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Identifying multiple sclerosis in women of childbearing age in six European countries: a contribution from the ConcePTION project

Marie Beslay Orcid Logo, Yvonne Geissbühler, Anna-Belle Beau, Davide Messina, Justine Benevent, Elisa Ballardini, Laia Barrachina-Bonet, Clara Cavero-Carbonell, Alex Coldea, Laura García-Villodre, Anja Geldhof, Rosa Gini, Kerstin Hellwig, Sue Jordan, Maarit K. Leinonen, Sandra Lopez-Leon, Marco Manfrini, Visa Martikainen, Vera R. Mitter, Amanda J. Neville, Hedvig Nordeng, Aurora Puccini, Sandra Vukusic, Joan K. Morris, Christine Damase-Michel

European Journal of Epidemiology

Swansea University Authors: Alex Coldea, Sue Jordan

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DOI (Published version): 10.1007/s10654-025-01264-3

Abstract

Prevalence of Multiple Sclerosis (MS) has increased over the last decades, primarily among women of childbearing age. Several algorithms for identifying MS have been described in the literature, providing heterogeneous prevalence estimates. We compared five algorithms to identify MS in women of chil...

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Published in: European Journal of Epidemiology
ISSN: 0393-2990 1573-7284
Published: Springer Science and Business Media LLC 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70072
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Several algorithms for identifying MS have been described in the literature, providing heterogeneous prevalence estimates. We compared five algorithms to identify MS in women of childbearing age and estimated MS prevalence by time period and age-group. The study population included women aged 15 to 49 years-old between 2005 and 2019, from three data sources including all women (from Italy, Norway, and Wales), and three including pregnant women only (from France, Finland, and Spain; data collected around pregnancy). Five algorithms were tested: MS1 to MS3 combined MS diagnoses and MS-medicine prescriptions/dispensations, requiring 1, 2, or 3 occurrences, respectively; MS4 and MS5 used only MS diagnoses, requiring at least 2 occurrences (MS4 allowed just 1 if diagnosis was from inpatient care). In 2015-2019, MS prevalence based on MS1 ranged from 109 to 359 per 100,000 women: 109 in France, 121 in Spain, 195 in Wales, 232 in Finland, 264 in Italy, and 359 in Norway. More restrictive algorithms led to greater disparity, with MS3 ranging from 53 in Spain to 325 in Norway, and MS5 from 21 in France to 345 in Norway. All algorithms showed expected prevalence trends by time and age among women of childbearing age, though lower than in the literature. Overall, MS1 provided prevalence estimates most closely aligned with existing literature. 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spelling 2025-08-13T15:01:40.9413572 v2 70072 2025-07-31 Identifying multiple sclerosis in women of childbearing age in six European countries: a contribution from the ConcePTION project 7f1029b5e3ee0447553de9072e79859f Alex Coldea Alex Coldea true false 24ce9db29b4bde1af4e83b388aae0ea1 Sue Jordan Sue Jordan true false 2025-07-31 MDA Prevalence of Multiple Sclerosis (MS) has increased over the last decades, primarily among women of childbearing age. Several algorithms for identifying MS have been described in the literature, providing heterogeneous prevalence estimates. We compared five algorithms to identify MS in women of childbearing age and estimated MS prevalence by time period and age-group. The study population included women aged 15 to 49 years-old between 2005 and 2019, from three data sources including all women (from Italy, Norway, and Wales), and three including pregnant women only (from France, Finland, and Spain; data collected around pregnancy). Five algorithms were tested: MS1 to MS3 combined MS diagnoses and MS-medicine prescriptions/dispensations, requiring 1, 2, or 3 occurrences, respectively; MS4 and MS5 used only MS diagnoses, requiring at least 2 occurrences (MS4 allowed just 1 if diagnosis was from inpatient care). In 2015-2019, MS prevalence based on MS1 ranged from 109 to 359 per 100,000 women: 109 in France, 121 in Spain, 195 in Wales, 232 in Finland, 264 in Italy, and 359 in Norway. More restrictive algorithms led to greater disparity, with MS3 ranging from 53 in Spain to 325 in Norway, and MS5 from 21 in France to 345 in Norway. All algorithms showed expected prevalence trends by time and age among women of childbearing age, though lower than in the literature. Overall, MS1 provided prevalence estimates most closely aligned with existing literature. This study offers key insights into choosing algorithms for identifying MS in women of childbearing age and in pregnant women. Journal Article European Journal of Epidemiology 0 Springer Science and Business Media LLC 0393-2990 1573-7284 Disease identification algorithms; Multiple sclerosis; Prevalence; Administrative healthcare data sources; Women of childbearing age; Pregnant women 18 7 2025 2025-07-18 10.1007/s10654-025-01264-3 COLLEGE NANME Development and Engagement COLLEGE CODE MDA Swansea University Another institution paid the OA fee Open access funding provided by Université de Toulouse. The ConcePTION project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 821520. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. The research leading to these results was conducted as part of the ConcePTION consortium. 2025-08-13T15:01:40.9413572 2025-07-31T11:39:55.4391061 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Marie Beslay 0000-0002-5153-1747 1 Yvonne Geissbühler 2 Anna-Belle Beau 3 Davide Messina 4 Justine Benevent 5 Elisa Ballardini 6 Laia Barrachina-Bonet 7 Clara Cavero-Carbonell 8 Alex Coldea 9 Laura García-Villodre 10 Anja Geldhof 11 Rosa Gini 12 Kerstin Hellwig 13 Sue Jordan 14 Maarit K. Leinonen 15 Sandra Lopez-Leon 16 Marco Manfrini 17 Visa Martikainen 18 Vera R. Mitter 19 Amanda J. Neville 20 Hedvig Nordeng 21 Aurora Puccini 22 Sandra Vukusic 23 Joan K. Morris 24 Christine Damase-Michel 25 70072__34881__f70996752f3b4f27b92c1afb86ef7ba9.pdf 70072.VOR.pdf 2025-07-31T11:46:04.8947380 Output 1942737 application/pdf Version of Record true © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). true eng http://creativecommons.org/licenses/by/4.0/
title Identifying multiple sclerosis in women of childbearing age in six European countries: a contribution from the ConcePTION project
spellingShingle Identifying multiple sclerosis in women of childbearing age in six European countries: a contribution from the ConcePTION project
Alex Coldea
Sue Jordan
title_short Identifying multiple sclerosis in women of childbearing age in six European countries: a contribution from the ConcePTION project
title_full Identifying multiple sclerosis in women of childbearing age in six European countries: a contribution from the ConcePTION project
title_fullStr Identifying multiple sclerosis in women of childbearing age in six European countries: a contribution from the ConcePTION project
title_full_unstemmed Identifying multiple sclerosis in women of childbearing age in six European countries: a contribution from the ConcePTION project
title_sort Identifying multiple sclerosis in women of childbearing age in six European countries: a contribution from the ConcePTION project
author_id_str_mv 7f1029b5e3ee0447553de9072e79859f
24ce9db29b4bde1af4e83b388aae0ea1
author_id_fullname_str_mv 7f1029b5e3ee0447553de9072e79859f_***_Alex Coldea
24ce9db29b4bde1af4e83b388aae0ea1_***_Sue Jordan
author Alex Coldea
Sue Jordan
author2 Marie Beslay
Yvonne Geissbühler
Anna-Belle Beau
Davide Messina
Justine Benevent
Elisa Ballardini
Laia Barrachina-Bonet
Clara Cavero-Carbonell
Alex Coldea
Laura García-Villodre
Anja Geldhof
Rosa Gini
Kerstin Hellwig
Sue Jordan
Maarit K. Leinonen
Sandra Lopez-Leon
Marco Manfrini
Visa Martikainen
Vera R. Mitter
Amanda J. Neville
Hedvig Nordeng
Aurora Puccini
Sandra Vukusic
Joan K. Morris
Christine Damase-Michel
format Journal article
container_title European Journal of Epidemiology
container_volume 0
publishDate 2025
institution Swansea University
issn 0393-2990
1573-7284
doi_str_mv 10.1007/s10654-025-01264-3
publisher Springer Science and Business Media LLC
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
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description Prevalence of Multiple Sclerosis (MS) has increased over the last decades, primarily among women of childbearing age. Several algorithms for identifying MS have been described in the literature, providing heterogeneous prevalence estimates. We compared five algorithms to identify MS in women of childbearing age and estimated MS prevalence by time period and age-group. The study population included women aged 15 to 49 years-old between 2005 and 2019, from three data sources including all women (from Italy, Norway, and Wales), and three including pregnant women only (from France, Finland, and Spain; data collected around pregnancy). Five algorithms were tested: MS1 to MS3 combined MS diagnoses and MS-medicine prescriptions/dispensations, requiring 1, 2, or 3 occurrences, respectively; MS4 and MS5 used only MS diagnoses, requiring at least 2 occurrences (MS4 allowed just 1 if diagnosis was from inpatient care). In 2015-2019, MS prevalence based on MS1 ranged from 109 to 359 per 100,000 women: 109 in France, 121 in Spain, 195 in Wales, 232 in Finland, 264 in Italy, and 359 in Norway. More restrictive algorithms led to greater disparity, with MS3 ranging from 53 in Spain to 325 in Norway, and MS5 from 21 in France to 345 in Norway. All algorithms showed expected prevalence trends by time and age among women of childbearing age, though lower than in the literature. Overall, MS1 provided prevalence estimates most closely aligned with existing literature. This study offers key insights into choosing algorithms for identifying MS in women of childbearing age and in pregnant women.
published_date 2025-07-18T05:29:53Z
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