Beyond diagnoses: The importance of social circumstances and lifestyle factors in explaining health-related quality of life and subjective well-being

Abstract

Background: There exist different descriptive system for reporting Health Related Quality of Life (HRQoL) and Subjective Well-being (SWB). Comparisons of results obtained from respondents shows not only diseases or health status that influence the result, but also social circumstance and behavioural factors are important to consider when analysing the results obtained when measuring HRQoL and SWB. Methods: The thesis is using a data set obtained from the Multi Instrument Comparison-study comparing different values on outcome measurements regarding health related quality of life and subjective well-being. The data set contains almost 8000 respondents from six different countries, divided into eight groups. In seven of the groups, the respondents have different chronic disease condition, and the last group consists of healthy respondents. In this thesis it is done a multivariate linear regression to compare outcome score on EQ-5D, SF6D, VAS and SWLS. It is also done comparisons between the outcomes by the use of a decomposition table explaining the total variance seen by the different regression models. Result: The linear regression model explains between 34-40 % of the total variance seen on the outcome measurements. Improved standard of living, higher education and marital status improve the outcome scoring. Smoking and obesity affects the outcome score negatively. Improvement is seen in the score when increasing levels of physical activity. Age and gender influence the outcomes in different ways. Discussion: The analysis shows that social position and health related behaviour have impact on the outcome score, and that it is necessary to include in analysis regarding HRQoL and SWB. In addition, it also shows a gender difference and differences caused by age, so these variables also needs to be included when examining differences in HRQoL and SWB. Conclusion: It is necessary to adjust for social position and health related behaviour when analysing measurements of HRQoL and SWB. Social position can account for almost 70 % of the variance seen by the regression model for SWLS, and around 30% of the variance in HRQoL.