Predicting Health-Related Quality of Life Among Chinese Residents: Latent Class Analysis Based on Panel Survey Data.

Purpose: This study aimed to identify distinct trends among Chinese residents based on their health-related quality of life (HR-QoL) outcomes and to analyze the demographic characteristics that contribute to these trends.

Materials and methods: The study conducted latent class analysis using baseline data obtained from a survey of health service utilization behaviors (from July to December 2016) among residents of Hubei Province, China (N = 1517). Latent classes were used to implement the HR-QoL grouping of different trends among the respondents. Multinomial logistic regression analysis was used to identify demographic characteristic factors affecting HR-QoL in the trend groups.

Results: A three-class model emerged as the most suitable grouping classification for HR-QoL among Chinese residents: the low HR-QoL class, exhibiting a downward trend (5.5%); the medium HR-QoL class, exhibiting an upward trend (12.1%); and the stable HR-QoL class, exhibiting high HR-QoL (82.4%). Participants in the medium class were more likely to be without chronic diseases, aged 45-64 years, and employed than those in the low class. Conversely, urban participants had a higher likelihood of belonging to the low class. Participants in the stable class were more likely to be without chronic diseases, aged 15-44 years or 45-64 years, and employed than those in the low class. Conversely, urban participants had a higher likelihood of belonging to the low class.

Conclusion: Three latent trend classes of HR-QoL were observed, which exhibited distinct characteristics. Residents without chronic diseases, residents under 65 years of age, and employed residents had better HR-QoL than individuals in other classes, while urban residents had poorer HR-QoL than individuals in other classes.