Use of dental practices for the identification of adults with undiagnosed type 2 diabetes mellitus or non-diabetic hyperglycaemia: a systematic review.

AIM: Type 2 diabetes is a growing global challenge. Evidence exists demonstrating the use of primary care (non-hospital based) dental practices to identify, through risk assessments, those who may be at increased risk of type 2 diabetes or who may already unknowingly have the condition. This review aimed to synthesize evidence associated with the use of primary care dental services for the identification of undiagnosed non-diabetic hyperglycaemia or type 2 diabetes in adults, with particular focus on the pick-up rate of new cases. METHOD: Electronic databases were searched for studies reporting the identification of non-diabetic hyperglycaemia/type 2 diabetes in primary care dental settings. Returned articles were screened and two independent reviewers completed the data-extraction process. A descriptive synthesis of the included articles was undertaken due to the heterogeneity of the literature returned. RESULTS: Nine studies were identified, the majority of which utilized a two-stage risk-assessment process with risk score followed by a point-of-care capillary blood test. The main barriers cited were cost, lack of adequate insurance cover and people having previously been tested elsewhere. The pick-up rate of new cases of type 2 diabetes and non-diabetic hyperglycaemia varied greatly between studies, ranging from 1.7% to 24% for type 2 diabetes and from 23% to 45% for non-diabetic hyperglycaemia, where reported. CONCLUSION: This review demonstrates that although it appears there may be benefit in using the dental workforce to identify undiagnosed cases of non-diabetic hyperglycaemia and type 2 diabetes, further high-quality research in the field is required assessing both the clinical and cost effectiveness of such practice. (Prospero Registration ID: PROSPERO 2018 CRD42018098750).

Enamel Caries Detection and Diagnosis: An Analysis of Systematic Reviews.

Detection and diagnosis of caries-typically undertaken through a visual-tactile examination, often with supporting radiographic investigations-is commonly regarded as being broadly effective at detecting caries that has progressed into dentine and reached a threshold where restoration is necessary. With earlier detection comes an opportunity to stabilize disease or even remineralize the tooth surface, maximizing retention of tooth tissue and preventing a lifelong cycle of restoration. We undertook a formal comparative analysis of the diagnostic accuracy of different technologies to detect and inform the diagnosis of early caries using published Cochrane systematic reviews. Forming the basis of our comparative analysis were 5 Cochrane diagnostic test accuracy systematic reviews evaluating fluorescence, visual or visual-tactile classification systems, imaging, transillumination and optical coherence tomography, and electrical conductance or impedance technologies. Acceptable reference standards included histology, operative exploration, or enhanced visual assessment (with or without tooth separation) as appropriate. We conducted 2 analyses based on study design: a fully within-study, within-person analysis and a network meta-analysis based on direct and indirect comparisons. Nineteen studies provided data for the fully within-person analysis and 64 studies for the network meta-analysis. Of the 5 technologies evaluated, the greatest pairwise differences were observed in summary sensitivity points for imaging and all other technologies, but summary specificity points were broadly similar. For both analyses, the wide 95% prediction intervals indicated the uncertainty of future diagnostic accuracy across all technologies. The certainty of evidence was low, downgraded for study limitations, inconsistency, and indirectness. Summary estimates of diagnostic accuracy for most technologies indicate that the degree of certitude with which a decision is made regarding the presence or absence of disease may be enhanced with the use of such devices. However, given the broad prediction intervals, it is challenging to predict their accuracy in any future "real world" context.