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I In nt te er rn na at ti io on na al l s st tu ud dy y o of f a as st th hm ma a a an nd d a al ll le er rg gi ie es s i in n c ch hi il ld dh ho oo od d ( (I ISIts specific aims are: 1) to describe the prevalence and severity of asthma, rhinitis and eczema in children living in different centres, and to make comparisons within and between countries; 2) to obtain baseline measures for assessment of future trends in the prevalence and severity of these diseases; and 3) to provide a framework for further aetiological research into genetic, lifestyle, environmental, and medical care factors affecting these diseases.The ISAAC design comprises three phases. Phase 1 uses core questionnaires designed to assess the prevalence and severity of asthma and allergic disease in defined populations. Phase 2 will investigate possible aetiological factors, particularly those suggested by the findings of Phase 1. Phase 3 will be a repetition of Phase 1 to assess trends in prevalence.

International audience1. Models for predicting the distribution of organisms from environmental data are widespread in ecology and conservation biology. Their performance is invariably evaluated from the percentage success at predicting occurrence at test locations. 2. Using logistic regression with real data from 34 families of aquatic invertebrates in 180 Himalayan streams, we illustrate how this widespread measure of predictive accuracy is affected systematically by the prevalence (i.e. the frequency of occurrence) of the target organism. Many evaluations of presence-absence models by ecologists are inherently misleading. 3. With the same invertebrate models, we examined alternative performance measures used in remote sensing and medical diagnostics. We particularly explored receiver-operating characteristic (ROC) plots, from which were derived (i) the area under each curve (AUC), considered an effective indicator of model performance independent of the threshold probability at which the presence of the target organism is accepted, and (ii) optimized probability thresholds that maximize the percentage of true absences and presences that are correctly identified. We also evaluated Cohen's kappa, a measure of the proportion of all possible cases of presence or absence that are predicted correctly after accounting for chance effects. 4. AUC measures from ROC plots were independent of prevalence, but highly significantly correlated with the much more easily computed kappa. Moreover, when applied in predictive mode to test data, models with thresholds optimized by ROC erroneously overestimated true occurrence among scarcer organisms, often those of greatest conservation interest. We advocate caution in using ROC methods to optimize thresholds required for real prediction. 5. Our strongest recommendation is that ecologists reduce their reliance on prediction success as a performance measure in presence-absence modelling. Cohen's kappa provides a simple, effective, standardized and appropriate statistic for evaluating or comparing presence-absence models, even those based on different statistical algorithms. None of the performance measures we examined tests the statistical significance of predictive accuracy, and we identify this as a priority area for research and development

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ObjectivesThe aim of this study was to develop a critical appraisal (CA) tool that addressed study design and reporting quality as well as the risk of bias in cross-sectional studies (CSSs). In addition, the aim was to produce a help document to guide the non-expert user through the tool.DesignAn initial scoping review of the published literature and key epidemiological texts was undertaken prior to the formation of a Delphi panel to establish key components for a CA tool for CSSs. A consensus of 80% was required from the Delphi panel for any component to be included in the final tool.ResultsAn initial list of 39 components was identified through examination of existing resources. An international Delphi panel of 18 medical and veterinary experts was established. After 3 rounds of the Delphi process, the Appraisal tool for Cross-Sectional Studies (AXIS tool) was developed by consensus and consisted of 20 components. A detailed explanatory document was also developed with the tool, giving expanded explanation of each question and providing simple interpretations and examples of the epidemiological concepts being examined in each question to aid non-expert users.ConclusionsCA of the literature is a vital step in evidence synthesis and therefore evidence-based decision-making in a number of different disciplines. The AXIS tool is therefore unique and was developed in a way that it can be used across disciplines to aid the inclusion of CSSs in systematic reviews, guidelines and clinical decision-making.