RationaleFrom June of 2009 to August of 2010 the influenza subtype H1N1pdm09 caused a worldwide pandemic. The impact on populations and health care systems around the globe evolved differently. Substantial data come from the German national surveillance network in an outpatient and private practice setting, while information on hospitalized patients in Germany is rather limited.MethodsData from the Federal Statistics Office comprising health insurance claims of the entire nationwide inpatient sample from 2005 to 2012 were used to identify patients who were hospitalized for laboratory-confirmed influenza and to analyse demographical aspects, comorbidities, hospitalization duration, outcomes and ventilator use during the pandemic and seasonal waves of influenza.Measurements and main resultsA number of 34,493 admissions for laboratory-confirmed influenza occurred during waves between 2005 and 2012. During the pandemic seasonal waves, the number of hospitalizations vastly surpassed the level that was seen in any of the seasonal waves. A major demographic shift was seen with respect to patient age, as younger patients (< 60 years old) were more frequently hospitalized. Mean length of stay was shorter (149 vs. 193 hours), mean time on ventilation tended to be shorter (261 vs. 305 hours) in young children (< 4 years old) and longer (393 vs. 339 hours) in the elderly (> 60 years old). Time to ventilation was shorter in non-fatal cases (328 vs. 349 hours) and longer in fatal cases (419 vs. 358 hours). Logistic regression was used to show the impact of comorbidities and co-diagnoses on mortality and the need for ventilation, as well as differences between pandemic and seasonal influenza.ConclusionsInpatient data suggest differences in patient populations during pandemic and seasonal influenza. Younger patients were more frequently hospitalized. Differences with respect to the presence of certain comorbidities and co-diagnoses, length of stay, time to ventilation and ventilation time could be identified.

The study aims to understand the social and organizational factors that influence knowledge sharing. A model of knowledge management and knowledge sharing was developed inspired by the work of Nahapiet and Ghoshal. Data on KM processes and various social capital measures were collected from a sample of 262 members of a tertiary educational institution in Singapore. Rewards and incentives, open-mindedness, and cost-benefit concerns of knowledge hoarding turned out to be the strongest predictors of knowledge sharing rather than prosocial motives or organizational care. Individuals who are highly competent in their work abilities are less likely to share what they know when they perceive that there are few rewards or when sharing is not recognized by the organization. The findings provide evidence for the importance of social capital as a lubricant of knowledge sharing and engaging performance management systems in knowledge-intensive organizations.

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In the knowledge-based economy, universities are encountering dramatic changes. Their missions and functions are 'pragmatized' because of emerging new players and competing markets for knowledge production, the availability of higher education to a wider range of social classes and age groups, as well as the assimilation of information technology into the university environment. The dynamics and conduct of university research, in particular, has correspondingly become more sensitive to industry collaboration opportunities, commercial exploitation, and is increasingly transdisciplinary. This paper argues that knowledge management (KM) practices and tools can support universities in addressing these demands. Institutions of higher education can benefit from KM by creating and maintaining relevant knowledge repositories, improving knowledge access, enhancing the knowledge environment, and valuing knowledge. This is exemplified with reference to the Singapore Management University (SMU) where KM is increasingly being applied in the area of research.