Validity of denture usage definitions based on claims data in Japanese older adults: The Longevity Improvement and Fair Evidence (LIFE) Study

BackgroundAdministrative claims data are increasingly used in oral health research, however, their validity for identifying denture use has not been established. ObjectiveTo evaluate the accuracy of algorithms based on dental claims codes for identifying denture users, dentist-reported oral health screening records were used as the reference standard. MethodsWe analyzed data from 4,053 adults aged [≥]65 years in the Longevity Improvement and Fair Evidence (LIFE) Study. Twelve algorithms incorporating 56 denture-related disease and procedure codes were developed from claims in the 12 months preceding the screening. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Sensitivity analyses used a 6-month claims window. A post hoc bias analysis was also performed. ResultsDuring screening, 59.9% of the participants were classified as denture users. Algorithms based solely on disease codes showed very high specificity (98.3-100%) but low sensitivity (1.4- 20.8%). Among the procedure-based definitions, oral rehabilitation (algorithm f) had the highest sensitivity (63.6%). The combined algorithm using both disease and procedure codes (algorithm C) achieved the best balance, with 65.3% sensitivity, 96.6% specificity, 96.6% PPV, and 65.1% NPV. Similar findings were observed using the 6-month claims data. Bias analysis indicated that the risks for denture use could be underestimated by 26-59%, with algorithm C showing the least bias. ConclusionsDenture use can be identified from dental claims data with moderate accuracy. Algorithms combining denture-related disease and procedure codes, or using oral rehabilitation codes, provide practical definitions for research on oral and systemic health outcomes.