# Influence of geographical accessibility to specialist and primary care givers on excess mortality of multiple sclerosis patients in France

Sarah Wilson, Emmanuelle Leray, Floriane Calocer, Fabien Rollot, Mathieu Fauvernier, Laurent Remontet, Ludivine Launay, Sandra Vukusic,

Emmanuelle Le Page, Marc Debouverie, et al.

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## ► To cite this version:

Sarah Wilson, Emmanuelle Leray, Floriane Calocer, Fabien Rollot, Mathieu Fauvernier, et al. Influence of geographical accessibility to specialist and primary care givers on excess mortality of multiple sclerosis patients in France. BMC Neurology, 2025, 25 (1), pp.441. 10.1186/s12883-025-04459-z. hal-05339351

HAL Id: hal-05339351

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# Influence of geographical accessibility to specialist and primary care givers on excess mortality of multiple sclerosis patients in France

Sarah Wilson<sup>1,37*</sup>, Emmanuelle Leray<sup>2</sup>, Floriane Calocer<sup>3</sup>, Fabien Rollot<sup>4,5,6,7</sup>, Mathieu Fauvernier<sup>8,9</sup>, Laurent Remontet<sup>8,9</sup>, Ludivine Launay<sup>10,11,12</sup>, Sandra Vukusic<sup>4,5,6,7</sup>, Emmanuelle Le Page<sup>13</sup>, Marc Debouverie<sup>14,15</sup>, Jonathan Ciron<sup>16,17</sup>, Aurélie Ruet<sup>18,19,20</sup>, Jérôme De Seze<sup>21</sup>, Hélène Zephir<sup>22</sup>, Thibault Moreau<sup>23</sup>, David-Axel Laplaud<sup>24</sup>, Christine Lebrun-Frenay<sup>25</sup>, Pierre Clavelou<sup>26,27</sup>, Pierre Labauge<sup>28,29</sup>, Eric Berger<sup>30</sup>, Jean Pelletier<sup>31</sup>, Olivier Heinzlef<sup>32</sup>, Eric Thouvenot<sup>33,34</sup>, Jean Philippe Camdessanche<sup>35</sup>, Charlotte Arnaud<sup>36</sup>, Nathalie Derache<sup>36</sup>, Pierre Branger<sup>36</sup>, Lydia Guittet<sup>37</sup>, Gilles Defer<sup>36†</sup> and Olivier Dejardin<sup>1†</sup>

## Abstract

**Background** Access to healthcare and socioeconomic deprivation are intricately linked. No studies have been led to measure the effect of healthcare accessibility on mortality in patients with MS so far. The objective was to examine the influence of travel time to the expert MS centre and of the accessibility to primary healthcare services on excess mortality in MS.

**Methods** A retrospective observational cohort study recruited patients from 18 French MS expert centres, with an onset of MS between 1960 and 2015 and a follow-up of up to 30 years. Primary health facility accessibility was measured by the Spatial Accessibility Multisectorial index. Specialist care accessibility was measured by road travel time to the expert MS centre. Excess death rates (EDR) and excess hazard ratios were studied using additive excess hazard models with multidimensional penalised splines.

**Results** The study included 33,697 patients. Patients with relapsing-onset MS (R-MS) with a travel time of 40 min had the lowest EDR (Men: 1.2 deaths per 100 person-years (95%CI [0.8;1.8))), women: 0.8 deaths per 100 person-years (95%CI[0.6;1.2]), lower than patients who lived further from the centre. No effect of primary care access was found for patients with R-MS, and no effect of accessibility to primary or specialised care was found for patients with primary progressive MS.

Full list of author information is available at the end of the article

<footnote>†Gilles Defer and Olivier Dejardin contributed equally to this work.</footnote>

<footnote>*Correspondence:
Sarah Wilson
sarah.wilson@unicaen.fr</footnote>
**Conclusion** This study reveals the impact of travel time to neurologists on excess mortality in patients with R-MS in France. This distance bias association highlights the importance of preventing a potential selection of patients followed in MS expert centres.

**Keywords** Multiple sclerosis, Excess mortality, Healthcare accessibility, Socioeconomic status, Observational cohort study

**Introduction**

Multiple sclerosis (MS) is an autoimmune debilitating disease which results in varying degrees of physical disability and cognitive decline, leading to impaired quality of life and shortened life expectancy [1–3]. Multidisciplinary care is necessary to prevent relapses, slow disability progression and manage symptoms, and MS expert centres appear to be the best way to organize such multidisciplinary care [4, 5]. A study in Canada found that 51% of patients followed in MS clinics had filled a prescription for an MS-specific DMT against only 1% of patients who received not specialist-treatment [6]. Once this treatment has been initiated by a neurologist, their expertise to monitor effectiveness as well as side effects is important to avoid any delays in switching to second-line treatment [7]. The pooling of specialised health resources in expert centres could reduce accessibility for patients who live far away or who do not have the financial resources to travel to expert centres [8]. This phenomenon is more and more frequent due to subspecialisation of neurological care with a concentration of healthcare professionals specialized in MS in expert centres [9]. Barriers to healthcare access can include healthcare resources, geographic access to these resources as well as individual factors such as disability and socioeconomic deprivation, interlinked with insurance status depending on the country. In France the universal health coverage associated with 100% reimbursement of outpatient and hospital care, together with medical transport of specific Long-Term Diseases, including MS, means insurance status should not constitute a barrier in access to care once the chronic disease is diagnosed. MS expert centres are located in university public hospitals (one per region), and patients can theoretically choose from which health professional or health centre they wish to receive care. Criteria other than the quality of care received can intervene in this decision process, namely access to healthcare professionals. Studies on other chronic diseases have revealed that patients sometimes favour proximity over other criteria, which could lead to patients missing out on the best care available [10, 11]. It is possible that patients living at a distance from an expert centre may benefit from less frequent follow-up at this centre, later in the course of their disease, or even not be cared for at this type of centre. Ultimately, distance from expert centre could be associated with an increased risk of MS-related mortality. Patients suffering from chronic disease also need

local support from primary care professionals. Territorial inequalities in primary care distribution are observed in France, and an association between accessibility to primary care and excess mortality has been demonstrated in patients with cancer, who, like those with MS, may benefit from expert centre care for their chronic illness [12, 13].

Previous work has highlighted the effect of socioeconomic status (SES) on excess mortality in patients with MS, and showed higher mortality for more socio-economically deprived patients with relapsing-onset MS (R-MS) [14, 15]. The impact of healthcare accessibility on mortality due to MS has not yet been extensively studied in patients with MS, despite the level of care required for these patients.

The objective of this study was to examine the influence of both travel time to the expert neurology centre from the patient's home address and of the accessibility to a range of primary healthcare services on excess mortality of patients with MS, whilst accounting for the patient's SES.

**Materials and methods**

**Study population**

The cohort was constituted by patients with MS from 18 of the 36 French MS expert centres participating in the French MS database OFSEP (www.ofsep.org), [16] with an onset of MS between 1960 and 2015. This population has been studied in two previous publications where further details are available [14, 17].

**Mortality in the cohort and in the general population**

Linkage to the National Repertory for the Identification of Physical Persons (“Répertoire National d’Identification des Personnes Physiques” (RNIPP) was performed at an individual level to obtain deaths that occured between 1976, the minimum date of systematic registration and 2016 were registered, and follow-up was censored beyond 30 years. Further details on mortality are available in a previous publication. [14] For patients for whom no follow-up data was obtained, exclusion from the analysis was performed. In addition, patients with no data on travel time to centre (n = 1299), on EDI (n = 7), as well as on SCALe index (n = 277) were also excluded from the analysis (complete case analysis). Other covariates of interest in the database were sex, initial MS phenotype (R-MS or PPMS), as well as age at MS onset. The method
used to obtain all-cause mortality rates is described in the study on the same cohort by Rollot et al. [17].

## Travel time to the MS expert centre

Travel time was defined as the time between the residential location of patients to the nearest regional expert MS centre, in minutes. These travel times were estimated using a Geographical Information System (GIS ArcGIS 10.5® and network analyst extension provided by ESRI France) combined with a road-map database (Navstreets® V15 provided by HERE and ESRI France). Travel times, computed in minutes, were estimated according to legal speeds for the different road classes. Travel times over 600 min were reclassed as missing data since these values suggested that these patients were from very remote areas and travelled to expert centres in a different region entirely. A map is provided in supplementary data to show the distribution of the 18 MS centres included on the French territory(eFigure 1).

## Accessibility to primary care resources

An overall health facility accessibility score was used: the Spatial accessibility multiscALar index (SCALe index) [18], enriched with its newest version which encompasses

road networks (not yet published). This aggregated index is based on residential buildings as the initial spatial unit, with 3 million units registered in the SCALe index. Health facilities in metropolitan France were obtained by the Permanent Facilities Database (BPE) of 2013. These facilities are divided into primary care equipment (GPs, physiotherapists, nurses, pharmacists, dentists) and tertiary care equipment (obstetrician-gynaecologists, maternity wards, paediatricians, ophthalmologists, short stay care and accident and emergency departments). Accessibility was defined as the distance that the population had to travel to access a service, whilst accounting for the pressure exerted on services by users. Each healthcare facility was weighted. The result was then standardized and combined into a score. The SCALe index was attributed to each patient according to their home address in the OFSEP database, following geolocalisation by the Geographic Information System (GIS ArcGIS 10.2®). The values ranged from -19.6 to 24.0 (median of -5.8) with a higher SCALe index meaning worse accessibility to primary healthcare facilities.

All of these indicators, EDI, travel time to the expert centre and accessibility to primary health care resources, were based on the patient's residential address for which

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Flow chart of the study population. MS: Multiple Sclerosis. OFSEP: Observatoire Français de la Sclérose en Plaques. PPMS: progressive-onset MS. R-MS: relapsing-onset MS. SCALe: Spatial accessibility multiscALar index (overall health facility accessibility score)</figcaption></figure>
we did not have a corresponding date or longitudinal data in case of variation over the study period.

## Socioeconomic indicator

Patient's socio-economic position was measured using the 2011 version of the European Deprivation Index (EDI) [14, 19]. This ecological index is attributed at a census level, according to the patient's IRIS ("Ilot Regroupé pour l'Information Statistique"), a neighbourhood measure. The score is based on ten weighted variables (low level of education, no access to a car, overcrowding, no access to a heating system, non-owner, unemployment, foreign nationality, unskilled worker - farm worker, household with more than six persons, single-parent household). It was analysed as a continuous variable, the higher the index, the greater the deprivation in the IRIS.

## Statistical analyses

The additive framework used is based on the following: the observed mortality rate (whatever the cause of death) of MS patients ($h_0$) is assumed to be equal to the sum of excess mortality due to MS ($h_E$) added to the expected death rate of the general French population ($h_P$):

$$h_0 = h_E + h_P$$

These mortality rates are defined for a given time, and for each individual according to their age at MS onset, and matched to the mortality rate of the general population by sex, age and year at death and area of residence ("Département"). Further information on the statistical methods used, namely penalized splines and excess death rate (EDR) are provided in a previous publication [14].

Models were computed separately for each initial phenotype of the disease, since effect of SES on excess mortality was found in R-MR but not PPMS [14].

Model building strategy was performed by adding the covariates of interest and potential interactions, and the model with the lowest corrected Akaike information criterion (AIC) was selected as the best fitting model, considering a difference of at least 4 units between two AIC values [20]. For R-MS, the first model was constructed considering that adjustment on socio-economic status should be performed, and that the potential interaction between EDI and time since disease onset should be tested. The covariates of interest, SCALe and travel time, were then added step by step. Finally, interactions between the covariates of interest and time since disease onset were tested.

$$M0: \log(h_E(t,a)) = \text{tensor}(\text{time since onset, age at onset}) + \text{sex} + s_1(\text{EDI}) + s_1(\text{EDI})*\text{time since onset}.$$

$$M1a: \log(h_E(t,a)) = M0 + s_2(\text{scale}).$$

$$M1b: \log(h_E(t,a)) = M0 + s_3(\text{traveltime}).$$

$$M1c: \log(h_E(t,a)) = M0 + s_2(\text{scale}) + s_3(\text{traveltime}).$$

M2a:

$$\log(h_E(t,a)) = M0 + s_3(\text{traveltime}) + s_3(\text{traveltime})*\text{time since onset}.$$

$$M2b: \log(h_E(t,a)) = M0 + s_2(\text{scale}) + s_2(\text{scale})*\text{time since onset}.$$

For PPMS, the first model was constructed without including EDI. The same model construction process used for R-MS was then applied for the following models. Excess mortality hazard ratios (EHR) were then estimated according to travel time to the expert MS centre, SCALe and EDI scores.

Following methods employed in previous work, knots were fixed at the percentiles of the distribution of time, age, EDI, travel time and SCALe among deceased patients [21]. There were 6 knots for time, and 5 knots for the other covariates.

In order to allow a two-dimensional representation of results, values of the variables sex, age at onset, and EDI had to be set. Graphs were constructed for an age at onset of 30 years for R-MS patients, approximately the median age at onset, and for an EDI score of 0 (corresponding to the median value). Curves for percentiles 2.5, 97.5 and median values of travel times were shown. The statistical significance of these variables did not depend on these values chosen for graphical representation.

A sensitivity analysis was led with stratification of the analyses by year of disease onset (before 2000 and 2000 and after) in order to account for changes in care practices and treatment options over time.

All analyses were led using R software (4.1.2) with the "survPen" package (1.0.1) [22].

## Results

A total of 33,697 patients were included in the analysis. R-MS accounted for 88% of the total number, and PPMS for 12% (Fig. 1).

The description of the study population is presented in Table 1. The mean age at MS onset was 30.5 years (sd: 9.8, range: 1.5–84.0) for R-MS, and 43.7 years (sd: 10.9, range: 6.1–77.8) for PPMS. There were 1,820 deaths in the overall population, and the percentage of deaths was 4.3% for R-MS and 13.1% for PPMS. There were respectively 1,794 (6.0%) and 279 (6.9%) lost to follow-up patients in R-MS and PPMS patients. For R-MS patients, SCALe index ranged from -19.6 to 24.0 (median: -5.8) and for PPMS patients, from -18.9 to 21.7 (median: -5.2). Travel time for R-MS patients ranged from 0.4 min to 180 min (median: 40.5 min), and for PPMS patients ranged from 1.3 min to 172 min (median: 46.2 min) (Fig. 2). Characteristics for patients excluded due to missing data are described in supplementary material (etables 1a and 1b).
<table><caption>**Table 1** General characteristics of the study population</caption><thead><tr><th></th><th></th><th>Overall<br>n (%)<br>(n=33,697)</th><th>R-MS n (%)<br>(n=29,659)</th><th>PPMS<br>n (%)<br>(n=4038)</th></tr></thead><tbody><tr><td rowspan="2">Sex</td><td>Men</td><td>9721 (28.8%)</td><td>7957 (26.8%)</td><td>1764<br>(43.7%)</td></tr><tr><td>Women</td><td>23,976<br>(71.2%)</td><td>21,702<br>(73.2%)</td><td>2274<br>(56.3%)</td></tr><tr><td rowspan="2">Age at MS<br>onset</td><td>Median<br>[quartiles<br>1-3]</td><td>31.7<br>[24.9-40.0]</td><td>30.5<br>[24.3-38.1]</td><td>43.2<br>[35.0-50.4]</td></tr><tr><td>Deceased</td><td>1820 (5.4%)</td><td>1291 (4.3%)</td><td>529 (13.1%)</td></tr><tr><td rowspan="2">Vital<br>status as of<br>2016/01/01</td><td>Lost to<br>follow-up</td><td>2073 (6.2%)</td><td>1794 (6.1%)</td><td>279 (6.9%)</td></tr><tr><td>Alive</td><td>29,804<br>(88.4%)</td><td>26,574<br>(89.6%)</td><td>3230<br>(80.0%)</td></tr><tr><td rowspan="2">Follow-up<br>(years)</td><td>Median<br>[quartiles<br>1-3]</td><td>15.7 [9.1;<br>23.7]</td><td>15.8 [9.0;<br>24.0]</td><td>15.2 [9.6;<br>22.0]</td></tr><tr><td>Median<br>[percentiles<br>2.5; 97.5]</td><td>-0.6 [-5.6;<br>11.9]</td><td>-0.6 [-5.6;<br>11.8]</td><td>-0.6 [-5.5;<br>12.6]</td></tr><tr><td rowspan="2">EDI</td><td>Median<br>[percentiles<br>2.5; 97.5]</td><td>41.2 [5.7;<br>125.1]</td><td>40.5 [5.7;<br>125.0]</td><td>46.2 [6.4;<br>126.4]</td></tr><tr><td>Median<br>[percentiles<br>2.5; 97.5]</td><td>-5.8 [-15.4;<br>5.9]</td><td>-5.8 [-15.4;<br>5.8]</td><td>-5.2<br>[-15.2; 6.2]</td></tr></tbody></table>

EDI European Deprivation Index (the higher the EDI score, the greater the deprivation), MS Multiple Sclerosis, PPMS Progressive-onset MS, R-MS Relapsing-onset MS, SCAle Spatial Accessibility multiscalar index (the higher the SCAle index the worse accessibility to primary healthcare facilities)

Tables 2a and b describe model building strategy to measure the effects of travel time and of SCAle index, in order to select significant variables. Model 1b was selected according to the lowest corrected AIC value for R-MS. There was a significant effect of travel time on EDR for patients with R-MS, which was non-linear (since the effective degrees of freedom for the spline for travel time (M1b) were > 1). No effect of SCAle on EDR was found. M0a was selected for PPMS, given that none of the models tested reduced the value of AIC by more than 4 units.

Excess Hazard Ratio (EHR) and Excess death rate (EDR) dynamics according to travel time to expert MS centre for patients with R-MS (figs 3 and 4)

Figure 3 shows the effect of travel times to nearest expert centre on the EHR in patients with R-MS. A statistically significant effect of travel time under 65 min was found (for 65 min EHR = 0.6911, 95%CI [0.4779; 0.9993]) compared to patients living closer to the expert centre. The EHR for a travel time of 120 min was not statistically significant compared to the reference of 0 min (EHR = 1.02, 95%CI [0.69; 1.51]).

Figure 4 presents the EDR for men and women with R-MS, for an EDI score of 0, and for travel times of

10 min, 40 min and 120 min; these travel times correspond respectively approximately to the 2.5th percentile, the median and the 97.5th percentile of travel times for R-MS patients. Patients with a travel time of 40 min had the lowest EDR. At thirty years of disease duration, EDR for men for a travel time of 10 min was 1.7 deaths per 100 person-years (95% confidence interval [1.1; 2.5]), for a time of 40 min was 1.2 deaths per 100 person-years (95% confidence interval [0.8; 1.8]) and for a travel time of 120 min was 2.1 deaths per 100 person-years (95% confidence interval [1.4; 3.2]). For women, EDR was 1.1 deaths per 100 person-years (95% confidence interval [0.8; 1.7]) for 10 min, 0.8 deaths per 100 person-years (95% confidence interval [0.6; 1.2]) for 40 min and 1.4 deaths per 100 person-years (95% confidence interval [1.0; 2.2]) for 120 min.

There were 1820 deaths for patients with R-MS, of which 1128 were for patients with disease onset before 2000 and 163 in 2000 and after. The sensitivity analysis revealed that travel time to the expert centre had a significant effect on excess mortality for patients with R-MS with disease onset before 2000 but not after the year 2000 (supplementary data etables 2a and 2b).

Discussion

In patients with R-MS, the excess mortality hazard ratio was lowest for patients living under one hour from the expert MS centre. No significant negative effect on mortality was found for patients who travelled longer than one hour. Whilst the effect of travel time to the expert centre was statistically significant for patients with R-MS, the clinical effect is likely moderate with only a small difference in EDR between patients living closer or further away from the expert centre. No effect of travel time to expert MS centre was found in patient with PPMS. No effect of access to primary healthcare services on excess mortality was found for either form of MS.

Two types of relationships have been found referring to the effect of distance. Distance decay association describes the fact that patients living closer to healthcare facilities have better health outcomes and/or higher access rates to these services compared to those who live further away. Distance bias association is a term which describes the opposite trend, whereby patients who reside further away from the healthcare facility have better health outcomes compared to patients who live closer to healthcare facilities [23–25]. One explanation to the latter association is a selection of patients who receive care in expert centres. Patients with more resources and better health can feasibly travel further, and therefore could benefit from a potentially higher quality of care. On the contrary, referral bias is also a possibility whereby more severe cases and/or younger patients are treated in secondary or tertiary centres whereas less severe cases
<figure><img src="image_5.png" /><figcaption>**Fig. 2** Travel time to the expert multiple sclerosis (MS) centre. MS: Multiple Sclerosis. PPMS: progressive-onset MS. R-MS: relapsing-onset MS</figcaption></figure>

<table><caption>**Table 2** Model building strategy to measure the association between travel time to expert multiple sclerosis (MS) expert centre or accessibility to primary healthcare facilities (SCALe) and excess mortality according to phenotype of MS</caption><thead><tr><th>Model</th><th>Formula</th><th>Number of regression parameters</th><th>Number of smoothing parameters</th><th>Effective degrees of freedom</th><th>Akaike Information Criteria</th></tr></thead><tbody><tr><td colspan="6">a: Model building strategy for relapsing-onset MS patients</td></tr><tr><td>M0</td><td>tensor(t,a) + sex + s<sub>1</sub>(EDI) + s<sub>1</sub>(EDI)*t</td><td>39</td><td>4</td><td>17.45</td><td>13578.22</td></tr><tr><td>M1a</td><td>M0 + s<sub>2</sub>(scale)</td><td>43</td><td>5</td><td>18.45</td><td>13579.28</td></tr><tr><td>M1b</td><td>M0 + s<sub>3</sub>(traveltime)</td><td>43</td><td>5</td><td>21.21</td><td>13569.93</td></tr><tr><td>M1c</td><td>M0 + s<sub>2</sub>(SCALe) + s<sub>3</sub>(traveltime)</td><td>47</td><td>6</td><td>22.18</td><td>13571.91</td></tr><tr><td>M2a</td><td>M0 + s<sub>3</sub>(traveltime) + s<sub>3</sub>(traveltime)*t</td><td>47</td><td>6</td><td>21.90</td><td>13568.77</td></tr><tr><td>M2b</td><td>M0 + s<sub>2</sub>(SCALe) + s<sub>2</sub>(scale)*t</td><td>47</td><td>6</td><td>19.48</td><td>13581.30</td></tr><tr><td colspan="6">b: Model building strategy for progressive-onset MS patients</td></tr><tr><td>M0a</td><td>tensor(t,a) + sex</td><td>31</td><td>2</td><td>11.08</td><td>4846.90</td></tr><tr><td>M0b</td><td>tensor(t,a) + sex + s<sub>1</sub>(EDI) + s<sub>1</sub>(EDI)*t</td><td>39</td><td>4</td><td>14.94</td><td>4847.33</td></tr><tr><td>M1a</td><td>M0b + s<sub>2</sub>(SCALe)</td><td>43</td><td>5</td><td>16.61</td><td>4849.45</td></tr><tr><td>M1b</td><td>M0b + s<sub>3</sub>(traveltime)</td><td>43</td><td>5</td><td>15.93</td><td>4849.29</td></tr><tr><td>M1c</td><td>M0b + s<sub>2</sub>(SCALe) + s<sub>3</sub>(traveltime)</td><td>47</td><td>6</td><td>17.58</td><td>4851.33</td></tr><tr><td>M2a</td><td>M0b + s<sub>3</sub>(traveltime) + s<sub>3</sub>(traveltime)*t</td><td>47</td><td>6</td><td>18.28</td><td>4844.73</td></tr><tr><td>M2b</td><td>M0b + s<sub>2</sub>(SCALe) + s<sub>2</sub>(SCALe)*t</td><td>47</td><td>6</td><td>17.40</td><td>4850.85</td></tr></tbody></table>

<footnote>t Time since disease onset, EDI European Deprivation Index, SCALe Spatial accessibility to primary healthcare facilities (SCALe) index (accessibility to primary healthcare facilities)</footnote>
<figure><img src="image_6.png" /><figcaption>**Fig. 3** Excess Hazard Ratio according to travel times to expert multiple sclerosis (MS) centre in patients with relapsing-onset (R-MS)</figcaption></figure>

or older patients are more likely to be treated by non-specialized neurologists and/or general practitioners [26, 27].

In our study, a significant distance bias association was observed. Patients who lived further away from the expert MS centre had a higher EHR than patients who lived closer. The beneficial effect of travel time to the expert centre was maximal at 40 min. Patients necessarily attended an MS expert centre to be included in the study population. It is plausible that nearly all patients with MS living within a small distance from the nearest expert centre attend this facility for their care. Beyond a certain distance, patients may decide to seek care from a neurologist who isn't part of an expert centre. Patients who travel the extra distance to the expert centre instead of attending a physician closer to their home could therefore potentially be healthier or more motivated to do so. The sensitivity analysis revealed that travel time to the expert centre had a significant effect on excess mortality for patients with R-MS with disease onset before 2000, but not after the year 2000. Our hypothesis for this result is that the commercialisation of DMTs in more recent years has increased the importance and central role of expert centres in patient's care. Centralised multidisciplinary discussions on therapeutic strategies, along with the development of regional networks anchored around expert care centres, may explain why distance to the expert centre no longer significantly affects excess

mortality in patients with onset of disease in more recent years. This approach promotes greater equity in therapeutic decisions and treatment choices, regardless of the initial place of care. Before the year 2000, although DMTs were not yet available for most patients, a benefit of specialized care on mortality was observed, and we can hypothesise that only the more motivated and/or healthy patients were prepared to travel to these expert centres. Another explanation for the non-significant effect of travel time to the expert centre for a disease onset after the year 2000 is lack of statistical power, since there are fewer deaths for this more recent time period.

The lack of a significant effect on excess mortality for patients living further away than one hour to the expert centre could be due to the fact that our study population may not reflect this population with MS who live in very rural areas, far from expert centres. Therefore a negative effect of living far from an expert centre, the aforementioned distance decay association, could exist but may not be revealed in our study. Other studies have found that patients with MS who lived further away had reduced access to expert centres and to DMT prescription [28–31]. A coordination in patient care by the expert centre of local neurology resources (neurologists, MS nurses, physiotherapists etc.) could be a major step in reducing inequalities in access to care. Mobile care units where healthcare providers travel to patients who do not have easy access is a solution. However, the shortage of
<figure><img src="image_7.png" /><figcaption>**Fig. 4** Excess Death Rate (EDR) according to time since disease onset (in years) for patients with relapsing-onset multiple sclerosis (R-MS)</figcaption></figure>

subspecialised neurologists is a barrier in developing these practices. Regional MS networks provide an alternative solution: the patient receives care from their local healthcare providers (neurologists, nurses, physiotherapists etc.) which is coordinated by the regional expert centres. Such structures exist in a few regions in France (regional MS networks), and thus patients can benefit from the same level of care via their local healthcare system [8, 32]. Studies have found that healthcare professionals also benefit from this coordination with benefits for time management and better working conditions [5, 33, 34]. Telemedicine is another way to increase accessibility to expert care for patients who live far away from expert centres. A review by Mcginley found that development of telemedicine solutions in the US could mitigate inequalities in access to care for patients with MS [9]. Digital literacy, as well as limitations on the proportion of care provided via telemedicine by health professionals are barriers that could be improved to further develop the impact of telemedicine on healthcare access for patients with MS.

The lack of effect of primary healthcare services on excess mortality could be explained by the importance of

neurologist-led care and more specifically of MS expert care to reduce mortality directly or indirectly due to MS. The importance of other healthcare services in reducing MS-related deaths may therefore be of lower importance than care coordinated by the patient's neurologist. The other potential explanation is the availability in MS expert centres of a wide panel of health professionals able to provide multidisciplinary care for patients with MS. Per Soelberg et al. highlighted the importance of MS Care Units, and recommended the following requirements: an MS-neurologist and MS-nurse, physiotherapists, neuro-psychologists, occupational therapists, dietitians, speech therapists, social workers, pain specialists, as well as continence and spasticity specialists. [4] Within these structures, patients with MS could receive a sufficient standard of care that renders accessibility to other health-care services less important.

The main limitation of this study is the absence of a national registry of MS patients in France, which would have allowed us to study the factors that condition access to expert MS centres. This limit prevents us to properly detect any distance decay association. Information on treatment and disease progression are key to define the
profile of patients who seek care from expert MS centres over local neurologist care. A comparison with the only registry included in the database revealed that treated patients, as well as patients treated with second-line and off-label MS therapies, may be over-represented, suggesting that there are more active MS patients in the database than in the general French MS population [16, 35].

Another limitation is the risk of reverse causation, since patients' addresses could have been recorded at the first visit following inclusion or updated since. It is possible that place of residence is indirectly due to disease progression, thus explaining the higher excess death rate for patients living very far from the expert centre (although not statistically significant). In France, no large-scale databases of patients with MS include both exhaustive longitudinal clinical and administrative data covering the course of the patients' disease. The effect of access to healthcare on outcomes in multiple sclerosis has gained growing interest in recent years [9, 28], and our results highlight the importance of recording administrative data as well as clinical data over long periods of time to answer these critical questions.

Information on public transport would have been informative to complete data on travel time to the expert centre. This data would be difficult to analyse however over the 55-year study period with MS symptoms onset between 1960 and 2015 with the development of public transport methods and routes over the decades. Moreover, in France, once a chronic disease such as multiple sclerosis has been diagnosed, medical transport to various healthcare providers regarding their chronic disease is free of charge for patients. Access to public transport likely affects patients more significantly in the period after disease onset but before diagnosis, after which universal health insurance covers the cost of transport related to care for their disease.

Lastly, no data on comorbidities was available for the study population. The effect of distance to the expert centre was significant on excess mortality for patients with R-MS, and information on comorbidities could help to better understand how this association affects mortality.

Our study has numerous strengths. Access to DMTs has been associated with socioeconomic status in studies in France, Canada and the UK where in all three countries more deprived patients were found to have reduced access to DMTs or to second-line treatment, hence the importance of adjusting our models on socio-economic status [29, 31, 36–38]. Some studies on the influence of distance on health outcomes in other chronic diseases found that distance no longer had an effect when controlling for socioeconomic deprivation and may therefore overestimate the impact of distance on health outcomes when not taken into account [23]. Our modelling

strategy adjusted on SES allows us to prevent this potential overestimation.

A further strength is the study of excess mortality and not overall mortality; it appears essential to study excess mortality to measure the effect of healthcare accessibility on mortality due to MS and therefore avoid highlighting differences in other cause mortality depending on urban or rural areas. A study on overall survival in England and Wales revealed longer survival rates for rural areas than for urban areas [39]. In Northern Ireland, a gradient of poorer health was found from rural to urban areas [40]. Other studies have found poorer health in rural populations [35, 41].

Moreover, use of SCALe, a composite index measuring various primary healthcare resources best captures access to primary care by accounting for demand on these healthcare resources by the local population. The lack of effect found on excess mortality is unlikely to be due to an inaccurate measure of primary care resources. The consideration of travel time as a continuous variable minimises loss of information. Studies have found effects of travel time or distances on health outcomes, but most studies categorised these variables, leading to a potential loss of information [23]. In addition, the statistical methods used in our study allow potential non-linear and non-proportional effects of continuous variables.

## Conclusion

This study reveals the impact of travel time to neurologists on excess mortality in patients with R-MS in France, which persists when controlling for material deprivation. Accessibility to primary health care professionals does not appear to influence mortality due to MS. The distance bias association observed highlights the importance of preventing a potential selection of patients followed in MS expert centres and providing an equal level of care for all patients with MS.

## Abbreviations

MS Multiple Sclerosis

R-MS Relapsing-Onset Multiple Sclerosis

PPMS Primary Progressive onset Multiple Sclerosis

EDI European Deprivation Index

DMT Disease-Modifying Therapy

SES Socio-Economic Status

OFSEP Observatoire Français de la Sclérose en Plaques

SCALe Spatial Accessibility multiscALar

EDR Excess Death Rate

EHR Excess Hazard Ratio

AIC Akaike Information Criterion

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12883-025-04459-z.

Supplementary material 1.
## Acknowledgements

The authors thank the patients and neurologists for their participation in the OFSEP project by contributing and facilitating access to clinical data; the research assistants in all participating centers who contributed to data collection; and the geographical platform MAPinMED and "Cancers and Preventions" group for performing geolocation of our study population of MS patients. Data collection has been supported by a grant provided by the French State and handled by the "Agence Nationale de la Recherche," within the framework of the "Investments for the Future" programme, under the reference ANR-10-COHO-002, Observatoire Français de la Sclérose en Plaques (OFSEP). This study was funded by the ARSEP foundation "Fondation pour l'aide à la recherche sur la Sclérose en Plaques" (Grant Reference Number 1122).

## Authors' contributions

S.W.: analysis and interpretation, writing of the original draft and reviewing and editing. E.L.: Study concept and design, interpretation and critical revision of the manuscript for important intellectual detail. F.C.: study concept and design, acquisition of data, critical revision of the manuscript for important intellectual detail. F.R.: acquisition of data, analysis, interpretation and critical revision of the manuscript for important intellectual content. M.F.: Analysis, interpretation and critical revision of the manuscript for important intellectual content. L.R.: Analysis, interpretation and critical revision of the manuscript for important intellectual content. L.L.: Methodology and critical revision of the manuscript for important intellectual content. S.V., E.L.P., M.D., J.C., A.R., J.D.S., H.Z., T.M., C.LF., D.A.L., P.C., P.L., E.B., J.P., O.H., E.T., J.P.C., C.A, N.D, P.B: acquisition of data and critical revision of the manuscript for important intellectual content. L.G.: Study concept and design, analysis and interpretation, writing and critical revision of the manuscript for important intellectual content. O.D.: Study concept and design, analysis and interpretation, writing and critical revision of the manuscript for important intellectual content. G.D.: Study concept and design, acquisition of data, analysis and interpretation, writing and critical revision of the manuscript for important intellectual content. O.D. and G.D. contributed equally to this work. S.W., F.C., E.L. and O.D. have accessed and verified the data. S.W., L.G., O.D. and G.D. are responsible for the decision to submit the manuscript.

## Funding

This study was funded by the ARSEP foundation "Fondation pour l'aide à la recherche sur la Sclérose en Plaques" (Grant Reference Number 1122).
This study was funded by the ARSEP foundation "Fondation pour l'aide à la recherche sur la Sclérose en Plaques" (Grant Reference Number 1122). Data collection has been supported by a grant provided by the French State and handled by the "Agence Nationale de la Recherche," within the framework of the "Investments for the Future" programme, under the reference ANR-10-COHO-002, Observatoire Français de la Sclérose en Plaques (OFSEP). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication.

## Data availability

Data that support the findings of this study are available upon a motivated request to the OFSEP coordinator, and will be evaluated by the study coordinator and the OFSEP scientific committee for approval according to OFSEP bylaws and access to data procedures.

## Declarations

### Ethics approval and consent to participate

Patients enrolled in the *Observatoire Français de la Sclérose en Plaques* (OFSEP) (registered to clinicaltrials.gov [NCT04028232] on 2019-07-22) provide written informed consent for participation. In accordance with the French legislation, the present study was approved by both the national data protection agency (Commission Nationale Informatique et Libertés [CNIL]; approval DR-2019-132) and the French expert committee for research, studies and evaluations on health (Comité d'expertise pour les recherches, les études et les évaluations dans le domaine de la santé [CEREES]; approval TPS 216966). This study adhered to the Declaration of Helsinki.

### Consent for publication

Not applicable (no images or other personal or clinical details of participants that could compromise anonymity have been included).

## Competing interests

The author(s) would like to disclose the following. S. Wilson reports no disclosures relevant to the manuscript; E. Leray received consulting fees from Alexion, Merck, Novartis, Roche and Biogen, received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Sanofi Genzyme, and received support for attending meetings and/or travel from Sanofi Genzyme, all of the above unrelated to this work; Floriane Calocer received funding for the present research from the ARSEP foundation for a Postdoctoral Fellowship (payment to the institution), from the "Réseau Bas-Normand pour la SEP" for a Postdoctoral Fellowship (payment to the institution), from the Regional Council of Normandy (payment to the institution), from the Ecole Doctorale of Caen University for a training in LSHTD to conduct this research (payment to the institution). She received support for attending meetings and/or travel from the ARSEP Foundation (paid directly to herself, unrelated to this work); F. Rollot reports no disclosures relevant to the manuscript; M. Fauvernier reports no disclosures relevant to the manuscript; L. Remontet reports no disclosures relevant to the manuscript; S. Vukusic reports lecturing fees, travel grants and research support from Biogen, BMS-Celgene, Janssen, Merck, Novartis, Roche, Sanofi-Genzyme, Teva, all of the above unrelated to this work; E. Le Page reports "consulting or lectures, and invitations for national and international congresses from Biogen, Merck, Teva, Sanofi-Genzyme, Novartis Alexion, research support from Teva and Biogen academic research grants from PHRC and LFSEP, and travel grant from ARSEP Foundation, all of the above unrelated to this work; M. Debouverie reports no disclosures relevant to the manuscript; J. Ciron reports consulting, serving on a scientific advisory board, speaking, or other activities with Biogen, Novartis, Merck, Sanofi-Genzyme, Roche, Celgene-BMS, Alexion, all of the above unrelated to this work; A. Ruet reports consultancy fees, speaker fees, research grants (non-personal), and/or honoraria approved by the institutions from Novartis, Biogen Idec, Genzyme, Roche, and Merck, all of the above unrelated to this work; J. De Sèze reports consulting and lecturing fees, travel grants and unconditional research support from Biogen, Genzyme, Novartis, Roche, Sanofi Aventis and Teva Pharma, all of the above unrelated to this work; H; Zephir reports consulting or lectures, and invitations for national and international congresses from Biogen, Merck, Teva, Sanofi-Genzyme, Novartis and Bayer, as well as research support from Teva and Roche, and academic research grants from Académie de Médecine, LFSEP, FHU Imminent and ARSEP Foundation, all of the above unrelated to this work; T. Moreau reports fees as scientific adviser from Biogen, Medday, Novartis, Genzyme, Sanofi, all of the above unrelated to this work; D.A Laplaud served on scientific advisory boards for Alexion, BMS, Roche, Sanofi, Novartis, Merck, Janssen and Biogen, received conference travel support and/or speaker honoraria from Alexion, Novartis, Biogen, Roche, Sanofi, BMS, Teva Pharma and Merck and received research support from Fondation ARSEP, Fondation EDMUS and Agence Nationale de la Recherche, all of the above unrelated to this work; C. Lebrun-Frenay reports no disclosures relevant to the manuscript; P. Clavelou reports consulting and lecturing fees, travel grants and unconditional research support from Biogen, Janssen, Medday, Merck, Novartis, Roche, Sanofi-Genzyme and Teva Pharma, all of the above unrelated to this work; P. Labauge reports consulting and lecturing fees, travel grants and unconditional research support from Biogen, Genzyme, Novartis, Merck Serono, Roche, and Teva Pharma, all of the above unrelated to this work; E. Berger reports honoraria and consulting fees from Novartis, Sanofi Aventis, Biogen, Genzyme, Roche and Teva Pharma, all of the above unrelated to this work; J. Pelletier reports no disclosures relevant to the manuscript; O. Heinzlef reports consulting and lecturing fees from Bayer Schering, Merck, Teva, Genzyme, Novartis, Almirall and BiogenIdec, travel grants from Novartis, Teva, Genzyme, Merck Serono and Biogen Idec and research support from Roche, Merck and Novartis, all of the above unrelated to this work; E. Thouvenot reports consulting and lecturing fees, travel grants or unconditional research support from the following pharmaceutical companies: Actelion, Biogen, Celgene, Genzyme, Merck Serono, Novartis, Roche, Teva pharma; has a patent pending for biomarkers of neurodegeneration and neuroregeneration and a patent pending for a diagnosis method of multiple sclerosis (EP18305630.8), and received academic research support from PHRC and ARSEP Foundation, all of the above unrelated to this work; J-P. Camdessanché reports consulting and lecturing fees from Akcea, Alexion, Alnylam, Argenx, Biogen, Bristol Myers Squibb, CSL-Behring, Genzyme, Grifols, Laboratoire Français des Biotechnologies, Merck-Serono, Natus, Novartis, Pfizer, Pharmalliance, UCB Pharma, Teva, SNF-Floerger; travel grants from Akcea, Alexion, Alnylam, Argenx, Biogen, CSL-Behring, Genzyme, Grifols, Laboratoire Français des Biotechnologies, Merck-Serono, Natus,
Novartis, Pfizer, Teva, SNF-Floerger, all of the above unrelated to this work; C; Arnaud reports no disclosures relevant to the manuscript; N. Derache reports no disclosures relevant to the manuscript; P. Branger reports no disclosures relevant to the manuscript; L. Guittet reports no disclosures relevant to the manuscript; G. Defer received research grants (paid to institution) from Biogen, Merck Serono, Novartis, Sanofi Genzyme; payment for speaker honoraria from Biogen, Merck Serono, Novartis, Sanofi Genzyme, Teva Pharmaceuticals, BMS; funding for travel from Biogen, Merck Serono, Novartis, Sanofi Genzyme, Teva Pharmaceuticals; and personal compensation for scientific advisory boards from Biogen, Merck Serono, Novartis, Sanofi Genzyme, Teva Pharmaceuticals, and BMS, all of the above unrelated to this work; O. Dejardin reports no disclosures relevant to the manuscript.

Author details

Received: 22 July 2025 / Accepted: 23 September 2025

Published online: 27 October 2025

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