Beslay, MarieMarieBeslayGeissbühler, YvonneYvonneGeissbühlerBeau, Anna-BelleAnna-BelleBeauMitter, VeraVeraMitter2025-09-102025-09-102025https://doi.org/10.24451/dspace/1209710.1007/s10654-025-01264-3https://arbor.bfh.ch/handle/arbor/45542Prevalence 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 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 the 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.enRA0421I. Health, Education, and Welfare::I1 Health::I18 Government Policy • Regulation • Public Healtharticle