Background Cardio-vascular disease and depression are thought to be closely related, due to shared risk factors. The aim of the study was to determine the association between cardio-vascular risk ...(CVR) factors and depressive status in a population (55–75 years) with metabolic syndrome (MetS) from the PREDIMED-Plus trial. Methods and findings Participants were classified into three groups of CVR according to the Framingham-based REGICOR function: (1) low (LR), (2) medium (MR) or (3) high/very high (HR). The Beck Depression Inventory-II (BDI-II) was used to assess depressive symptoms at baseline and after 2 years. The association between CVR and depressive status at baseline (n = 6545), and their changes after 2 years (n = 4566) were evaluated through multivariable regression models (logistic and linear models). HR women showed higher odds of depressive status than LR OR (95% CI) = 1.78 (1.26, 2.50). MR and HR participants with total cholesterol <160 mg/mL showed higher odds of depression than LR OR (95% CI) = 1.77 (1.13, 2.77) and 2.83 (1.25, 6.42) respectively) but those with total cholesterol ≥280 mg/mL showed lower odds of depression than LR OR (95% CI) = 0.26 (0.07, 0.98) and 0.23 (0.05, 0.95), respectively. All participants decreased their BDI-II score after 2 years, being the decrease smaller in MR and HR diabetic compared to LR adjusted mean±SE = -0.52±0.20, -0.41±0.27 and -1.25±0.31 respectively). MR and HR participants with total cholesterol between 240–279 mg/mL showed greater decreases in the BDI-II score compared to LR (adjusted mean±SE = -0.83±0.37, -0.77±0.64 and 0.97±0.52 respectively). Conclusions Improving cardiovascular health could prevent the onset of depression in the elderly. Diabetes and total cholesterol in individuals at high CVR, may play a specific role in the precise response. International Standard Randomized Controlled Trial (ISRCTN89898870).
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
El estudio de eficiencia en unidades de salud contribuye a diseñar estrategias que permitan alcanzar mejores resultados con los recursos disponibles. El objetivo de la presente investigación fue ...desarrollar un algoritmo que permitiera la medición de la eficiencia en instituciones sanitarias cubanas y mexicanas, con el propósito de identificar las unidades de mejor práctica productiva e impulsar la eficiencia productiva mediante la incorporación de procesos gerenciales de benchmarking. El algoritmo se construyó sobe la base de la revisión bibliográfica y la experiencia de los autores. Se aplicó para medir la eficiencia de los centros de salud de Tabasco, México y de los policlínicos de las provincias de Cienfuegos y Matanzas, Cuba. Se definieron 15 operaciones que combinaron técnicas cualitativas y cuantitativas. Se seleccionó el Análisis Envolvente de Datos como técnica para medir la eficiencia. Más de la mitad de las entidades estudiadas resultaron ineficientes. En todas las unidades ineficientes, se identificaron áreas potenciales de mejoramiento de la eficiencia. The study of the efficiency of health units contributes to design strategies for attaining better results with the available resources. The objective was to develop an algorithm for measuring the efficiency at a health care institution, in order to identify the units with better productive practice; and to impel the productive efficiency by means of the incorporation of management processes of benchmarking. The algorithm developed was made on the basis of bibliographic revision and the knowledge of experts. Qualitative and quantitative techniques were combined in the algorithm. It was applied to measure the efficiency of centers of health of Tabasco, Mexico and the efficiency of polyclinics of the provinces of Cienfuegos and Matanzas, Cuba. 15 operations were defined. Data Envelopment Analysis was the technique used to measure the efficiency. More 50% of the studied units were inefficient. In all the inefficient units, potential areas for improving the efficiency and references units were found. KEY WORDS: efficiency, data envelopment analysis, health care institutions
