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Journal articles
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.
Cited literature [38 references]
https://www.hal.inserm.fr/inserm-00598500
Contributor : Annie Schmaus <>
Submitted on : Wednesday, October 23, 2013 - 3:55:47 PM
Last modification on : Wednesday, September 16, 2020 - 5:09:03 PM
Long-term archiving on: : Friday, January 24, 2014 - 2:20:12 AM
Contributor : Annie Schmaus <>
Submitted on : Wednesday, October 23, 2013 - 3:55:47 PM
Last modification on : Wednesday, September 16, 2020 - 5:09:03 PM
Long-term archiving on: : Friday, January 24, 2014 - 2:20:12 AM
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