Prediction model of Parkinson's disease based on antiparkinsonian drug claims.
Identifieur interne : 000952 ( PubMed/Corpus ); précédent : 000951; suivant : 000953Prediction model of Parkinson's disease based on antiparkinsonian drug claims.
Auteurs : Frédéric Moisan ; Véronique Gourlet ; Jean-Louis Mazurie ; Jean-Luc Dupupet ; Jean Houssinot ; Marcel Goldberg ; Ellen Imbernon ; Christophe Tzourio ; Alexis ElbazSource :
- American journal of epidemiology [ 1476-6256 ] ; 2011.
English descriptors
- KwdEn :
- MESH :
- chemical , therapeutic use : Antiparkinson Agents.
- geographic , epidemiology : France.
- drug therapy : Parkinson Disease.
- epidemiology : Parkinson Disease.
- Aged, Databases, Factual, Female, Humans, Logistic Models, Male, Middle Aged, Models, Statistical, Prevalence, ROC Curve, Reproducibility of Results.
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.
DOI: 10.1093/aje/kwr081
PubMed: 21606234
Links to Exploration step
pubmed:21606234Le document en format XML
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<author><name sortKey="Gourlet, Veronique" sort="Gourlet, Veronique" uniqKey="Gourlet V" first="Véronique" last="Gourlet">Véronique Gourlet</name>
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<front><div type="abstract" xml:lang="en">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.</div>
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<Abstract><AbstractText>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.</AbstractText>
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