Monitoring de la problématique du cannabis en Suisse: étude sentinelle 2004-2009

Le monitoring de la problématique du cannabis en Suisse constitue un ensemble de travaux qui permettent le suivi de la situation au niveau national et qui sont mis en oeuvre par un consortium d'institutions de recherche. Ce monitoring comprend l'étude présentée dans ce rapport, l'étude sentinelle. Celle-ci s'intéresse à l'évolution de la situation en matière de cannabis ainsi qu'à la gestion de cette situation au niveau local. Il s'agit de répondre aux questions suivantes : - quelle est la situation en matière de consommation de cannabis et de marché et quelle est son évolution ? - quels sont les principaux problèmes rencontrés sur le terrain ? - quelles sont les mesures et interventions qui ont été développées dans ce domaine ? Pour y répondre, on a choisi de suivre la situation dans quatre cantons suisses dits "sentinelle" (St-Gall, Tessin, Vaud, Zurich). Les critères de choix de ces cantons font appel à leur taille, au rapport ville/campagne et à la présence de frontière avec des états voisins, à la langue, au type de politique drogue pratiqué. Dans chaque canton on a constitué des panels d'experts formés par des profes-sionnels de terrain dans trois domaines différents (santé et social, école, police et justice). Leurs observations ainsi que les données cantonales disponibles sont récoltées et discutées lors d'un workshop et analysées sur plusieurs années. Le présent rapport fait état des résultats des quatre workshops de suivi (2005, 2006, 2008, 2009). [Résumé, p. 5]

Evaluation de la filière coordonnée de prise en charge des patients diabétiques «Diabaide» : période 2004-2006

La filière coordonnée "diabaide" a été mise en place à fin 2004 dans l'objectif d'améliorer la prise en charge des patients diabétiques par une organisation des soins fondée sur la collaboration, le partage de l'information et la coordination des prestations, afin de renforcer l'autonomie des patients (éducation et auto-prise en charge), d'améliorer la qualité des soins (recommandations thérapeutiques et protocoles de soins) et de maîtriser les coûts. La filière, à ses débuts, était constituée principalement de la cellule multidisciplinaire "diabaide", qui offrait des consultations ambulatoires et hospitalières par des professionnels spécialisés. Cette évaluation, intermédiaire, avait pour objectif d'estimer si le programme avait atteint ses objectifs après deux années d'activités. [....] Le développement de programmes de prise en charge des maladies chroniques est encore à ses débuts en Suisse et "diabaide" fait image de pionnier dans ce domaine. Après cette évaluation, le programme a été modifié en 2007 et ne correspond plus à la description fournie dans ce document. De nouveaux programmes ont également été mis en place en Suisse depuis 2007 (par exemple makora Diabetes-Disease Management Programm à Zürich). Dans le canton de Vaud, le département de la santé de l'action sociale a créé en 2010 un programme cantonal visant à réduire l'impact du diabète sur la population en agissant sur la prévention et sur l'amélioration de la prise en charge des personnes diabétiques. Le programme cantonal a pour objectif notamment de développer une prise en charge globale, inspirée en partie du programme "diabaide", qui sera stratifiée en fonction de la sévérité de la maladie et des besoins des patients, intégrera l'auto-gestion (self-management), sera organisée en filières interdisciplinaires, et sera fondée sur les preuves. [Auteurs, p. 5]

Plasma and urine profiles of Delta9-tetrahydrocannabinol and its metabolites 11-hydroxy-Delta9-tetrahydrocannabinol and 11-nor-9-carboxy-Delta9-tetrahydrocannabinol after cannabis smoking by male volunteers to estimate recent consumption by athletes.

Since 2004, cannabis has been prohibited by the World Anti-Doping Agency for all sports competitions. In the years since then, about half of all positive doping cases in Switzerland have been related to cannabis consumption. In doping urine analysis, the target analyte is 11-nor-9-carboxy-Delta(9)-tetrahydrocannabinol (THC-COOH), the cutoff being 15 ng/mL. However, the wide urinary detection window of the long-term metabolite of Delta(9)-tetrahydrocannabinol (THC) does not allow a conclusion to be drawn regarding the time of consumption or the impact on the physical performance. The purpose of the present study on light cannabis smokers was to evaluate target analytes with shorter urinary excretion times. Twelve male volunteers smoked a cannabis cigarette standardized to 70 mg THC per cigarette. Plasma and urine were collected up to 8 h and 11 days, respectively. Total THC, 11-hydroxy-Delta(9)-tetrahydrocannabinol (THC-OH), and THC-COOH were determined after hydrolysis followed by solid-phase extraction and gas chromatography/mass spectrometry. The limits of quantitation were 0.1-1.0 ng/mL. Eight puffs delivered a mean THC dose of 45 mg. Plasma levels of total THC, THC-OH, and THC-COOH were measured in the ranges 0.2-59.1, 0.1-3.9, and 0.4-16.4 ng/mL, respectively. Peak concentrations were observed at 5, 5-20, and 20-180 min. Urine levels were measured in the ranges 0.1-1.3, 0.1-14.4, and 0.5-38.2 ng/mL, peaking at 2, 2, and 6-24 h, respectively. The times of the last detectable levels were 2-8, 6-96, and 48-120 h. Besides high to very high THC-COOH levels (245 +/- 1,111 ng/mL), THC (3 +/- 8 ng/mL) and THC-OH (51 +/- 246 ng/mL) were found in 65 and 98% of cannabis-positive athletes' urine samples, respectively. In conclusion, in addition to THC-COOH, the pharmacologically active THC and THC-OH should be used as target analytes for doping urine analysis. In the case of light cannabis use, this may allow the estimation of more recent consumption, probably influencing performance during competitions. However, it is not possible to discriminate the intention of cannabis use, i.e., for recreational or doping purposes. Additionally, pharmacokinetic data of female volunteers are needed to interpret cannabis-positive doping cases of female athletes.

A temporal hierarchy for conspecific vocalization discrimination in humans.

Introduction: Discrimination of species-specific vocalizations is fundamental for survival and social interactions. Its unique behavioral relevance has encouraged the identification of circumscribed brain regions exhibiting selective responses (Belin et al., 2004), while the role of network dynamics has received less attention. Those studies that have examined the brain dynamics of vocalization discrimination leave unresolved the timing and the inter-relationship between general categorization, attention, and speech-related processes (Levy et al., 2001, 2003; Charest et al., 2009). Given these discrepancies and the presence of several confounding factors, electrical neuroimaging analyses were applied to auditory evoked-potential (AEPs) to acoustically and psychophysically controlled non-verbal human and animal vocalizations. This revealed which region(s) exhibit voice-sensitive responses and in which sequence. Methods: Subjects (N=10) performed a living vs. man-made 'oddball' auditory discrimination task, such that on a given block of trials 'target' stimuli occurred 10% of the time. Stimuli were complex, meaningful sounds of 500ms duration. There were 120 different sound files in total, 60 of which represented sounds of living objects and 60 man-made objects. The stimuli that were the focus of the present investigation were restricted to those of living objects within blocks where no response was required. These stimuli were further sorted between human non-verbal vocalizations and animal vocalizations. They were also controlled in terms of their spectrograms and formant distributions. Continuous 64-channel EEG was acquired through Neuroscan Synamps referenced to the nose, band-pass filtered 0.05-200Hz, and digitized at 1000Hz. Peri-stimulus epochs of continuous EEG (-100ms to 900ms) were visually inspected for artifacts, 40Hz low-passed filtered and baseline corrected using the pre-stimulus period . Averages were computed from each subject separately. AEPs in response to animal and human vocalizations were analyzed with respect to differences of Global Field Power (GFP) and with respect to changes of the voltage configurations at the scalp (reviewed in Murray et al., 2008). The former provides a measure of the strength of the electric field irrespective of topographic differences; the latter identifies changes in spatial configurations of the underlying sources independently of the response strength. In addition, we utilized the local auto-regressive average distributed linear inverse solution (LAURA; Grave de Peralta Menendez et al., 2001) to visualize and statistically contrast the likely underlying sources of effects identified in the preceding analysis steps. Results: We found differential activity in response to human vocalizations over three periods in the post-stimulus interval, and this response was always stronger than that to animal vocalizations. The first differential response (169-219ms) was a consequence of a modulation in strength of a common brain network localized into the right superior temporal sulcus (STS; Brodmann's Area (BA) 22) and extending into the superior temporal gyrus (STG; BA 41). A second difference (291-357ms) also followed from strength modulations of a common network with statistical differences localized to the left inferior precentral and prefrontal gyrus (BA 6/45). These two first strength modulations correlated (Spearman's rho(8)=0.770; p=0.009) indicative of functional coupling between temporally segregated stages of vocalization discrimination. A third difference (389-667ms) followed from strength and topographic modulations and was localized to the left superior frontal gyrus (BA10) although this third difference did not reach our spatial criterion of 12 continuous voxels. Conclusions: We show that voice discrimination unfolds over multiple temporal stages, involving a wide network of brain regions. The initial stages of vocalization discrimination are based on modulations in response strength within a common brain network with no evidence for a voice-selective module. The latency of this effect parallels that of face discrimination (Bentin et al., 2007), supporting the possibility that voice and face processes can mutually inform one another. Putative underlying sources (localized in the right STS; BA 22) are consistent with prior hemodynamic imaging evidence in humans (Belin et al., 2004). Our effect over the 291-357ms post-stimulus period overlaps the 'voice-specific-response' reported by Levy et al. (Levy et al., 2001) and the estimated underlying sources (left BA6/45) were in agreement with previous findings in humans (Fecteau et al., 2005). These results challenge the idea that circumscribed and selective areas subserve con-specific vocalization processing.