Monitoring population health for Healthy People 2020: evaluation of the NIH PROMIS® Global Health, CDC Healthy Days, and satisfaction with life instruments
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Healthy People 2020 identified health-related quality of life and well-being (WB) as indicators of population health for the next decade. This study examined the measurement properties of the NIH PROMIS® Global Health Scale, the CDC Healthy Days items, and associations with the Satisfaction with Life Scale.
A total of 4,184 adults completed the Porter Novelli’s HealthStyles mailed survey. Physical and mental health (9 items from PROMIS Global Scale and 3 items from CDC Healthy days measure), and 4 WB factor items were tested for measurement equivalence using multiple-group confirmatory factor analysis.
The CDC items accounted for similar variance as the PROMIS items on physical and mental health factors; both factors were moderately correlated with WB. Measurement invariance was supported across gender and age; the magnitude of some factor loadings differed between those with and without a chronic medical condition.
The PROMIS, CDC, and WB items all performed well. The PROMIS items captured a broad range of functioning across the entire continuum of physical and mental health, while the CDC items appear appropriate for assessing burden of disease for chronic conditions and are brief and easily interpretable. All three measures under study appear to be appropriate measures for monitoring several aspects of the Healthy People 2020 goals and objectives.
KeywordsHealth-related quality of life Well-being Measurement invariance Structural equation modeling Population health Healthy People 2020
Barile and Luncheon were supported in part by an appointment to the Research Participation Program for the Centers for Disease Control and Prevention (CDC) administered by the Oak Ridge Institute for Science and Education through an agreement between the US Department of Energy and CDC. Cella was supported by a grant from the National Institutes of Health to the PROMIS Statistical Center (U54AR057951-02). We would like to thank Adam Burns and Bill Pollard of Porter Novelli for reviewing drafts of this manuscript.
Conflict of interest
The authors do not report any conflicts of interest with presenting these findings.
- 1.US Department of Health and Human Services. (2011). Foundation health measures. Healthy People 2020. Retrieved July 5, 2011.Google Scholar
- 2.Centers for Disease Control and Prevention. (2000). Measuring Healthy Days. Atlanta, GA: U.S. Department of Health and Human Services.Google Scholar
- 6.Fayers, P. M., & Machin, D. (2007). Quality of life: The assessment, analysis and interpretation of patient-reported outcomes. Chichester: Wiley.Google Scholar
- 7.National Center for Health Statistics. (1994). In Proceedings of the 1993 NCHS conference on the cognitive aspects of self-reported health status. Unpublished manuscript.Google Scholar
- 8.National Center for Chronic Disease Prevention and Health Promotion. (1993). Consultation on functional status surveillance for states and communities: Decatur, Georgia, June 4–5, 1992: Meeting report. US Dept. of Health & Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.Google Scholar
- 9.Booske, B. C., Kindig, D. A., Remington, P. L., Kempf, A. M., & Peppard, P. E. (2006). How should we measure health-related quality of life in Wisconsin? University of Wisconsin Population Health Institute Brief Report (Vol. 1). University of Wisconsin Population Health Institute.Google Scholar
- 11.Jiang, Y., & Hesser, J. E. (2009). Using item response theory to analyze the relationship between health-related quality of life and health risk factors. Preventing Chronic Disease, 6(1), 1–10.Google Scholar
- 12.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 and Quality of Life Outcomes, 4, 66–84.PubMedCrossRefGoogle Scholar
- 13.Horner-Johnson, W., Suzuki, R., Krahn, G., Andresen, E., Drum, C., & The RRTC Expert Panel on Health. (2010). Structure of health-related quality of life among people with and without functional limitations. Quality of Life Research, 19(7), 977–984.Google Scholar
- 14.Parrish, R. G. (2010). Measuring population health outcomes. Preventing Chronic Disease: Public Health Research, Practice, and Policy, 7(4), 1–11.Google Scholar
- 15.Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (2010). The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194.PubMedCrossRefGoogle Scholar
- 16.Gallup-Healthways Well-Being Index. (2008). www.well-beingindex.com.
- 21.Bann, C. M., Kobau, R., Lewis, M. A., Zack, M. M., Luncheon, C., & Thompson, W. W. (2011). Development and psychometric evaluation of the public health surveillance well-being scale. Quality of Life Research, 21(6), 1031–1043.Google Scholar
- 22.Porter Novelli International. (2010). HealthStyles survey. Washington, DC: Porter Novelli.Google Scholar
- 26.Pavot, W. (2008). The assessment of subjective well-being. New York: The Guilford Press.Google Scholar
- 28.Larsen, R. J., & Eid, M. (2008). Ed Diener and the science of subjective well-being. New York: The Guilford Press.Google Scholar
- 31.Muthen, B. O., & Muthen, L. K. (2010). Mplus statistical analysis with latent variables (Version 6.0). Los Angeles: Muthen & Muthen.Google Scholar
- 36.Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: The Guilford Press.Google Scholar
- 41.Gandek, B., Ware, J. E., Jr, Aaronson, N. K., Alonso, J., Apolone, G., Bjorner, J., et al. (1998). Tests of data quality, scaling assumptions, and reliability of the SF-36 in eleven countries: Results from the IQOLA Project. International quality of life assessment. Journal of Clinical Epidemiology, 51(11), 1149–1158.PubMedCrossRefGoogle Scholar
- 45.Rothrock, N., Hays, R., Spritzer, K., Yount, S., Riley, W., & Cella, D. (2010). Relative to the general US population, chronic diseases are associated with poorer health-related quality of life as measured by the Patient-Reported Outcomes Measurement Information System (PROMIS). Journal of Clinical Epidemiology.Google Scholar
- 46.U.S. Department of Health and Human Services. (2010). Multiple chronic conditions—A strategic framework: Optimum health and quality of life for individuals with multiple chronic conditions. Washington, DC: U.S. Department of Health and Human Services.Google Scholar
- 47.Boyd, C. M., & Fortin, M. (2010). Future of multimorbidity research: How should understanding of multimorbidity inform health system design? Public Health Reviews, 32(2), 451–474.Google Scholar