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Backgroundand Purpose: The purpose was to estimate the risk of epilepsy in a cohort of young individuals with celiac disease (CD) compared to that of matched references., Methods: The cohort consisted of 213 635 individuals born during 1989-2011 and residing in Friuli-Venezia Giulia (Italy). 1215 individuals affected by CD and 6075 reference individuals matched by sex and age were identified. Epilepsy was defined by means of hospital diagnosis or drug prescriptions. Conditional logistic regression was used to estimate the odds ratios (ORs) of having epilepsy amongst individuals with CD, before CD diagnosis and in the entire period, compared with those of their matched references. Cox regression was used to calculate the hazard ratios for epilepsy diagnosed after CD diagnosis. Different definitions of epilepsy were used for sensitivity analyses., Results: Thirty-one (2.6%) individuals with CD and 78 (1.3%) reference individuals had epilepsy [adjusted OR 2.03; 95% confidence interval (CI) 1.33-3.10]. The risk of epilepsy was increased prior to CD (adjusted OR 2.29; 95% CI 1.33-3.94), with similar estimates after CD diagnosis (adjusted hazard ratio 1.96; 95% CI 0.95-4.02). The increased risk of epilepsy was not explained by a peak in epilepsy diagnosis just around CD diagnosis. Sex stratification found a significantly higher risk of epilepsy amongst female individuals with CD. Sensitivity analyses confirmed the positive association between CD and epilepsy., Conclusion: Children and youths with CD were at increased risk of epilepsy. Patients with epilepsy without a clear etiology should be screened for CD since an early diagnosis and treatment might improve the response to antiepileptic therapies., (© 2020 European Academy of Neurology.)

Background: Parkinson's Disease (PD), Multiple Sclerosis (MS), and Epilepsy are three highly impactful health conditions affecting the nervous system. PD, MS, and epilepsy cases can be identified by means of Healthcare Administrative Databases (HADs) to estimate the occurrence of these diseases, to better monitor the adherence to treatments, and to evaluate patients' outcomes. Nevertheless, the absence of a validated and standardized approach makes it hard to quantify case misclassification., Objectives: to identify and describe all PD, MS, and epilepsy case-identification algorithms by means of Italian HADs, through the review of papers published in the past 10 years., Methods: this study is part of a project that systematically reviewed case-identification algorithms for 18 acute and chronic conditions by means of HADs in Italy. PubMed was searched for original articles, published between 2007 and 2017, in Italian or English. The search string consisted of a combination of free text and MeSH terms with a common part that focused on HADs and a disease-specific part. All identified papers were screened by two independent reviewers. Pertinent papers were classified according to the objective for which the algorithm had been used, and only articles that used algorithms for primary objectives (I disease occurrence; II population/cohort selection; III outcome identification) were considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms have been reported. Further information on specific objective(s), accuracy measures, sensitivity analyses and the contribution of each HAD, have also been recorded., Results: the search strategy led to the identification of 70 papers for PD, 154 for MS, and 100 for epilepsy, of which 3 papers for PD, 6 for MS, and 5 for epilepsy were considered pertinent. Most articles were published in the last three years (2014-2017) and focused on a region-wide setting. Out of all pertinent articles, 3 original algorithms for PD, 4 for MS, and 4 for epilepsy were identified. The Drug Prescription Database (DPD) and Hospital Discharge record Database (HDD) were used by almost all PD, MS, and epilepsy case-identification algorithms. The Exemption from healthcare Co-payment Database (ECD) was used by all PD and MS case-identification algorithms, while only 1 epilepsy case-identification algorithm used this source. All epilepsy case-identification algorithms were based on at least a combination of electroencephalogram (EEG) and drug prescriptions. An external validation had been performed by 2 papers for MS, 2 for epilepsy, and only 1 for PD., Conclusion: the results of our review highlighted the scarce use of HADs for the identification of cases affected by neurological diseases in Italy. While PD and MS algorithms are not so heterogeneous, epilepsy case-identification algorithms have increased in complexity over time. Further validations are needed to better understand the specific characteristics of these algorithms.