Determinants of generic drug substitution in Switzerland.

Background: Since generic drugs have the same therapeutic effect as the original formulation but at generally lower costs, their use should be more heavily promoted. However, a considerable number of barriers to their wider use have been observed in many countries. The present study examines the influence of patients, physicians and certain characteristics of the generics' market on generic substitution in Switzerland.Methods: We used reimbursement claims' data submitted to a large health insurer by insured individuals living in one of Switzerland's three linguistic regions during 2003. All dispensed drugs studied here were substitutable. The outcome (use of a generic or not) was modelled by logistic regression, adjusted for patients' characteristics (gender, age, treatment complexity, substitution groups) and with several variables describing reimbursement incentives (deductible, co-payments) and the generics' market (prices, packaging, co-branded original, number of available generics, etc.).Results: The overall generics' substitution rate for 173,212 dispensed prescriptions was 31%, though this varied considerably across cantons. Poor health status (older patients, complex treatments) was associated with lower generic use. Higher rates were associated with higher out-of-pocket costs, greater price differences between the original and the generic, and with the number of generics on the market, while reformulation and repackaging were associated with lower rates. The substitution rate was 13% lower among hospital physicians. The adoption of the prescribing practices of the canton with the highest substitution rate would increase substitution in other cantons to as much as 26%.Conclusions: Patient health status explained a part of the reluctance to substitute an original formulation by a generic. Economic incentives were efficient, but with a moderate global effect. The huge interregional differences indicated that prescribing behaviours and beliefs are probably the main determinant of generic substitution.

Adaptive strategy for the statistical analysis of connectomes.

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores.

Argumentation et narration dans le discours publicitaire

De la publicité au discours politique, les procédés d'argumentation sont variés. Un seul d'entre eux, aux multiples occurrences, occupe ce mémoire : l'emploi de la narration dans le discours publicitaire. Il s'agit plus précisément d'étudier les manières dont s'articulent l'argumentation et la narration dans le contexte publicitaire. Les exemples analysés se réfèrent à deux modalités narratives distinctes, les récits factuels et les récits fictionnels. Ils sont au nombre de quatre, relatifs à trois produits différents : une publicité pour la gamme Excellence de Lindt publiée en avril 2008 dans Femina ; un publi-reportage consacré au café Jinogalpa de Nespresso diffusé en février 2008 dans un magazine promotionnel ; un fascicule promotionnel consacré à Jinogalpa, distribué avec le même magazine ; un iconotexte publicitaire datant de février 1938 pour le Cognac Hennessy issu de L'Illustration. D'une part, l'analyse confirme les observations faites par les études rhétoriques: l'usage de la narration par l'argumentation publicitaire recouvre les catégories de l'exemplum (l'illustration ou l'analogie) et de la narratio (narration orientée favorisant une réception pertinente de l'argumentation). D'autre part, l'analyse pointe également des phénomènes nouveaux : l'emploi d'assertions présuppositionnelles par la reformulation des cadres spatio-temporels implicites du récit en pôles d'argumentation explicites, l'utilisation de fragments de récit et de scripts narratifs comme embrayeurs d'une interprétation orientée ou encore l'usage des genres narratifs comme brouilleurs du contrat de communication préétabli.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.