Structure of health-related quality of life among people with and without functional limitations
- 158 Downloads
The objective of this study was to assess the factor structure of nine health-related quality of life (HRQOL) survey items among people with and without disabilities or functional limitations (FL) and determine whether factor loadings were similar for the two groups.
Data were from US states and territories in the 2001 and 2002 Behavioral Risk Factor Surveillance System (BRFSS). Confirmatory factor analyses assessed fit of the data to a previously found factor structure.
A two-factor structure was confirmed, conceptually representing physical and mental health. Although this structure fit data for both people with and without FL, factor loadings were significantly different for the two groups. In all but one instance, factor loadings were higher for people with FL than for people without FL.
Results suggest that people with and without FL conceptualize physical and mental HRQOL similarly. However, the nine items analyzed appear to be a better reflection of the latent constructs of physical and mental HRQOL in the population of people with FL than those without FL.
KeywordsHealth-related quality of life Functional limitation Questionnaires Factor analysis
Behavioral Risk Factor Surveillance System
Centers for Disease Control and Prevention
Comparative fit index
Health-related quality of life
Mean and variance-adjusted weighted least squares
Root mean square error of approximation
The members of the Rehabilitation Research and Training Center (RRTC) Expert Panel on Health Status Measurement are: Elena Andresen, PhD, University of Florida, Gainesville, Florida; Vincent Campbell, PhD, Centers for Disease Control and Prevention, Atlanta, Georgia; Bradley J. Cardinal, PhD, Oregon State University, Corvallis, Oregon; Charles Drum, JD, PhD, Oregon Health & Science University, Portland, Oregon; Glenn Fujiura, PhD, University of Illinois at Chicago, Chicago, Illinois; Trevor Hall, PsyD, Oregon Health & Science University; Willi Horner-Johnson, PhD, Oregon Health & Science University; Gloria Krahn, PhD, Centers for Disease Control and Prevention; and Margaret Nosek, PhD, Baylor College of Medicine, Houston, Texas. The authors thank Mo Wang for expert consultation and data analysis and Susan Wingenfeld for assistance with references and formatting. The contents of this article were developed under a grant from the Department of Education, NIDRR grant number H133B040034. However, those contents do not necessarily represent the policy of the Department of Education, and you should not assume endorsement by the Federal Government.
- 3.Miilunpalo, S., Vuori, I., Oja, P., Pasanen, M., & Urponen, H. (1997). Self-rated health status as a health measure: The predictive value of self-reported health status on the use of physician services and on mortality in the working-age population. Journal of Clinical Epidemiology, 50, 517–528.CrossRefPubMedGoogle Scholar
- 8.CDC. (1994). Quality of life as a new public health measure–Behavioral Risk Factor Surveillance System, 1993. Morbidity and Mortality Weekly Reports, 43, 375–380.Google Scholar
- 9.CDC. (2000). Measuring healthy days. Atlanta, GA: Centers for Disease Control and Prevention.Google Scholar
- 11.CDC. (2005). Prevalence of epilepsy and health-related quality of life and disability among adults with epilepsy–South Carolina, 2003 and 2004. Morbidity and Mortality Weekly Report, 54, 1080–1082.Google Scholar
- 12.McGuire, L. C., Strine, T. W., Okoro, C. A., Ahluwalia, I. B., Ford, E. S. (2006). Healthy lifestyle behaviors among older US adults with and without disabilities, Behavioral Risk Factor Surveillance System, 2003. Preventing Chronic Disease, 4, 11 (published online).Google Scholar
- 16.Jiang, Y., Hesser, J. E. (2006). Associations between health-related quality of life and demographics and health risks. Results from Rhode Island’s 2002 behavioral risk factor survey. Health & Quality of Life Outcomes, 4, 10 (published online).Google Scholar
- 18.Schwartz, C. E., Andresen, E. M., Nosek, M. A., Krahn, G. L., Rapkin, B., & The RRTC Expert Panel on Health Measurement. (2007). Response shift theory: Important implications for measuring quality of life in individuals with disability. Archives of Physical Medicine and Rehabilitation, 88, 529–536.CrossRefPubMedGoogle Scholar
- 23.Mielenz, T., Jackson, E., Currey, S., DeVellis, R., Callahan, L. F. (2006). Psychometric properties of the Centers for Disease Control and Prevention health-related quality of life (CDC HRQOL) items in adults with arthritis. Health & Quality of Life Outcomes, 4:66 (p. 10) (published online).Google Scholar
- 24.Qualls-Hampton, R. (2007) Health-related quality of life and pain in an SCI population: A descriptive and factor analysis study. Chicago, IL: University of Illinois at Chicago (Unpublished dissertation).Google Scholar
- 25.SAS Institute Inc. (2004). SAS 9.12 for windows. Cary: NC SAS Institute, Inc.Google Scholar
- 26.Muthén, L. K., & Muthén, B. O. (2005). Mplus 5.2. Los Angeles, CA: Muthén & Muthén.Google Scholar
- 27.Muthén, B., du Toit, S. H. C., Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Accessed December 30, 2008 from http://www.gseis.ucla.edu/faculty/muthen/articles/Article_075.pdf.
- 29.Yu, C. Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Accessed December 30, 2008 from http://www.statmodel.com/download/Yudissertation.pdf.