Appropriate definition of diabetes using an administrative database: A cross‐sectional cohort validation study. Issue 2 (24th August 2021) - Nishioka, Yuichi; Takeshita, Saki; Kubo, Shinichiro; Myojin, Tomoya; Noda, Tatsuya; Okada, Sadanori; Ishii, Hitoshi; Imamura, Tomoaki; Takahashi, Yutaka
Abstract: Aims/Introduction: The purpose of the present study was to quantify errors in the diagnosis of diabetes for use in the national database, using a sufficient population size. Materials and methods: A claims database constructed by the JMDC (Tokyo, Japan), using standardized disease classifications and anonymous record linkage, was used in this validation study. We included patients with health insurance claims data from April 2005 to March 2019 in the JMDC claims database. We excluded patients without a record of specific health checkups in Japan. Sample size calculation was based on a 5% prevalence of diabetes and 0.4% absolute accuracy (i.e., 1, 250, 000 individuals), to calculate the sensitivity, specificity, positive predictive value and negative predictive value. Results: In total, 2, 999, 152 patients were included in this study, of which 165, 515 were classified as having diabetes based on specific health checkups (validation cohort prevalence of 5.5%). The newly devised algorithm had three elements – the diagnosis‐related codes for diabetes without suspected flag, the medication codes for diabetes and then these two codes on the same record – and yielded a sensitivity of 74.6%, positive predictive value of 88.4% and Kappa Index of 0.80 (the highest values). Conclusions: In future claims database studies, our validated algorithms will be useful as diagnostic criteria for diabetes. Abstract : Algorithms 9 and 12 had three elements: (i) the diagnosis‐related codes for diabetes without suspected flag; (ii) the medication codes for diabetes; and (iii) then these two codes on the same record. These algorithms yielded a diagnosis that agrees with the results of the specific health checkups based on specificity, positive predictive value and Kappa Index. In future claims database studies, these validated algorithms will be useful as diagnostic criteria for diabetes.