How critical is timing for the diagnosis of influenza in general practice?

QUESTIONS UNDER STUDY: The diagnostic significance of clinical symptoms/signs of influenza has mainly been assessed in the context of controlled studies with stringent inclusion criteria. There was a need to extend the evaluation of these predictors not only in the context of general practice but also according to the duration of symptoms and to the dynamics of the epidemic. PRINCIPLES: A prospective study conducted in the Medical Outpatient Clinic in the winter season 1999-2000. Patients with influenza-like syndrome were included, as long as the primary care physician envisaged the diagnosis of influenza. The physician administered a questionnaire, a throat swab was performed and a culture acquired to document the diagnosis of influenza. RESULTS: 201 patients were included in the study. 52% were culture positive for influenza. By univariate analysis, temperature >37.8 degrees C (OR 4.2; 95% CI 2.3-7.7), duration of symptoms <48 hours (OR 3.2; 1.8-5.7), cough (OR 3.2; 1-10.4) and myalgia (OR 2.8; 1.0-7.5) were associated with a diagnosis of influenza. In a multivariable logistic analysis, the best model predicting influenza was the association of a duration of symptom <48 hours, medical attendance at the beginning of the epidemic (weeks 49-50), fever >37.8 and cough, with a sensitivity of 79%, specificity of 69%, positive predictive value of 67%, negative predictive value of 73% and an area under the ROC curve of 0.74. CONCLUSIONS: Besides relevant symptoms and signs, the physician should also consider the duration of symptoms and the epidemiological context (start, peak or end of the epidemic) in his appraisal, since both parameters considerably modify the value of the clinical predictors when assessing the probability of a patient having influenza.

A spatial modelling framework for assessing climate change impacts on freshwater ecosystems: Response of brown trout (Salmo trutta L.) biomass to warming water temperature

Mountain regions worldwide are particularly sensitive to on-going climate change. Specifically in the Alps in Switzerland, the temperature has increased twice as fast than in the rest of the Northern hemisphere. Water temperature closely follows the annual air temperature cycle, severely impacting streams and freshwater ecosystems. In the last 20 years, brown trout (Salmo trutta L) catch has declined by approximately 40-50% in many rivers in Switzerland. Increasing water temperature has been suggested as one of the most likely cause of this decline. Temperature has a direct effect on trout population dynamics through developmental and disease control but can also indirectly impact dynamics via food-web interactions such as resource availability. We developed a spatially explicit modelling framework that allows spatial and temporal projections of trout biomass using the Aare river catchment as a model system, in order to assess the spatial and seasonal patterns of trout biomass variation. Given that biomass has a seasonal variation depending on trout life history stage, we developed seasonal biomass variation models for three periods of the year (Autumn-Winter, Spring and Summer). Because stream water temperature is a critical parameter for brown trout development, we first calibrated a model to predict water temperature as a function of air temperature to be able to further apply climate change scenarios. We then built a model of trout biomass variation by linking water temperature to trout biomass measurements collected by electro-fishing in 21 stations from 2009 to 2011. The different modelling components of our framework had overall a good predictive ability and we could show a seasonal effect of water temperature affecting trout biomass variation. Our statistical framework uses a minimum set of input variables that make it easily transferable to other study areas or fish species but could be improved by including effects of the biotic environment and the evolution of demographical parameters over time. However, our framework still remains informative to spatially highlight where potential changes of water temperature could affect trout biomass. (C) 2015 Elsevier B.V. All rights reserved.-