Algorithms to define diabetes type using data from administrative databases: A systematic review of the evidence

Seyedeh Forough Sajjadi; Julian W Sacre; Lei Chen; Sarah H Wild; Jonathan E Shaw; Dianna J Magliano

doi:10.1016/j.diabres.2023.110859

Algorithms to define diabetes type using data from administrative databases: A systematic review of the evidence

Diabetes Res Clin Pract. 2023 Sep:203:110859. doi: 10.1016/j.diabres.2023.110859. Epub 2023 Jul 28.

Authors

Seyedeh Forough Sajjadi¹, Julian W Sacre², Lei Chen³, Sarah H Wild⁴, Jonathan E Shaw², Dianna J Magliano²

Affiliations

¹ Baker Heart and Diabetes Institute, Melbourne, Australia; Monash University, School of Public Health and Preventive Medicine, Melbourne, Australia. Electronic address: Forough.Sajjadi@baker.edu.au.
² Baker Heart and Diabetes Institute, Melbourne, Australia; Monash University, School of Public Health and Preventive Medicine, Melbourne, Australia.
³ Baker Heart and Diabetes Institute, Melbourne, Australia.
⁴ Usher Institute, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland.

PMID: 37517777
DOI: 10.1016/j.diabres.2023.110859

Abstract

Aims: To find the best-performing algorithms to distinguish type 1 and type 2 diabetes in administrative data.

Methods: Embase and MEDLINE databases were searched from January 2000 until January 2023. Papers evaluating the performance of algorithms to define type 1 and type 2 diabetes by reporting diagnostic metrics against a range of reference standards were selected. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies.

Results: Of the 24 studies meeting the eligibility criteria, 19 demonstrated a low risk of bias and low concerns about the applicability of the study population across all domains. Algorithms considering multiple diabetes diagnostic codes alone were sensitive and specific approaches to classify diabetes type (both metrics >92.1% for type 1 diabetes; >86.9% for type 2 diabetes). Among the top 10-performing algorithms to detect type 1 and type 2 diabetes, 70% and 100% featured multiple criteria, respectively. Information on insulin use was more sensitive and specific for detecting diabetes type than were criteria based on use of oral hypoglycaemic agents.

Conclusions: Algorithms based on multiple diabetes diagnostic codes and insulin use are the most accurate approaches to distinguish type 1 from type 2 diabetes using administrative data. Approaches with more than one criterion may also increase sensitivity in distinguishing diabetes type.

Keywords: Administrative data; Algorithm; Classification; Systematic review; Type 1 Diabetes; Type 2 Diabetes.

Publication types

Review