Validity of Denture Usage Definitions Based on Claims Data in Japanese Older Adults

Administrative claims data are increasingly used in oral health research, but their validity for identifying denture use has not been established.

To 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.

We analyzed data from 4,053 adults aged ≥65 y in the Longevity Improvement and Fair Evidence (LIFE) Study in Japan. Twelve algorithms incorporating 56 denture-related diagnosis and procedure codes were developed from claims in the 12 mo preceding the screening. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Sensitivity analyses used a 6-mo claims window. A post hoc bias analysis was also performed.

During screening, 59.9% of the participants were classified as denture users. Algorithms based solely on diagnosis codes showed very high specificity (98.3%-100%) but low sensitivity (1.4%-20.8%). Among the procedure-based definitions, oral rehabilitation codes (algorithm f; denture adjustment or instruction) had the highest sensitivity (63.6%). The combined algorithm using both diagnosis 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-mo claims data. Bias analysis indicated that the risks for denture use could be underestimated by 26% to 59%, with algorithm C showing the least bias.

Denture use can be identified from dental claims data with moderate accuracy. Algorithms combining denture-related diagnosis and procedure codes or using oral rehabilitation codes provide practical definitions for research on oral and systemic health outcomes.Knowledge Transfer Statement:The result of this validated study can be identified as denture use from dental claims with moderate accuracy. In combination with validated denture use and the number of remaining teeth, it can expand opportunities to assess oral health status and its relationship with systemic health outcomes in large-scale epidemiologic studies using claims data.