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Accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis

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...

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Autores principales: Mbizvo, Gashirai K, Bennett, Kyle, Simpson, Colin R, Duncan, Susan E, Chin, Richard F M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042541/
https://www.ncbi.nlm.nih.gov/pubmed/29961014
http://dx.doi.org/10.1136/bmjopen-2017-020824
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author Mbizvo, Gashirai K
Bennett, Kyle
Simpson, Colin R
Duncan, Susan E
Chin, Richard F M
author_facet Mbizvo, Gashirai K
Bennett, Kyle
Simpson, Colin R
Duncan, Susan E
Chin, Richard F M
author_sort Mbizvo, Gashirai K
collection PubMed
description 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 from 1975 to current (01 June 2018). Included studies will validate the International Classification of Disease (ICD), Ninth Revision (ICD-9) onwards (ie, ICD-9 code 345 and ICD-10 codes G40–G41) as well as other non-ICD disease classification systems used, such as Read Codes in the UK. 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 I(2) 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.
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spelling pubmed-60425412018-07-16 Accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis Mbizvo, Gashirai K Bennett, Kyle Simpson, Colin R Duncan, Susan E Chin, Richard F M BMJ Open Neurology 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 from 1975 to current (01 June 2018). Included studies will validate the International Classification of Disease (ICD), Ninth Revision (ICD-9) onwards (ie, ICD-9 code 345 and ICD-10 codes G40–G41) as well as other non-ICD disease classification systems used, such as Read Codes in the UK. 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 I(2) 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. BMJ Publishing Group 2018-06-30 /pmc/articles/PMC6042541/ /pubmed/29961014 http://dx.doi.org/10.1136/bmjopen-2017-020824 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Neurology
Mbizvo, Gashirai K
Bennett, Kyle
Simpson, Colin R
Duncan, Susan E
Chin, Richard F M
Accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis
title Accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis
title_full Accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis
title_fullStr Accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis
title_full_unstemmed Accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis
title_short Accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis
title_sort accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042541/
https://www.ncbi.nlm.nih.gov/pubmed/29961014
http://dx.doi.org/10.1136/bmjopen-2017-020824
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