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Assessment of the French National Health Insurance Information System as a tool for epidemiological surveillance of malaria.

  • Delon, François1
  • Mayet, Aurélie1, 2
  • Thellier, Marc3
  • Kendjo, Eric3
  • Michel, Rémy1, 4
  • Ollivier, Lénaïck5
  • Chatellier, Gilles6
  • Desjeux, Guillaume7
  • 1 French Armed Forces Center for Epidemiology and Public Health, Marseille, France. , (France)
  • 2 UMR 912: INSERM-IRD-Aix-Marseille University, Marseille, France. , (France)
  • 3 National Reference Center for Malaria, Paris, France. , (France)
  • 4 French Military Health Service Academy, Paris, France. , (France)
  • 5 Central Directorate of the French Military Health Service, Paris, France. , (France)
  • 6 Department of Computer Science, Biostatistics and Public Health, Georges Pompidou European Hospital, Paris, France. , (France)
  • 7 National Military Social Security Fund, Toulon, France. , (France)
Published Article
Journal of the American Medical Informatics Association
Oxford University Press
Publication Date
May 01, 2017
DOI: 10.1093/jamia/ocw164
PMID: 28040684


Epidemiological surveillance of malaria in France is based on a hospital laboratory sentinel surveillance network. There is no comprehensive population surveillance. The objective of this study was to assess the ability of the French National Health Insurance Information System to support nationwide malaria surveillance in continental France. A case identification algorithm was built in a 2-step process. First, inclusion rules giving priority to sensitivity were defined. Then, based on data description, exclusion rules to increase specificity were applied. To validate our results, we compared them to data from the French National Reference Center for Malaria on case counts, distribution within subgroups, and disease onset date trends. We built a reusable automatized tool. From July 1, 2013, to June 30, 2014, we identified 4077 incident malaria cases that occurred in continental France. Our algorithm provided data for hospitalized patients, patients treated by private physicians, and outpatients for the entire population. Our results were similar to those of the National Reference Center for Malaria for each of the outcome criteria. We provided a reliable algorithm for implementing epidemiological surveillance of malaria based on the French National Health Insurance Information System. Our method allowed us to work on the entire population living in continental France, including subpopulations poorly covered by existing surveillance methods. Traditional epidemiological surveillance and the approach presented in this paper are complementary, but a formal validation framework for case identification algorithms is necessary. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: [email protected]

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