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

  • Baiju R. Shah
    Correspondence
    Corresponding author: Baiju R. Shah, G106 – 2075 Bayview Avenue, Toronto, Ontario, Canada M4N 3M5. Tel 416-480-4706. Fax 416-480-6048.
    Affiliations
    Department of Medicine, University of Toronto, Ontario, Canada

    ICES, Toronto, Ontario, Canada

    Division of Endocrinology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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  • Gillian L. Booth
    Affiliations
    Department of Medicine, University of Toronto, Ontario, Canada

    ICES, Toronto, Ontario, Canada

    Division of Endocrinology, Unity Health, Toronto, Ontario, Canada
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  • Denice S. Feig
    Affiliations
    Department of Medicine, University of Toronto, Ontario, Canada

    ICES, Toronto, Ontario, Canada

    Division of Endocrinology, Sinai Health Systems, Toronto, Ontario, Canada
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  • Lorraine L. Lipscombe
    Affiliations
    Department of Medicine, University of Toronto, Ontario, Canada

    ICES, Toronto, Ontario, Canada

    Division of Endocrinology, Women’s College Hospital, Toronto, Ontario, Canada
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      Abstract

      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

      Key words

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