(33) Identification and prevalence assessment of multiple sclerosis in women: a multicentre database study across Europe: A contribution from the IMI2 ConcePTION project

Presenting Author(s)

MB

Marie Beslay, PhD

Postdoctoral researcher
Toulouse University Hospital, CERPOP-SPHERE Team, Inserm UMR 1295, Toulouse University, France., France

Author(s)

AB

Anna-Belle Beau, PhD

Postdoctoral researcher
Toulouse University Hospital, CERPOP-SPHERE Team, Inserm UMR 1295, Toulouse University, Toulouse, France
Toulouse, France
JB

Justine Benevent

Researcher
PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway, Norway
SJ

Sue Jordan, MB.BCh., PhD. PGCE (FE),

professor
Faculty of Medicine, Health and Life Sciences, Swansea University, Swansea, United Kingdom
Maarit K. Leinonen, MD, PhD

Associate professor
Data and Analytics, Knowledge Brokers, Finnish Institute for Health and Welfare, Helsinki, Finland
Helsinki, Uusimaa, Finland
VM

Visa Martikainen

Data manager, statistician
Department of Knowledge Brokers, Finnish Institute for Health and Welfare, Helsinki, Finland, Finland
MM

Marco Manfrini

Specialist in health statistics and biometrics
Department of Medical Sciences, Centre for Clinical and Epidemiological Research, Ferrara University, Italy, Italy
Hedvig Nordeng, PhD

Professor
Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
Oslo, Oslo, Norway
VM

Vera Ruth Mitter

Postdoctoral researcher
Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
CC

Clara Cavero-Carbonell

Doctor of Medicine
Rare Disease Research Unit, Foundation for Promotion of Health and Biomedical Research of Valencian Region (FISABIO), Spain
LB

Laia Barrachina Bonet

Doctorate Student / Research Assistant
Rare Disease Research Unit, Foundation for Promotion of Health and Biomedical Research of Valencian Region (FISABIO), Spain
SL

Séverine Lignot

Project Manager
Bordeaux PharmacoEpi, INSERM CIC-P1401, Univ. Bordeaux, 33000 Bordeaux, France, United States
RL

Régis Lassalle, MSc

Head of Statistics and Data management
Bordeaux PharmacoEpi, INSERM CIC-P1401, Univ. Bordeaux, 33000 Bordeaux, France
Bordeaux, Aquitaine, France
MB

Marie-Agnès Bernard

Statistician
Bordeaux PharmacoEpi, INSERM CIC-P1401, Univ. Bordeaux, 33000 Bordeaux, France, France
Anja Geldhof, PhD (she/her/hers)

Director of Medical Affairs
Janssen Biologics BV, Leiden, The Netherlands, Netherlands
Rosa Gini, PhD (she/her/hers)

Head of Unit at ARS Toscana, Florence, Italy
ARS Toscana, Florence, Italy
Florence, Toscana, Italy
DM

Davide Messina

Programmer
ARS Toscana, Florence, Italy, Italy
JM

Joan K. Morris

Professor
St George's, University of London, London, United Kingdom
Yvonne Geissbühler, PhD

Director RWE and Data Science
Real World Evidence, Novartis Pharma AG, Basel, Switzerland, Switzerland
Christine Damase-Michel, PhD, PharmD

Associate Professor in Pharmacology
Université Toulouse III
Toulouse, Midi-Pyrenees, France

ABSTRACT: Introduction

Multiple sclerosis (MS) is frequently diagnosed during women's reproductive years, raising the issue of the safety of Disease Modifying Therapies (DMTs) during pregnancy. To assess the safety of DMTs using population databases, women with MS must be identified. The objective was to explore strategies for identifying MS patients and calculating MS prevalence in large-population databases.

Material and Methods

Five algorithms were tested to identify MS in 8 European databases one of them being EFEMERIS, a French cohort of pregnant women. Three methods for MS-prevalence per 100,000 women calculation were performed: period prevalence (number of cases/total number of women), person-time prevalence (number of person-days after MS identification /total number of person-days), and average prevalence (average of point prevalence calculated on each first day of the month).

Results

We present results on data from EFEMERIS. The algorithm selected to identify MS in EFEMERIS required at least one DMT prescription or one MS-diagnosis. The study included 103,332 women. 105 women were identified as having MS, with a median follow-up of 360 days. MS prevalence according to the different methods was: 101.6 for the period prevalence, 49.0 for the person-time prevalence, and 53.9 for the average prevalence.

Discussion/Conclusion

MS prevalence in EFEMERIS was lower than previously published for France (210.0 per 100,000 women[1]). This underestimation was expected because of the short follow-up of women included in EFEMERIS, and, consequently, the short time frame in which the disease might be identified. The prevalence was lower when time was considered (person-time and average methods) compared with the period prevalence, which may be explained by the MS identification being predominantly at the end of follow-up. The choice of the algorithm for identifying MS and the method for prevalence calculation must be considered and adapted to each database. This study will be replicated in 7 other large databases in the coming months.

References

[1] S. Foulon et al., « Prevalence and mortality of patients with multiple sclerosis in France in 2012: a study based on French health insurance data », J. Neurol., vol. 264, no 6, p. 1185‑1192, 2017, doi: 10.1007/s00415-017-8513-0.