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Background: Primary brain tumors are a heterogeneous group of benign and malignant tumors arising from the brain parenchyma and its surrounding structures. The epidemiology of these tumors is poorly understood. The aim of our study is to systematically review the latest literature on the incidence and prevalence of primary brain tumors., Methods: The systematic review and meta-analysis were conducted according to a predetermined protocol and established guidelines. Only studies reporting on data from 1985 onward were included. Articles were included if they met the following criteria: (i) original research, (ii) population based, (iii) reported an incidence or prevalence estimate of primary brain tumors., Results: From the 53 eligible studies overall, 38 were included in the meta-analysis. A random-effects model found the overall incidence rate of all brain tumors to be 10.82 (95% CI: 8.63-13.56) per 100 000 person-years. The incidence proportion estimates were heterogeneous, even among the same tumor subtypes, and ranged from 0.051 per 100 000 (germ cell tumors) to 25.48 per 100 000 (all brain tumors). There were insufficient data to conduct a meta-analysis of the prevalence of primary brain tumors., Conclusions: There is a need for more accurate and comparable incidence and prevalence estimates of primary brain tumors across the world. A standardized approach to the study of the epidemiology of these tumors is needed to better understand the burden of brain tumors and the possible geographical variations in their incidence., (© The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The objective of this study was to develop and validate coding algorithms for epilepsy using ICD-coded inpatient claims, physician claims, and emergency room (ER) visits. 720/2049 charts from 2003 and 1533/3252 charts from 2006 were randomly selected for review from 13 neurologists' practices as the "gold standard" for diagnosis. Epilepsy status in each chart was determined by 2 trained physicians. The optimal algorithm to identify epilepsy cases was developed by linking the reviewed charts with three administrative databases (ICD 9 and 10 data from 2000 to 2008) including hospital discharges, ER visits and physician claims in a Canadian health region. Accepting chart review data as the gold standard, we calculated sensitivity, specificity, positive, and negative predictive value for each ICD-9 and ICD-10 administrative data algorithm (case definitions). Of 18 algorithms assessed, the most accurate algorithm to identify epilepsy cases was "2 physician claims or 1 hospitalization in 2 years coded" (ICD-9 345 or G40/G41) and the most sensitive algorithm was "1 physician clam or 1 hospitalization or 1 ER visit in 2 years." Accurate and sensitive case definitions are available for research requiring the identification of epilepsy cases in administrative health data., (Copyright © 2012 Elsevier B.V. All rights reserved.)