodontitis predictors (Se = 0.28; Sp = 0.89; AUROC = 0.61). Combining 13 variables for (Model 2) improved prediction (Se = 0.55; Sp = 0.81; AUROC = 0.77). The addition of rCAL (Model 3) improved diagnostic capacity considerably (AUROC = 0.86). Conclusions: Self-reported questions combined with classic risk indicators are “useful” for periodontitis screening. Addition of radiographs markedly improved diagnostic validity. Based on modelling, nondental health care professionals may provisionally screen for periodontitis with minimal training.Kostas Kapellas, Anna Ali, Lisa M. Jamieso