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Supplementary Material for: In-Office Application of Fluoride Gel or Varnish: Cost-Effectiveness and Expected Value of Perfect Information Analysis

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journal contribution
posted on 07.04.2017, 10:34 by Schwendicke F., Stolpe M.

Application of fluoride gel/varnish (FG/FV) reduces caries increments but generates costs. Avoiding restorative treatments by preventing caries might compensate for these costs. We assessed the cost-effectiveness of dentists applying FG/FV in office and the expected value of perfect information (EVPI). EVPI analyses estimate the economic value of having perfect knowledge, assisting research resource allocation. A mixed public-private-payer perspective in Germany was adopted. A population of 12-year-olds was followed over their lifetime, with caries increments modelled using wide intervals to reflect the uncertainty of caries risk. Biannual application of FV/FG until age 18 years was compared to no fluoride application. Effectiveness parameters and their uncertainty were derived from systematic reviews. The health outcome was caries increment (decayed, missing, or filled teeth; DMFT). Cost calculations were based on fee catalogs or microcosting, including costs for individual-prophylactic fluoridation and, for FG, an individualized tray, plus material costs. Microsimulations, sensitivity, and EVPI analyses were performed. On average and applied to a largely low-risk population, no application of fluoride was least costly but also least effective (EUR 230; 11 DMFT). FV was more costly and effective (EUR 357; 7 DMFT). FG was less effective than FV and also more costly when using individualized trays. FV was the best choice for payers willing to invest EUR 39 or more per avoided DMFT. This cost-effectiveness will differ in different settings/countries or if FG/FV is applied by other care professionals. The EVPI was mainly driven by the individual's caries risk, as FV/FG were significantly more cost-effective in high-risk populations than in low-risk ones. Future studies should focus on caries risk prediction.