Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Decades of population genetic research reveal the need for harmonization of molecular markers: the grey wolf Canis lupus as a case study

Following protection measures implemented since the 1970s, large carnivores are currently increasing in number and returning to areas from which they were absent for decades or even centuries. Monitoring programmes for these species rely extensively on non-invasive sampling and genotyping. However, attempts to connect results of such studies at larger spatial or temporal scales often suffer from the incompatibility of genetic markers implemented by researchers in different laboratories. This is particularly critical for long-distance dispersers, revealing the need for harmonized monitoring schemes that would enable the understanding of gene flow and dispersal dynamics. Based on a review of genetic studies on grey wolves Canis lupus from Europe, we provide an overview of the genetic markers currently in use, and identify opportunities and hurdles for studies based on continent-scale datasets. Our results highlight an urgent need for harmonization of methods to enable transnational research based on data that have already been collected, and to allow these data to be linked to material collected in the future. We suggest timely standardization of newly developed genotyping approaches, and propose that action is directed towards the establishment of shared single nucleotide polymorphism panels, next-generation sequencing of microsatellites, a common reference sample collection and an online database for data exchange. Enhanced cooperation among genetic researchers dealing with large carnivores in consortia would facilitate streamlining of methods, their faster and wider adoption, and production of results at the large spatial scales that ultimately matter for the conservation of these charismatic species.

Escalating incidence of eosinophilic esophagitis in Canton of Vaud, Switzerland, 1993-2013: a population-based study.

BACKGROUND: Eosinophilic esophagitis (EoE) is a chronic, inflammatory disease of the esophagus with a rapidly increasing incidence. However, population-based epidemiologic data on EoE are rare and limited to regions with less than 200 000 inhabitants. We evaluated the incidence and prevalence of EoE over time in Canton of Vaud, Switzerland. MATERIALS AND METHODS: Canton of Vaud lies in the French-speaking, Western part of Switzerland. As of December 2013, it had a population of 743 317 inhabitants. We contacted all pathology institutes (n = 6) in this canton to identify patients that have been diagnosed with esophageal eosinophilia between 1993 and 2013. We then performed a chart review in all adult and pediatric gastroenterology practices to identify patients with EoE. RESULTS: Of 263 patients with esophageal eosinophilia, a total of 179 fulfilled the diagnostic criteria for EoE. Median diagnostic delay was 4 (IQR 1-9) years. No patient was diagnosed with EoE prior to 2003. Incidence of EoE increased from 0.16/100 000 inhabitants in 2004 to 6.3/100 000 inhabitants in 2013 (P < 0.001). The cumulative EoE prevalence in 2013 was 24.1/100 000. The incidence in males was 2.8 times higher (95% CI 2.01-3.88, P < 0.001) when compared to that in females. The annual EoE incidence was 10.6 times higher (95%-CI 7.61-14.87, P < 0.001) in the period from 2010 to 2013 when compared to that in the period from 1993 to 2009. CONCLUSIONS: The incidence and cumulative prevalence of EoE in Canton of Vaud, Switzerland, has rapidly increased in the past 10 years.

Appropriateness criteria for surgery improve clinical outcomes in patients with low back pain and/or sciatica.

STUDY DESIGN: Prospective, controlled, observational outcome study using clinical, radiographic, and patient/physician-based questionnaire data, with patient outcomes at 12 months follow-up. OBJECTIVE: To validate appropriateness criteria for low back surgery. SUMMARY OF BACKGROUND DATA: Most surgical treatment failures are attributed to poor patient selection, but no widely accepted consensus exists on detailed indications for appropriate surgery. METHODS: Appropriateness criteria for low back surgery have been developed by a multispecialty panel using the RAND appropriateness method. Based on panel criteria, a prospective study compared outcomes of patients appropriately and inappropriately treated at a single institution with 12 months follow-up assessment. Included were patients with low back pain and/or sciatica referred to the neurosurgical department. Information about symptoms, neurologic signs, the health-related quality of life (SF-36), disability status (Roland-Morris), and pain intensity (VAS) was assessed at baseline, at 6 months, and at 12 months follow-up. The appropriateness criteria were administered prospectively to each clinical situation and outside of the clinical setting, with the surgeon and patients blinded to the results of the panel decision. The patients were further stratified into 2 groups: appropriate treatment group (ATG) and inappropriate treatment group (ITG). RESULTS: Overall, 398 patients completed all forms at 12 months. Treatment was considered appropriate for 365 participants and inappropriate for 33 participants. The mean improvement in the SF-36 physical component score at 12 months was significantly higher in the ATG (mean: 12.3 points) than in the ITG (mean: 6.8 points) (P = 0.01), as well as the mean improvement in the SF-36 mental component score (ATG mean: 5.0 points; ITG mean: -0.5 points) (P = 0.02). Improvement was also significantly higher in the ATG for the mean VAS back pain (ATG mean: 2.3 points; ITG mean: 0.8 points; P = 0.02) and Roland-Morris disability score (ATG mean: 7.7 points; ITG mean: 4.2 points; P = 0.004). The ATG also had a higher improvement in mean VAS for sciatica (4.0 points) than the ITG (2.8 points), but the difference was not significant (P = 0.08). The SF-36 General Health score declined in both groups after 12 months, however, the decline was worse in the ITG (mean decline: 8.2 points) than in the ATG (mean decline: 1.2 points) (P = 0.04). Overall, in comparison to ITG patients, ATG patients had significantly higher improvement at 12 months, both statistically and clinically. CONCLUSION: In comparison to previously reported literature, our study is the first to assess the utility of appropriateness criteria for low back surgery at 1-year follow-up with multiple outcome dimensions. Our results confirm the hypothesis that application of appropriateness criteria can significantly improve patient outcomes.

The management of brain metastases

Brain metastases occur in 20-50% of NSCLC and 50-80% of SCLC. In this review, we will look at evidence-based medicine data and give some perspectives on the management of BM. We will address the problems of multiple BM, single BM and prophylactic cranial irradiation. Recursive Partitioning Analysis (RPA) is a powerful prognostic tool to facilitate treatment decisions. Dealing with multiple BM, the use of corticosteroids was established more than 40 years ago by a unique randomized trial (RCT). Palliative effect is high (_80%) as well as side-effects. Whole brain radiotherapy (WBRT) was evaluated in many RCTs with a high (60-90%) response rate; several RT regimes are equivalent, but very high dose per fraction should be avoided. In multiple BM from SCLC, the effect of WBRT is comparable to that in NSCLC but chemotherapy (CXT) although advocated is probably less effective than RT. Single BM from NSCLC occurs in 30% of all BM cases; several prognostic classifications including RPA are very useful. Several options are available in single BM: WBRT, surgery (SX), radiosurgery (RS) or any combination of these. All were studied in RCTs and will be reviewed: the addition of WBRT to SX or RS gives a better neurological tumour control, has little or no impact on survival, and may be more toxic. However omitting WBRT after SX alone gives a higher risk of cerebro-spinal fluid dissemination. Prophylactic cranial irradiation (PCI) has a major role in SCLC. In limited disease, meta-analyses have shown a positive impact of PCI in the decrease of brain relapse and in survival improvement, especially for patients in complete remission. Surprisingly, this has been recently confirmed also in extensive disease. Experience with PCI for NSCLC is still limited, but RCT suggest a reduction of BM with no impact on survival. Toxicity of PCI is a matter of debate, as neurological or neuro-cognitive impairment is already present prior to PCI in almost half of patients. However RT toxicity is probably related to total dose and dose per fraction. Perspectives : Future research should concentrate on : 1) combined modalities in multiple BM. 2) Exploration of treatments in oligo-metastases. 3) Further exploration of PCI in NSCLC. 4) Exploration of new, toxicity-sparing radiotherapy techniques (IMRT, Tomotherapy etc).

Changing Religiosity, Changing Politics? The Influence of 'Belonging' and 'Believing' on Political Attitudes in Switzerland

Starting from theories of secularization and of religious individualization, we propose a two-dimensional typology of religiosity and test its impact on political attitudes. Unlike classic conceptions of religiosity used in political studies, our typology simultaneously accounts for an individual's sense of belonging to the church (institutional dimension) and his/her personal religious beliefs (spiritual dimension). Our analysis, based on data from the World Values Survey in Switzerland (1989-2007), shows two main results. First, next to evidence of religious decline, we also find evidence of religious change with an increase in the number of people who "believe without belonging." Second, non-religious individuals and individuals who believe without belonging are significantly more permissive on issues of cultural liberalism than followers of institutionalized forms of religiosity.

Magnetic resonance imaging determinants of intraindividual variability in the elderly: combined analysis of grey and white matter.

Elderly individuals display a rapid age-related increase in intraindividual variability (IIV) of their performances. This phenomenon could reflect subtle changes in frontal lobe integrity. However, structural studies in this field are still missing. To address this issue, we computed an IIV index for a simple reaction time (RT) task and performed magnetic resonance imaging (MRI) including voxel based morphometry (VBM) and the tract based spatial statistics (TBSS) analysis of diffusion tensor imaging (DTI) in 61 adults aged from 22 to 88 years. The age-related IIV increase was associated with decreased fractional anisotropy (FA) as well as increased radial (RD) and mean (MD) diffusion in the main white matter (WM) fiber tracts. In contrast, axial diffusion (AD) and grey matter (GM) densities did not show any significant correlation with IIV. In multivariate models, only FA has an age-independent effect on IIV. These results revealed that WM but not GM changes partly mediated the age-related increase of IIV. They also revealed that the association between WM and IIV could not be only attributed to the damage of frontal lobe circuits but concerned the majority of interhemispheric and intrahemispheric corticocortical connections.

From the vendor in the hemp shop to the dealer in the alleyway? A comparison of the sources of supply of cannabis in a time of 'de-normalising' cannabis use.

Objectives: After several years of increasing 'normalisation' of cannabis use in Switzerland at the beginning of the new millennium, a reversed tendency, marked among others by a more stringent law-enforcement, set in. The presentation examines the question of where adolescents and young adults obtained cannabis, within the context of this societal change. In addition, it compares the sources of supply for cannabis with those found in studies of other European countries. Methods: Analyses are based on data from the Swiss Cannabis Monitoring Study. As part of this longitudinal, representative population survey, more than 5000 adolescents and young adults were interviewed by telephone on the topic of cannabis. Within the total sample, 593 (2004) or 554 (2007) respectively, current cannabis users replied to the questions on sources of supply. Changes in law-enforcement and societal climate concerning cannabis are assessed based on relevant literature, media reports and parliamentary discussions. Results: Whereas 22% of cannabis users stated in 2004 that they bought their cannabis from vendors in hemp shops, this proportion drastically decreased to 6% three years later. At the same time, cannabis was obtained increasingly from friends, while the proportion of users who purchased cannabis from dealers in the alleyway, more than doubled from 6% (2004) to 13% (2007). It was male cannabis users, and in particular, young adult and frequent users, who have moved into the alleyways. Generally, users who buy cannabis in the alleyway show more cannabis-related problems than those who mainly name other sources of supply, even when adjusted for sex, age and frequency of cannabis use. Discussion: Possible consequences of these changes in cannabis supply, like the risk of merging a previously cannabis-only market with other 'harder' drugs markets, are discussed.

Alcohol and nonlethal injuries: a Swiss emergency department study on the risk relationship between acute alcohol consumption and type of injury.

BACKGROUND: Acute alcohol consumption has been reported to be an important risk factor for injury, but clear scientific evidence on issues such as injury type is not available. The present study aims to improve the knowledge of the importance of alcohol consumption as an injury determinant with regards to two dimensions of the type of injury, namely the nature and the body region involved. METHODS: Risk relationships between two injury type components and acute alcohol use were estimated through multinomial and logistic regression models based on data from 7,529 patients-among whom 3,682 had injury diagnoses-gathered in a Swiss emergency department. RESULTS: Depending on the type of injury, between 31.1% and 48.7% of casualties report alcohol use before emergency department attendance. The multinomial regression models show that even low alcohol levels are consistently associated with nearly all natures of injury and body regions. A persistent dose-response effect between alcohol levels and risk associations was observed for almost all injury types. CONCLUSIONS: The results highlight the importance and consistency of the risk association between low and moderate levels of acute alcohol consumption and all types of injury. None of the body regions and natures of injury could pride on absence of association between alcohol and injury. Public health, prevention, and care implications are considered.

The cost-effectiveness and public health benefit of nalmefene added to psychosocial support for the reduction of alcohol consumption in alcohol-dependent patients with high/very high drinking risk levels: a Markov model.

OBJECTIVES: To determine whether nalmefene combined with psychosocial support is cost-effective compared with psychosocial support alone for reducing alcohol consumption in alcohol-dependent patients with high/very high drinking risk levels (DRLs) as defined by the WHO, and to evaluate the public health benefit of reducing harmful alcohol-attributable diseases, injuries and deaths. DESIGN: Decision modelling using Markov chains compared costs and effects over 5 years. SETTING: The analysis was from the perspective of the National Health Service (NHS) in England and Wales. PARTICIPANTS: The model considered the licensed population for nalmefene, specifically adults with both alcohol dependence and high/very high DRLs, who do not require immediate detoxification and who continue to have high/very high DRLs after initial assessment. DATA SOURCES: We modelled treatment effect using data from three clinical trials for nalmefene (ESENSE 1 (NCT00811720), ESENSE 2 (NCT00812461) and SENSE (NCT00811941)). Baseline characteristics of the model population, treatment resource utilisation and utilities were from these trials. We estimated the number of alcohol-attributable events occurring at different levels of alcohol consumption based on published epidemiological risk-relation studies. Health-related costs were from UK sources. MAIN OUTCOME MEASURES: We measured incremental cost per quality-adjusted life year (QALY) gained and number of alcohol-attributable harmful events avoided. RESULTS: Nalmefene in combination with psychosocial support had an incremental cost-effectiveness ratio (ICER) of £5204 per QALY gained, and was therefore cost-effective at the £20,000 per QALY gained decision threshold. Sensitivity analyses showed that the conclusion was robust. Nalmefene plus psychosocial support led to the avoidance of 7179 alcohol-attributable diseases/injuries and 309 deaths per 100,000 patients compared to psychosocial support alone over the course of 5 years. CONCLUSIONS: Nalmefene can be seen as a cost-effective treatment for alcohol dependence, with substantial public health benefits. TRIAL REGISTRATION NUMBERS: This cost-effectiveness analysis was developed based on data from three randomised clinical trials: ESENSE 1 (NCT00811720), ESENSE 2 (NCT00812461) and SENSE (NCT00811941).

Estimating the incidence of cancers in Switzerland: 1983-1987.

Estimates have recently been made of the incidence of cancers in the countries of the European Community. Similar estimates are given for Switzerland, based on data from the six Swiss cantonal cancer registries, all of which have been operating for at least 12 years. These registries cover Basel, Geneva, Neuchatel, St Gall and Appenzell, Vaud and Zurich, which account for about 50% of the Swiss population as a whole. Two different methods were used to extrapolate from the incidences observed in the regions covered by cancer registration to the entire country. The first method is based solely on the distribution of populations according to the country's main linguistic groups, whereas the second relies on mortality data. Estimates obtained by the second approach are presented and their reliability is discussed. Comparison of the age incidence curve with that of Denmark tends to confirm the validity of the estimations. Estimated standardised rates (world population) for all sites except nonmelanomatous skin cancer are 294.3 for males and 214.2 for females. Comparisons with other European countries show that in males, lung cancer is relatively less common in Switzerland, whereas in females, breast cancer is relatively more frequent.

Inventory and perspectives of chronic disease management programs in Switzerland: an exploratory survey.

OBJECTIVE: To describe chronic disease management programs active in Switzerland in 2007, using an exploratory survey. METHODS: We searched the internet (Swiss official websites and Swiss web-pages, using Google), a medical electronic database (Medline), reference lists of pertinent articles, and contacted key informants. Programs met our operational definition of chronic disease management if their interventions targeted a chronic disease, included a multidisciplinary team (>/=2 healthcare professionals), lasted at least six months, and had already been implemented and were active in December 2007. We developed an extraction grid and collected data pertaining to eight domains (patient population, intervention recipient, intervention content, delivery personnel, method of communication, intensity and complexity, environment, clinical outcomes). RESULTS: We identified seven programs fulfilling our operational definition of chronic disease management. Programs targeted patients with diabetes, hypertension, heart failure, obesity, psychosis and breast cancer. Interventions were multifaceted; all included education and half considered planned follow-ups. The recipients of the interventions were patients, and healthcare professionals involved were physicians, nurses, social workers, psychologists and case managers of various backgrounds. CONCLUSIONS: In Switzerland, a country with universal healthcare insurance coverage and little incentive to develop new healthcare strategies, chronic disease management programs are scarce. For future developments, appropriate evaluations of existing programs, involvement of all healthcare stakeholders, strong leadership and political will are, at least, desirable.

Qualités psychométriques du Consumer satisfaction questionnaire (CSQ-8) et du Helping alliance questionnaire (HAQ) [Psychometric properties of the Consumer Satisfaction Questionnaire (CSQ-8) and the Helping Alliance Questionnaire (HAQ)].

BACKGROUND: This study was based on data from a quality of care assessment survey conducted in 2011 in outpatient polyclinics of the Vaud Canton in Switzerland, comprising questionnaires completed by 568 children over the age often and 672 parents of children of all ages. The objective of this study was to evaluate the psychometric qualities of the eight-item French versions for children of the Helping Alliance Questionnaire (HAQ) and the Consumer Satisfaction Questionnaire (CSQ-8) to allow formal validation and clinical application of these tools in the context of French-speaking child psychiatry. METHODOLOGY: Responses from children over the age often to the HAQ and CSQ-8 questionnaires were submitted to confirmatory factorial analysis (CFA) for ordinal data to verify their good fit with the original long versions. Construct validity (correspondence between scores on the scales and other external criteria considered to evaluate similar concepts) of the child questionnaires was tested by Spearman's correlation with the parents' responses and their feeling of being reassured or in agreement with respect to the first visit, and with the perception of the help provided by individual and family interviews. RESULTS: CFA showed an acceptable fit with the one-dimensional model of the original scales, both for the HAQ and the CSQ-8. Significant positive correlations of the scales with the parents' responses and with other convergent external criteria confirmed the good construct validity. CONCLUSIONS: These psychometric analyses provide a basis for the validation and clinical application of the abridged French versions of the HAQ and CSQ-8 in quality of care assessment in child psychiatry.

HER2 shedding and serum HER2 extracellular domain: Biology and clinical utility in breast cancer.

The transmembrane protein HER2 is over-expressed in approximately 15% of invasive breast cancers as a result of HER2 gene amplification. HER2 proteolytic cleavage (HER2 shedding) generates soluble truncated HER2 molecules that include only the extracellular domain and the concentration of which can be measured in the serum fraction of blood. HER2 shedding also generates a constitutively active truncated intracellular receptor of 95kDa (p95(HER2)). Another soluble truncated HER2 protein (Herstatin), which can also be found in serum, is the product of an alternatively spliced HER2 transcript. Recent preclinical findings may provide crucial insights into the biological and clinical relevance of increased sHER2 concentrations for the outcome of HER2-positive breast cancer and sensitivity to trastuzumab and lapatinib treatment. We present here the most recent findings about the role and biology of sHER2 based on data obtained using a standardized test, which has been cleared by FDA in 2000, for measuring sHER2. This test includes quality control assessments and has been already widely used to evaluate the clinical utility of sHER2 as a biomarker in breast cancer. We will describe in detail data concerning the assessment of sHER2 as a surrogate maker to optimize the evaluation of the HER2 status of a primary tumor and as a prognosis and predictive marker of response to therapies, both in early and metastatic breast cancer.