BackgroundLifetime stroke risk has been calculated in a limited number of selected populations. We determined lifetime risk of stroke globally and at the regional and country level.MethodsUsing Global Burden of Disease Study estimates of stroke incidence and the competing risks of non-stroke mortality, we estimated the cumulative lifetime risk of ischemic stroke, hemorrhagic stroke, and total stroke (with 95% uncertainty intervals [UI]) for 195 countries among adults over 25 years) for the years 1990 and 2016 and according to the GBD Study Socio-Demographic Index (SDI).ResultsThe global estimated lifetime risk of stroke from age 25 onward was 24.9% (95% UI: 23.5–26.2): 24.7% (23.3–26.0) in men and 25.1% (23.7–26.5) in women. The lifetime risk of ischemic stroke was 18.3% and of hemorrhagic stroke was 8.2%. The risk of stroke was 23.5% in high SDI countries, 31.1% in high-middle SDI countries, and 13.2% in low SDI countries with UIs not overlapping for these categories. The greatest estimated risk of stroke was in East Asia (38.8%) and Central and Eastern Europe (31.7 and 31.6 %%), and lowest in Eastern Sub-Saharan Africa (11.8%). From 1990 to 2016, there was a relative increase of 8.9% in global lifetime risk.ConclusionsThe global lifetime risk of stroke is approximately 25% starting at age 25 in both men and women. There is geographical variation in the lifetime risk of stroke, with particularly high risk in East Asia, Central and Eastern Europe.

Differentiating epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) is commonly based on electroencephalogram and concurrent video recordings (vEEG). Here, we demonstrate that these two types of seizures can be discriminated based on signals related to autonomic nervous system activity recorded via wearable sensors. We used Empatica E4 Wristband sensors worn on both arms in vEEG confirmed seizures, and machine learning methods to train classifiers, specifically, extreme gradient boosting (XGBoost). Classification performance achieved a predictive accuracy of 78 ± 1.5% on previously unseen data for whether a seizure was epileptic or psychogenic, which is 6 standard deviations above the baseline of 68% accuracy. Our dataset contained altogether 35 seizures from 18 patients out of which 8 patients had 13 convulsive seizures. Prediction of seizure type was based on simple features derived from the segments of autonomic activity measurements (electrodermal activity, body temperature, blood volume pulse, and heart rate) and forearm acceleration. Features related to heart rate and electrodermal activity were ranked as the top predictors in XGBoost classifiers. We found that patients with PNES had a higher ictal heart rate and electrodermal activity than patients with ES. In contrast to existing published studies of mainly convulsive seizures, our classifier focuses on autonomic signals to differentiate convulsive or nonconvulsive semiology ES from PNES. Our results show that autonomic activity recorded via wearable sensors provides promising signals for detection and discrimination of psychogenic and epileptic seizures, but more work is necessary to improve the predictive power of the model.

There are no comprehensive, spatially referenced databases of public and private health facilities in any of the countries of the Eastern Mediterranean Region. This study in Pakistan was conducted to demonstrate the feasibility of creating a spatially referenced health facility database for a medium-sized city, in a low-cost, non-resource intensive manner and to visualize the spatial relationship between hospitals and clinics in the city of Islamabad. Cumulatively, 166 (77.6% of all clinics mapped) were in close proximity (within 1 km) to a hospital. Repeating such studies elsewhere would help us to better understand different spatial distribution patterns, the reasons for them and the implications for health-care planning. RÉSUMÉ Il n'existe pas de bases de données exhaustives référençant la répartition spatiale des établissements de santé publics et privés des pays de la Région de la Méditerranée orientale. La présente étude menée au Pakistan visait à démontrer la faisabilité de la création d'une base de données fournissant les références spatiales des établissements de santé dans une ville de taille moyenne, sur un mode économique et nécessitant peu de ressources pour visualiser les liens spatiaux entre les hôpitaux et les établissements de soins dans la ville d'Islamabad. Au total, 166 établissements de soins cartographiés (77,6 %) étaient situés à proximité d'un hôpital (moins d'un kilomètre). Reproduire de telles études ailleurs permettrait de mieux comprendre les différentes répartitions spatiales, les raisons motivant ces répartitions et les implications pour la planification des soins de santé. ‫باكستان‬ ‫أباد،‬ ‫إسالم‬ ‫يف‬ ‫الصحية‬ ‫للمرافق‬ ‫املكاين‬ ‫التوزيع‬

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