Epidemiology and therapeutic management of highly active relapsing-remitting multiple sclerosis adults in the French national health insurance database

Abstract

Background: In France, no specific information on the Highly Active Relapsing-Remitting Multiple Sclerosis (HA-RRMS) population is available.Objective: To describe the epidemiology and therapeutic management of HA-RRMS patients in France.Methods: In this cohort study, HA-RRMS patients were identified in the health data system with a new algorithm using outpatient healthcare consumption and hospital discharge data. Results: Over 2010–2015, 9,596 incident HA-RRMS patients were identified (sex ratio: 2.8; mean age 39.9 years old) and followed-up for 4.0 years, on average. In 2015, the incidence and mortality rates in patients aged 20 and above were 3.6 and 389 per 100,000, respectively (lower mortality than in the MS population but twice as high as in the French population). During the study, 39.7% of patients took a disease-modifying drug (DMD) and 83.5% a high efficacy DMD (HE-DMD, fingolimod or natalizumab, with a mean treatment duration of 3.5 years). When patients treated by an HE-DMD required to switch to another treatment, it was more often to the other HE-DMD (more often from natalizumab to fingolimod) than to a DMD. Conclusions: According to this first real-world study, HA-RRMS patients could represent 8% of MS patients. The results validated the algorithm to identify HA-RRMS patients.

Framework for personalized prediction of treatment response in relapsing-remitting multiple sclerosis: a replication study in independent data

“…We avoid statements on optimal treatment effects for individual patients [9], because our approach may be impaired by unobserved confounding. For example, if a new drug is introduced on the market, changes from older drugs to the new drug may be motivated by market strategies of the respective company [10]. Effects observed after such switches are "prognostic" in terms that they reflect the effect of the drug and not interactions between drug and patient's features.…”

Framework for Personalized Prediction of Treatment Response in Relapsing-Remitting Multiple Sclerosis: A Replication Study in Independent Data

“…We avoid statements on optimal treatment effects for individual patients (9), because our approach may be impaired by unobserved confounding. For example, if a new drug is introduced on the market, changes from older drugs to the new drug may be motivated by market strategies of the respective company (10). Effects observed after such switches are "prognostic" in terms that they re ect the effect of the drug and not interactions between drug and patient's features.…”