Comparative well-being burden of common health conditions and behaviors

Objectives To assess well-being among Chinese university students and quantify the well-being burden associated with common health conditions and health-related behaviors using the EQ Health and Wellbeing Short Version (EQ-HWB-9). Methods A nationwide cross-sectional survey of Chinese university students was conducted. Well-being of the students was assessed using the EQ Health and Wellbeing Short Version (EQ-HWB-9). Seven common health determinants were evaluated: self-reported chronic disease, symptoms, injuries, vision problems, sleep problems, smoking, and alcohol consumption. The EQ-HWB-9 utility decrement associated with each determinant was estimated using multiple regression models, and their annual quality-adjusted life-year (QALY) losses per 100,000 persons were calculated based on their respective prevalence and utility decrement. Logistic regression analyses were further performed to examine associations between health determinants and specific EQ-HWB dimensions. Results A total of 3,589 university students were included (mean age 20.7 years; 53.4% female). Despite relatively high overall well-being (mean EQ-HWB-9 utility score: 0.86), the well-being burden was disproportionately concentrated in several health determinants. Sleep problems (prevalence 60.9%) were associated with the largest utility decrement (β = −0.208) and the greatest annual QALY loss per 100,000 persons (5,420.1), followed by vision problems (prevalence 45.8%; β = −0.148; QALY loss 4,076.2) and chronic diseases (prevalence 18.3%; β = −0.052; QALY loss 1,079.7). Sleep problems also exerted broad multidimensional effects across cognitive, emotional, and psychosocial domains. Conclusions Although overall well-being among Chinese university students remained relatively high, the well-being burden was highly concentrated in sleep problems, vision problems, and chronic diseases. These findings highlight the value of preference-based measures for well-being burden assessment. By applying the EQ-HWB-9 in a university population, this study also extends the evidence base for preference-based well-being measurement and provides a framework for identifying priority targets for student well-being promotion.