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

Area of Science:

• Public Health
• Health Services Research
• Quality of Life Studies

Background:

• Health-related quality of life (HR-QoL) is a crucial indicator of population health.
• Understanding trends in HR-QoL and associated demographic factors is essential for targeted health interventions.
• Previous research has highlighted variations in HR-QoL across different populations, necessitating localized studies.

Purpose of the Study:

• To identify distinct health-related quality of life (HR-QoL) trends among Chinese residents.
• To analyze demographic characteristics associated with these identified HR-QoL trends.
• To inform public health strategies by understanding factors influencing HR-QoL in China.

Main Methods:

• Latent class analysis was employed using baseline data from a 2016 survey of health service utilization behaviors in Hubei Province, China (N=1517).
• Latent classes were utilized to group respondents based on distinct HR-QoL trends.
• Multinomial logistic regression analysis identified demographic factors influencing HR-QoL within these trend groups.

Main Results:

• A three-class model best represented HR-QoL trends: low HR-QoL (5.5%, downward trend), medium HR-QoL (12.1%, upward trend), and stable high HR-QoL (82.4%).
• The medium HR-QoL class was associated with being without chronic diseases, aged 45-64, and employed, compared to the low HR-QoL class.
• Urban residents were more likely to belong to the low HR-QoL class, while those without chronic diseases and employed individuals (aged 15-64) were more likely to be in the stable high HR-QoL class.

Conclusions:

• Three distinct latent trend classes of HR-QoL were identified among Chinese residents.
• Factors such as absence of chronic diseases, younger age (under 65), and employment status were linked to better HR-QoL.
• Urban residency was associated with a higher likelihood of experiencing poorer HR-QoL.