Objectives: Our aim in this study was to determine the test characteristics of algorithms using hospitalization and physician claims data to predict gestational diabetes (GDM).
Methods: Using population-level health-care 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%, a specificity of 99.2% and a positive predictive value of 87.6%. The prevalence of GDM increased from 4.2% of pregnancies in 1999 to 12.0% in 2019.
Conclusion: Algorithms using hospitalization or physician claims administrative data can accurately identify GDM.
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