Validation of Register-Based Diabetes Classifiers in Danish Data

Abstract

Purpose: To validate two register-based algorithms classifying type 1 (T1D) and type 2 diabetes (T2D) in a general population using Danish register data. Patients and Methods: After linking data on prescription drug usage, hospital diagnoses, laboratory results and diabetes-specific healthcare services from nationwide healthcare registers, diabetes type was defined for all individuals in Central Denmark Region age 18–74 years on 31 December 2018 according to two distinct register-based classifiers: 1) a novel register-based diabetes classifier incorporating diagnostic hemoglobin-A1C measurements, the Open-Source Diabetes Classifier (OSDC), and 2) an existing Danish diabetes classifier, the Register for Selected Chronic Diseases (RSCD). These classifications were validated against self-reported data from the Health in Central Denmark survey – overall and stratified by age at onset of diabetes. The source-code of both classifiers was made available in the open-source R package osdc. Results: A total of 2633 (9.0%) of 29,391 respondents reported having any type of diabetes, divided across 410 (1.4%) self-reported cases of T1D and 2223 (7.6%) cases of T2D. Among all self-reported diabetes cases, 2421 (91.9%) were classified as diabetes cases by both classifiers. In T1D, sensitivity of OSDC-classification was 0.773 [95% CI 0.730–0.813] (RSCD: 0.700 [0.653–0.744]) and positive predictive value (PPV) 0.943 [0.913–0.966] (RSCD: 0.944 [0.912–0.967]). In T2D, sensitivity of OSDC-classification was 0.944 [0.933–0.953] (RSCD: 0.905 [0.892–0.917]) and PPV 0.875 [0.861–0.888] (RSCD: 0.898 [0.884–0.910]). In age at onset-stratified analyses of both classifiers, sensitivity and PPV were low in individuals with T1D onset after age 40 and T2D onset before age 40. Conclusion: Both register-based classifiers identified valid populations of T1D and T2D in a general population, but sensitivity was substantially higher in OSDC compared to RSCD. Register-classified diabetes type in cases with atypical age at onset of diabetes should be interpreted with caution. The validated, open-source classifiers provide robust and transparent tools for researchers.