• Richard Nicholas, Emma Clare Tallantyre, James Witts, Ruth Ann Marrie, Elaine M Craig, Sarah Knowles, Owen Rhys Pearson, Katherine Harding, Karim Kreft, J Hawken, Gillian Ingram, Bethan Morgan, Rodden M Middleton, Neil Robertson, and Ukms Register Research Group.
• Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK.
• J. Neurol. Neurosurg. Psychiatr. 2024 Oct 16; 95 (11): 103210351032-1035.

BackgroundIdentification 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.AimTo develop an algorithm to reliably identify MS cases within a national health data bank.MethodRetrospective 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.ResultsFrom 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.DiscussionThe 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.© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

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