Predictors of health-related quality of life in the general population

Abstract

Objective To evaluate health-related quality of life (HRQoL) in the general population in China and research on the predictive factors of HRQoL by using the short form 36 item (SF-36) questionnaire. Methods Using large sample data of health status cross-section survey from general population of nine provinces and municipalities (Jiangsu, Anhui, Gansu, Qinghai, Fujian, Beijing, Jilin, Jiangxi and He'nan) from December 2005 to January 2007,8448 samples in accord with sample data from 1% nationwide population survey in 2005 were randomly selected with sex and age structures, which could representative the general population of China. The relationship between HRQoL score and socio-demographic characteristics,behavior and health-related factors were studied by using variables single factor analysis,and found out the predictive factors of HRQoL by using multiple stepwise regression analysis. Results All sub-scales scores were similar trend of change between Chinese general population and Hong Kong general norm. Single factor analysis showed that the decreasing of HRQoL score was related with sex, age, marital status, education level, occupation, sleep time, exercise habit, body mass index and past history of chronic disease. Multiple stepwise regression analysis showed that there were differences among significant predictive factors of each domain of SF-36;in all eight domains, the first six significant predictive factors have twelve indexes. Among them, the predictive factors of age increase, low-level exercises, past history of chronic disease, farmers, inoccupation people, students,women, less sleep time and low weight leaded to the decrement in HRQoL score, married and higher education were protective factors for HRQoL. Conclusion Age, sex, marital status, education level, occupation, sleep time, exercise habit, body mass index and past history of chronic disease were the predictive factors for HRQoL. Key words: Health-related quality of life; SF-36; Multiple stepwise regression analysis; Predictive factors