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Identifying diabetes cases in health administrative databases: a validation study based on a large French cohort

Author

Listed:
  • Sonsoles Fuentes

    (Santé publique France (SpF))

  • Emmanuel Cosson

    (Paris 13 University, Sorbonne Paris Cité, CRNH-IdF, CINFO
    Sorbonne Paris Cité, UMR U1153 Inserm/U1125 Inra/Cnam/Université Paris 13)

  • Laurence Mandereau-Bruno

    (Santé publique France (SpF))

  • Anne Fagot-Campagna

    (Caisse nationale de l’assurance maladie)

  • Pascale Bernillon

    (Santé publique France (SpF))

  • Marcel Goldberg

    (Inserm UMS 011)

  • Sandrine Fosse-Edorh

    (Santé publique France (SpF))

Abstract

Objectives In the French national health insurance information system (SNDS) three diabetes case definition algorithms are applied to identify diabetic patients. The objective of this study was to validate those using data from a large cohort. Methods The CONSTANCES cohort (Cohorte des consultants des Centres d’examens de santé) comprises a randomly selected sample of adults living in France. Between 2012 and 2014, data from 45,739 participants recorded in a self-administrated questionnaire and in a medical examination were linked to the SNDS. Two gold standards were defined: known diabetes and pharmacologically treated diabetes. Sensitivity, specificity, positive and negative predictive values (PPV, NPV) and kappa coefficients (k) were estimated. Results All three algorithms had specificities and NPV over 99%. Their sensitivities ranged from 73 to 77% in algorithm A, to 86 and 97% in algorithm B and to 93 and 99% in algorithm C, when identifying known and pharmacologically treated diabetes, respectively. Algorithm C had the highest k when using known diabetes as the gold standard (0.95). Algorithm B had the highest k (0.98) when testing for pharmacologically treated diabetes. Conclusions The SNDS is an excellent source for diabetes surveillance and studies on diabetes since the case definition algorithms applied have very good test performances.

Suggested Citation

  • Sonsoles Fuentes & Emmanuel Cosson & Laurence Mandereau-Bruno & Anne Fagot-Campagna & Pascale Bernillon & Marcel Goldberg & Sandrine Fosse-Edorh, 2019. "Identifying diabetes cases in health administrative databases: a validation study based on a large French cohort," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(3), pages 441-450, April.
    • Handle: RePEc:spr:ijphth:v:64:y:2019:i:3:d:10.1007_s00038-018-1186-3
    DOI: 10.1007/s00038-018-1186-3
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    References listed on IDEAS

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    1. Aaron Leong & Kaberi Dasgupta & Sasha Bernatsky & Diane Lacaille & Antonio Avina-Zubieta & Elham Rahme, 2013. "Systematic Review and Meta-Analysis of Validation Studies on a Diabetes Case Definition from Health Administrative Records," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-11, October.
    2. Linda S Geiss & Karen Kirtland & Ji Lin & Sundar Shrestha & Ted Thompson & Ann Albright & Edward W Gregg, 2017. "Changes in diagnosed diabetes, obesity, and physical inactivity prevalence in US counties, 2004-2012," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-10, March.
    3. Grégoire Lagasnerie & Anne-Sophie Aguadé & Pierre Denis & Anne Fagot-Campagna & Christelle Gastaldi-Menager, 2018. "The economic burden of diabetes to French national health insurance: a new cost-of-illness method based on a combined medicalized and incremental approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(2), pages 189-201, March.
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