Nicholas, Richard;
Tallantyre, Emma Clare;
Witts, James;
Marrie, Ruth Ann;
Craig, Elaine M;
Knowles, Sarah;
Pearson, Owen Rhys;
... Robertson, Neil; + view all
(2024)
Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales.
Journal of Neurology, Neurosurgery & Psychiatry
, 95
(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.
| Type: | Article |
|---|---|
| Title: | Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1136/jnnp-2024-333532 |
| Publisher version: | https://doi.org/10.1136/jnnp-2024-333532 |
| Language: | English |
| Additional information: | This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Clinical Neurology, Psychiatry, Surgery, Neurosciences & Neurology, MULTIPLE SCLEROSIS, EPIDEMIOLOGY |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203107 |
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