# Updated Multiple Sclerosis Incidence in France, 2011–2021

Octave Guinebretiere, Chloé Pierret, Quentin Calonge, Edouard Januel, Celine Louapre, Thomas Nedelec

► To cite this version:

Octave Guinebretiere, Chloé Pierret, Quentin Calonge, Edouard Januel, Celine Louapre, et al. Updated Multiple Sclerosis Incidence in France, 2011–2021. Neurology, 2025, 104 (9), 10.1212/WNL.0000000000213586. hal-05034515

HAL Id: hal-05034515

https://hal.science/hal-05034515v1

Submitted on 15 Apr 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 Updated Multiple Sclerosis incidence in France, 2011-2021

2 Octave Guinebretiere¹, Chloé Pierret², Quentin Calonge³,⁴,⁵, Edouard Januel⁶,⁷, Celine

3 Louapre¹,⁸, Thomas Nedelec¹

4 1. Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Inria, Paris, France

5 2. Rennes University, EHESP, CNRS, Inserm, ARENES UMR 6051, RSMS U 1309, Rennes,

6 France

7 3. Sorbonne Université, Paris Brain Institute - Institut du Cerveau, ICM, INSERM, CNRS,

8 Pitié-Salpêtrière Hospital, Paris, France

9 4. AP-HP, Epilepsy Unit, Pitié-Salpêtrière Hospital, DMU Neurosciences, Paris, France

10 5. AP-HP, Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France

11 6. Neurology Department and Clinical Research Unit, Hôpital fondation A. de Rothschild,

12 Paris, France

13 7. Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique,

14 Equipe PEPITES, F75013, Paris, France

15 8. Department of Neurology, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Assistance

16 Publique-Hôpitaux de Paris, Paris, France

17

18

19

20

21

22

23

24

25

26

27

28
29 **Abstract**

30 **Background and objectives:**

31 Multiple sclerosis (MS) is a chronic neurological disorder with significant implications for
32 public health as being the first cause of non-traumatic neurological disability in young adults.
33 Although the global prevalence of MS has been increasing, recent temporal trends in
34 incidence remain unclear. We aimed to evaluate current MS incidence trends in France over
35 11 years using the Système National des Données de Santé (SNDS), a nationwide
36 administrative database covering 99% of the French population.

37 **Methods:**

38 We utilized a published algorithm that incorporates multiple data sources, including benefits
39 from long-term diseases, specific disease-modifying treatment prescriptions, and hospital
discharge, to identify incident MS cases from January 1, 2011 to December 31, 2021. Sex and
40 age-standardized incidence and prevalence were estimated using a “specific” and a “sensitive”
41 definition providing bounds on the evolution of recent incidence. We used multivariable
42 Poisson regression models to estimate temporal trends in incidence rates, calculating
43 incidence rate ratios (IRR) along with corresponding 95% confidence intervals. In a
44 sensitivity analysis, the time lag between past visits to neurologists and the database recording
45 of MS was analyzed to ensure diagnosis extraction date was reliable.
46

47 **Results:**

48 A total of 67,311 suspected MS cases were identified between 2011 and 2021, with 50,320
49 cases classified as incident MS using the specific definition. The sensitive definition identified
50 56,918 incident cases. The median age at diagnosis was 40.6 years for the specific definition
51 and 41.5 years for the sensitive definition. The study found stable incidence of MS over the
52 11-year period (adjusted IRR: 0.998 (95% CI, 0.996-1.001) for the specific cohort). The
53 female-to-male ratio of incident MS cases remained stable, while sex and age-standardized
54 prevalence of MS continued to rise. The median time lag between probable diagnosis and
55 database recording was estimated to less than 18 months, with variations depending on age
56 and method of patient identification.

57 **Conclusion:**

58 This study provides a comprehensive analysis of MS epidemiology in France, demonstrating
stable incidence and sex ratio. The increase in prevalence suggests improved management and survival and highlights the ongoing need for healthcare systems to support the growing population of individuals with MS.

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83
84

# 1. Introduction

Multiple sclerosis (MS) is a chronic neurological disorder characterized by inflammation, demyelination, and neurodegeneration of the central nervous system. Identifying incident patients with MS is crucial for understanding the epidemiology and trends of this disease, as well as interplay with environmental factors, which may interfere with incidence. While previous studies have documented a global increase in the prevalence of MS,¹⁻³ there is no consensus on the temporal trends in the incidence of the disease.⁴⁻⁶ A recent meta-analysis has underscored the lack of studies covering entire countries and using standardized case ascertainment methods.⁷ Research specifically targeting the recent evolution of incidence rates is crucial to ascertain the effects of new risk factors and the implications of updated diagnostic criteria.⁸⁻¹⁰

A key challenge in studying incident MS through healthcare databases is to determine the time lag between the first clinical diagnosis or first presumed symptoms and the point at which the disease is recorded in the database. Depending on the database or EHR system and the available information, the incidence date may be marked by the first physician claim related to MS, the first hospitalization discharge code for MS, or the first prescription of a disease-modifying treatment (DMTs). The time lag between the neurologist's diagnosis and this recorded incidence date can significantly impact the accuracy of incidence and prevalence estimates. Previous research has highlighted the challenges associated with accurately identifying the diagnosis date of MS in administrative health databases, emphasizing the need for robust methodologies to minimize these delays.¹¹,¹² Addressing this gap is critical for improving the timeliness and accuracy of MS identification in large-scale health databases.

In light of these considerations, we aim to describe the incidence of MS in France over the last 11 years within the French National Health Data System (Système National des Données de Santé, SNDS), covering 99% of the French population. The French healthcare system is highly centralized, allowing for comprehensive tracking of all patients prescribed MS DMTs or having hospitalization discharge code related to MS. We report the evolution of MS incidence, prevalence and the sex ratio at diagnosis over 11 years (2011-2021). We also analyze the timing of neurologist visits conducted before and after the recorded MS identification date. By examining these markers, we aim to quantify the diagnostic lag in recording MS in large health registries.
117 We utilized the French National Health Data System (SNDS), a comprehensive and large-
118 scale repository of healthcare information derived from the French national health insurance
119 system. Established in 2006, the SNDS initially covered 87% of the French population and
120 has provided exhaustive data on the entire French population since 2009. Its primary purpose
121 is to systematically record all healthcare reimbursements issued through the French public
122 health insurance system, which offers universal coverage and finances the majority of medical
123 expenses incurred in both public and private healthcare settings. The SNDS incorporates data
124 from two primary sources: the national hospital discharge database for inpatient care,
125 including discharge diagnosis code based on ICD-10 classification, and the national health
126 insurance claims information system for outpatient care, including reimbursed drugs delivery
127 coded according to ATC classification. SNDS also includes information on clinical diagnoses
128 from hospitalization records and long-term disease (LTD) scheme, which lists and reimburses
129 conditions specifically to alleviate the financial hardship. Information on age, sex, residence,
130 and vital status are also available. The date of death was extracted directly from the SNDS,
131 from the Epidemiology Center on Medical Causes of Death (CepiDC), linked to the SNDS
132 using an indirect matching procedure.

133 We reported the study using the Reporting of studies Conducted using Observational
134 Routinely-collected health Data (RECORD) guidelines (eTable 1 supplementary).

135

136 2.2 Study population

137 2.2.1 MS-identification algorithm

138 Our methodology for identifying MS patients used an algorithm introduced recently.² This
139 algorithm incorporated three key components: (1) LTD status related to MS, identified using
140 the ICD-10 code G35. LTD status is used in France to cover 100% of healthcare costs for 30
141 chronic diseases including MS, diabetes, and cancer, due to their expensive or prolonged
142 treatment needs – the registration is initiated by the patient's general practitioner and requires
143 approval from a physician within the health insurance system; (2) reimbursement for MS-
144 specific disease modifying drugs (DMT) such as beta-interferon, cladribine, dimethyl
145 fumarate, fingolimod, glatiramer acetate, natalizumab, ocrelizumab, ponesimod, and
146 teriflunomide; and (3) hospitalization discharge (HD) specifically related to MS, identified
147 using the ICD-10 code G35. The analysis period for HD records and DMT reimbursement
148 data extended from January 1, 2006, to December 31, 2023, while LTD status data was
149 considered up to December 31, 2023, without any minimum date constraint. The algorithm
150 was applied to the whole French territory (including the overseas departments listed in
151 eMethod 1 supplementary).

152 The inclusion criteria for susceptible MS were established as follows: (1) presence of LTD
153 status for MS; or (2) at least one reimbursement for a DMT (eTable 2 supplementary) or (3) at
154 least one HD record where MS was recorded as discharge diagnosis. Following the
155 methodology of a prevalence study conducted on the same database,² individuals with any
156 hospitalization coded as neuromyelitis optica spectrum disorder (NMOSD) (ICD-10: G36)
157 were excluded.

158 Amongst the identified subjects who fulfilled at least one of the above-mentioned criteria, we
159 defined two subgroups according to the likelihood of MS and the precision of the date of
160 record in the database following a previous work on Parkinson's disease identification.¹³
161 Those classified as 'highly probable' had LTD status for MS. Those classified as 'probable'
162 had either (1) at least two reimbursements for a DMT within one year without MS-LTD; or
163 (2) at least two hospital discharges for MS without MS-LTD. The others, who had only one
164 DMT reimbursement, multiple DMT reimbursements not occurring within a year, or only one
165 HD record for MS could potentially have a disease other than MS and were therefore
166 classified as 'possible'.

167 We then selected patients among the 'highly probable' and 'probable' groups to build both a
168 specific and a sensitive definition to enhance the robustness of our analyses. The "specific"
169 definition, which included subjects classified as 'highly probable', is designed to refine the
170 subset of patients with LTD related to MS, ensuring that our analysis focuses on a population,
171 with a precise date of recording. In contrast, the "sensitive" definition, which encompass both
172 patients classified as 'highly probable' and 'probable', will capture a broader group by
173 including patients with at least two hospitalizations with a code related to MS or those with a
174 at least two prescriptions for a treatment specific to MS. This approach allows us to account
175 for varying levels of MS-related activity and treatment. Individuals from the 'possible' group 176 were not included in either MS case definition. When presenting statistical data, we will 177 report the characteristics and epidemiological outcomes for both populations. The specific 178 code used for patient identification is available in the referenced GitLab repository 179 (https://github.com/octaveguinebretiere/ms-incidence-snds).

180

181 2.2.2 Date of detection of MS in the database

182 To ascertain the date of detection of MS in the database, we compute the earliest date among 183 the following: the initiation of LTD status, the first occurrence of an HD record with a 184 diagnosis code related to MS, and the first reimbursement of an MS-specific DMT. An 185 individual was classified as a prevalent MS case in 2010 if they (1) fulfilled the sensitive 186 definition selection criteria before December 31, 2010, (2) were alive on December 31, 2010, 187 and (3) had at least one medical reimbursement in 2010. All other patients meeting the 188 selection criteria for specific or sensitive definitions after this point are considered incident 189 cases. Because there can be a time lag between the first record indicating MS and the 190 fulfillment of the criteria, we analyzed incident cases between 2011 and 2021, an 191 period. 192

192 2.3 Standard Protocol Approvals, Registrations, and Patient Consents

193 To access and process data from the SNDS, permanent access to the Caisse Nationale 194 d'Assurance Maladie (CNAM) data portal is granted via the affiliation of the authors to the 195 National Institute for Research in Digital Science and Technology (INRIA), in application of 196 the provisions of Articles R. 1461-11 to R. 1461-17 of the French Public Health Code and the 197 French data protection authority decision CNIL-2016-316. As permanent users of the SNDS, 198 the authors declared the study to the INRIA's SNDS registry and were exempted from 199 Institutional Review Board approval. There was no need for written informed consent from 200 participants.

201

202
203 2.4 Statistical Analysis

204 2.4.1 Incidence

205 For calculating the incidence rates of MS, we identified all new cases diagnosed within the
206 study period from January 1, 2011, to December 31, 2021. The incidence rate was determined
207 annually and was calculated as the number of new MS cases in a year divided by the size of
208 the French population of that same year, according to data from the French National Institute
209 of Statistics and Economic Studies (Institut National de la Statistique et des Études
210 Économiques, INSEE).¹⁴ The incidence rate will be expressed per 100,000 person-years. We
211 used sex- and age-standardization methods to adjust for the varying age structures of the
212 population over time, ensuring comparability of incidence rates across different years.
213 Standardized incidence rates were obtained using the age and sex distribution of the starting
214 year, i.e., 2011. Specifically, using the proportional distribution of inhabitants in each five-
215 year age and sex group in the year 2011 as the standard, we calculated the age and sex-
216 standardized incidence rates of MS at subsequent years. Confidence intervals (95% CI) of
217 incidence rates were calculated assuming a Poisson distribution.

218 Temporal trends were assessed by computing incidence rate ratios (IRRs) and 95%
219 confidence intervals using multivariable Poisson regression adjusting for 10-year age groups
220 and sex as covariates and the natural logarithm of person-years as offset. The age band 30-40
221 years was used as the reference. We estimated the time trends in incidence by including time
222 period as a linear variable. The Poisson model assumptions were tested by computing the
223 dispersion statistic as the ratio of the sum of squared Pearson residuals to the residual degrees
224 of freedom.

225 2.4.2 Prevalence and mortality

226 We assessed the prevalence of MS by counting all individuals living with MS according to the
227 sensitive definition as of December 31st of each year within the study period. The prevalence
228 will be expressed per 1000 individuals. To calculate age at death, we identified all deaths
229 among individuals with a diagnosis of MS and calculated the average age at death for all
230 patients who died with MS in a given year.

231
232

### 2.4.3 Time lag between MS presumed onset and database identification

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

To analyze the time lag between the presumed clinical onset of MS and its recording in the SNDS, we used consultations with neurologists as the primary indicator of diagnostic opportunities. We hypothesized that a visit to a neurologist could be prompted by the presence of characteristic MS symptoms. To distinguish consultations directly related to MS and those that may be coincidental, we estimated the diagnostic delay using a case-control design combined with a bootstrapping method.¹⁵ The case-control design was adapted from a case-crossover design (eMethod 2 supplementary). Outpatient neurologist visits from public and private medical care practices, along with stays in the neurology department during inpatient stays were extracted from the SDNS between 1st January 2009 and 31st December 2021. For each one-month period within the observation window, the number of visits and the visit ratio, defined as the ratio of the number of patients with at least one visit to a neurologist to the total number of individuals at risk during that period, were computed. We stratified this study based on the two MS-likelihood subgroups ('highly probable' and 'probable'). To calculate the expected trend in the number of visits we computed the trends in neurologist visits in the database for a cohort of patients without MS. To ensure sufficient pre and post-encoding follow-up, we included patient from 2019 to 2021 (10 years pre-encoding) and from 2014 (5 years pre and 7 years post-encoding) respectively for the pre and pre and post-encoding studies. Each MS case was matched to 5 controls without MS based on exact year of birth, sex, and department of residence (closest measure of residence to the index date if several residences were recorded) using risk set sampling.¹⁶ The exact index date of each case was assigned to the matched controls.

254

## 2.5 Data Availability

255

256

257

Data from the SNDS used in this work cannot be shared by the Authors due to French laws restricting access to health data (https://www.snds.gouv.fr/). Applications to access the SNDS must be submitted to the French Health Data Hub (https://www.health-data-hub.fr/).

258
259

260

## 3. Results

261

### 3.1 Population Characteristics

262

263

264

265

266

267

268

269

270

271

272

273

274

A total of 67,311 individuals with suspected incident MS were selected from the SNDS database between 2011 and 2021: MS was “highly probable” for 74.1%, ‘probable’ for 10.5%, and ‘possible’ for 15.4%. 49,862 were considered to have incident MS using the specific definition and 56,918 using the sensitive definition (Figure 1 and Table 1). Among people with incident MS according to the sensitive definition (n=56,918), 49,865 (87.5%) had an MS-related LTD status, 3,064 (5.4%) had no LTD but received at least two MS-specific drug prescriptions, and 5,797 (10.2%) had an MS-related hospitalization diagnosis without LTD status (eFigure 1 supplementary). Of the 35,602 patients treated for MS, 32,538 patients (91.3%) had an MS-specific LTD, 1,549 (4.3%) had an LTD for another condition before the index date, and 1,515 (4.4%) had no LTD. Among the 49,862 individuals identified with the specific definition (MS-LTD presence), 32,538 (65.2%) received an MS-specific drug prescription during follow-up, and 42,414 (85.1%) had either an MS-related prescription or hospitalization.

275

276

277

The sensitive and specific definition showed concordant female predominance of 69.9% for the specific and 69.1% for the sensitive definition. However, the ‘probable’ cohort only had 63.7% (Table 1).

278

279

280

281

The median age at MS identification in the database was 40.6 years for the specific definition and 41.5 years for the sensitive definition, reflecting discrepancies in the age at identification between the ‘highly probable’ cohort and ‘probable’ cohort (‘highly probable’: 40.6 years vs. ‘probable’: 50.7 years) (Table 1).

282

283

284

We identified 152,859 patients with prevalent MS in the SNDS between 2011 and 2021. The median age during follow-up was 51.9 years, and prevalent cases were predominantly women (70.4%) (eTable 3 supplementary).

285

### 3.2 Epidemiology of incident and prevalent MS

286

287

288

The number of newly identified MS cases was between 4642 and 5295 in 2011, and between 4690 and 5392 in 2021, according to the specific and sensitive definitions, respectively. The age- and sex-standardized incidence of MS during the study period was 6.98 (95% CI: 6.78–
7.18) cases per 100,000 person-years for the specific definition and 7.95 (95% CI: 7.73-8.16) cases per 100,000 person-years for the sensitive definition. Statistically, there were no significant secular trend of incidence between 2011 and 2021 for the specific and sensitive definitions (specific IRR: 0.998 (95% CI, 0.996-1.001) vs. sensitive IRR: 0.998 (0.996-1.001)) (figure 2). Incidence increased to a peak in the 30-39 years age band, a pattern consistent for both sexes and MS definition (figure 3 a). The F/M sex ratio of incidence rates decreased with age from 2.31 in the 15-19 age group to 1.24 in the 80-84 age group (figure 3 b). The sex ratio for incident MS cases remained relatively stable over the study period, with a female-to-male ratio of sex and age-standardized incidence of 2.17 in 2011 and 2.19 in 2021 (figure 4 a). The age at identification of MS in the database was stable during the period (figure 4 b). Using the sensitive definition, the prevalence of MS on December 31, 2021, was 130,676 cases (eTable 3 supplementary). Notably, the sex and age-standardized prevalence increased from 1.47 cases per 1000 individuals in 2011 to 1.95 cases per 1000 individuals in 2021, representing an increase of 33% (figure 4 c). We observed that the prevalent MS population was getting older, with a median age of 49.9 years in 2011 and 53.6 years in 2021 (eTable 3 supplementary). The average age at death for MS patients during the study period was 67.8 years, with an increase from 66.0 years to 70.6 years between 2011 and 2021 (figure 4 c).

### 3.3 Time Lag Between Initial Clinical Diagnosis and Record in the Database

In this analysis, we included 15,169 and 5,235 patients with MS, and 75,845 and 26,175 patients without MS respectively for the pre and post-encoding analyses (eTable 4 supplementary). The visits increased as the date of MS encoding approached, suggesting a probable clinical diagnosis (Figure 5 a and eFigure 2 supplementary). The 'probable' cohort showed a significantly higher proportion (figure 5 a and eFigure 2 supplementary) of neurologist visits than its control group years before the identification date, which invalidated the core assumption of the case-crossover method that cases serve as controls before the MS identification date. Consequently, we used the case-control method for the two MS incident cohorts. The median time lag between the initial diagnosis of MS by a neurologist and its recording in the SNDS was 1.45 years (95% CI: 1.44-1.46 years) for the 'highly probable' cohort and 3.17 years (95% CI: 3.16-3.18 years) for the 'probable' cohort. The time lag was independent of sex but increased with age (eFigure 3 a and b supplementary). Notably, when
321 comparing neurologist visits before and after the MS encoding date, the 'probable' subgroup
322 appeared to have been actively monitored by neurologists several years before their
323 identification in the database, as the monthly period prevalence of neurologist visits remained
324 consistent before and after MS was recorded in the database (figure 5 b and eFigure 2
325 supplementary).

326

327 4. Discussion

328 Our study provides a comprehensive analysis of the incidence, prevalence, and demographic
329 trends of MS using data from the SNDS covering 99% of the French population. We found
330 that the incidence of MS remained stable over the past 11 years, with no significant increase
331 in incidence rates from 2011 to 2021. Additionally, our findings highlight that while the
332 prevalence of MS continues to rise, the sex ratio of new cases has remained relatively
333 constant, and the average age at first MS-related encounter and death for MS patients had
334 increased over time. The robustness of these findings is enhanced by our approach to account
335 for diagnostic delays through a newly proposed algorithm that refines case identification and
336 ensures the accuracy of recorded data.

337 Although systematic reviews have quantified MS incidence rates across different
338 geographical regions¹¹,¹⁷,¹⁸ and compared these rates across countries and regions¹,¹⁹, few
339 studies have examined changes in MS incidence rates over time at a national level with a
340 consistent case definition.⁷ Notably, only one recent study has focused on how the 2017
341 diagnostic criteria changes have impacted MS incidence.²⁰ Our study documents a stable
342 incidence of MS in a large country like France from 2011 to 2021, utilizing population-based
343 data. This comprehensive dataset allows us to provide robust evidence of the stability of MS
344 incidence rates in the face of evolving diagnostic criteria and global trends. Previous meta-
345 analyses have highlighted the scarcity of data from many regions and the lack of standardized
346 methodologies, which complicates the assessment of global trends.⁷ For instance, studies from
347 Denmark reported increasing MS incidence over long periods⁵,⁶, but those in Germany and
348 Canada did not reveal a predominant pattern of incidence changes.⁴,²⁰ Especially, the German
349 study was nationwide, covered a similar timeframe as ours, and also found stable MS
350 incidence rates. Another study focusing on a small sub-region of France (Lorraine) with a
351 specific MS registry also reported stable incidence between 1996 and 2015, with incidence
352 rates within the range we reported.²¹ Our findings contribute valuable insights into the debate
over global MS incidence trends, demonstrating that in France, despite evolving diagnostic criteria and increasing global awareness, the incidence of MS has remained stable over the past decade. Interestingly, we observed a bimodal distribution of age at encoding of MS in women that peaked around 35 years and 45 years of age, similar to a Norwegian population-based study.²² The COVID-19 pandemic has further complicated the current epidemiology of chronic diseases, potentially affecting disease incidence through disruptions in healthcare access, or other indirect mechanisms.²³,²⁴ However, and concordant with the recent German study on MS incidence,²⁰ the COVID-19 pandemic did not seem to affect the MS incidence in our study.

Our study shows that the sex ratio of new MS cases has remained relatively stable over the study period. This finding contrasts with previous research that reported an increase in the female-to-male ratio, which cannot be easily attributed to genetic factors, new diagnostic technologies, or greater awareness.¹⁹,²⁵ The observed shift in the female-to-male ratio in other studies is generally attributed to smoking, as research has shown a significant correlation between the ratios of smoking prevalence among women and men in different countries and birth cohorts, and corresponding MS incidence ratios. It was estimated that smoking increased the risk of MS by an average of 40%.²⁶ The discrepancy between our findings and those of other studies may be due to our relatively limited observation period, during which changes in smoking habits between men and women remained relatively stable.²⁷ Nevertheless, the stability of MS incidence is particularly notable given that, although overall smoking rates have remained stable in France in recent decades, the proportion of regular teenage smokers (age 17) has significantly decreased from 41% in 2000 to 17% in 2020,²⁸ similarly among both men and women, without being offset by an increase in vaping. Therefore, we would have expected to see a decline in incidence. This observation prompts inquiry into the relationship between tobacco use and early-onset MS, despite the potential for a tobacco-associated decline in MS incidence to be counteracted by factors that could contribute to an increase in cases, such as expanded diagnostic criteria, enhanced disease awareness, or increased prevalence of obesity (across all age) in France over the last 20 years.²⁹,³⁰

Our study corroborates the ongoing increase in MS prevalence, which aligns with trends observed in other recent epidemiological studies.²,⁴,¹¹,³¹ As incidence and age at diagnosis are stable, this increase is likely influenced by improved survival rates. The increasing prevalence aligns with the rise in average age and age at death for MS patients noted in our study. This is consistent with trends showing improved life expectancy among individuals with MS due to
386 advancements in treatment and disease management. 4,6 We identified 130,676 prevalent MS
387 cases in 2021, slightly fewer than the 134,062 reported in the latest SNDS study. 2 This
388 difference likely stems from our incident MS definition, requiring at least two MS-specific
389 DMTs within one year, whereas their study had no time restriction for the DMTs.

390 The 2017 McDonald criteria introduced refinements to the 2010 criteria, such as the inclusion
391 of cerebrospinal fluid oligoclonal bands as a marker of dissemination in time and allowing the
392 simultaneous presence of gadolinium-enhancing and non-enhancing lesions on MRI to fulfill
393 dissemination in time. While these changes improve diagnostic accuracy and may accelerate
394 the time to diagnosis,32,33 studies conducted in various European cohorts, including the
395 MAGNIMS study34 and Italian registries,33 consistently show that the overall sensitivity of
396 the 2017 criteria is not significantly different from the 2010 criteria over the long term. This is
397 further supported by large European observational studies,6,20 which corroborates our findings
398 that the adoption of the 2017 criteria has had limited impact on incidence rates.
399

400 Studying MS incidence through health registries is challenging, particularly for forms like
401 primary progressive multiple sclerosis (PPMS), which are often managed without DMTs —a
402 common marker for MS identification. Consequently, non-DMT cases may be underreported
403 or missed entirely. To address this issue, our study combines hospitalization data, LTD data—
404 specific to France for reimbursing all health costs for certain diseases, including MS—and
405 prescription data to improve case detection sensitivity. Additionally, the accuracy of diagnosis
406 and the timeliness of data entry are crucial when studying incidence in large health databases.
407 The timing of diagnosis is particularly difficult to study in administrative health databases for
408 MS, especially for PPMS patients, who often lack MS-specific medications, do not have LTD
409 coverage, and are only hospitalized when complications arise. For example, patients with only
410 two hospitalizations for MS likely has the disease, but the timing of diagnosis may be unclear,
411 especially if the patient has PPMS and was hospitalized due to complications rather than the
412 initial diagnosis. The ‘probable’ cases, exhibiting long identification time lag, likely represent
413 PPMS, as their demographics (e.g., older age, balanced sex ratio) align with known features
414 of PPMS. Some cases could also include “benign” MS patients without DMTs, and stable,
415 disability-free disease. To address this, our approach introduces two definitions: a sensitive
416 one, aimed at ensuring that identified MS patients are indeed MS patients, and a specific one,
417 focused on accurately determining the time of diagnosis. Failure to account for these delays
418 may result in erroneous conclusions. We recommend researchers assess the time between the
onset of specific symptoms, inferred from neurologist visits, and disease documentation in the database. Cases with long delays should be excluded from analyses of risk factors or early symptoms to avoid reverse causation. However, excluding these patients, despite their late identification, may still omit genuine MS cases, potentially limiting external validity.

The strengths of our study lie in the comprehensive coverage of the SNDS, which captures extensive data on MS diagnoses, treatments, and outcomes for nearly the entire French population. However, reliance on administrative data could introduce misclassification of MS cases, particularly forms like PPMS that may not involve DMTs and could be underrepresented. To address this, we integrated multiple data sources (e.g., hospitalization data, LTD registrations, and prescriptions). One limitation of our study is that we could not validate the specificity and sensitivity of our two definitions using clinical reports. To improve the specificity-sensitivity tradeoff reported in a preliminary validation study,¹² we included a requirement of at least two hospitalizations or prescriptions within one year. This approach was inspired by the study of Culpepper et al., which evaluated case definitions based on a combination of data sources and often required at least two occurrences to optimize case identification.³⁵ The presumed lag between clinical diagnosis and its recording in the SNDS is an assumption, as there is no direct evidence a diagnosis occurred at that time. Pre-diagnosis visits to the neurologist may reflect specific symptoms (e.g., optic neuritis or paresthesia) leading to diagnosis or non-specific symptoms (e.g., migraine, sensory issues) linked to a prodromal phase or comorbidity. Unfortunately, the reasons for the visits are not recorded in the SNDS database. Moreover, the lack of clinical granularity limits the ability to draw conclusions about the heterogeneity of MS presentation and the progression of incidence across different subtypes.

442

## Conclusion

Our study provides valuable insights into the epidemiology of MS in France, highlighting the stability of incidence rates, increasing prevalence, and unchanged sex ratio over the past decade. The increase in the average age at death reflects advancements in disease management. The proposed algorithm for identifying MS cases and analyzing delays between diagnosis and database recording offers a robust method for improving data accuracy in large-scale health databases.

450
451

452

453

454

455

456 Study Funding

457 The research leading to these results has received funding from the joint program in

458 neurodegenerative diseases (JPND) ANR-21-JPW2-0002-01 (LeMeReND) and the program

459 "Investissements d'avenir" ANR-10-IAIHU-06. This work was funded in part by the French

460 government under management of Agence Nationale de la Recherche as part of the

461 "Investissements d'avenir" program, reference ANR-19- P3IA-0001 (PRAIRIE 3IA Institute).

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477
478

479

480

481

482 References

483 1. Koch-Henriksen N, Sørensen PS. The changing demographic pattern of multiple sclerosis epidemiology. The Lancet Neurology. mai 2010;9(5):520-32.

484 2. Pierret C, Mainguy M, Leray E. Prevalence of multiple sclerosis in France in 2021: Data from the French health insurance database. Revue Neurologique. mai 2024;180(5):429-37.

488 3. Walton C, King R, Rechtman L, Kaye W, Leray E, Marrie RA, et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult Scler. déc 2020;26(14):1816-21.

491 4. Rotstein DL, Chen H, Wilton AS, Kwong JC, Marrie RA, Gozdyra P, et al. Temporal trends in multiple sclerosis prevalence and incidence in a large population. Neurology. 2018;90(16):e1435-41.

494 5. Koch-Henriksen N, Thygesen LC, Stenager E, Laursen B, Magyari M. Incidence of MS has increased markedly over six decades in Denmark particularly with late onset and in women. Neurology. 2018;90(22):e1954-63.

497 6. Holm R, Wandall-Holm M, Magyaru M. Multiple sclerosis in Denmark (1950-2023): mean age, sex distribution, incidence and prevalence. Brain. 2024;

499 7. Lane J. Multiple sclerosis incidence: A systematic review of change over time by geographical region. Multiple Sclerosis and Related Disorders. 2022;

501 8. McDonald WI, Compston A, Edan G, Goodkin D, Hartung H, Lublin FD, et al. Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis. Annals of Neurology. juill 2001;50(1):121-7.

504 9. Filippi M, Rocca MA, Ciccarelli O, De Stefano N, Evangelou N, Kappos L, et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. The Lancet Neurology. mars 2016;15(3):292-303.

507 10. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al.

508 Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. The Lancet Neurology. 2018;17(2):162-73.

510 11. Kingwell E, Marriott JJ, Jetté N, Pringsheim T, Makhani N, Morrow SA, et al.

511 Incidence and prevalence of multiple sclerosis in Europe: a systematic review. BMC Neurol. déc 2013;13(1):128.

513 12. Ducatel P, Debouverie M, Soudant M, Guillemin F, Mathey G, Epstein J. Performance of administrative databases for identifying individuals with multiple sclerosis. Sci Rep. 25 oct 2023;13(1):18310.

516 13. Blin P, Dureau-Pournin C, Foubert-Samier A, Grolleau A, Corbillon E, Jové J, et al. Parkinson's disease incidence and prevalence assessment in France using the national healthcare insurance database. Eur J Neurol. mars 2015;22(3):464-71.

519 14. INSEE. Opération statistique: Estimations de population [04/09/2024]. Available at: http://www.insee.fr/fr/methodes/default.asp?page1/4sources/ope-adm-estimations-population.htm.

522 15. Miller AC, Cavanaugh JE, Arakkal AT, Koeneman SH, Polgreen PM. A

523 comprehensive framework to estimate the frequency, duration, and risk factors for diagnostic
524 delays using bootstrapping-based simulation methods. BMC Med Inform Decis Mak. 14 avr
525 2023;23(1):68.

526 16. Goldstein L, Langholz B. Risk set sampling in epidemiologic cohort studies. Statist
527 Sci [Internet]. 1 janv 1996 [cité 6 sept 2024];11(1). Disponible sur:

528 https://projecteuclid.org/journals/statistical-science/volume-11/issue-1/Risk-set-sampling-in-
529 epidemiologic-cohort-studies/10.1214/ss/1032209663.full

530 17. Evans C, Beland SG, Kulaga S, Wolfson C, Kingwell E, Marriott J, et al. Incidence
531 and prevalence of multiple sclerosis in the Americas: a systematic review.

532 Neuroepidemiology. 2013;40(3):195-210.

533 18. Makhani N, Morrow S, Fisk J, Evans C, Beland S, Kulaga S, et al. MS incidence and
534 prevalence in Africa, Asia, Australia and New Zealand: a systematic review. Multiple
535 sclerosis and related disorders. 2014;3(1):48-60.

536 19. Alonso A, Hernán MA. Temporal trends in the incidence of multiple sclerosis: a
537 systematic review. Neurology. 2008;71(2):129-35.

538 20. Graf J, Akmatov MK, Meuth SG, Tremlett H, Holstiege J. Updated Multiple Sclerosis
539 Incidence, 2015-2022. JAMA Neurol. 1 oct 2024;81(10):1100.

540 21. Gbaguidi B, Guillemin F, Soudant M, Debouverie M, Mathey G, Epstein J. Age-
541 period-cohort analysis of the incidence of multiple sclerosis over twenty years in Lorraine,
542 France. Sci Rep. 19 janv 2022;12(1):1001.

543 22. Habbestad A, Willumsen JS, Aarseth JH, Grytten N, Midgard R, Wergeland S, et al.
544 Increasing age of multiple sclerosis onset from 1920 to 2022: a population-based study. J
545 Neurol. avr 2024;271(4):1610-7.

546 23. Fekadu G, Bekele F, Tolossa T, Fetensa G, Turi E, Getachew M, et al. Impact of
547 COVID-19 pandemic on chronic diseases care follow-up and current perspectives in low
548 resource settings: a narrative review.

549 24. Wikström K, Linna M, Laatikainen T. The impact of the COVID-19 pandemic on
550 incident cases of chronic diseases in Finland. European Journal of Public Health. 29 nov
551 2022;32(6):982-4.

552 25. Palacios N, Gao X, McCullough ML, Jacobs EJ, Patel AV, Mayo T, et al. Obesity,
553 diabetes, and risk of Parkinson's disease. Movement Disorders. oct 2011;26(12):2253-9.

554 26. Palacios N, Alonso A, Brønnum-Hansen H, Ascherio A. Smoking and Increased Risk
555 of Multiple Sclerosis: Parallel Trends in the Sex Ratio Reinforce the Evidence. Annals of
556 Epidemiology. juill 2011;21(7):536-42.

557 27. Journée mondiale sans tabac, 31 mai 2023 / World No Tobacco Day, May 31, 2023.
558 2023;

559 28. https://www.ofdt.fr/statistiques-et-infographie/ tableau-de-bord-tabac/.

560 29. Fontbonne A, Currie A, Tounian P, Picot MC, Foulatier O, Nedelcu M, et al.

561 Prevalence of Overweight and Obesity in France: The 2020 Obepi-Roche Study by the “Ligue
562 Contre l’Obésité”. JCM. 25 janv 2023;12(3):925.

563 30. Huppke B, Ellenberger D, Hummel H, Stark W, Röbl M, Gärtner J, et al. Association
564 of Obesity With Multiple Sclerosis Risk and Response to First-line Disease Modifying Drugs
565 in Children. JAMA Neurol. 1 oct 2019;76(10):1157.

566 31. Foulon S, Maura G, Dalichampt M, Alla F, Debouverie M, Moreau T, et al.

567 Prevalence and mortality of patients with multiple sclerosis in France in 2012: a study based
568 on French health insurance data. J Neurol. juin 2017;264(6):1185-92.

569 32. Ferrand M, Epstein J, Soudant M, Guillemin F, Pittion-Vouyovitch S, Debouverie M,
570 et al. Real-life evaluation of the 2017 McDonald criteria for relapsing–remitting multiple
571 sclerosis after a clinically isolated syndrome confirms a gain in time-to-diagnosis. Journal of
572 Neurology. 2024;271(1):125-33.

573 33. Gaetani L, Prosperini L, Mancini A, Eusebi P, Cerri MC, Pozzilli C, et al. 2017
574 revisions of McDonald criteria shorten the time to diagnosis of multiple sclerosis in clinically

575 isolated syndromes. Journal of neurology. 2018;265:2684-7.

576 34. Filippi M, Preziosa P, Meani A, Dalla Costa G, Mesaros S, Drulovic J, et al.

577 Performance of the 2017 and 2010 revised McDonald criteria in predicting MS diagnosis after

578 a clinically isolated syndrome: a MAGNIMS study. Neurology. 2022;98(1):e1-14.

579 35. Culpepper WJ, Marrie RA, Langer-Gould A, Wallin MT, Campbell JD, Nelson LM, et

580 al. Validation of an algorithm for identifying MS cases in administrative health claims

581 datasets. Neurology [Internet]. 5 mars 2019 [cité 21 déc 2023];92(10). Disponible sur:

582 https://www.neurology.org/doi/10.1212/WNL.00000000000007043

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606
607

608

609

610 **Figures**

611 **Figure1.** Identification and extraction of MS incident patients from the French health database (SNDS). Amongst eligible patients of the SNDS database, those with an MS-LTD were considered for specific estimations, to which were added those with (at least two HD encounters for MS) or (at least two MS-DMT within a year) or (at least one HD encounter for MS and at least one DMT) for sensitive estimations. Abbreviations: MS-LTD, long-term disease benefit with an ICD-10 diagnosis code G35; MS-HD, hospital discharge with an ICD-10 diagnosis code G35; MS-DMT, reimbursement for a MS-specific disease modifying treatment (listed in supplementary table 1). Abbreviations: LTD, long-term disease; HD, hospitalization discharge; DMT, disease modifying therapy.
612
613
614
615
616
617
618
619

620 **Figure 2.** Secular trend in overall crude and sex and age-standardized incidence of MS cases per 100,000 person-years in the SNDS from 2011 to 2021. (a), and by sex crude and standardized on age (b: women, c: men). The 2011 French population was used as the reference for standardization.
621
622
623

624 **Figure 3.** Crude incidence rate and sex ratio od MS by age in France. (a) Incidence rate by age, and (b) the F/M sex ratios computed as the ratio of the incidence rates per age group for the “sensitive” definition during 2011-2021 in France. We restricted the sex ratios to age groups with at least 100 incident cases.
625
626
627

628 **Figure 4.** The epidemiology of MS. (a) incidence sex ratio for women to men during the 2011-2021 period. (b) median age at encoding of incident MS in the database (c) secular trend in prevalence rate between 2011 and 2021, with the size of the square proportional to the median age of the population, and (d) mean age at death for the prevalent cases from 2011 to 2021. Prevalent cases were identified according to the sensitive definition, as the distinction between the specific and sensitive definitions is primarily driven by the precision of the incidence date rather than the actual disease status.
629
630
631
632
633
634

635 **Figure 5.** Analysis of neurologist visit trends for incident MS cases. We investigated the (a) neurologist visit rates over one-month intervals for incident MS cases from 2019 to 2021 in the SNDS, comparing ‘highly probable’ and ‘probable’ subgroups with a matched control cohort. We also examined the (b) monthly neurologist visit rates for the 2014 MS cohort, providing insights into visit patterns before and after the encoding of MS in the database. This analysis aims to determine whether patients were already being monitored for active MS before their identification in the database.
636
637
638
639
640
641

642

643
644

645

646

<caption>Table 1: Characteristics of the overall population susceptible to incident MS, according to the likelihood subgroup to have MS, and according to MS incidence definition during the 2011-2021 period in the SNDS.</caption>

650

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Overall<br>(N=67,311)</th><th colspan="3">Likelihood of MS subgroups</th><th colspan="2">Incident cohorts</th></tr><tr><th>Highly probable<br>(N=49,862)</th><th>Probable<br>(N=7,056)</th><th>Possible<br>(N=10,393)</th><th>Specific<br>definition<br>(N=49,862)</th><th>Sensitive<br>definition<br>(N=56,918)</th></tr></thead><tbody><tr><td>Sex (number<br>(%))</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Men</td><td>21559 (32.0%)</td><td>15030 (30.1%)</td><td>2559 (36.3%)</td><td>3970 (38.2%)</td><td>15030 (30.1%)</td><td>17589 (30.9%)</td></tr><tr><td>Women</td><td>45752 (68.0%)</td><td>34832 (69.9%)</td><td>4497 (63.7%)</td><td>6423 (61.8%)</td><td>34832 (69.9%)</td><td>39329 (69.1%)</td></tr><tr><td>Age (median<br>[IQR])</td><td>43.0 (32.8 - 54.3)</td><td>40.6 (31.6 - 50.5)</td><td>50.7 (36.9 - 63.6)</td><td>53.5 (40.1 - 67.9)</td><td>40.6 (31.6 - 50.5)</td><td>41.5 (32.0 - 52.0)</td></tr><tr><td>Year of diagnosis<br>(number (%))</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2011</td><td>6465 (9.6%)</td><td>4642 (9.3%)</td><td>653 (9.3%)</td><td>1170 (11.3%)</td><td>4642 (9.3%)</td><td>5295 (9.3%)</td></tr><tr><td>2012</td><td>6156 (9.1%)</td><td>4501 (9.0%)</td><td>615 (8.7%)</td><td>1040 (10.0%)</td><td>4501 (9.0%)</td><td>5115 (9.0%)</td></tr><tr><td>2013</td><td>6114 (9.1%)</td><td>4482 (9.0%)</td><td>641 (9.1%)</td><td>992 (9.5%)</td><td>4482 (9.0%)</td><td>5122 (9.0%)</td></tr><tr><td>2014</td><td>6155 (9.1%)</td><td>4576 (9.2%)</td><td>643 (9.1%)</td><td>936 (9.0%)</td><td>4576 (9.2%)</td><td>5220 (9.2%)</td></tr><tr><td>2015</td><td>6141 (9.1%)</td><td>4605 (9.2%)</td><td>633 (9.0%)</td><td>902 (8.7%)</td><td>4605 (9.2%)</td><td>5239 (9.2%)</td></tr><tr><td>2016</td><td>6184 (9.2%)</td><td>4604 (9.2%)</td><td>586 (8.3%)</td><td>994 (9.6%)</td><td>4604 (9.2%)</td><td>5190 (9.1%)</td></tr><tr><td>2017</td><td>5948 (8.8%)</td><td>4428 (8.9%)</td><td>620 (8.8%)</td><td>900 (8.7%)</td><td>4428 (8.9%)</td><td>5048 (8.9%)</td></tr><tr><td>2018</td><td>6091 (9.0%)</td><td>4573 (9.2%)</td><td>697 (9.9%)</td><td>821 (7.9%)</td><td>4573 (9.2%)</td><td>5270 (9.3%)</td></tr><tr><td>2019</td><td>6080 (9.0%)</td><td>4400 (8.8%)</td><td>758 (10.7%)</td><td>922 (8.9%)</td><td>4400 (8.8%)</td><td>5158 (9.1%)</td></tr><tr><td>2020</td><td>5852 (8.7%)</td><td>4361 (8.7%)</td><td>608 (8.6%)</td><td>883 (8.5%)</td><td>4361 (8.7%)</td><td>4969 (8.7%)</td></tr><tr><td>2021</td><td>6125 (9.1%)</td><td>4690 (9.4%)</td><td>602 (8.5%)</td><td>833 (8.0%)</td><td>4690 (9.4%)</td><td>5292 (9.3%)</td></tr></tbody></table>

651

652

653
<figure><img src="image_2.png" /></figure>

654

655

656

657

658

659

660

661

662

663

**Figure1. Identification and extraction of MS incident patients from the French health database (SNDS).** Amongst eligible patients of the SNDS database, those with an MS-LTD were considered for specific estimations, to which were added those with (at least two HD encounters for MS) or (at least two MS-DMT within a year) or (at least one HD encounter for MS and at least one DMT) for sensitive estimations. Abbreviations: MS-LTD, long-term disease benefit with an ICD-10 diagnosis code G35; MS-HD, hospital discharge with an ICD-10 diagnosis code G35; MS-DMT, reimbursement for a MS-specific disease modifying treatment (listed in supplementary table 1). Abbreviations: LTD, long-term disease; HD, hospitalization discharge; DMT, disease modifying therapy.

664

665

666

667

668

669

670

671

672
673

674

675

676

677

678

a all

679

Sensitive : IRR=0.998 (95%CI, 0.996-1.001)

Specific : IRR=0.998 (95%CI, 0.996-1.001)

680

681

682

683

684

685

686

687

688

689

690

691

b women

<figure><img src="image_3.png" /></figure>

Sensitive : IRR=0.998 (95%CI, 0.995-1.001)

Specific : IRR=0.999 (95%CI, 0.995-1.002)

c men

<figure><img src="image_4.png" /></figure>

Sensitive : IRR=0.999 (95%CI, 0.994-1.003)

Specific : IRR=0.998 (95%CI, 0.992-1.003)

<caption>Figure 2. Secular trend in overall crude and sex and age-standardized incidence of MS cases per 100,000 person-years in the SNDS from 2011 to 2021. (a), and by sex crude and standardized on age (b: women, c: men). The 2011 French population was used as the reference for standardization.</caption>
a, incidence rate by age

<figure><img src="image_5.png" /></figure>

b, F/M sex ratio of incidence rates ("sensitive definition")

<figure><img src="image_6.png" /></figure>

**Figure 3.** Crude incidence rate and sex ratio od MS by age in France. (a) Incidence rate by age, and (b) the F/M sex ratios computed as the ratio of the incidence rates per age group for the “sensitive” definition during 2011-2021 in France. We restricted the sex ratios to age groups with at least 100 incident cases.
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 4.** The epidemiology of MS. (a) incidence sex ratio for women to men during the 2011-2021 period. (b) median age at encoding of incident MS in the database (c) secular trend in prevalence rate between 2011 and 2021, with the size of the square proportional to the median age of the population, and (d) mean age at death for the prevalent cases from 2011 to 2021. Prevalent cases were identified according to the sensitive definition, as the distinction between the specific and sensitive definitions is primarily driven by the precision of the incidence date rather than the actual disease status.</figcaption></figure>
731

a

732

Controls highly probable

Controls probable

Highly probable

Probable

733

<figure><img src="image_13.png" /></figure>

734

735

736

737

738

739

740

741

b

<figure><img src="image_14.png" /></figure>

742

<caption>**Figure 5.** Analysis of neurologist visit trends for incident MS cases. We investigated the (a) neurologist visit rates over one-month intervals for incident MS cases from 2019 to 2021 in the SNDS, comparing ‘highly probable’ and ‘probable’ subgroups with a matched control cohort. We also examined the (b) monthly neurologist visit rates for the 2014 MS cohort, providing insights into visit patterns before and after the encoding of MS in the database. This analysis aims to determine whether patients were already being monitored for active MS before their identification in the database.</caption>

743

744

745

746

747

748

749

750
751 Supplementary materials:

752 Supplementary eMethod 1. Mainland France and overseas French
departments/regions.
753

754 We used the data from the whole French National Healthcare Database – covering
755 99% of the French population in the whole French territory - hence there was no
756 restriction to any geographical location. The French territory spans (north to south)
757 from 51° 05' 21" N, 2° 32' 43" E to 42° 19' 58" N, 2° 31' 58" E mainland France and
758 from 51° 05' 21" N, 2° 32' 43" E to 21° 22' S, 55° 37' E when including the overseas
759 territories. The mainland France population represents 96% of the French population.
760 Overseas departments/regions include: French Guiana, Guadeloupe, Martinique,
761 Mayotte, and Réunion.

762 Supplementary eMethod 2. Time lag between MS presumed onset and database identification
763

764 The lag between the presumed onset of MS and its identification in the database is quantified
765 by comparing the observed and expected patterns of neurologist visits within the observation
766 window (0 to 10 years prior to the disease's recording in the database). The expected trend,
767 representing the baseline, corresponds to the frequency of neurologist visits that would be
768 anticipated in the absence of MS (eFigure 5 a and b supplementary). This baseline is
769 estimated using the visit rate observed in a control cohort comprising individuals matched to
770 the MS cohort by age and sex. In contrast, the observed trend reflects the actual frequency
771 of neurologist visits among MS patients during the observation period. The divergence
772 between these two trends indicates the number of visits that may be attributed to the early
773 clinical manifestation of MS. For each time point within the 10-year pre-index period, the
774 process involves two main steps. First, the number of visits attributed to the disease
775 (calculated as the difference between the observed visits in the MS cohort and the expected
776 visits in the matched control cohort) is randomly sampled, and the corresponding MS
777 patients who contributed to this difference are identified (Step 1). Second, the time lag for
778 each patient is determined as the interval between the first visit attributed to MS and the
779 index date (Step 2). This two-step procedure is repeated using a bootstrapping approach,
780 and the results are aggregated to obtain a robust estimate of the time lag distribution.

781 a

782

<figure><img src="image_15.png" /></figure>

b

<figure><img src="image_16.png" /></figure>
788 Supplementary eTable 1. The RECORD statement – a checklist of items, extended from the STROBE statement, that should be reported in observational studies using routinely collected health data.

791

<table><thead><tr><th></th><th>Item No.</th><th>STROBE items</th><th>Location in manuscript where items are reported</th><th>RECORD items</th><th>Location in manuscript where items are reported</th></tr></thead><tbody><tr><td colspan="6"><strong>Title and abstract</strong></td></tr><tr><td></td><td>1</td><td>(a) Indicate the study's design with a commonly used term in the title or the abstract<br>(b) Provide in the abstract an informative and balanced summary of what was done and what was found</td><td>Title p1<br>Abstract p2.</td><td>RECORD 1.1: The type of data used should be specified in the title or abstract. When possible, the name of the databases used should be included.<br>RECORD 1.2: If applicable, the geographic region and timeframe within which the study took place should be reported in the title or abstract.<br>RECORD 1.3: If linkage between databases was conducted for the study, this should be clearly stated in the title or abstract.</td><td>Title p1<br>Abstract p2.<br>Title p1<br>Abstract p2.<br>No linkage.</td></tr><tr><td colspan="6"><strong>Introduction</strong></td></tr><tr><td>Background rationale</td><td>2</td><td>Explain the scientific background and rationale for the investigation being reported</td><td>P 4.</td><td></td><td></td></tr><tr><td>Objectives</td><td>3</td><td>State specific objectives, including any prespecified hypotheses</td><td>P 4.</td><td></td><td></td></tr><tr><td colspan="6"><strong>Methods</strong></td></tr><tr><td>Study Design</td><td>4</td><td>Present key elements of study design early in the paper</td><td>P4.</td><td></td><td></td></tr><tr><td>Setting</td><td>5</td><td>Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection</td><td>P5.<br>P6. Supplementary eMethod 1</td><td></td><td></td></tr><tr><td>Participants</td><td>6</td><td>(a) Cohort study - Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up<br>Case-control study - Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls<br>Cross-sectional study - Give the eligibility criteria, and the sources and methods of selection of participants<br>(b) Cohort study - For matched studies, give matching criteria and number of exposed and unexposed<br>Case-control study - For matched studies, give matching criteria and the number of controls per case</td><td></td><td>RECORD 6.1: The methods of study population selection (such as codes or algorithms used to identify subjects) should be listed in detail. If this is not possible, an explanation should be provided.<br>RECORD 6.2: Any validation studies of the codes or algorithms used to select the population should be referenced. If validation was conducted for this study and not published elsewhere, detailed methods and results should be provided.<br>RECORD 6.3: If the study involved linkage of databases, consider use of a flow diagram or other graphical display to demonstrate the data linkage process, including the number of individuals with linked data at each stage.</td><td>P5.<br>P6.<br>P5.<br>No linkage.</td></tr><tr><td>Variables</td><td>7</td><td>Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable.</td><td>P5.<br>P6.</td><td>RECORD 7.1: A complete list of codes and algorithms used to classify exposures, outcomes, confounders, and effect modifiers should be provided. If these cannot be reported, an explanation should be provided.</td><td>P5.<br>P6.</td></tr><tr><td>Data sources/ measurement</td><td>8</td><td>For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group</td><td>P7.<br>P8.<br>P9.</td><td></td><td></td></tr><tr><td>Bias</td><td>9</td><td>Describe any efforts to address potential sources of bias</td><td>P8.<br>P9.</td><td></td><td></td></tr><tr><td>Study size</td><td>10</td><td>Explain how the study size was arrived at</td><td>P5.<br>P6.</td><td></td><td></td></tr><tr><td>Quantitative variables</td><td>11</td><td>Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen, and why</td><td>P8.</td><td></td><td></td></tr><tr><td>Statistical methods</td><td>12</td><td>(a) Describe all statistical methods, including those used to control for confounding<br>(b) Describe any methods used to examine subgroups and interactions<br>(c) Explain how missing data were addressed<br>(d) Cohort study - If applicable, explain how loss to follow-up was addressed<br>Case-control study - If applicable, explain how matching of cases and controls was addressed<br>Cross-sectional study - If applicable, describe analytical methods taking</td><td>P7.<br>P8.<br>P9.<br>P9.</td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th>account of sampling strategy<br>(e) Describe any sensitivity analyses</th><th></th><th></th></tr></thead><tbody><tr><td>Data access and<br>cleaning methods</td><td></td><td>..</td><td>RECORD 12.1: Authors should describe the extent to which the investigators had access to the database population used to create the study population.<br>RECORD 12.2: Authors should provide information on the data cleaning methods used in the study.</td><td>P5.</td></tr><tr><td>Linkage</td><td></td><td>..</td><td>RECORD 12.3: State whether the study included person-level, institutional-level, or other data linkage across two or more databases. The methods of linkage and methods of linkage quality evaluation should be provided.</td><td>None.</td></tr><tr><td colspan="5"><strong>Results</strong></td></tr><tr><td>Participants</td><td>13</td><td>(a) Report the numbers of individuals at each stage of the study (e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed)<br>(b) Give reasons for non-participation at each stage.<br>(c) Consider use of a flow diagram</td><td>P9.</td><td>RECORD 13.1: Describe in detail the selection of the persons included in the study (i.e., study population selection) including filtering based on data quality, data availability and linkage. The selection of included persons can be described in the text and/or by means of the study flow diagram.</td><td>P5.<br>P6.<br>Figure 1.</td></tr><tr><td>Descriptive data</td><td>14</td><td>(a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders<br>(b) Indicate the number of participants with missing data for each variable of interest<br>(c) Cohort study - summarise follow-up time (e.g., average and total amount)</td><td>Figure 1.<br>P9.<br>P10.<br>Table 1.</td><td></td><td></td></tr><tr><td>Outcome data</td><td>15</td><td>Cohort study - Report numbers of outcome events or summary measures over time<br>Case-control study - Report numbers in each exposure category, or summary measures of exposure<br>Cross-sectional study - Report numbers of outcome events or summary measures</td><td></td><td></td><td></td></tr><tr><td>Main results</td><td>16</td><td>(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence interval). Make clear which confounders were adjusted for and why they were included<br>(b) Report category boundaries when continuous variables were categorized<br>(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period</td><td>P10.</td><td></td><td></td></tr><tr><td>Other analyses</td><td>17</td><td>Report other analyses done—e.g., analyses of subgroups and interactions, and sensitivity analyses</td><td>P10.<br>P11.</td><td></td><td></td></tr><tr><td colspan="5"><strong>Discussion</strong></td></tr><tr><td>Key results</td><td>18</td><td>Summarise key results with reference to study objectives</td><td>P11.<br>P12.</td><td></td><td></td></tr><tr><td>Limitations</td><td>19</td><td>Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias</td><td>P14.</td><td>RECORD 19.1: Discuss the implications of using data that were not created or collected to answer the specific research question(s). Include discussion of misclassification bias, unmeasured confounding, missing data, and changing eligibility over time, as they pertain to the study being reported.</td><td>P13.<br>P14.</td></tr><tr><td>Interpretation</td><td>20</td><td>Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence</td><td>P12.<br>P13.</td><td></td><td></td></tr><tr><td>Generalisability</td><td>21</td><td>Discuss the generalisability (external validity) of the study results</td><td>P14.</td><td></td><td></td></tr><tr><td colspan="5"><strong>Other Information</strong></td></tr><tr><td>Funding</td><td>22</td><td>Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based</td><td>P15.</td><td></td><td></td></tr><tr><td>Accessibility of<br>protocol, raw data,<br>and programming<br>code</td><td></td><td>..</td><td>P7.</td><td>RECORD 22.1: Authors should provide information on how to access any supplemental information such as the study protocol, raw data, or programming code.</td><td>P7.</td></tr></tbody></table>
795 REporting of studies Conducted using Observational Routinely-collected health Data

796 (RECORD) Statement. *PLoS Medicine* 2015; in press.

797

798 *Checklist is protected under Creative Commons Attribution (CC BY) license.

799

800

801

802

803

804

805

806

807

808

809

810

811

812

813

814

815

816

817

818

819 Supplementary table 2. Disease-modifying treatments specific of MS

<table><thead><tr><td>Name</td><td>ATC code</td></tr></thead><tbody><tr><td>Interféron béta-1A</td><td>L03AB07</td></tr><tr><td>Interféron bêta-1B</td><td>L03AB08</td></tr><tr><td>Peginterféron bêta-1a</td><td>L03AB13</td></tr><tr><td>Acétate de glatiramère</td><td>L03AX13</td></tr><tr><td>Natalizumab</td><td>L04AA23</td></tr><tr><td>Fingolimod</td><td>L04AA27</td></tr></tbody></table>
<table><tr><td>Tériflunomide</td><td>L04AA31</td></tr><tr><td>Ocrelizumab</td><td>L04AA36</td></tr><tr><td>Cladribine</td><td>L04AA40</td></tr><tr><td>Ponesimod</td><td>L04AA50</td></tr><tr><td>Ofatumumab</td><td>L04AA52</td></tr><tr><td>Dimethyl fumarate</td><td>L04AX07</td></tr><tr><td>Diroximel fumarate</td><td>L04AX09</td></tr><tr><td>Fampridine</td><td>N07XX07</td></tr><tr><td>Fumarate de diméthyle</td><td>N07XX09</td></tr></table>

820

821

822

823

824

825

826

827

828

829

830

831

832

833

834

835

836

837

838

839

Supplementary Figure 1. Venn diagram showing the repartition of sources of identification (MS-LTD, MS-IP, and MS-DMT) for the sensitive definition (n=59,229). Here, IP = inpatient, is the same as hospital discharge described in the article.

840

841

842

843

<figure><img src="image_17.png" /></figure>
844

845

846

847

<table><thead><tr><th></th><th>Prevalent<br>(N=152,859)</th><th></th></tr></thead><tbody><tr><td>Sex (number (%))</td><td></td><td>848</td></tr><tr><td>Men</td><td>45,210 (29,6%)</td><td>850</td></tr><tr><td>Women</td><td>107,649 (70,4%)</td><td>851</td></tr><tr><td>Age (median [IQR])</td><td>51.9 (41.6 - 62.2)</td><td>852</td></tr><tr><td>Year of diagnosis (number / prevalence (95% CI) per 1000 people / age (median))</td><td></td><td>853</td></tr><tr><td>2011</td><td>94,438 / 1.454 (1.445-1.463) / 49.8</td><td>854</td></tr><tr><td>2012</td><td>98,373 / 1.556 (1.546-1.566) / 50.7</td><td>855</td></tr><tr><td>2013</td><td>102,291 / 1.605 (1.595-1.614) / 50.7</td><td>856</td></tr><tr><td>2014</td><td>106,174 / 1.653 (1.643-1.663) / 51.0</td><td>857</td></tr><tr><td>2015</td><td>110,029 / 1.702 (1.692-1.712) / 51.4</td><td>858</td></tr><tr><td>2016</td><td>113,728 / 1.747 (1.737-1.758) / 51.6</td><td>859</td></tr><tr><td>2017</td><td>117,180 / 1.793 (1.783-1.803) / 52.2</td><td>860</td></tr><tr><td>2018</td><td>120,776 / 1.832 (1.822-1.843) / 52.6</td><td>861</td></tr><tr><td>2019</td><td>124,156 / 1.873 (1.862-1.883) / 52.9</td><td>862</td></tr><tr><td>2020</td><td>127,319 / 1.904 (1.893-1.914) / 53.2</td><td>863</td></tr><tr><td>2021</td><td>130,676 / 1.931 (1.921-1.942) / 53.6</td><td>864</td></tr></tbody></table>

865

866

867

868

869

870

871

872

873

874

Supplementary table 3: Characteristic of the population with prevalent MS according to the sensitive definition during the 2011 to 2021 period in the SNDS.
875

876

877

878

879

880

881

882

883

884

885

Supplementary table 4. Characteristics of the cohorts used to study the delay between the first symptoms specific of MS and the encoding of the disease in the SNDS.

<table><thead><tr><th rowspan="2">Period</th><th colspan="3">MS pre-encoding cohorts</th><th colspan="3">MS Pre- and post-encoding cohorts</th></tr><tr><th>Highly probable<br>(n=13,508)</th><th>Controls highly probable<br>(n=67,540)</th><th>Probable<br>(n=1661)</th><th>Controls probable<br>(n=8305)</th><th>Highly probable<br>(n=4647)</th><th>Controls highly probable<br>(n=23,235)</th><th>Probable<br>(n=588)</th><th>Controls probable<br>(n=2940)</th></tr></thead><tbody><tr><td><strong>Sex (number<br>(%))</strong></td><td colspan="3">2019-2021</td><td colspan="3">2015</td></tr><tr><td>Men</td><td>4049<br>(30.0%)</td><td>20,245<br>(30.0%)</td><td>565 (34.0%)</td><td>2827 (34.0%)</td><td>1396<br>(30.0%)</td><td>6980<br>(30.0%)</td><td>187<br>(31.8%)</td><td>936 (31.8%)</td></tr><tr><td>Women</td><td>9459<br>(70.0%)</td><td>47,295<br>(70.0%)</td><td>1096<br>(66.0%)</td><td>5478 (66.0%)</td><td>3251<br>(70.0%)</td><td>16,255<br>(70.0%)</td><td>401<br>(68.2%)</td><td>2004<br>(68.2%)</td></tr><tr><td>Age (median<br>[IQR])</td><td>40.6 (31.9 -<br>51.0)</td><td>40.6 (31.9 -<br>51.0)</td><td>50.1 (34.5 -<br>62.5)</td><td>50.1 (34.5 -<br>62.5)</td><td>41.3 (32.0 -<br>50.7)</td><td>41.3 (32.0 -<br>50.7)</td><td>50.4 (38.5 -<br>63.0)</td><td>50.4 (38.5 -<br>63.0)</td></tr></tbody></table>

886

887

888

889

890

891

892

893
894

895

896

897

898

899

900

901

902

903 Supplementary figure 2: Analysis of neurologist visit trends for incident MS cases.

904 We investigated the (a) number of neurologist visit over one-month intervals for

905 incident MS cases from 2019 to 2021 in the SNDS, comparing ‘highly probable’ and

906 ‘probable’ subgroups with a matched control cohort (the number of visits for controls

907 was divided by 5 to account for the 1:5 matching ratio). We also examined the (b)

908 monthly number of visits for the 2014 MS cohort, providing insights into visit patterns

909 before and after the encoding of MS in the database. This analysis aims to determine

910 whether patients were already being monitored for active MS before their

911 identification in the database.

912

<figure><img src="image_18.png" /><figcaption>a</figcaption></figure>

<figure><img src="image_19.png" /></figure>

913

914

915

916

917

918

<figure><img src="image_20.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_21.png" /></figure>

919

920

921

922

923

924
Supplementary figure 3: Monthly period prevalence of neurologist visits over one-month intervals for incident MS cases from 2019 to 2021 in the SNDS, according to sex (a) and age (b).

<figure><img src="image_22.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_23.png" /></figure>

Supplementary figure 4: Evolution of the number of cases identified with incident MS according to the likelihood of MS. The ‘highly probable’ subgroup is composed of patients with MS-LTD, the ‘Probable’ subgroup had either (≥2 MS-HD) or (≥2 MS-DMT within a year) or (≥1 MS-HD or ≥1 MS-DMT) without MS-LTD, while the ‘Possible’ group had either (≥1 MS-HD or ≥1 MS-DMT) without being in the two previous subgroups.

<figure><img src="image_24.png" /></figure>