Quels facteurs sociodémographiques influencent l'attitude des médecins généralistes envers la prévention ?

Objectifs En Belgique, les mesures de prévention médicalisées restent inéquitablement distribuées dans plusieurs domaines (vaccins, dépistages, conseil cardio-vasculaire, etc.). Une recherche antérieure au moyen d’une échelle d’attitude a mis en évidence trois facteurs pouvant modeler l’attitude des médecins généralistes (MG) envers la prévention : 1. Evaluation de pratique. 2. Sentiment de responsabilité envers la santé des patients. 3. Compétences professionnelles particulières (CPP) : références scientifiques, positionnement dans le système de soins, délégation de tâches à des paramédicaux. Cette étude a cherché à établir des profils de MG par rapport à la prévention, en croisant les scores sur ces trois facteurs et les variables sociodémographiques. Méthode Les réponses des 457 MG répondants à l’échelle d’attitude ont été soumis à des analyses multivariées, en prenant comme variables dépendantes les scores obtenus sur les trois facteurs, et comme variables indépendantes l’ancienneté, le sexe, la langue, le lieu de pratique, le travail dans un centre de prévention (ONE, planning, PSE), le type de pratique (solo ou divers types d’association). Résultats Le type de pratique est le seul déterminant qui influence les 3 facteurs, avec un gradient des pratiques solo vers les maisons médicales, en passant par les associations mono- puis pluridisciplinaires. Les MG ayant moins de 20 ans de pratique ont de meilleurs scores en évaluation et CPP. Les hommes ont de meilleurs scores en évaluation, et les pratiques urbaines se distinguent en CPP. La langue influence tantôt dans un sens tantôt dans l’autre. Il n’y a pas d’influence du fait de travailler dans un centre de prévention. Conclusion Une diffusion plus large et équitable des actes préventifs passe par la prise en compte de facteurs personnels et organisationnels propres aux MG. Les associations pluridisciplinaires semblent mieux préparées pour atteindre cet objectif

Protocol for a New Index Validation in Prosthodontics Clinical Research

Protocols to validate indices in prosthodontics research have been scarcely reported. Meanwhile, there is no appropriate index gathering all different aspects of implant restorations. This work introduces a protocol to validate a new index to Score Implant Restorations (SIR index

Comprehensive Cluster Analysis for COPD Including Systemic and Airway Inflammatory Markers

Chronic obstructive pulmonary disease (COPD) is a complex, multidimensional and heterogeneous disease. The main purpose of the present study was to identify clinical phenotypes through cluster nalysis in adults suffering from COPD. A retrospective study was conducted on 178 COPD patients in stable state recruited from ambulatory care at University hospital of Liege. All patients were above 40 years, had a smoking history of more than 20 pack years, post bronchodilator FEV1/FVC <70% and denied any history of asthma before 40 years. In this study, the patients were described by a total of 84 mixed sets of variables with some missing values. Hierarchical clustering on principal components (HCPC) was applied on multiple imputation. In the final step, patients were classified into homogeneous distinct groups by consensus clustering. Three different clusters, which shared similar smoking history were found. Cluster 1 included men with moderate airway obstruction (n¼67) while cluster 2 comprised men who were exacerbation-prone, with severe airflow limitation and intense granulocytic airway and neutrophilic systemic inflammation (n¼56). Cluster 3 essentially included women with moderate airway obstruction (n¼55). All clusters had a low rate of bacterial colonization (5%), a low median FeNO value (<20 ppb) and a very low sensitization rate toward common aeroallergens (0-5%). CAT score did not differ between clusters. Including markers of systemic airway inflammation and atopy and applying a comprehensive cluster analysis we provide here evidence for 3 clusters markedly shaped by sex, airway obstruction and neutrophilic inflammation but not by symptoms and T2 biomarkers

Health Literacy and Its Associations with Understanding and Perception of Front-of-Package Nutrition Labels among Higher Education Students

peer reviewed(1) Background: Nutrition labels on the front of food packages have increasingly become the focus of research. However, too few studies have placed special emphasis on nutritionally at-risk subpopulations, such as young adults or those with low literacy/numeracy skills. The present study aimed to assess both the perception and objective understanding of three front-of-package labeling (FOPL) formats currently in use on the Belgian market, i.e., the Nutri-Score, Reference Intakes, and Multiple Traffic Lights, among students of varying health literacy (HL) levels. (2) Methods: A web-based survey was carried out among 2295 students of tertiary education in the province of Liège, Belgium. The questionnaire included questions related to general characteristics, objective understanding, and perception in response to the assigned FOPL format and level of HL. (3) Results: With respect to objective understanding, the Nutri-Score outperformed all other labels across all HL levels, and it was similarly understood in students of varying HL levels. Several students’ characteristics appeared to be associated with each cluster of perception, with the Nutri-Score cluster having the highest percentages of disadvantaged students, i.e., those with inadequate HL, from non-university institutions, with low self-estimated nutrition knowledge, and with low self-estimated diet quality. (4) Conclusion: Overall, the findings supported the Nutri-Score as particularly effective in guiding students in their food choices. Of particular importance is the fact that the summarized and graded color-coded nutritional label would be a useful strategy for those disadvantaged by limited HL.4. Quality educatio

Are type-2 biomarkers of any help in asthma diagnosis?

Peer reviewe

Cluster analysis on emergency COVID-19 data: A result-based imputation method for missing data

editorial reviewedBackground and Objective In 2020, hospitals have been confronted with an influx of COVID-19 confirmed patients. Grouping patients based on clinical features could help clinicians to identify a structure of patients who needs more attention. The present study considers cluster analysis to identify different clinical phenotypes with similar properties while accounting for the presence of missing data. Although several frameworks exist for handling missing data in cluster analysis, in this study, a new perspective was introduced for multiple imputation in cluster analysis that focused on the result of clustering. Method To handle the uncertainty of missing values, m imputed datasets were generated. The model-based clustering strategy was applied on the imputed datasets. Based on BIC criterion, the best method and the best number of groups were defined for all imputed datasets. Subsequently, the most repetitive number of groups and types was fixed. In the next step, cluster analysis was re-applied on m imputed datasets by the fixed number of clusters and type. The results of the statistical analysis were reported for each of the groups in imputed datasets. According to Rubin’s rules, in the pooled step, the final results were combined by mean and the statistical inferences were applied by considering between and within variance. Results The performance of the proposed framework was compared and assessed in several scenarios. The proposed method with 20 clinical features was performed on 628 confirmed COVID-19 patients who presented at University Hospital of Liege from March to May 2020. Based on model-based clustering and BIC criterion for multiple imputation, the patients were classified into four clusters. The rate of hospitalization in Cluster2 with older patients was higher than those in Cluster1. The oldest patients were assigned to Cluster3 and Cluster 4. The rate of comorbidity was almost close to 100% in Cluster 4 and percentage of infectious disease in cluster3 was less than Cluster4; however, Cluster3 had a higher rate of hospitalization than Cluster4. Conclusions The proposed method handled cluster analysis on missing data by multiple imputations. Also, the present study identified four clusters of patients confirmed with COVID-19 and the corresponding rate of hospitalization based on clinical features

Determinants of COVID-19 vaccine intention in a University population: a serial mediation approach

editorial reviewe