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Published in: Neurological Sciences 4/2017

01-04-2017 | Original Article

Identification of prevalent patients with epilepsy using administrative data: the Tuscany experience

Authors: Irene Bellini, Laura Policardo, Gaetano Zaccara, Pasquale Palumbo, Eleonora Rosati, Elena Torre, Paolo Francesconi

Published in: Neurological Sciences | Issue 4/2017

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Abstract

Overall prevalence of epilepsy ranges from 4 to 10 cases per 1000. Italy lacks recent epidemiological studies on large populations. In the present study, prevalence of epilepsy has been assessed in Tuscany, an Italian Region with 3,750,000 habitants, implementing an algorithm based on administrative data from the Regional Information Health System. To identify patients with epilepsy, we used at least one the following criteria: (a) at least one EEG and at least two dispensations of any antiepileptic drug (AEDS) at a minimum distance of 12 months; (b) at least two dispensations of one “specific” AED (authorized for use only for patients with epilepsy) at a minimum distance of 12 months; and (c) hospital admission for epilepsy or recurrent relapses (cod. ICD-IX-345.*). This algorithm was validated through comparison with lists of true patients with epilepsy and subjects without neurological disorders (gold standard). 35,950 cases were identified. Total crude prevalence was 9.6/1000. Prevalence increased in older patients up to 16/1000 without gender differences. Overall sensitivity of the algorithm was 87.3%, and specificity was 99.9%. This algorithm identifies patients with epilepsy with acceptable sensitivity and specificity and can be used to assess the burden of disease and for monitoring health services.
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Metadata
Title
Identification of prevalent patients with epilepsy using administrative data: the Tuscany experience
Authors
Irene Bellini
Laura Policardo
Gaetano Zaccara
Pasquale Palumbo
Eleonora Rosati
Elena Torre
Paolo Francesconi
Publication date
01-04-2017
Publisher
Springer Milan
Published in
Neurological Sciences / Issue 4/2017
Print ISSN: 1590-1874
Electronic ISSN: 1590-3478
DOI
https://doi.org/10.1007/s10072-016-2798-0

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