The Accuracy and Utility of Using Administrative Healthcare Databases to Identify People with Epilepsy: A Protocol for a Systematic Review and Meta-Analysis

Gashirai Mbizvo, Colin Simpson, Susan Duncan, Richard Chin, Kyle H Bennett

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Introduction: In an increasingly digital age for healthcare around the world, administrative data have become rich and accessible tools for potentially identifying and monitoring population trends in diseases including epilepsy. However, it remains unclear (1) how accurate administrative data are at identifying epilepsy within a population, and (2) the optimal algorithms needed for administrative data to correctly identify people with epilepsy within a population. To address this knowledge gap, we will conduct a novel systematic review of all identified studies validating administrative healthcare data in epilepsy identification. We provide here a protocol that will outline the methods and analyses planned for the systematic review.
Methods and analysis: The systematic review described in this protocol will be conducted to follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE and EMBASE will be searched for studies validating administrative data in epilepsy published between 01/01/1975 and 03/01/2018. Included studies will validate the International Classification of Disease (ICD) 9th revision onwards (ICD-9 code 345 and ICD-10 codes G40-G41). The primary outcome will be providing pooled estimates of accuracy for identifying epilepsy within the administrative databases validated using sensitivity, specificity, positive and negative predictive values, and area under the receiver operating characteristic curves. Heterogeneity will be assessed using the I2 statistic and descriptive analyses used where this is present. The secondary outcome will be the optimal administrative data algorithms for correctly identifying epilepsy. These will be identified using multivariable logistic regression models. 95% confidence intervals will be quoted throughout. We will make an assessment of risk of bias, quality of evidence, and completeness of reporting for included studies.
Ethics and dissemination: Ethical approval is not required as primary data will not be collected. Results will be disseminated in peer-reviewed journals, conference presentations, and in press releases.
PROSPERO registration: CRD42017081212.
Strengths and limitations of this study
Strengths
• The protocol describes what will be the first systematic review to conduct a worldwide assessment of the accuracy of administrative data in identifying epilepsy and the optimal disease-identification algorithms.
• This protocol also describes what will be the first systematic review to make an assessment of risk of bias, quality of evidence, and completeness of reporting for studies validating administrative healthcare data in epilepsy identification.
Limitations
• The review described in this protocol will be limited to assessing the use of administrative data in diagnosing epilepsy within observational studies, which are more prone to bias than randomised controlled trials.
• A systematic review of the diagnostic accuracy of administrative data within randomised controlled trials in epilepsy remains to be completed, and is out of the scope of the current review.
Original languageEnglish
JournalBMJ Open
DOIs
Publication statusPublished - 30 Jun 2018

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