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Background There is a need for a reliable, easy-to-use, widely available, and validated tool for timely cognitive impairment identification. We created a computerized cognitive screening tool (Santé-Cerveau digital tool (SCD-T)) including validated questionnaires and the following neuropsychological tests: 5 Word Test (5-WT) for episodic memory, Trail Making Test (TMT) for executive functions, and a number coding test (NCT) adapted from the Digit Symbol Substitution Test for global intellectual efficiency. This study aimed to evaluate the performance of SCD-T to identify cognitive deficit and to determine its usability. Methods Three groups were constituted including 65 elderly Controls, 64 patients with neurodegenerative diseases (NDG): 50 AD and 14 non-AD, and 20 post-COVID-19 patients. The minimum MMSE score for inclusion was 20. Association between computerized SCD-T cognitive tests and their standard equivalent was assessed using Pearson's correlation coefficients. Two algorithms (a simple clinician-guided algorithm involving the 5-WT and the NCT; and a machine learning classifier based on 8 scores from the SCD-T tests extracted from a multiple logistic regression model, and data from the SCD-T questionnaires) were evaluated. The acceptability of SCD-T was investigated through a questionnaire and scale. Results AD and non-AD participants were older (mean ± standard deviation (SD): 72.61 ± 6.79 vs 69.91 ± 4.86 years old, p  = 0.011) and had a lower MMSE score (Mean difference estimate ± standard error: 1.74 ± 0.14, p  < 0.001) than Controls; post-COVID-19 patients were younger than Controls (mean ± SD: 45.07 ± 11.36 years old, p  < 0.001). All the computerized SCD-T cognitive tests were significantly associated with their reference version. In the pooled Controls and NDG group, the correlation coefficient was 0.84 for verbal memory, -0.60 for executive functions, and 0.72 for global intellectual efficiency. The clinician-guided algorithm demonstrated 94.4% ± 3.8% sensitivity and 80.5% ± 8.7% specificity, and the machine learning classifier 96.8% ± 3.9% sensitivity and 90.7% ± 5.8% specificity. The acceptability of SCD-T was good to excellent. Conclusions We demonstrate the high accuracy of SCD-T in screening cognitive disorders and its good acceptance even in individuals with prodromal and mild dementia stages. SCD-T would be useful in primary care to faster refer subjects with significant cognitive impairment (and limit unnecessary referrals) to specialized consultation, improve the AD care pathway and the pre-screening in clinical trials.

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.