The Impact of Heat Islands on Mortality in Paris during the August 2003 Heat Wave

Background: Heat waves have a drastic impact on urban populations, which could increase with climate change

Accuracy of syndrome definitions based on diagnoses in physician claims

<p>Abstract</p> <p>Background</p> <p>Community clinics offer potential for timelier outbreak detection and monitoring than emergency departments. However, the accuracy of syndrome definitions used in surveillance has never been evaluated in community settings. This study's objective was to assess the accuracy of syndrome definitions based on diagnostic codes in physician claims for identifying 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory including influenza-like illness) in community clinics.</p> <p>Methods</p> <p>We selected a random sample of 3,600 community-based primary care physicians who practiced in the fee-for-service system in the province of Quebec, Canada in 2005-2007. We randomly selected 10 visits per physician from their claims, stratifying on syndrome type and presence, diagnosis, and month. Double-blinded chart reviews were conducted by telephone with consenting physicians to obtain information on patient diagnoses for each sampled visit. The sensitivity, specificity, and positive predictive value (PPV) of physician claims were estimated by comparison to chart review.</p> <p>Results</p> <p>1,098 (30.5%) physicians completed the chart review. A chart entry on the date of the corresponding claim was found for 10,529 (95.9%) visits. The sensitivity of syndrome definitions based on diagnostic codes in physician claims was low, ranging from 0.11 (fever) to 0.44 (respiratory), the specificity was high, and the PPV was moderate to high, ranging from 0.59 (fever) to 0.85 (respiratory). We found that rarely used diagnostic codes had a higher probability of being false-positives, and that more commonly used diagnostic codes had a higher PPV.</p> <p>Conclusions</p> <p>Future research should identify physician, patient, and encounter characteristics associated with the accuracy of diagnostic codes in physician claims. This would enable public health to improve syndromic surveillance, either by focusing on physician claims whose diagnostic code is more likely to be accurate, or by using all physician claims and weighing each according to the likelihood that its diagnostic code is accurate.</p

Modelling of the risk Cryptosporidium parvum infection through drinking tap-water. A pragmatic approach in France

Poster *INRA Biométrie Jouy-en-Josas (FRA) Collation : 1 p. Diffusion du document : INRA Biométrie Jouy-en-Josas (FRA

Investigation of humoral and cellular immunity of dairy cattle after one or two year of vaccination with a phase I Coxiella vaccine

AbstractQ fever is a worldwide zoonosis that may cause reproductive disorders such as abortion, endometritis or infertility in livestock. The implementation of a vaccination with a phase I vaccine is the nowadays the most relevant way to control the spread of the infection within herds. Annual boosters are recommended for ruminants by the manufacturer whereas in humans, to prevent side effects, no booster must be done before 5 years and the lack of humoral and cellular immunity has to be confirmed before any additional vaccination. The aim of this study was to investigate, in dairy cattle, the interest of such annual booster by assessing the level of different immune markers among 142 animals (from infected and uninfected herds) vaccinated either 2 year (i.e. 2 times) or 1 year before with an efficient commercial phase I Q fever vaccine. One year after vaccination, more than 80 % of the vaccinated cows had still immune markers, whereas 68 % of the heifers from uninfected herd did not. These data suggested that an annual booster would not be necessary for all vaccinated animals within a herd. In order to detect the immune animals and then to optimize the number of animals needing a boost, the skin test method, performed at least 3 days before the vaccination could be used

Limits in Using Multiresolution Analysis to Forecast Turbidity by Neural Networks. Case Study on the Yport Basin, Normandie-France

International audienceApproximately, 25% of the world population drinking water depends on karst aquifers. Nevertheless, due to their poor filtration properties, karst aquifers are very sensitive to pollution and specifically to turbidity. As physical processes involved in transport of solid/suspended particles (advection, diffusion, deposit…) are complicated and badly known in underground conditions, a black-box modeling approach using neural networks is promising. Despite the well-known ability of universal approximation of multilayer perceptron, it appears difficult to efficiently take into account hydrological conditions of the basin. Indeed, these conditions depend both on the initial state of the basin (schematically wet or dry: long timescale component), and on the intensity of rainfall, usually associated to short timescale component. In this context, the present paper addresses the application of the multiresolution analysis to decompose the turbidity on several timescales in order to better consider various phenomena at various timescales (flow in thin or wide fissures for example). Because of “boundary effects”, usually neglected by authors, a specific adaptation was shown as necessary that diminishes the quality of results for real-time forecasting. Decomposing turbidity using multiresolution analysis adds thus questionable improvements