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

Nicholas, Richard; Tallantyre, Emma Clare; 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; Evangelou, Nikos; Allen, Kellie; Schmierer, Klaus; Galea, Ian; Craner, Matt; Chataway, Jeremy; McDonnell, Gavin; Fox, Annemieke; Wilson, Heather; Rog, David; Kipps, Chris; Gale, Andrew; Marta, Monica; Fuller, Sarah; Archer, Judy; McLean, Brendan; Straukiene, Agne; Guadango, Joe; Kitley, Jo; Graham, Andrew; Canepa, Carlo; Ford, Helen; Coles, Alasdair; Emsley, H.; Hobart, Jeremy; Foxton, Julie; Harikrishnan, Dreedharan; Petzold, Laura; Harrower, Tim; London, Ruth Dobson; Slowinski, Zbignew; Sharrack, Basil

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.

Citation

Nicholas, R., Tallantyre, E. C., Witts, J., Marrie, R. A., Craig, E. M., Knowles, S., Pearson, O. R., Harding, K., Kreft, K., Hawken, J., Ingram, G., Morgan, B., Middleton, R. M., Robertson, N., Evangelou, N., Allen, K., Schmierer, K., Galea, I., Craner, M., Chataway, J., …Sharrack, B. (2024). Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales. Journal of Neurology, Neurosurgery and Psychiatry, 95(11), 1032-1035. https://doi.org/10.1136/jnnp-2024-333532

• Journal Article Type: Article
• Acceptance Date: Apr 29, 2024
• Online Publication Date: Oct 16, 2024
• Publication Date: May 23, 2024
• Deposit Date: Jul 22, 2025
• Publicly Available Date: Jul 25, 2025
• Journal: Journal of Neurology, Neurosurgery and Psychiatry
• Print ISSN: 0022-3050
• Electronic ISSN: 1468-330X
• Publisher: BMJ Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 95
• Issue: 11
• Pages: 1032-1035
• DOI: https://doi.org/10.1136/jnnp-2024-333532
• Public URL: https://nottingham-repository.worktribe.com/output/41929848
• Publisher URL: https://jnnp.bmj.com/content/95/11/1032