47 resultados para socio-demographic differences
Resumo:
The present study estimated the prevalence of metabolic syndrome (MS) according to the criteria established by the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATPIII) and the International Diabetes Federation (IDF) and analyzed the contribution of social factors in an adult urban population in the Southeastern region of Brazil. The sample plan was based on multistage probability sampling according to family head income and educational level. A random sample of 1116 subjects aged 30 to 79 years was studied. Participants answered a questionnaire about socio-demographic variables and medical history. Fasting capillary glucose (FCG), total cholesterol, high-density lipoprotein cholesterol (HDL-C), and triglycerides were determined and all non-diabetic subjects were submitted to the 75-g oral glucose tolerance test. Body mass index (BMI, kg/m²), waist circumference and blood pressure (BP) were determined. Age- and gender-adjusted prevalence of MS was 35.9 and 43.2% according to NCEP-ATPIII and IDF criteria, respectively. Substantial agreement was found between NCEP-ATPIII and IDF definitions. Low HDL-C levels and high BP were the most prevalent MS components according to NCEP-ATPIII criteria (76.3 and 59.2%, respectively). Considering the diagnostic criteria adopted, 13.5% of the subjects had diabetes and 9.7% had FCG ≥100 mg/dL. MS prevalence was significantly associated with age, skin color, BMI, and educational level. This cross-sectional population-based study in the Southeastern region of Brazil indicates that MS is highly prevalent and associated with an important social indicator, i.e., educational level. This result suggests that in developing countries health policy planning to reduce the risk of MS, in particular, should consider improvement in education.
Resumo:
Introduction: Numerous studies examined the associations between socio-demographic, economic and individual factors and chronic kidney disease (CKD) outcomes and observed that the associations were complex and multifactorial. Socioeconomic factors can be evaluated by a model of social vulnerability (SV). Objective: To analyze the impact of SV on the outcomes of predialysis patients. Methods: Demographic, clinical and laboratory data were collected from a cohort of patients with predialysis stage 3 to 5 who were treated by an interdisciplinary team (January 2002 and December 2009) in Minas Gerais, Brazil. Factor, cluster and discriminant analysis were performed in sequence to identify the most important variables and develop a model of SV that allowed for classification of the patients as vulnerable or non-vulnerable. Cox regression was performed to examine the impact of SV on the outcomes of mortality and need for renal replacement therapy (RRT). Results: Of the 209 patients examined, 29.4% were classified as vulnerable. No significance difference was found between the vulnerable and non-vulnerable groups regarding either mortality (log rank: 0.23) or need for RRT (log rank: 0.17). In the Cox regression model, the hazard ratios (HRs) for the unadjusted and adjusted impact of SV on mortality were found to be 1.87 (confidence interval [CI]: 0.64-5.41) and 1.47 (CI: 0.35-6.0), respectively, and the unadjusted and adjusted impact of need for RRT to be 1.85 (CI: 0.71-4.8) and 2.19 (CI: 0.50-9.6), respectively. Conclusion: These findings indicate that SV did not influence the outcomes of patients with predialysis CKD treated in an interdisciplinary center.