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Updated: Jun 25, 2025
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales
Richard Nicholas1, Emma Clare Tallantyre2, James Witts3
1Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK.
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.
