Search

Purpose: To investigate if sociodemographic characteristics increase the adverse effects of cardiovascular diseases (CVD) and cardiometabolic risk factors (CMRF) on health-related quality of life (HRQoL).Methods: Cross-sectional, face-to-face survey investigating 2379 adults living in South Australia in 2015 (57.1 ± 14 years; 51.7% females). Questions included diagnosis of CMRF (obesity, diabetes, hypertension, dyslipidaemia) and CVD. Physical and mental HRQoL were assessed using the SF-12v1 questionnaire. Multiple linear regression models including confounders (sociodemographic, lifestyle, use of preventive medication) and interaction terms between sociodemographic variables and cardiometabolic conditions were used in adjusted analysis.Results: The prevalence of CMRF (one or more) was 54.6% and CVD was 13.0%. The physical HRQoL reduced from 50.8 (95%CI 50.2-51.4) in healthy individuals to 45.1 (95%CI 44.4-45.9) and 39.1 (95%CI 37.7-40.5) among those with CMRF and CVD, respectively. Adjustment for sociodemographic variables reduced these differences in 33%, remaining stable after controlling for lifestyle and use of preventive medications (p < 0.001). Differences in physical HRQoL according to cardiometabolic conditions were twice as high among those with lower educational level, or if they were not working. Among unemployed, having a CMRF or a CVD had the same impact on the physical HRQoL (9.7 lower score than healthy individuals). The inverse association between cardiometabolic conditions and mental HRQoL was subtle (p = 0.030), with no evidence of disparities due to sociodemographic variables.Conclusions: A lower educational level and unemployment increase the adverse effects of cardiometabolic conditions on the physical HRQoL. Targeted interventions for reducing CMRF and/or CVD in these groups are necessary to improve HRQoL. [ABSTRACT FROM AUTHOR]

Purpose: To compare the measurement properties of the physical component summary (PCS) and mental component summary (MCS) scores of the SF-36 and SF-12 based on the traditional orthogonal scoring algorithms with the performance of the PCS and MCS scored based on structural equation model coefficients from a correlated model. Methods: This study used three large-scale representative population studies to compare the measurement properties of the PCS and MCS scores of the SF-36 and SF-12 with the performance of the PCS and MCS scores based on structural equation models producing coefficients from a correlated model. We assessed the relationships of these scores with selected important mental health measures and chronic conditions from three representative Australian population studies that address clinical conditions of high prevalence and health service importance. Results: Structural equation model scoring methods produced summary scores with higher correlations than the recommended orthogonal methods across a range of disease and health conditions. The problem experienced in using the orthogonal methods is that negative scoring coefficients are applied to negative z-scores for sub-scales, inflating the resulting summary scores. Effect sizes over a half of a standard deviation were common. Conclusions: If health policy or investment decisions are made based on the results of studies employing the recommended orthogonal scoring methods then the expected outcome of such decisions or investments may not be achieved. [ABSTRACT FROM AUTHOR]