Evidence map›Paper›PMID 38782573›Full record

ArticleJournal of neurology, neurosurgery, and psychiatry2024

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

Richard Nicholas, Emma Clare Tallantyre, James Witts, Ruth Ann Marrie, Elaine M Craig, Sarah Knowles, Owen Rhys Pearson, Katherine Harding, Karim Kreft, J Hawken and 5 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neurology, neurosurgery, and psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Measuring health-related quality of life in multiple sclerosis: comparing the acceptability, validity and responsiveness of the EQ-5D-3L and MSIS-8D.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Richard NicholasDivision of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK.ORCID 0000-0003-0414-1225
Emma Clare TallantyreDivision of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.ORCID 0000-0002-3760-6634
James WittsPopulation Data Science, Singleton Park, Swansea University Medical School, Swansea, UK.
Ruth Ann MarrieDepartments of Medicine and Community Health Sciences, University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada.ORCID 0000-0002-1855-5595
Elaine M CraigPopulation Data Science, Singleton Park, Swansea University Medical School, Swansea, UK.ORCID 0000-0002-3432-9942
Sarah KnowlesPopulation Data Science, Singleton Park, Swansea University Medical School, Swansea, UK.
Owen Rhys PearsonDepartment of Neurology, Swansea Bay University Health Board, Swansea, UK.ORCID 0000-0002-2712-0200
Katherine HardingRoyal Gwent Hospital, Aneurin Bevan University Health Board, Newport, UK.
Karim KreftDivision of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
J HawkenDivision of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
Gillian IngramDepartment of Neurology, Swansea Bay University Health Board, Swansea, UK.
Bethan MorganUplands and Mumbles Surgery, Swansea Bay University Health Board, Swansea, UK.
Rodden M MiddletonPopulation Data Science, Singleton Park, Swansea University Medical School, Swansea, UK R.M.Middleton@swansea.ac.uk.ORCID 0000-0002-2130-4420
Neil RobertsonDivision of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
Ukms Register Research GroupUK MS Register, Swansea, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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. 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.

Indexed as

AlgorithmsMultiple SclerosisRegistriesAdultDatabases, FactualFemaleHumansMaleMiddle AgedPrevalenceRetrospective StudiesSensitivity and SpecificityWalesEPIDEMIOLOGYMULTIPLE SCLEROSIS

Identifiers

PMID38782573
PMCPMC11503172

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.