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.

Keywords:

• type 1 diabetes
• type 2 diabetes
• classification
• population-based
• open-source

Ethics Approval Statement

This study was approved by the Health in Central Denmark steering committee. The Health in Central Denmark project is registered in the Central Denmark Region internal register of research projects (reg. no. 1-16-02-165-20). Access to register data was provided and approved by the Danish Health Data Authority and Statistics Denmark. In Denmark, studies based entirely on survey and register data do not require specific ethical approval.

Acknowledgments

The authors are grateful to all participants of the Health in Central Denmark cohort for their contributions to the study. The authors are grateful to J. Støy and S.T. Knudsen from Steno Diabetes Center Aarhus for providing valuable clinical inputs to algorithm design during development of OSDC, and to the Clinical Epidemiology Research group at Steno Diabetes Center Copenhagen for insightful feedback on preliminary analyses.

Disclosure

Financial support was provided to AAI by a research training supplement grant from Aarhus University, Denmark, as well as unrestricted grants from the Public Health in Central Denmark Region foundation and Steno Diabetes Center Aarhus, Denmark. AS and LB are employed by Steno Diabetes Center Aarhus, Denmark, which is partially funded by an unrestricted donation from the Novo Nordisk Foundation. The authors report no other conflicts of interest in this work.