Obesity and health related quality of life in the general adult population of the Canary Islands.

PURPOSE: To explore the relationship between excess weight and health related quality of life (HRQL) in the general population by means of the EuroQol-5D questionnaire. METHODS: The relationship between excess weight and HRQL was explored by the 2004 Canary Islands Health Survey. Survey participants from 16 years of age and older were classified according to their body mass index (BMI). The relationship between excess weight and the dichotomized EuroQol-5D was examined by multiple logistic regression analysis, adjusting by socio-demographic variables, smoking status, alcohol consumption, and self-reported disease status. RESULTS: People with excess weight were older, less educated, and had more associated diseases than the population without excess weight. Excess weight and HRQL are inversely correlated. Severely obese participants showed EuroQol-5D index scores significantly lower than those of normal weight participants (0.65 vs. 0.87). The adjusted odds ratio for the lower HRQL is 3.17 for severely obese people compared to normal weight people. CONCLUSIONS: Excess weight has a negative impact on HRQL, even for people without chronic diseases. A better understanding of the relationships between excess weight, its associated comorbidities, and HRQL may have important implications for the design and assessment of prevention and treatment strategies.

[Interim analysis in clinical trials: a methodological guide]. / Análisis interino en ensayos clínicos: una guía metodológica.

Interim analysis of data accumulated in clinical trials is one aspect of the monitoring of the study progress. It is usually done to assess whether there are significant differences in efficacy between the experimental and control treatment groups, in order to decide whether to stop or no the trial prematurely. Among many reasons for early interruption of a trial is the ethical consideration that subjects should not be exposed to an unsafe, inferior or ineffective treatment. Statistical methods suited for doing interim analysis, that allow to control the probability of incorrectly rejecting the null hypothesis of no treatment differences, are often not well understood by researchers. In this article we present an intuitive, non-mathematical explanation and review of the statistical methods for doing interim analysis in clinical trials along with an illustrative example of the application of the methods on a hypothetical dataset.

An audit of appropriate use of blood products in adult patients in a Venezuelan general university hospital.

OBJECTIVE: To audit appropriate use of blood products in adult patients. DESIGN: A cross-sectional study with pre-set criteria. SETTING: Ciudad Hospitalaria 'Dr. Enrique Tejera', the main public tertiary-care hospital in Valencia, Venezuela. STUDY PARTICIPANTS: We studied 700 adult patients from the Medicine, Surgery, Emergency and Obstetrics departments. MAIN OUTCOME MEASURE: Appropriate use of blood products. RESULTS: Seven hundred patients who had an average of 2.45 transfusions (95% confidence interval = 2.28-2.62) were studied. Prevalence of appropriate use was 51.3% for all departments. Prevalence by department was: 72% for Medicine, 36% for Surgery, 56% for Emergency, and 47% for Obstetrics. The average number of transfusions per subject in each department was: Medicine, 3.41; Surgery, 1.75; Obstetrics, 2.09; and Emergency: 2.81 (F-test: P=0.005). Using the department of Medicine as the reference group, it was found that the departments of Surgery, Emergency and Obstetrics had a higher 'risk' of inappropriate use of transfusions, showing odds ratios of 4.4, 1.38 and 2.79 respectively. CONCLUSION: The main conclusions of this study are: (i) the prevalence of the appropriate use of blood products was 51%; (ii) packed red cells and fresh frozen plasma were the blood products with the lowest prevalence of appropriate use; and (iii) none of the departments showed rates of appropriate use of transfusions greater than 80%, implying a higher cost in health care and putting patients at a higher risk for acquiring a transfusion-transmitted disease.