Validation of Algorithms to Identify Gestational Diabetes From Population-level Health-care Administrative Data

Our aim in this study was to determine the test characteristics of algorithms using hospitalization and physician claims data to predict gestational diabetes (GDM).

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.

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.

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