ABSTRACT
Background
Changes over time in the incidence of Motor Neuron Disease (MND) remain uncertain. We aimed to examine time trends in the incidence and survival of MND over 14 years using the Système National des Données de Santé, a nationwide French administrative database.
Methods
We utilized a published algorithm that integrates riluzole prescriptions and hospital discharge to identify incident MND cases from January 1, 2010, to December 31, 2023. Crude and standardized incidences were calculated per 100,000 person‐years. Multivariate Poisson regression models determined time trends in MND incidence by age and sex. Survival was analyzed using Kaplan–Meier methods and Cox proportional hazards models to calculate adjusted hazard ratios for different time periods.
Results
A total of 30,028 incident cases were identified. Crude incidence rose from 2.99 to 3.49 cases per 100,000 person‐years between 2010 and 2019, reflecting an annual increase of 1.7% (IRR 1.017, 95% CI 1.012–1.021). After accounting for population aging, there was still an annual increase of 0.7% (IRR: 1.007 [95% CI 1.002–1.012]) between 2010 and 2019. From 2020 to 2023, observed incidence rates deviated from the expected trend, particularly in 2022, which showed a 15% decrease. The median survival time after diagnosis was 18.1 months (2010), 17.8 months (2015), and 15.6 months (2019).
Conclusions
Although population aging explains much of the rise in case numbers, it does not fully account for the increase. Mortality rates remained stable between 2010 and 2015, but the COVID‐19 pandemic had a notable impact, leading to reduced incidence and survival rates.
Keywords: epidemiology, incidence studies, motor neuron diseases (MND)
1. Introduction
Motor neuron disease (MND) encompasses three primary phenotypes—amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS), and progressive spinal muscular atrophy (PSMA)—with ALS being the most prevalent and severe form, accounting for approximately 90% of cases [1, 2]. ALS is a progressive neurodegenerative disease characterized by the degeneration of motor neurons, leading to muscle weakness, paralysis, and ultimately, respiratory failure. Research suggests that ALS may result from a complex interplay of genetic and environmental factors, with various studies identifying potential genetic markers and environmental exposures that contribute to disease risk [3, 4]. Given the high mortality rate of ALS, with 50% of patients dying within 15–20 months of diagnosis [1], closely tracking any potential increases in its incidence is essential to identify new risk factors and improve patient care and resource allocation [5].
Despite its importance, there is a lack of recent large‐scale epidemiological studies on ALS. Few real‐world studies exist [2], and research cohorts tend to involve much younger populations compared to the general ALS patient population [6]. To obtain more accurate incidence estimates, it is essential to gather robust data from large populations, especially considering the rare nature of ALS, which has a reported all‐age global incidence of 0.78 cases per 100,000 person‐years according to a meta‐analysis [1, 7, 8]. In terms of incidence trends, evidence is conflicting [9]; recent findings from two nationwide studies in Sweden and Denmark and a French regional study suggest an increasing incidence [2, 10], whereas some regional studies in Italy report stable or decreasing rates [11, 12, 13, 14].
The COVID‐19 pandemic has further complicated the current epidemiology of MND, potentially affecting disease incidence through disruptions in healthcare access or other indirect mechanisms. It is plausible that the pandemic contributed to underdiagnosis or diagnostic delays during peak periods, subsequently leading to a posterior increase in MND incidence due to healthcare service disruptions [15]. Conflicting results also emerge concerning MND‐related mortality. While a few studies have explored mortality rates, comprehensive analyses at the population level remain scarce, making it difficult to draw definitive conclusions about the recent evolution of MND mortality and the pandemic's overall impact on MND outcomes [6, 11, 15, 16, 17].
This study aims to bridge these knowledge gaps by conducting a detailed analysis of MND incidence time trends in France over a 14‐year period, from 2010 to 2023, utilizing nationwide data. Additionally, by focusing on the period from 2020 to 2023, this study also aims to assess how the pandemic may have influenced MND incidence in terms of underdiagnosis, increased case numbers, or diagnostic catch‐up effect.
2. Methods
2.1. Data Sources and Study Populations
The French National Health Data System (SNDS) is an extensive healthcare database that catalogs nearly all public health insurance reimbursements for residents in France, covering about 99% of the population. It includes detailed records on prescriptions, clinical diagnoses, and causes of death, integrating inpatient and outpatient data to provide comprehensive insights into healthcare utilization and outcomes. To identify patients with MND, we used an algorithm described elsewhere [5], identifying cases based on multiple criteria including riluzole prescriptions, hospitalizations, and requests for long‐term disease benefits (LTD) with motor neuron disease codes (ICD‐10 code G12.2 for hospitalizations and G12 for LTD). The detailed list of diagnostic codes included in the ICD‐10 G12.2 MND category is detailed in Table S1. Infantile spinal muscular atrophy (ICD‐10 code G12.0), other inherited spinal muscular atrophy (ICD‐10 code G12.1), other spinal muscular atrophies (ICD‐10 G12.8), and spinal muscular atrophy, unspecified (ICD‐10 code G12.9) were not included in our analysis. The precise algorithm is detailed in Method S1. The incidence date was defined as the earliest date of the following conditions: (1) riluzole delivery, (2) hospitalization code G12.2, and (3) LTD benefit with an ICD‐10 code G12. For most patients, the incidence date corresponds to the date of the first riluzole prescription. In the remaining subset of patients without riluzole prescriptions, a sensitivity analysis was conducted to assess whether the first hospitalization code, used as the incidence date for this group, closely aligned with visits to a neurologist (Method S2) [18]. Additionally, because MND patients are not always first referred to neurologists [19], we also investigated in a sensitivity analysis whether the detection in the database aligned with visits to neurologist, otorhinolaryngologists, orthopedists, rheumatologists, or pneumologists. Outpatient visits to the specialists mentioned above from public and private medical care practices, along with stays in the specialty department during inpatient stays were extracted from the SDNS between January 1, 2009 and December 31, 2023.
We reported the study using the Reporting of studies Conducted using Observational Routinely‐collected health Data (RECORD) guidelines (Table S2).
2.2. Statistical Analysis
2.2.1. Incidence and Age and Sex‐Standardization
The incidence rate was determined annually and was calculated as the number of new MND cases in a year divided by the size of the French population on the January 1 of the same year (INSEE data) [20], while standardized incidence rates were obtained using the age and sex distribution of the starting year, that is, 2010. Specifically, using the proportional distribution of inhabitants in each 10‐year age and sex group in the year 2010 as the standard, we calculated the age and sex‐standardized incidence rates of MND at subsequent years. Confidence intervals (95% CI) of incidence rates were calculated assuming a Poisson distribution. Temporal trends were assessed by computing incidence rate ratios (IRRs) and 95% CIs using multivariable Poisson regression adjusting for age and sex. The time period was fitted as a linear variable. Females and the 30–39 years age group were taken as the reference, respectively, for the sex and age variable. Stratified incidences were calculated across sex (age standardized), 10‐year age groups. We also ran separate Poisson regression models for men and women, adjusting for age to investigate incidence time trends stratified by sex. Finally, to investigate time trends across age at diagnosis, we ran separate Poisson regression models for each 10‐year age band. IRRs and 95% CIs were computed. All models were applied to the 2010–2019 period and 2019–2023 period separately to account for the effect of the COVID‐19 pandemic.
2.2.2. Survival Analysis
We standardized the death rates to the year 2012 and used a multivariable Poisson regression model to compute the IRR and its 95% CI adjusted for age and sex. In the survival analysis, survival time was defined as the time from the MND incidence date to either death or the end of follow‐up (December 31, 2023), whichever came first. The Kaplan–Meier method was used to estimate survival. To assess statistical differences in survival time, we used the log‐rank test. We compared survival between three cohorts, namely cohorts of patients identified with incident MND in 2010, 2015, and 2019. To account for potential demographic differences between the cohorts, we ran a multivariate Cox proportional hazards model adjusted for age at incidence and sex, and including year as a factor (2010 was taken as the reference year). The adjusted hazard ratio (HR) with 95% CI was reported. In the Cox proportional hazards models, the 3‐year survival was investigated.
2.2.3. Impact of the COVID‐19 Pandemic
In order to take into account the effect of the COVID‐19 pandemic, we ran separate Poisson regression models for the periods 2010–2019 and 2019–2023. We then compared the observed incidence rates of the 2020–2023 period to the expected values predicted by the Poisson regression model for the period 2010–2019. We also stratified incidence trends on age (age groups considered: 0–49 years, 50–69 years, 70+ years) and investigated the impact of the COVID‐19 pandemic on survival by computing the hazard ratios for each year between 2011 and 2021 using a Cox regression model.
2.3. Standard Protocol Approvals, Registrations, and Patient Consents
To access and process data from the SNDS, permanent access to the Caisse Nationale d'Assurance Maladie (CNAM) data portal is granted via the affiliation of the authors to the National Institute for Research in Digital Science and Technology (INRIA), in application of the provisions of Articles R. 1461–11 to R. 1461–17 of the French Public Health Code and the French data protection authority decision CNIL‐2016‐316. As permanent users of the SNDS, the authors declared the study to the INRIA's SNDS registry and were exempted from Institutional Review Board approval. There was no need for written informed consent from participants.
3. Results
3.1. Patient Characteristics
Overall, 30,028 patients with incident MND were identified between January 1, 2010 and December 31, 2023, corresponding to an average of 2145 new cases per year (flowchart Figure S1). Among these patients, 16,946 (56.4%) were male, and 13,082 (43.6%) were female. The overall median age at diagnosis was 69.6 years (IQR 61.1–77.1 years), with a median of 70.9 years [62.7, 78.4] for women and 68.5 years [60.0, 75.9] for men (Table 1). Among incident MND cases (n = 30,028), 28,208 (94%) had a hospital discharge with an ICD‐10 code G12.2, 23,823 (79%) had a reimbursement for riluzole, and 16,468 had LTD benefit coded with ICD‐10 G12 (Figure S2). The cohort of MND patients and their matched controls used to compute the lag in the encoding of the disease in the database had similar characteristics to the whole MND cohort (Table S3).
TABLE 1.
Sociodemographic and epidemiological characteristics of MND incident cases in the SNDS.
| MND incident cohort | |
|---|---|
| Numbers | |
| Patients | |
| Overall | 30,028 |
| 2010 | 1916 |
| 2015 | 2105 |
| 2020 | 2189 |
| 2023 | 2327 |
| Period | |
| Years | 2010–2023 |
| Sex | |
| Male | 16,946 (56.4%) |
| Age | |
|
Diagnosis/index date Median (IQR) |
69.6 (61.1–77.1) |
|
Riluzole start Median (IQR) |
68.7 (59.4–75.4) |
|
Incidence rates (95% CI) per 100,000 person‐years |
|
| Crude | 3.27 (3.13–3.40) |
| Standardized a | 3.06 (2.92–3.19) |
| Riluzole use | |
| At least once | 23,842 (79.4%) |
| Never use | 6186 (20.6%) |
| N.I. | 0 (0%) |
French population 2010. The exact age at onset cannot be computed; however, we provided a study on the average time delay between specific symptoms leading to a neurologist visit and the encoding of the disease in the database (Figure S2).
3.2. Incidence Rates
The crude and standardized incidence rates were 3.27 (95% CI, 3.13–3.40) and 3.06 (2.92–3.19) cases per 100,000 person‐years, respectively, for the 2010–2019 period and 2019–2023 period. Incidence, while accounting for time periods and age, was higher in males than females (2010–2019 sex‐related IRR: 1.56 [95% CI, 1.525–1.611], 2019–2023 sex‐related IRR: 1.635 [1.575–1697], Table S4). Incidence increased with age to a peak in the 75–79‐year‐olds age group, a pattern consistent for both sexes (Figure 1a,b).
FIGURE 1.
Incidence and sex ratio of MND by age in France. Number of cases (a) and incidence rate (b) by age (stratified on sex), and the sex ratios (reference men) computed as (c) the ratio of the number of cases, and (d) the ratio of the incidence rates per age groups during 2010–2023 in France. We restricted the sex ratios to age groups with a sufficient number of cases.
3.3. Time Trends in Incidence Rates
The overall crude incidence rate increased during the 2010–2019 period, rising from 2.99 to 3.49 cases per 100,000 person‐years by 2019, which represents an annual increase of 1.7%, p < 0.001 (Figure 2a). The number of cases increased from 1916 in 2010 to 2301 in 2019, a rise of 385 patients, which represents an increase of 20% (Figure S3). Notably, the number of cases diagnosed in the 70–79 years age group rose from 621 to 728 during the period, reflecting an increase of 107 cases, equivalent to a 17% rise for this specific age group (Figure 2c).
FIGURE 2.
Incidence rates during 2010–2023 in France. Crude, and age‐ and sex‐standardized to the age distribution of 2010 French population (a) by sex, age‐standardized (b), by age (c, number of cases, and d, rate per age group). (a) Crude and standardized incidence rates. (b) Incidence rates per age group. (c) Number of cases per age group. (d) Incidence rates per sex.
Age and sex‐standardized incidence rates of MND also increased from 2.99 cases in 2010 to 3.20 cases per 100,000 person‐years in 2019, with an annual rate of increase in incidence of 0.7% (age and sex‐adjusted IRR: 1.007 (95% CI, 1.002–1.012), Figure 2a, Table S1). This increasing trend was consistent across riluzole treatment status between 2010 and 2019 (riluzole use IRR: 1.005 [95% CI, 1.000–1.010], no riluzole use IRR 1.015 [95% CI, 1.005–1.026]; Figure S4). In the 2019–2023 period, the incidence rate of MND significantly decreased at an annual rate of 1.8% (age and sex‐adjusted IRR: 0.982 (95% CI, 0.969–0.995), Figure 2a, Table S4). The standardized incidence increased significantly in men and no trend was observed for women while accounting for age: men IRR: 1.011 (95% CI, 1.005–1.017) and women IRR: 1.002 (95% CI, 0.995–1.009) in the 2010–2019 period (Figure 2b, Table S5).
The stratification on age at diagnosis revealed a significant increase in MND incidence in the 30–39 age group (overall IRR: 1.040 [95% CI: 1.005–1.076], men: 1.009 [0.968–1.052] and women: 1.104 [1.041–1.171]), the 70–79 age group (overall IRR: 1.009 [95% CI 1.001–1.018], men: 1.012 [1.001–1.024] and women: 1.005 [0.993–1.018]) and the 90–99 age group (overall IRR: 1.050 (95% CI 1.006–1.096), men: 1.046 (0.990–1.106) and women: 1.057 (0.987–1.132)) (Figure 2d, Table 2, Figure S5). The highest IRRs were observed in the 30–39 and 90–99 age groups, and the increasing trend in the 30–39 age group was driven by women.
TABLE 2.
Time trends in incidence according to age groups.
| Age at diagnosis stratification | IRR a | 95% CI |
|---|---|---|
| 20–29 | ||
| Overall: Time period (per increase in calendar year) | 0.993 | 0.931–1.060 |
| Women: Time period (per increase in calendar year) | 1.007 | 0.901–1.125 |
| Men: Time period (per increase in calendar year) | 0.986 | 0.911–1.068 |
| 30–39 | ||
| Overall: Time period (per increase in calendar year) | 1.040 | 1.005–1.076 |
| Women: Time period (per increase in calendar year) | 1.104 | 1.041–1.171 |
| Men: Time period (per increase in calendar year) | 1.009 | 0.968–1.052 |
| 40–49 | ||
| Overall: Time period (per increase in calendar year) | 1.005 | 0.985–1.025 |
| Women: Time period (per increase in calendar year) | 0.997 | 0.963–1.032 |
| Men: Time period (per increase in calendar year) | 1.009 | 0.985–1.033 |
| 50–59 | ||
| Overall: Time period (per increase in calendar year) | 1.002 | 0.989–1.014 |
| Women: Time period (per increase in calendar year) | 0.987 | 0.968–1.007 |
| Men: Time period (per increase in calendar year) | 1.011 | 0.995–1.027 |
| 60–69 | ||
| Overall: Time period (per increase in calendar year) | 1.008 | 0.999–1.017 |
| Women: Time period (per increase in calendar year) | 1.002 | 0.988–1.015 |
| Men: Time period (per increase in calendar year) | 1.012 | 1.001–1.024 |
| 70–79 | ||
| Overall: Time period (per increase in calendar year) | 1.009 | 1.001–1.018 |
| Women: Time period (per increase in calendar year) | 1.005 | 0.993–1.018 |
| Men: Time period (per increase in calendar year) | 1.012 | 1.001–1.024 |
| 80–89 | ||
| Overall: Time period (per increase in calendar year) | 1.011 | 0.999–1.023 |
| Women: Time period (per increase in calendar year) | 1.004 | 0.988–1.021 |
| Men: Time period (per increase in calendar year) | 1.018 | 1.001–1.036 |
| 90–99 | ||
| Overall: Time period (per increase in calendar year) | 1.050 | 1.006–1.096 |
| Women: Time period (per increase in calendar year) | 1.046 | 0.990–1.106 |
| Men: Time period (per increase in calendar year) | 1.057 | 0.987–1.132 |
IRRs and their 95% CI from multivariable Poisson regressions to identify time trends associated with MND incidence stratified by age at diagnosis. Only the 2010–2019 period is considered. We did not consider age groups below 20 years because there were not enough cases.
3.4. Impact of the COVID‐19 Pandemic
The comparison between the observed and expected rates showed that rates in the 2020–2023 period did not follow the trend estimated by the model for the period 2010–2019, especially for the year 2022 with a 15% decrease in incidence when comparing to expected incidence (Figure S6a). This pattern was more pronounced in the 50–69 and 70+ year age groups, with minimal impact observed in early‐onset (0–49 years) MND cases (Figure S6b). The COVID‐19 pandemic appears to have had a sustained effect on MND incidence in the 70+ age group, as rates in this cohort remained below the expected levels from 2020 to 2023.
3.5. Survival Analysis
A total of 1915 and 2100 cases were included in the survival analysis for the year 2010 and 2015, respectively. The median survival after diagnosis was 18.1 months (95% CI 17.2–19.9) for the 2010 cohort, 17.8 months (16.6–19.1) for the 2015 cohort, and 15.6 months for the 2019 cohort (Figure 3a,b). The difference did not reach statistical significance (log‐rank p‐value = 0.4). When adjusting for demographic variables, we found a concordant result (adjusted HR 1.044 [95% CI 0.98–1.12], reference 2010). The survival analysis revealed a higher mortality rate from the 2019–2021 cohorts compared to the 2010 cohort (HR2019: 1.08 [95% CI, 1.002–1.157], HR2020: 1.14 [95% CI, 1.06–1.22], and HR2021: 1.20 [95% CI, 1.11–1.28]) (Figure 3c). Survival stratified on riluzole status is shown in Figure S7.
FIGURE 3.
Kaplan–Meier survival curves from date of diagnosis and Cox regression model to investigate the secular trend in mortality. The survival curves were computed for two time periods: Incident MND cases in 2010 and 2015 (a) and 2010 and 2019 (b). Using a multivariate Cox regression model, we computed hazard ratios adjusted for age and sex for each year between 2011 and 2021, taking the year 2010 as reference (c). (a) 2010 vs. 2015. (b) 2010 vs. 2019. (c) Secular trend of survival.
3.6. Lag in the Encoding of the Disease in the Database
The overall mean delay from symptoms leading to referral visits and encoding of the disease in the database was 1.17 years for neurologists and 2.11 years for neurologists, otorhinolaryngologists, orthopedists, rheumatologists, or pneumologists; interestingly, this delay was consistent across the riluzole treated and untreated patients (Figures S8a,b and S9a,b).
4. Discussion
We used the French national healthcare database to estimate trends in crude incidence rates of MND from 2010 to 2023, as well as age‐ and sex‐specific rates. Additionally, we analyzed survival differences among incident cases identified in 2010, 2015, and 2019, showing no significant changes in MND mortality over the past decade in France, except during the COVID‐19 pandemic. With 30,028 cases, this study represents the most extensive analysis of MND incidence trends to date. Our findings demonstrate a rise in MND incidence in the 2010–2019 period, even after adjusting for sociodemographic factors. While population aging explains a substantial part of the increase in cases, it does not fully account for the rise in incidence.
Age at diagnosis is a crucial factor in MND incidence studies. In European population‐based studies, the median age at diagnosis for MND or ALS in Europe consistently hovers around 70 years [2, 5, 21]. Our analysis aligns with this, showing a median diagnosis age of 69.6 years, which is consistent with other French and Swedish registry data (Table S6) [2, 5, 21]. These observations emphasize the importance of utilizing epidemiological data derived from real‐world sources when studying MND or ALS cohorts in research and clinical trials. Younger, less representative populations in these studies may affect the generalizability of findings. Our analysis, which used neurologist visits to estimate the delay between symptom onset and the recording of the disease in the database, revealed a maximum delay of 14 months between neurologist visits and the point at which the disease is detectable in the database. This delay extended to 25 months when accounting for visits to other specialists, potentially triggered by early specific symptoms of MND. This finding aligns with previous research, which showed that patients first referred to non‐neurologist specialists had higher diagnostic delay [22]. However, our delay should not be interpreted as purely diagnostic, as we lack precise data on symptom onset. Additionally, visits to neurologists or other specialists might include consultations related to a potential MND prodrome [23, 24], potentially inflating the actual diagnostic delay. Nonetheless, unlike studies on diagnostic delay that often suffer from recall bias, our study is based on routinely recorded healthcare visits, minimizing this limitation. This sensitivity analysis enhances the reliability of the age at diagnosis calculated for the general population in past observational studies.
In terms of crude incidence rates, external comparison to both French ALS registries and international administrative health data registers showed good consistency (Table S5). We calculated a crude incidence rate of 3.27 cases per 100,000 person‐years during the study period, which aligns with the upper range when compared to a recent systematic review of the MND burden, reporting crude incidence rates between 0.86 and 3.22 cases per 100,000 person‐years in Europe, including studies ran before ours [1]. When comparing European data, our study revealed fewer MND incident cases than a Swedish study (France: 3.27 vs. Sweden: 4.1 cases per 100,000 person‐years), supporting the idea of a geographical gradient in MND incidence [2]. Factors such as differences in study design, population demographics, diagnostic methods, and environmental or genetic factors could explain these variations, complicating direct comparisons between studies.
The time trend in MND incidence remains a debated issue. Our findings indicate a significant upward trend in incidence from 2010 to 2019, though this trend was weakened by standardization. Some studies have reported an increasing trend, as seen in four separate investigations [2, 12, 25, 26], while four others have suggested a stable incidence rate over time [13, 27, 28, 29]. Most of these articles did not use a Poisson regression model to statistically test the time trend in MND incidence, and the incidence was often fluctuating during the period of interest because of small sample sizes. A previous Swedish study used administrative health data to estimate MND incidence rates in a period like ours (2002–2021). Consistent with our study, the Swedish study suggested an increase in the standardized incidence rate of MND, though they did not assess whether the trend was significant using Poisson regression models [2]. However, the gender‐specific time trends were different, with their study showing a more pronounced increase in incidence for women, while ours showed an increase only for men. In contrast, a population‐wide study in the Italian Marche region relying on administrative data showed a decrease in incidence rate from 2014 to 2019, though not reaching significance (p = 0.057) [11]. 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 [30, 31]. This is consistent with our observation that the rate of incident cases in the 2020–2023 period was lower than the expected incidence rates obtained using the predictions of Poisson regression for the 2010–2019 period.
In our study, the rise in crude MND incidence was primarily driven by men aged 70–79, a finding that aligns with demographic shifts. As the population ages, more men are surviving to older age, a key risk factor for MND development, contributing to the rise in cases. Even after adjusting for population aging, an upward trend in MND incidence persisted, with an annual increase of 0.7%, suggesting that factors beyond aging are influencing incidence rates. Notably, the increase over the 2010–2019 period was significant for men but not for women, and particularly pronounced in the younger (30–39 years) and older (70–79 years and 90–99 years) age groups. These results highlight potential differences in disease dynamics across sex and age, warranting further investigation into sex‐specific and age‐related risk factors.
Our study was conducted over an extended period of time using a consistent methodology and had sufficient statistical power to detect a modest yet significant increasing trend of MND incidence during the 2010–2019 period. Having a nationwide database provided a sufficient sample size to conduct stratification of time trends of MND incidence across sex and age at diagnosis, which is not feasible in regional MND or ALS registries. Despite the strengths of our study, several limitations should be considered. First, the French national healthcare database, while comprehensive, lacks detailed clinical information such as the proportion of familial MND cases, specific genetic mutations, and the clinical spectrum of the disease (e.g., bulbar vs. spinal onset). Although we employed a robust algorithm that included drug claims, hospitalization records, and LTD diagnosis codes to identify MND cases, there is a possibility of inclusion of non‐ALS diagnoses under the G12.2 coding. In comparison with the French ALS registry—FRALIM [21], our study observed slightly higher incidence rates of MND. Notably, the ICD‐10 code G12.2 encompasses not only ALS but also related conditions such as primary lateral sclerosis (PLS) and progressive spinal muscular atrophy (PSMA). Supporting this, a recent Swedish study using a motor neuron disease registry to analyze the diagnostic composition within the G12.2 code found that 90% of individuals coded as G12.2 had pure ALS, 4% had PLS, and 6% had PSMA [2]. Finally, our study did not assess the potential influence of environmental factors, healthcare access, or regional genetic predispositions, which may vary across different parts of France and contribute to the observed variations in incidence trends.
5. Conclusion
While the increase in incidence rates is partly attributed to the aging population—particularly the increased longevity among men—the fact that incidence rates continue to rise even after adjusting for demographic changes suggests other contributing factors. These findings highlight the importance of ongoing surveillance and further research to investigate the underlying causes of this increasing incidence.
Author Contributions
Octave Guinebretiere: conceptualization, investigation, writing – original draft, methodology, validation, visualization, writing – review and editing, software, formal analysis, project administration, data curation. Quentin Calonge: validation, methodology, writing – original draft, writing – review and editing, investigation. Gaelle Bruneteau: validation, writing – review and editing, conceptualization, investigation. Maria‐Del‐Mar Amador: conceptualization, investigation, writing – review and editing, validation. Thomas Nedelec: conceptualization, investigation, funding acquisition, writing – original draft, validation, methodology, writing – review and editing, formal analysis, project administration, resources, supervision.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Appendix S1.
Funding: This work was supported by EU Joint Programme—Neurodegenerative Disease Research (ANR‐21‐JPW2‐0002‐01).
Data Availability Statement
To access and process data from the SNDS, permanent access to the Caisse Nationale d'Assurance Maladie (CNAM) data portal is granted via the affiliation of the authors to the National Institute for Research in Digital Science and Technology (INRIA), in application of the provisions of Articles R. 1461‐11 to R. 1461‐17 of the French Public Health Code and the French data protection authority decision CNIL‐2016‐316. As permanent users of the SNDS, the authors declared the study to the INRIA's SNDS registry and were exempted from Institutional Review Board approval.
References
- 1. Logroscino G., Piccininni M., Marin B., et al., “Global, Regional, and National Burden of Motor Neuron Diseases 1990–2016: A Systematic Analysis for the Global Burden of Disease Study 2016,” Lancet Neurology 17, no. 12 (2018): 1083–1097. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Imrell S., Fang F., Ingre C., and Sennfält S., “Increased Incidence of Motor Neuron Disease in Sweden: A Population‐Based Study During 2002–2021,” Journal of Neurology 271, no. 5 (2024): 2730–2735, 10.1007/s00415-024-12219-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Kiernan M. C., Vucic S., Cheah B. C., et al., “Amyotrophic Lateral Sclerosis,” Lancet 377, no. 9769 (2011): 942–955, 10.1016/S0140-6736(10)61156-7. [DOI] [PubMed] [Google Scholar]
- 4. Talbott E. O., Malek A. M., and Lacomis D., “The Epidemiology of Amyotrophic Lateral Sclerosis,” in Handbook of Clinical Neurology, vol. 138 (Elsevier, 2016), 225–238. [DOI] [PubMed] [Google Scholar]
- 5. Kab S., Moisan F., Preux P. M., Marin B., and Elbaz A., “Nationwide Incidence of Motor Neuron Disease Using the French Health Insurance Information System Database,” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 18, no. 5–6 (2017): 426–433. [DOI] [PubMed] [Google Scholar]
- 6. Esselin F., De La Cruz E., Pageot N., Juntas‐Morales R., Alphandery S., and Camu W., “Increased Worsening of Amyotrophic Lateral Sclerosis Patients During Covid‐19‐Related Lockdown in France,” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 22, no. 7–8 (2021): 505–507. [DOI] [PubMed] [Google Scholar]
- 7. Logroscino G., Traynor B. J., Hardiman O., et al., “Incidence of Amyotrophic Lateral Sclerosis in Europe,” Journal of Neurology, Neurosurgery, and Psychiatry 81, no. 4 (2010): 385–390. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Logroscino G. and Piccininni M., “Amyotrophic Lateral Sclerosis Descriptive Epidemiology: The Origin of Geographic Difference,” Neuroepidemiology 52, no. 1–2 (2019): 93–103. [DOI] [PubMed] [Google Scholar]
- 9. Marin B., Boumédiene F., Logroscino G., et al., “Variation in Worldwide Incidence of Amyotrophic Lateral Sclerosis: A Meta‐Analysis,” International Journal of Epidemiology 16 (2016): dyw061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Levison L. S., Blicher J. U., and Andersen H., “Incidence and Mortality of ALS: A 42‐Year Population‐Based Nationwide Study,” Journal of Neurology 272, no. 1 (2025): 44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Sopranzi F. M., Faragalli A., Pompili M., Carle F., Gesuita R., and Ceravolo M. G., “Incidence of Amyotrophic Lateral Sclerosis Before and During the COVID‐19 Pandemic: Evidence From an 8‐Year Population‐Based Study in Central Italy Based on Healthcare Utilization Databases,” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 25, no. 5–6 (2024): 554–562. [DOI] [PubMed] [Google Scholar]
- 12. Cima V., Logroscino G., D'Ascenzo C., et al., “Epidemiology of ALS in Padova District, Italy, From 1992 to 2005,” European Journal of Neurology 16, no. 8 (2009): 920–924. [DOI] [PubMed] [Google Scholar]
- 13. Govoni V., Cesnik E., Casetta I., Tugnoli V., Tola M. R., and Granieri E., “Temporal Trend of Amyotrophic Lateral Sclerosis Incidence in Southern Europe: A Population Study in the Health District of Ferrara, Italy,” Journal of Neurology août 259, no. 8 (2012): 1623–1631, 10.1007/s00415-011-6390-5. [DOI] [PubMed] [Google Scholar]
- 14. Chiò A., Mora G., Calvo A., Mazzini L., Bottacchi E., and Mutani R., “Epidemiology of ALS in Italy: A 10‐Year Prospective Population‐Based Study,” Neurology 72, no. 8 (2009): 725–731. [DOI] [PubMed] [Google Scholar]
- 15. Musson L. S., Collins A., Opie‐Martin S., et al., “Impact of the Covid‐19 Pandemic on Amyotrophic Lateral Sclerosis Care in the UK,” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 24, no. 1–2 (2023): 91–99. [DOI] [PubMed] [Google Scholar]
- 16. Raymond J., Berry J. D., Larson T., Horton D. K., and Mehta P., “Effects of COVID‐19 on Motor Neuron Disease Mortality in the United States: A Population‐Based Cross‐Sectional Study,” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 26, no. 1‐2 (2025): 149–156. [DOI] [PubMed] [Google Scholar]
- 17. García‐Casanova P. H., Pérez‐Martínez P., Sevilla T., Doménech R., León M., and Vázquez‐Costa J. F., “Impact of SARS ‐ CoV‐2 Infection and COVID ‐19 Pandemic on the Morbidity and Mortality of Amyotrophic Lateral Sclerosis Patients in Valencia, Spain,” European Journal of Neurology (2024): e16465. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Miller A. C., Cavanaugh J. E., Arakkal A. T., Koeneman S. H., and Polgreen P. M., “A Comprehensive Framework to Estimate the Frequency, Duration, and Risk Factors for Diagnostic Delays Using Bootstrapping‐Based Simulation Methods,” BMC Medical Informatics and Decision Making 23, no. 1 (2023): 68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Turner M. R., Scaber J., Goodfellow J. A., Lord M. E., Marsden R., and Talbot K., “The Diagnostic Pathway and Prognosis in Bulbar‐Onset Amyotrophic Lateral Sclerosis,” Journal of the Neurological Sciences 294, no. 1–2 (2010): 81–85. [DOI] [PubMed] [Google Scholar]
- 20. INSEE , “Opération Statistique: Estimations de Population,” http://www.insee.fr/fr/methodes/default.asp?page1/4sources/ope‐adm‐estimations‐population.htm.
- 21. Luna J., Defressigne O., Erazo D., et al., “Epidemiological Time‐Trend of Amyotrophic Lateral Sclerosis (ALS) Over Two Decades: The French Population‐Based Register of ALS in Limousin (FRALim Register),” Revue Neurologique 178, no. 9 (2022): 914–923, 10.1016/j.neurol.2022.05.004. [DOI] [PubMed] [Google Scholar]
- 22. Palese F., Sartori A., Logroscino G., and Pisa F. E., “Predictors of Diagnostic Delay in Amyotrophic Lateral Sclerosis: A Cohort Study Based on Administrative and Electronic Medical Records Data,” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 20, no. 3–4 (2019): 176–185. [DOI] [PubMed] [Google Scholar]
- 23. Benatar M., Wuu J., Andersen P. M., Lombardi V., and Malaspina A., “Neurofilament Light: A Candidate Biomarker of Presymptomatic Amyotrophic Lateral Sclerosis and Phenoconversion,” Annals of Neurology 84, no. 1 (2018): 130–139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Bjornevik K., O'Reilly E. J., Molsberry S., et al., “Prediagnostic Neurofilament Light Chain Levels in Amyotrophic Lateral Sclerosis,” Neurology 97, no. 15 (2021): e1466–e1474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.on behalf of the CARE‐MND Consortium , Leighton D. J., Newton J., et al., “Changing Epidemiology of Motor Neurone Disease in Scotland,” Journal of Neurology 266, no. 4 (2019): 817–825. [DOI] [PubMed] [Google Scholar]
- 26. Lareau‐Trudel É., Fortin É., Gauthier M., Lavoie S., Morissette É., and Mathieu J., “Epidemiological Surveillance of Amyotrophic Lateral Sclerosis in Saguenay Region,” Canadian Journal of Neurological Sciences 40, no. 5 (2013): 705–709, 10.1017/S0317167100014955. [DOI] [PubMed] [Google Scholar]
- 27. Marin B., Gil J., Preux P. M., Funalot B., and Couratier P., “Incidence of Amyotrophic Lateral Sclerosis in the Limousin Region of France, 1997–2007,” Amyotrophic Lateral Sclerosis 10, no. 4 (2009): 216–220. [DOI] [PubMed] [Google Scholar]
- 28. Bonaparte J. P., Grant I. A., Benstead T. J., Murray T. J., and Smith M., “ALS Incidence in Nova Scotia Over a 20‐ Year‐Period: A Prospective Study,” Canadian Journal of Neurological Sciences 34, no. 1 (2007): 69–73. [DOI] [PubMed] [Google Scholar]
- 29. Sorenson E. J., Stalker A. P., Kurland L. T., and Windebank A. J., “Amyotrophic Lateral Sclerosis in Olmsted County, Minnesota, 1925 to 1998,” Neurology 59, no. 2 (2002): 280–282. [DOI] [PubMed] [Google Scholar]
- 30. Fekadu G., Bekele F., Tolossa T., et al., “Impact of COVID‐19 Pandemic on Chronic Diseases Care Follow‐Up and Current Perspectives in Low Resource Settings: A Narrative Review,” International Journal of Physiology, Pathophysiology and Pharmacology 13, no. 3 (2021): 86–93. [PMC free article] [PubMed] [Google Scholar]
- 31. Wikström K., Linna M., and Laatikainen T., “The Impact of the COVID‐19 Pandemic on Incident Cases of Chronic Diseases in Finland,” European Journal of Public Health 32, no. 6 (2022): 982–984. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Appendix S1.
Data Availability Statement
To access and process data from the SNDS, permanent access to the Caisse Nationale d'Assurance Maladie (CNAM) data portal is granted via the affiliation of the authors to the National Institute for Research in Digital Science and Technology (INRIA), in application of the provisions of Articles R. 1461‐11 to R. 1461‐17 of the French Public Health Code and the French data protection authority decision CNIL‐2016‐316. As permanent users of the SNDS, the authors declared the study to the INRIA's SNDS registry and were exempted from Institutional Review Board approval.