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Validation of algorithms to identify gestational diabetes from population-level healthcare administrative data

Shah BR, Booth GL, Feig DS, Lipscombe LL. Can J Diabetes. 2022; Jul 9 [Epub ahead of print]. DOI: https://doi.org/10.1016/j.jcjd.2022.06.010


Aims — To determine the test characteristics of algorithms using hospitalization and physician claim data to predict gestational diabetes (GDM).

Methods — Using population-level healthcare administrative data, we identified all pregnant women in Ontario in 2019. The presence of GDM was determined based on glucose screening laboratory results. Algorithms using hospitalization records and/or physician claims were tested against this gold standard. The selected algorithm was applied to administrative data records from 1999 to 2019 to determine GDM prevalence in each year.

Results — Identifying GDM based on either a diabetes mellitus code on the delivery hospitalization record, OR at least 1 physician claim with a diabetes diagnosis code with a 90 day lookback before delivery yielded a sensitivity of 95.9%, specificity of 99.2%, and positive predictive value of 87.6%. The prevalence of GDM increased from 4.2% of pregnancies in 1999 to 12.0% in 2019.

Conclusions — Algorithms using hospitalization or physician claims administrative data can accurately identify GDM

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