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Purpose: Identified factors associated with multiple emergency department (ED) visits (≥) for asthma, which is associated with death. Patients and Methods: We first conducted a qualitative study. We invited French-speaking adults (≥ 18 years old) with a diagnosis of asthma for more than 6 months. The identified concepts were transcribed into items. A Delphi method allowed for selecting items for a self-reported questionnaire. In an observational multicentric cross-sectional quantitative study, the resulting 20-item questionnaire and 12-item General Health Questionnaire, exploring psychological distress, were administered to adults visiting an ED for asthma exacerbation. Multivariable logistic regression was used to assess factors associated with ED visits. Results: Data saturation was obtained after 8 patient interviews. Patients who came to the ED seemed unfamiliar with their illness or treatments but were concerned by the disease. The questionnaire was administered to 182 patients. On multivariable logistic regression, multiple and systematic ED visits were associated with asthma exacerbation (adjusted odds ratio (aOR) = 6.89, 95% confidence interval [CI]: 2.25– 21.09), asthma perceived as a handicap (aOR=3.19, 95% CI: 1.55– 6.57) and reported atopy (OR=2.09, 95% CI: 1.03– 4.26). High educational level and lack of maintenance inhaled corticosteroids were protective for multiple ED visits. Conclusion: Inadequate medical care is frequent in patients attending the ED for an asthma exacerbation, associated with strong psychological impact. Questioning the reasons for consulting the ED may help quickly identify patients requiring asthma education and improve their referral. [ABSTRACT FROM AUTHOR]

We propose an objective framework for estimating rainfall cumulative distribution function within a region when data are only available at rain gauges. Our methodology is based on the evaluation of several goodness-of-fit scores in a cross-validation framework, allowing to assess goodness-of-fit of the full distribution but with a particular focus on its tail. Cross-validation is applied both to select the most appropriate statistical distribution at station locations and to validate the mapping of these distributions. Our methodology is applied to daily rainfall in the Ardèche catchment in South of France, a 2260 km2 catchment with strong disparities in rainfall distribution. Results show preference for a mixture of Gamma distribution over seasons and weather patterns, with parameters interpolated with thin plate spline across this region. However the framework presented in this paper is general and could be likewise applied in any region, with possibly different conclusion depending on the subsequent rainfall processes. [ABSTRACT FROM AUTHOR]

This paper analyzes the sensitivity of a hydrological model to different methods to statistically downscale climate precipitation and temperature over four western Mediterranean basins illustrative of different hydrometeorological situations. The comparison was conducted over a common 20-year period (1986-2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically downscaled data sets to reproduce various aspects of the hydrograph. Three different statistical downscaling models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function-transform approach (CDFt). We used the models to downscale precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSLCM5A- MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various downscaled data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution downscaled climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical downscaling models to be used in climate change impact studies to minimize the range of uncertainty associated with such downscaling methods. [ABSTRACT FROM AUTHOR]