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Prediction model of Parkinson's disease based on antiparkinsonian drug claims.

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.
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Frédéric Moisan, Véronique Gourlet, Jean-Louis Mazurie, Jean-Luc Dupupet, Jean Houssinot, et al.. Prediction model of Parkinson's disease based on antiparkinsonian drug claims.. American Journal of Epidemiology, Oxford University Press (OUP), 2011, 174 (3), pp.354-63. ⟨10.1093/aje/kwr081⟩. ⟨inserm-00598500⟩



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