Jiseon Kim, Ph.D., Hyewon Chung, Ph.D., Dagmar Amtmann, Ph.D., Rana Salem, M.A., Ryoungsun Park, M.S., Robert L. Askew, M.P.H.
DOI: http://dx.doi.org/10.1016/j.dhjo.2013.08.007
Background
Children with chronic conditions often experience numerous symptoms, but few research studies examine patterns of symptoms and quality of life (QoL) indicators.
Objective
To examine if reliable latent classes of children with chronic medical conditions can be identified based on the clustering of symptoms and QoL indicators.
Methods
Structured interviews were conducted with children ages 9–21 living with chronic medical conditions (N = 90). Multiple symptoms (e.g., pain, sleep, fatigue, and depression) and QoL indicators (e.g., life satisfaction and social support) were measured. Physical health and emotional, social, and school functioning were measured using the Pediatric Quality of Life Inventory (PedsQL). Latent class analysis was used to classify each child into a latent class whose members report similar patterns of responses.
Results
A three-class solution had the best model fit. Class 1 (high-symptom group; n = 15, 16.7%) reported the most problems with symptoms and the lowest scores on the QoL indicators. Class 2 (moderate-symptom group; n = 39, 43.3%) reported moderate levels of both symptoms and QoL indicators. Class 3 (low-symptom group; n = 36, 40.0%) reported the lowest levels of symptoms and the highest scores on the QoL indicators.
Conclusions
The three latent classes identified in this study were distributed along the severity continuum. All symptoms and QoL indicators appeared to move in the same direction (e.g., worse symptoms with lower QoL). The PedsQL psychosocial health summary score (combining emotional, social, and school functioning scores) discriminated well between children with different levels of disease burden.