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

  • Baiju R. Shah
    Correspondence
    Address for correspondence: Baiju R. Shah MD, PhD, Division of Endocrinology, Sunnybrook Health Sciences Centre, G106-075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada.
    Affiliations
    Department of Medicine, University of Toronto, 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, 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, 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, Toronto, Ontario, Canada

    ICES, Toronto, Ontario, Canada

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

      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.

      Résumé

      Objectifs

      L’objectif de notre étude était de déterminer les caractéristiques des tests des algorithmes utilisant les données relatives aux hospitalisations et aux demandes de paiement des médecins pour prédire le diabète gestationnel (DG).

      Méthodes

      À partir des données administratives relatives aux soins de santé de la population, nous avons recensé toutes les femmes enceintes de l’Ontario en 2019. Nous avons déterminé la présence du DG en fonction des résultats de laboratoire aux fins de dépistage du DG. Nous avons testé les algorithmes utilisant les dossiers d’hospitalisation et/ou les demandes de paiement des médecins par rapport à cet « étalon-or ». Nous avons appliqué l’algorithme retenu aux données administratives enregistrées de 1999 à 2019 pour déterminer la prévalence du DG chaque année.

      Résultats

      La détermination du DG soit en fonction du code de diabète sucré aux dossiers d’hospitalisation liée à l’accouchement, SOIT en fonction d’au moins une demande de paiement de 1 médecin indiquant un code de diagnostic du diabète en utilisant une fenêtre rétrospective de 90 jours avant l’accouchement a donné lieu à une sensibilité de 95,9 %, une spécificité de 99,2 % et une valeur prédictive positive de 87,6 %. La prévalence du DG est passée de 4,2 % des grossesses en 1999 à 12,0 % en 2019.

      Conclusion

      Les algorithmes utilisant les données administratives relatives aux hospitalisations et aux demandes de paiement des médecins peuvent permettre de déterminer avec précision le DG.

      Keywords

      Mots clés

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