Journal article 82 views 31 downloads
Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales
,
Emma Clare Tallantyre ,
James Witts,
Ruth Ann Marrie ,
Elaine Craig,
Sarah Knowles,
Owen Rhys Pearson ,
Katherine Harding,
Karim Kreft,
J Hawken,
Gillian Ingram,
Bethan Morgan,
Rod Middleton ,
Neil Robertson,
UKMS Register Research Group
Journal of Neurology, Neurosurgery and Psychiatry, Pages: jnnp - 2024
Swansea University Authors:
James Witts, Elaine Craig, Sarah Knowles, Rod Middleton
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DOI (Published version): 10.1136/jnnp-2024-333532
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 tha...
| Published in: | Journal of Neurology, Neurosurgery and Psychiatry |
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| ISSN: | 0022-3050 1468-330X |
| Published: |
BMJ
2024
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| Online Access: |
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| 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. |
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| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
This study was funded by Multiple Sclerosis Society (147). |
| Start Page: |
jnnp |
| End Page: |
2024 |