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. 2025 Aug;40(8):891-904.
doi: 10.1007/s10654-025-01264-3. Epub 2025 Jul 18.

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

Marie Beslay  1 , Yvonne Geissbühler  2 , Anna-Belle Beau  3 , Davide Messina  4 , Justine Benevent  3 , Elisa Ballardini  5 , Laia Barrachina-Bonet  6 , Clara Cavero-Carbonell  6 , Alex Coldea  7 , Laura García-Villodre  6 , Anja Geldhof  8 , Rosa Gini  4 , Kerstin Hellwig  9 , Sue Jordan  7 , Maarit K Leinonen  10 , Sandra Lopez-Leon  11   12 , Marco Manfrini  13 , Visa Martikainen  10 , Vera R Mitter  14 , Amanda J Neville  15 , Hedvig Nordeng  16 , Aurora Puccini  17 , Sandra Vukusic  18 , Joan K Morris  19 , Christine Damase-Michel  3
Affiliations

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

Marie Beslay et al. Eur J Epidemiol. 2025 Aug.

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 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.

Keywords: Administrative healthcare data sources; Ddisease identification algorithms; Multiple sclerosis; Pregnant women.; Prevalence; Women of childbearing age.

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Conflict of interest statement

Declarations. Conflict of interest: AG is an employee of Janssen Biologics B.V. and owns stock/stock options in Johnson & Johnson, of which Janssen is a subsidiary. SLL and YG are employees of Novartis and own stock. All other co-authors have no competing interests to disclose. Ethical approval: Finland: Ethical approval is not required for register-based studies. Institutional Review Board at the Finnish Institute for Health and Welfare approved the study and waived the requirement for obtaining informed consent for the secondary use of health administrative data from study participants (THL/543/6.02.00/2021). Data were handled and stored in accordance with the General Data Protection Regulation. France: The EFEMERIS cohort was approved by the French Data Protection Authority on 7 April 2005 (authorization number 05-1140). This study was performed on anonymized patient data. The women included in the EFEMERIS database were informed of their inclusion and of the potential use of their anonymized data for research purposes. They could oppose the use of their data at any time. The women included in the EFEMERIS database know that their collected and anonymized data can be used for medical research purposes and can thus be published. The study was approved by the EFEMERIS steering group. Data were handled and stored in accordance with the General Data Protection Regulation. Italy: The study was approved by the local ethical committee (approval number 593/2023/Oss/UniFe). Data were handled and stored in accordance with the General Data Protection Regulation and in agreement with the Authority for Healthcare and Welfare, Emilia Romagna Regional Health Service, Bologna, Italy. Norway: The study was approved by the Regional Committee for Research Ethics in South-East Norway (approval number 85224) and by the Data Protection Officer at the University of Oslo (approval number 519858). Data were handled and stored in accordance with the General Data Protection Regulation. Spain (Valencian Region): The study (code: IMI-IMN-2019-01) was classified as an Observational Post-authorisation Study “Other designs” (EPA-OD) by Spanish Medicines Agency (AEMPS), available on: https://sede.aemps.gob.es ; and approved by the Arnau de Vilanova Hospital’s Clinical Research Ethics Committee on 29th January 2020, according to the Spanish regulations (approval number 1/2020). At regional level following the national Personal Data Protection and guaranteeing digital rights (Law 3/2018), the study was approved by the Commission of the Regional Government (PROSIGA) that has the right of giving RDRU Fisabio authorisation to process the data (references: SD2556; SD2577; SD2578; SD2579; SD2580; SD2581; SD2582). Wales: This study uses anonymised data held in the Secure Anonymised Information Linkage (SAIL) Databank. The SAIL Databank independent Information Governance Review Panel (IGRP) approved the study as part of project 0823, on 16th October 2020.

Figures

Fig. 1
Fig. 1
For each data source, periods of available data for identifying Multiple Sclerosis (blue), periods when prevalence is stratified by time period (green stripes), and by age groups (yellow stripes)
Fig. 2
Fig. 2
Prevalence of Multiple Sclerosis (MS) per 100,000 women (95% Confidence Interval) according to five MS-identification algorithms (MS1 to MS5), stratified by period in data sources with women of childbearing age (a) and in data sources with pregnant women (b). Prevalence estimates are presented only when at least 5 cases were observed. Values are available in online supplementary Tables 6 and 7. Algorithms MS1 to MS5 are described in Table 2
Fig. 3
Fig. 3
Prevalence of Multiple Sclerosis (MS) per 100,000 women (95% Confidence Interval) according to five MS-identification algorithms (MS1 to MS5), in the 2015-2019 period stratified by age group in data sources with women of childbearing age (a) and in data sources with pregnant women (b). Prevalence estimates are presented only when at least 5 cases were observed. Values are available in online supplementary Tables 8 and 9. Algorithms MS1 to MS5 are described in Table 2
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