Identifying diabetes cases in health administrative databases: a validation study based on a large French cohort
- 111 Downloads
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.
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.
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.
The SNDS is an excellent source for diabetes surveillance and studies on diabetes since the case definition algorithms applied have very good test performances.
KeywordsInformation systems Diabetes Algorithms Validation studies CONSTANCES
The CONSTANCES cohort is supported by the Caisse Nationale d’Assurance Maladie des travailleurs salariés-CNAMTS. CONSTANCES is accredited as a “National Infrastructure for Biology and health” by the governmental Investissements d’avenir programme and was funded by the Agence nationale de la recherche (ANR-11-INBS-0002 Grant). CONSTANCES also receives funding from MSD, AstraZeneca and Lundbeck managed by INSERM-Transfert. This study has received a funding from the Interministerial Mission for Combating Drugs and Addictive Behaviors (“Mission Interministérielle de Lutte contre les Drogues et les Conduites Addictives”, MILDECA). None of the authors are salaried by the funders of the CONSTANCES cohort. The funders did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors declared no potential conflict of interest relevant to this article.
Compliance with ethical standards
Conflict of interest
All authors declared no potential conflict of interest relevant to this article.
Research involving human participants and/or animals
This article does not contain any studies with human participants performed by any of the authors.
The CONSTANCES study was approved by authorities regulating ethical data collection in France (CCTIRS: Comité Consultatif pour le Traitement des Informations Relatives à la Santé; CNIL-Commission Nationale Informatique et Liberté) and all participants signed an informed consent.
- Clottey C, Mo F, Lebrun B, Mickelson P, Niles J, Robbins G (2001) The development of the national diabetes surveillance system (NDSS) in Canada. Chronic Dis Can 22:67–69Google Scholar
- Day HR, Parker JD (2013) Self-report of diabetes and claims-based identification of diabetes among medicare beneficiaries. National Health Statistics Report 1–14Google Scholar
- De Lagasnerie G, Aguade AS, Denis P, Fagot-Campagna A, Gastaldi-Menager C (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. Eur J Health Econ 19:189–201. https://doi.org/10.1007/s10198-017-0873-y CrossRefGoogle Scholar
- Fosse-Edorh S, Rigou A, Morin S, Fezeu L, Mandereau-Bruno L, Fagot-Campagna A (2017) Algorithms based on medico-administrative data in the field of endocrine, nutritional and metabolic diseases, especially diabetes. Rev Epidemiol Sante Publique 65(Suppl 4):S168–S173. https://doi.org/10.1016/J.Respe.2017.05.001 CrossRefGoogle Scholar
- Maura G, Blotiere PO, Bouillon K, Billionnet C, Ricordeau P, Alla F, Zureik M (2015) Comparison of the short-term risk of bleeding and arterial thromboembolic events in nonvalvular atrial fibrillation patients newly treated with dabigatran or rivaroxaban versus vitamin K antagonists: a French nationwide propensity-matched cohort study. Circulation 132:1252–1260. https://doi.org/10.1161/circulationaha.115.015710 CrossRefGoogle Scholar
- Santin G et al (2016) Estimation De Prévalencesdans Constances: Premières explorations. Bull Épidémiologique Hebd 35–36:622–628Google Scholar
- Tuppin P et al (2017) Value of a national administrative database to guide public decisions: from the Systeme National D’information Interregimes De L’assurance Maladie (SNIIRAM) to the Systeme National Des Donnees De Sante (SNDS) in France. Rev Epidemiol Sante Publique 65(Suppl 4):S149–S167. https://doi.org/10.1016/j.respe.2017.05.004 CrossRefGoogle Scholar