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Methods of estimating prevalence of multiple sclerosis in six European healthcare data sources: a contribution from the ConcePTION project

Marie Beslay Orcid Logo, 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, Christine Damase-Michel, Yvonne Geissbühler, Joan K Morris

European Journal of Epidemiology

Swansea University Authors: Alex Coldea, Sue Jordan

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DOI (Published version): 10.1007/s10654-025-01243-8

Abstract

Multiple sclerosis (MS) is a chronic autoimmune condition primarily affecting women and often diagnosed during childbearing years. This study assessed the impact of the lookback period and calculation method on MS prevalence in three healthcare data sources including women of childbearing age (from...

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Published in: European Journal of Epidemiology
ISSN: 0393-2990 1573-7284
Published: Springer Nature 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70177
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This study assessed the impact of the lookback period and calculation method on MS prevalence in three healthcare data sources including women of childbearing age (from Italy, Norway and Wales) and three data sources including pregnant women (from France, Finland and Spain). Women aged 15 to 49 years from 2005 to 2019 were included, data from pregnant women were collected around the pregnancy period. MS cases were identified based on at least one MS diagnosis or one dispensation for an MS-specific medication. All data sources provided inpatient diagnoses and medication data; outpatient diagnoses were available in Norway and Finland, and primary care diagnoses in Norway, Finland and Wales. We assessed MS case detection rate by lookback period, and compared three methods for estimating yearly MS prevalence: period prevalence (PP), average point prevalence (APP) and person-time prevalence (PTP). The estimated lookback periods to identify 95% of MS cases ranged from 6 to 9 years. 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spelling 2025-08-13T10:51:40.6751696 v2 70177 2025-08-13 Methods of estimating prevalence of multiple sclerosis in six European healthcare data sources: a contribution from the ConcePTION project 7f1029b5e3ee0447553de9072e79859f Alex Coldea Alex Coldea true false 24ce9db29b4bde1af4e83b388aae0ea1 Sue Jordan Sue Jordan true false 2025-08-13 MDA Multiple sclerosis (MS) is a chronic autoimmune condition primarily affecting women and often diagnosed during childbearing years. This study assessed the impact of the lookback period and calculation method on MS prevalence in three healthcare data sources including women of childbearing age (from Italy, Norway and Wales) and three data sources including pregnant women (from France, Finland and Spain). Women aged 15 to 49 years from 2005 to 2019 were included, data from pregnant women were collected around the pregnancy period. MS cases were identified based on at least one MS diagnosis or one dispensation for an MS-specific medication. All data sources provided inpatient diagnoses and medication data; outpatient diagnoses were available in Norway and Finland, and primary care diagnoses in Norway, Finland and Wales. We assessed MS case detection rate by lookback period, and compared three methods for estimating yearly MS prevalence: period prevalence (PP), average point prevalence (APP) and person-time prevalence (PTP). The estimated lookback periods to identify 95% of MS cases ranged from 6 to 9 years. APP and PTP provided lower prevalence estimates than PP, especially when the lookback to identify MS was short. In women of childbearing age, MS prevalence increased over time with all calculation methods and the highest MS prevalence was observed in Norway (PP of 402 per 100,000 in 2019). Finland showed the highest MS prevalence in pregnant women (PP of 218 per 100,000 in 2018). This study highlights the importance of sufficient lookback and available data to accurately estimate MS prevalence. Journal Article European Journal of Epidemiology 0 Springer Nature 0393-2990 1573-7284 Prevalence calculation methods; Lookback; Multiple sclerosis; Administrative healthcare data sources; Women of childbearing age; Pregnant women 31 7 2025 2025-07-31 10.1007/s10654-025-01243-8 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. 2025-08-13T10:51:40.6751696 2025-08-13T10:22:29.7977208 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Marie Beslay 0000-0002-5153-1747 1 Anna-Belle Beau 2 Davide Messina 3 Justine Benevent 4 Elisa Ballardini 5 Laia Barrachina-Bonet 6 Clara Cavero-Carbonell 7 Alex Coldea 8 Laura García-Villodre 9 Anja Geldhof 10 Rosa Gini 11 Kerstin Hellwig 12 Sue Jordan 13 Maarit K Leinonen 14 Sandra Lopez-Leon 15 Marco Manfrini 16 Visa Martikainen 17 Vera R Mitter 18 Amanda J Neville 19 Hedvig Nordeng 20 Aurora Puccini 21 Sandra Vukusic 22 Christine Damase-Michel 23 Yvonne Geissbühler 24 Joan K Morris 25 70177__34946__43181ee2267f4363b57c6ee57ed5f7c7.pdf 70177.VOR.pdf 2025-08-13T10:30:42.6285392 Output 2641405 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 Methods of estimating prevalence of multiple sclerosis in six European healthcare data sources: a contribution from the ConcePTION project
spellingShingle Methods of estimating prevalence of multiple sclerosis in six European healthcare data sources: a contribution from the ConcePTION project
Alex Coldea
Sue Jordan
title_short Methods of estimating prevalence of multiple sclerosis in six European healthcare data sources: a contribution from the ConcePTION project
title_full Methods of estimating prevalence of multiple sclerosis in six European healthcare data sources: a contribution from the ConcePTION project
title_fullStr Methods of estimating prevalence of multiple sclerosis in six European healthcare data sources: a contribution from the ConcePTION project
title_full_unstemmed Methods of estimating prevalence of multiple sclerosis in six European healthcare data sources: a contribution from the ConcePTION project
title_sort Methods of estimating prevalence of multiple sclerosis in six European healthcare data sources: 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
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
Christine Damase-Michel
Yvonne Geissbühler
Joan K Morris
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/s10654-025-01243-8
publisher Springer Nature
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 Multiple sclerosis (MS) is a chronic autoimmune condition primarily affecting women and often diagnosed during childbearing years. This study assessed the impact of the lookback period and calculation method on MS prevalence in three healthcare data sources including women of childbearing age (from Italy, Norway and Wales) and three data sources including pregnant women (from France, Finland and Spain). Women aged 15 to 49 years from 2005 to 2019 were included, data from pregnant women were collected around the pregnancy period. MS cases were identified based on at least one MS diagnosis or one dispensation for an MS-specific medication. All data sources provided inpatient diagnoses and medication data; outpatient diagnoses were available in Norway and Finland, and primary care diagnoses in Norway, Finland and Wales. We assessed MS case detection rate by lookback period, and compared three methods for estimating yearly MS prevalence: period prevalence (PP), average point prevalence (APP) and person-time prevalence (PTP). The estimated lookback periods to identify 95% of MS cases ranged from 6 to 9 years. APP and PTP provided lower prevalence estimates than PP, especially when the lookback to identify MS was short. In women of childbearing age, MS prevalence increased over time with all calculation methods and the highest MS prevalence was observed in Norway (PP of 402 per 100,000 in 2019). Finland showed the highest MS prevalence in pregnant women (PP of 218 per 100,000 in 2018). This study highlights the importance of sufficient lookback and available data to accurately estimate MS prevalence.
published_date 2025-07-31T05:30:10Z
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score 11.089386

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