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

Richard Nicholas Orcid Logo, Emma Clare Tallantyre Orcid Logo, James Witts, Ruth Ann Marrie Orcid Logo, Elaine Craig, Sarah Knowles, Owen Rhys Pearson Orcid Logo, Katherine Harding, Karim Kreft, J Hawken, Gillian Ingram, Bethan Morgan, Rod Middleton Orcid Logo, 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 Orcid Logo

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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...

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Published in: Journal of Neurology, Neurosurgery and Psychiatry
ISSN: 0022-3050 1468-330X
Published: BMJ 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa66529
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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.</abstract><type>Journal Article</type><journal>Journal of Neurology, Neurosurgery and Psychiatry</journal><volume>0</volume><journalNumber/><paginationStart>jnnp</paginationStart><paginationEnd>2024</paginationEnd><publisher>BMJ</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0022-3050</issnPrint><issnElectronic>1468-330X</issnElectronic><keywords/><publishedDay>23</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-05-23</publishedDate><doi>10.1136/jnnp-2024-333532</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>This study was funded by Multiple Sclerosis Society (147).</funders><projectreference/><lastEdited>2024-05-29T17:11:30.9555823</lastEdited><Created>2024-05-29T17:03:30.6928179</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Richard</firstname><surname>Nicholas</surname><orcid>0000-0003-0414-1225</orcid><order>1</order></author><author><firstname>Emma Clare</firstname><surname>Tallantyre</surname><orcid>0000-0002-3760-6634</orcid><order>2</order></author><author><firstname>James</firstname><surname>Witts</surname><order>3</order></author><author><firstname>Ruth Ann</firstname><surname>Marrie</surname><orcid>0000-0002-1855-5595</orcid><order>4</order></author><author><firstname>Elaine</firstname><surname>Craig</surname><order>5</order></author><author><firstname>Sarah</firstname><surname>Knowles</surname><order>6</order></author><author><firstname>Owen Rhys</firstname><surname>Pearson</surname><orcid>0000-0002-2712-0200</orcid><order>7</order></author><author><firstname>Katherine</firstname><surname>Harding</surname><order>8</order></author><author><firstname>Karim</firstname><surname>Kreft</surname><order>9</order></author><author><firstname>J</firstname><surname>Hawken</surname><order>10</order></author><author><firstname>Gillian</firstname><surname>Ingram</surname><order>11</order></author><author><firstname>Bethan</firstname><surname>Morgan</surname><order>12</order></author><author><firstname>Rod</firstname><surname>Middleton</surname><orcid>0000-0002-2130-4420</orcid><order>13</order></author><author><firstname>Neil</firstname><surname>Robertson</surname><order>14</order></author><author><firstname>UKMS Register Research</firstname><surname>Group</surname><order>15</order></author></authors><documents><document><filename>66529__30487__88a72cf17813481dbcb835d60fa511e6.pdf</filename><originalFilename>66529.VoR.pdf</originalFilename><uploaded>2024-05-29T17:08:04.5357246</uploaded><type>Output</type><contentLength>618712</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by-nc/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 66529 2024-05-29 Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales c8d1e374a823863aae5d0dfaec19c7b5 James Witts James Witts true false 99f62fa657546df3f118c712d18e5595 Elaine Craig Elaine Craig true false 6c4c4acf0f27a0964f6f4b36c2a4ffac Sarah Knowles Sarah Knowles true false 005518f819ef1a2a13fdf438529bdfcd 0000-0002-2130-4420 Rod Middleton Rod Middleton true false 2024-05-29 MEDS 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. Journal Article Journal of Neurology, Neurosurgery and Psychiatry 0 jnnp 2024 BMJ 0022-3050 1468-330X 23 5 2024 2024-05-23 10.1136/jnnp-2024-333532 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University SU Library paid the OA fee (TA Institutional Deal) This study was funded by Multiple Sclerosis Society (147). 2024-05-29T17:11:30.9555823 2024-05-29T17:03:30.6928179 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Richard Nicholas 0000-0003-0414-1225 1 Emma Clare Tallantyre 0000-0002-3760-6634 2 James Witts 3 Ruth Ann Marrie 0000-0002-1855-5595 4 Elaine Craig 5 Sarah Knowles 6 Owen Rhys Pearson 0000-0002-2712-0200 7 Katherine Harding 8 Karim Kreft 9 J Hawken 10 Gillian Ingram 11 Bethan Morgan 12 Rod Middleton 0000-0002-2130-4420 13 Neil Robertson 14 UKMS Register Research Group 15 66529__30487__88a72cf17813481dbcb835d60fa511e6.pdf 66529.VoR.pdf 2024-05-29T17:08:04.5357246 Output 618712 application/pdf Version of Record true This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license. true eng http://creativecommons.org/licenses/by-nc/4.0/
title Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales
spellingShingle Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales
James Witts
Elaine Craig
Sarah Knowles
Rod Middleton
title_short Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales
title_full Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales
title_fullStr Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales
title_full_unstemmed Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales
title_sort Algorithmic approach to finding people with multiple sclerosis using routine healthcare data in Wales
author_id_str_mv c8d1e374a823863aae5d0dfaec19c7b5
99f62fa657546df3f118c712d18e5595
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author_id_fullname_str_mv c8d1e374a823863aae5d0dfaec19c7b5_***_James Witts
99f62fa657546df3f118c712d18e5595_***_Elaine Craig
6c4c4acf0f27a0964f6f4b36c2a4ffac_***_Sarah Knowles
005518f819ef1a2a13fdf438529bdfcd_***_Rod Middleton
author James Witts
Elaine Craig
Sarah Knowles
Rod Middleton
author2 Richard Nicholas
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
format Journal article
container_title Journal of Neurology, Neurosurgery and Psychiatry
container_volume 0
container_start_page jnnp
publishDate 2024
institution Swansea University
issn 0022-3050
1468-330X
doi_str_mv 10.1136/jnnp-2024-333532
publisher BMJ
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
active_str 0
description 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.
published_date 2024-05-23T17:11:29Z
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