Prediction model of Parkinsons disease based on antiparkinsonian drug claims.Report as inadecuate

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* Corresponding author 1 Neuroépidémiologie 2 Caisse départementale de la Gironde 3 Caisse centrale 4 CESP - Centre de recherche en épidémiologie et santé des populations 5 DST-InVS - Département santé travail

Abstract : Drug claims databases are increasingly available and provide opportunities to investigate epidemiologic questions. The authors used computerized drug claims databases from a social security system in 5 French districts to predict the probability that a person had Parkinson-s disease PD based on patterns of antiparkinsonian drug APD use. Clinical information for a population-based sample of persons using APDs in 2007 was collected. The authors built a prediction model using demographic variables and APDs as predictors and investigated the additional predictive benefit of including information on dose and regularity of use. Among 1,114 APD users, 320 29% had PD and 794 71% had another diagnosis as determined by study neurologists. A logistic model including information on cumulative APD dose and regularity of use showed good performance c statistic = 0.953, sensitivity = 92.5%, specificity = 86.4%. Predicted PD prevalence among persons aged ≥18 years was 6.66-1,000; correcting this estimate using sensitivity-specificity led to a similar figure 6.04-1,000. These data demonstrate that drug claims databases can be used to estimate the probability that a person is being treated for PD and that information on APD dose and regularity of use improves models- performances. Similar approaches could be developed for other conditions.

Keywords : antiparkinsonian agents Parkinson disease prediction predictive value of tests prescriptions prevalence

Author: Frédéric Moisan - Véronique Gourlet - Jean-Louis Mazurie - Jean-Luc Dupupet - Jean Houssinot - Marcel Goldberg - Ellen Imberno



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