Allergic rhinitis in patients with asthma: the Swiss LARA (Link Allergic Rhinitis in Asthma) survey.

OBJECTIVE: To determine the characteristics of asthma (A) and allergic rhinitis (AR) among asthma patients in primary care practice. RESEARCH DESIGN AND METHODS: Primary care physicians, pulmonologists, and allergologists were asked to recruit consecutive asthma patients with or without allergic rhinitis from their daily practice. Cross-sectional data on symptoms, severity, treatment and impact on quality of life of A and AR were recorded and examined using descriptive statistics. Patients with and without AR were then compared. RESULTS: 1244 asthma patients were included by 211 physicians. Asthma was controlled in 19%, partially controlled in 27% and not controlled in 54%. Asthma treatment was generally based on inhaled corticosteroids (ICS) with or without long acting beta 2 agonists (78%). A leukotriene receptor antagonist (LTRA) was used by 46% of the patients. Overall, 950 (76%) asthma patients had AR (A + AR) and 294 (24%) did not (A - AR). Compared to patients with A - AR, A + AR patients were generally younger (mean age +/- standard deviation: 42 +/- 16 vs. 50 +/- 19 years, p < 0.001) and fewer used ICS (75% vs. 88%, p < 0.001). LTRA usage was similar in both groups (46% vs. 48%). Asthma was uncontrolled in 53% of A + AR and 57% of A - AR patients. Allergic rhinitis was treated with a mean of 1.9 specific AR medications: antihistamines (77%), nasal steroids (66%) and/or vasoconstrictors (38%), and/or LTRA (42%). Rhinorrhoea, nasal obstruction, or nasal itching were the most frequently reported AR symptoms and the greatest reported degree of impairment was in daily activities/sports (55%). CONCLUSIONS: Allergic rhinitis was more common among younger asthma patients, increased the burden of symptoms and the need for additional medication but was associated with improved asthma control. However, most asthma patients remained suboptimally controlled regardl-ess of concomitant AR.

Analyse des besoins de la prise en charge des personnes dépendantes en Suisse : Rapport final

L'Office fédéral de la santé publique (OFSP) a mandaté deux groupes de recherche pour analyser les besoins de la prise en prise en charge des personnes dépendantes en Suisse : l'Unité d'évaluation de programmes de prévention (UEPP) de l'Institut universitaire de médecine sociale et préventive de Lausanne (IUMSP) du Centre hospitalier universitaire vaudois (CHUV) et Addiction Suisse de Lausanne. Plus précisément, le but de cette étude est d'explorer et de définir - par une analyse des besoins - si l'offre actuelle en services dans le domaine des addictions est encore adaptée à la situation épidémiologique actuelle des addictions, à l'évolution des types de comportements liés à la dépendance et aux besoins des clients. Il s'agit en particulier de répondre aux questions suivantes: ? Existe-t-il actuellement des besoins en traitement pour lesquels il n'existe aucune offre appropriée ? ? Quels groupes ne sont pas ou sont insuffisamment atteints par l'offre existante? ? A quels genres de problèmes liés à la dépendance et à quels nouveaux besoins des clients les structures oeuvrant dans le domaine de la dépendance sont -elles confrontées? ? Quels sont les besoins d'adaptation du système de prise en charge nécessaires concernant soit les groupes-cibles de services, soit les types d'offres - en particulier le besoin en nouveaux concepts/modèles de prise en charge pour répondre à l'évolution des besoins? ? Comment ces structures font-elles face à l'accroissement de l'usage de multiples substances (multi-consommation)?

Quelques abus de la statistique dans la recherche médicale [Various abuses of statistics in medical research].

Statistics has become an indispensable tool in biomedical research. Thanks, in particular, to computer science, the researcher has easy access to elementary "classical" procedures. These are often of a "confirmatory" nature: their aim is to test hypotheses (for example the efficacy of a treatment) prior to experimentation. However, doctors often use them in situations more complex than foreseen, to discover interesting data structures and formulate hypotheses. This inverse process may lead to misuse which increases the number of "statistically proven" results in medical publications. The help of a professional statistician thus becomes necessary. Moreover, good, simple "exploratory" techniques are now available. In addition, medical data contain quite a high percentage of outliers (data that deviate from the majority). With classical methods it is often very difficult (even for a statistician!) to detect them and the reliability of results becomes questionable. New, reliable ("robust") procedures have been the subject of research for the past two decades. Their practical introduction is one of the activities of the Statistics and Data Processing Department of the University of Social and Preventive Medicine, Lausanne.