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Detection of emerging diseases in occupational health: usefulness and limitations of application of pharmacovigilance methods to the database of the French national occupational disease surveillance and prevention network (RNV3P)
  1. Vincent BONNETERRE (vbonneterre{at}chu-grenoble.fr)
  1. Laboratoire EPSP–TIMC, Université Joseph Fourier, Faculté de Médecine, Grenoble, France
    1. Dominique J BICOUT (bicout{at}ill.fr)
    1. Laboratoire EPSP–TIMC, Université Joseph Fourier, Faculté de Médecine, Grenoble, France
      1. Linda LARABI (llarabi{at}chu-grenoble.fr)
      1. Service de consultation de pathologies professionnelles, CHU Grenoble France, France
        1. Cyril BERNARDET (cbernardet{at}chu-grenoble.fr)
        1. Service de consultation de pathologies professionnelles, CHU Grenoble France, France
          1. Anne MAITRE (amaitre{at}chu-grenoble.fr)
          1. Laboratoire EPSP–TIMC, Université Joseph Fourier, Faculté de Médecine, Grenoble, France
            1. Pascale TUBERT-BITTER (tubert{at}vjf.inserm.fr)
            1. Inserm U 472, Hôpital Paul Brousse, Villejuif, France
              1. Régis DE GAUDEMARIS (rdegaudemaris{at}chu-grenoble.fr)
              1. Laboratoire EPSP–TIMC, Université Joseph Fourier, Faculté de Médecine, France

                Abstract

                Objective: To test data mining methods used in pharmacovigilance in order to identify potential emerging disease-nuisance associations in the national occupational disease surveillance and prevention network (RNV3P) database. Methods: Proportional reporting ratios (PRR) used in pharmacovigilance were applied to detect disproportional reporting of disease-nuisance associations which are not compensated by the national social security system. Results: The 24,785 reports of the RNV3P were grouped into 1344 different disease-nuisance associations reported more than twice, of which 422 did not give entitlement to compensation by the social security system. Among these associations, 162 were potentially emergent and generated a signal, of which 8 associations involve cancer. Conclusion: This work is the first stage of an exploratory investigation submitting the questions raised to experts and involving participants in the network in reflection on the hypotheses generated.

                • data mining
                • occupational diseases (MeSH)
                • occupational network or database
                • pharmacovigilance methods
                • signal detection

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