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

Nicholas, Richard, Tallantyre, Emma Clare ORCID: https://orcid.org/0000-0002-3760-6634, Witts, James, Marrie, Ruth Ann, Craig, Elaine M., Knowles, Sarah, Pearson, Owen Rhys, Harding, Katherine, Kreft, Karim, Hawken, J., Ingram, Gillian, Morgan, Bethan, Middleton, Rodden M., Robertson, Neil ORCID: https://orcid.org/0000-0002-5409-4909 and Research Group, UKMS Register 2024. Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales. Journal of Neurology, Neurosurgery & Psychiatry 96 (11) , pp. 1032-1035. 10.1136/jnnp-2024-333532

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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 Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Publisher:	BMJ Publishing Group
ISSN:	0022-3050
Date of First Compliant Deposit:	20 June 2024
Date of Acceptance:	29 April 2024
Last Modified:	18 Nov 2024 14:09
URI:	https://orca.cardiff.ac.uk/id/eprint/169938

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