Management of preventative care for an ageing population

Müller, Frauke; Schimmel, Martín

doi:10.1111/j.1875-595x.2007.tb00159.x

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…This peak in prevalence in adults around 70 years old is attributed to the development of root caries [Kassebaum et al, 2015]. Root caries prevalence is increasing as populations age and retain more of their natural dentition [Müller and Schimmel, 2007;HSCIC, 2011;Slade et al, 2014;Schwendicke et al, 2018]. There is a wide range in the reported prevalence of root caries for differing populations, ranging from 4 to 100% [Fejerskov et al, 1991;Christensen et al, 2015].…”

Section: Introductionmentioning

confidence: 99%

Development of a Root Caries Prediction Model in a Population of Dental Attenders

et al. 2022

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Root caries prevalence is increasing as populations age and retain more of their natural dentition. However, there is generally no accepted practice to identify individuals at risk of disease. There is a need for the development of a root caries prediction model to support clinicians to guide targeted prevention strategies. The aim of this study was to develop a prediction model for root caries in a population of regular dental attenders. Clinical and patient-reported predictors were collected at baseline by routine clinical examination and patient questionnaires. Clinical examinations were conducted at the four-year timepoint by trained outcome assessors blind to baseline data to record root caries data at two thresholds - root caries present on any teeth (RC>0), and root caries present on three or more teeth (RC≥3). Multiple logistic regression analyses were performed with the number of participants with root caries at each outcome threshold utilised as the outcome and baseline predictors as the candidate predictors. An automatic backwards elimination process was conducted to select predictors for the final model at each threshold. The sensitivity, specificity and c-statistic of each model's performance was assessed. A total of 1432 patient participants were included within this prediction model, with 324 (22.6%) presenting with at least one root caries lesion, and 97 (6.8%) with lesions on three or more teeth. The final prediction model at the RC>0 threshold included increasing age, having ≥9 restored teeth at baseline, smoking, lack of knowledge of spitting toothpaste without rinsing following toothbrushing, decreasing dental anxiety and worsening OHRQoL. The model sensitivity was 71.4%, specificity 69.5% and c-statistic 0.79 (95% CI 0.76, 0.81). The predictors included in the final prediction model at the RC≥3 threshold included increasing age, smoking and lack of knowledge of spitting toothpaste without rinsing following toothbrushing. The model sensitivity was 76.5%, specificity 73.6% and c-statistic 0.81 (95% CI 0.77, 0.86). To the authors’ knowledge this is the largest published root caries prediction model, with statistics indicating good model fit and providing confidence in its robustness. The performance of the risk model indicates that adults at risk of developing root caries can be accurately identified, with superior performance in the identification of adults at risk of multiple lesions.

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