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Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales

Richard Nicholas Orcid Logo, Emma Clare Tallantyre Orcid Logo, James Witts Orcid Logo, Ruth Ann Marrie Orcid Logo, Elaine Craig, Sarah Knowles, Owen Rhys Pearson Orcid Logo, Katherine Harding, Karim Kreft, J Hawken, Gillian Ingram, Bethan Morgan, Rod Middleton Orcid Logo, Neil Robertson, UKMS Register Research Group

Journal of Neurology, Neurosurgery and Psychiatry, Volume: 95, Issue: 11, Pages: 1032 - 1035

Swansea University Authors: James Witts Orcid Logo, Elaine Craig, Sarah Knowles, Rod Middleton Orcid Logo

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Abstract

Background: Identification of multiple sclerosis (MS) cases in routine healthcare data repositories remains challenging. MS can have a protracted diagnostic process and is rarely identified as a primary reason for admission to the hospital. Difficulties in identification are compounded in systems th...

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Published in: Journal of Neurology, Neurosurgery and Psychiatry
ISSN: 0022-3050 1468-330X
Published: BMJ 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa66529
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Abstract: Background: Identification of multiple sclerosis (MS) cases in routine healthcare data repositories remains challenging. MS can have a protracted diagnostic process and is rarely identified as a primary reason for admission to the hospital. Difficulties in identification are compounded in systems that do not include insurance or payer information concerning drug treatments or non-notifiable disease. Aim: To develop an algorithm to reliably identify MS cases within a national health data bank. Method: Retrospective analysis of the Secure Anonymised Information Linkage (SAIL) databank was used to identify MS cases using a novel algorithm. Sensitivity and specificity were tested using two existing independent MS datasets, one clinically validated and population-based and a second from a self-registered MS national registry. Results: From 4 757 428 records, the algorithm identified 6194 living cases of MS within Wales on 31 December 2020 (prevalence 221.65 (95% CI 216.17 to 227.24) per 100 000). Case-finding sensitivity and specificity were 96.8% and 99.9% for the clinically validated population-based cohort and sensitivity was 96.7% for the self-declared registry population. Discussion: The algorithm successfully identified MS cases within the SAIL databank with high sensitivity and specificity, verified by two independent populations and has important utility in large-scale epidemiological studies of MS.
Item Description: Short report
College: Faculty of Medicine, Health and Life Sciences
Funders: This study was funded by Multiple Sclerosis Society (147).
Issue: 11
Start Page: 1032
End Page: 1035