Why do we choose to address health 2020?

What can we predict for 2020? Solar and lunar eclipses? Without a doubt. Climate change? Most likely. Rising sea levels? Signs point to yes. Beyond that, however, in the world of human events, it is best to be cautious. In the field of health and medicine (or anywhere else, for that matter), no one predicted the most important discoveries of the twentieth century. Economists were no more successful in foreseeing financial or economic crises. The pundits did not forecast any of the recent wars, disruptions or even the recent Arab Spring movements indeed, political experts turned out to be only slightly more accurate than dart-throwing chimpanzees in divining what was in store for the future.1 As the World Health Organization (WHO) and the wider scientific community looked to East Asia in anticipation of the next outbreak of H5N1, the influenza H1N1 pandemic took hold in Mexico. Tsunamis, volcanic eruptions, earthquakes, drug scandals, outbreaks of emerging diseases and political disruptions are notoriously unpredictable, as Nassim Nicholas Taleb brilliantly highlighted in his book The Black Swan

Plus forts ensemble - Swiss School of Public Health

De vénérables écoles de santé publique ont fêté leur 100 e anniversaire au cours des 20 dernières années. En Suisse, la pandémie a pris au dépourvu une institution adolescente - un modèle innovant qui fête son 16 e anniversaire cette année. L'âge adulte a-t-il été atteint

Precision global health in the digital age.

Precision global health is an approach similar to precision medicine, which facilitates, through innovation and technology, better targeting of public health interventions on a global scale, for the purpose of maximising their effectiveness and relevance. Illustrative examples include: the use of remote sensing data to fight vector-borne diseases; large databases of genomic sequences of foodborne pathogens helping to identify origins of outbreaks; social networks and internet search engines for tracking communicable diseases; cell phone data in humanitarian actions; drones to deliver healthcare services in remote and secluded areas. Open science and data sharing platforms are proposed for fostering international research programmes under fair, ethical and respectful conditions. Innovative education, such as massive open online courses or serious games, can promote wider access to training in public health and improving health literacy. The world is moving towards learning healthcare systems. Professionals are equipped with data collection and decision support devices. They share information, which are complemented by external sources, and analysed in real time using machine learning techniques. They allow for the early detection of anomalies, and eventually guide appropriate public health interventions. This article shows how information-driven approaches, enabled by digital technologies, can help improving global health with greater equity

Epidemic variability in complex networks

We study numerically the variability of the outbreak of diseases on complex networks. We use a SI model to simulate the disease spreading at short times, in homogeneous and in scale-free networks. In both cases, we study the effect of initial conditions on the epidemic's dynamics and its variability. The results display a time regime during which the prevalence exhibits a large sensitivity to noise. We also investigate the dependence of the infection time on nodes' degree and distance to the seed. In particular, we show that the infection time of hubs have large fluctuations which limit their reliability as early-detection stations. Finally, we discuss the effect of the multiplicity of shortest paths between two nodes on the infection time. Furthermore, we demonstrate that the existence of even longer paths reduces the average infection time. These different results could be of use for the design of time-dependent containment strategies

The effects of mindfulness training on weight-loss and health-related behaviours in adults with overweight and obesity: A systematic review and meta-analysis

The aim of this study was to conduct a comprehensive quantitative synthesis of the effects of mindfulness training interventions on weight-loss and health behaviours in adults with overweight and obesity using meta-analytic techniques. Studies included in the analysis (k =12) were randomised controlled trials investigating the effects of any form of mindfulness training on weight loss, impulsive eating, binge eating, or physical activity participation in adults with overweight and obesity. Random effects meta-analysis revealed that mindfulness training had no significant effect on weight loss, but an overall negative effect on impulsive eating (d =-1.13) and binge eating (d =-.90), and a positive effect on physical activity levels (d =.42). Meta-regression analysis showed that methodological features of included studies accounted for 100% of statistical heterogeneity of the effects of mindfulness training on weight loss (R 2 =1,00). Among methodological features, the only significant predictor of weight loss was follow-up distance from post-intervention (ß =1.18; p <.05), suggesting that the longer follow-up distances were associated with greater weight loss. Results suggest that mindfulness training has short-term benefits on health-related behaviours. Future studies should explore the effectiveness of mindfulness training on long-term post-intervention weight loss in adults with overweight and obesity

Mycobacterium ulcerans ecological dynamics and its association with freshwater ecosystems and aquatic communities : results from a 12-month environmental survey in Cameroon

Background: Mycobacterium ulcerans (MU) is the agent responsible for Buruli Ulcer (BU), an emerging skin disease with dramatic socioeconomic and health outcomes, especially in rural settings. BU emergence and distribution is linked to aquatic ecosystems in tropical and subtropical countries, especially to swampy and flooded areas. Aquatic animal organisms are likely to play a role either as host reservoirs or vectors of the bacilli. However, information on MU ecological dynamics, both in space and time, is dramatically lacking. As a result, the ecology of the disease agent, and consequently its mode of transmission, remains largely unknown, which jeopardizes public health attempts for its control. The objective of this study was to gain insight on MU environmental distribution and colonization of aquatic organisms through time. Methodology/Principal Findings: Longitudinal sampling of 32 communities of aquatic macro-invertebrates and vertebrates was conducted from different environments in two BU endemic regions in Cameroon during 12 months. As a result, 238,496 individuals were classified and MU presence was assessed by qPCR in 3,084 sample-pools containing these aquatic organisms. Our study showed a broad distribution of MU in all ecosystems and taxonomic groups, with important regional differences in its occurrence. Colonization dynamics fluctuated along the year, with the highest peaks in August and October. The large variations observed in the colonization dynamics of different taxonomic groups and aquatic ecosystems suggest that the trends shown here are the result of complex ecological processes that need further investigation. Conclusion/Perspectives: This is the largest field study on MU ecology to date, providing the first detailed description of its spatio-temporal dynamics in different aquatic ecosystems within BU endemic regions. We argue that coupling this data with fine-scale epidemiological data through statistical and mathematical models will provide a major step forward in the understanding of MU ecology and mode of transmission