Validation study on diabetes definitions using Japanese Diagnosis Procedure Combination data among hospitalized patients

J Epidemiol. 2021 Jul 17. doi: 10.2188/jea.JE20210024. Online ahead of print.


BackgroundValidation studies on diabetes definitions using nationwide healthcare databases are scarce. We evaluated the validity of diabetes definitions using disease codes and antidiabetic drug prescriptions in the Japanese Diagnosis Procedure Combination (DPC) data via medical chart review.MethodsWe randomly selected 500 records among 15,334 patients who participated in the Japan Public Health Center-Based Prospective Study for the Next Generation in Yokote City and who had visited a general hospital in Akita between October 2011 and August 2018. Of the 500 patients, 98 were linked to DPC data; however, only 72 had sufficient information in the medical chart. Gold standard confirmation was performed by board-certified diabetologists. DPC-based diabetes definitions were based on the International Classification of Diseases, 10th Revision codes, and antidiabetic prescriptions. Sensitivity, specificity, and the positive and negative predictive values (PPV and NPV, respectively) of DPC-based diabetes definitions were evaluated.ResultsOf 72 patients, 23 were diagnosed with diabetes by chart review; 19 had a diabetes code, and 13 had both a diabetes code and antidiabetic prescriptions. The sensitivity, specificity, PPV, and NPV were 89.5% (95% confidence interval: 66.9-98.7), 96.2% (87.0-99.5), 89.5% (66.9-98.7), and 96.2% (87.0-99.5), respectively, for (i) diabetes codes alone; 89.5% (66.9-98.7), 94.3% (84.3-98.8), 85.0% (62.1-96.8), and 96.2 (86.8-99.5) for (ii) diabetes codes and/or prescriptions; 68.4% (43.4-87.4), 100% (93.3-100), 100% (75.3-100), and 89.8% (79.2-96.2) for (iii) both diabetes codes and prescriptions.ConclusionsOur results suggest that DPC data can accurately identify diabetes among inpatients using (i) diabetes codes alone or (ii) diabetes codes and/or prescriptions.

Keywords: DPC data; diabetes diagnosis; healthcare database; validation.