title: 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.
authors: Unité–Neuroepidemiology, INSERM, Paris, France. frederic.moisan@upmc.fr
authors: Frédéric
authors: F
authors: Moisan
authors: Véronique
authors: V
authors: Gourlet
authors: Jean-Louis
authors: JL
authors: Mazurie
authors: Jean-Luc
authors: JL
authors: Dupupet
authors: Jean
authors: J
authors: Houssinot
authors: Marcel
authors: M
authors: Goldberg
authors: Ellen
authors: E
authors: Imbernon
authors: Christophe
authors: C
authors: Tzourio
authors: Alexis
authors: A
authors: Elbaz