Skip to main content
Mijn bibliotheek
Shop
Nieuws Boeken Tijdschriften Toetsvragen Web-tv Video Audio
Tips voor Mijn BSL
Inloggen
Top
Quality of Life Research
Gepubliceerd in:

01-06-2013

The utility of the behavioral risk factor surveillance system (BRFSS) in testing quality of life theory: an evaluation using structural equation modeling

Auteurs: Victoria M. Rizzo, Evelyn Kintner

Gepubliceerd in: Quality of Life Research | Uitgave 5/2013

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail
    Link delen
publish
Publiceer nu

Abstract

Purpose

This study uses structural equation modeling to assess the utility of the behavioral risk factor surveillance system (BRFSS)—the Centers for Disease Control’s premier surveillance tool for monitoring behavioral risk factors—in predicting health-related quality of life (HRQoL).

Methods

Using SPSS/AMOS (version 18), the study utilizes New York State data extracted from the 2007 BRFSS national dataset to test a well-known HRQoL model developed by Wilson and Cleary (J Am Med Assoc 59–65, 2). The analysis represents an exploratory study that seeks to identify new applications for this important epidemiological database as well as a theoretical evaluation that examines the robustness of our current understanding of HRQoL.

Results

Findings support the Wilson and Cleary (J Am Med Assoc 59–65, 2) model, with the final model producing fit indices well within the thresholds traditionally used as benchmarks of good fit.

Conclusions

The integrity of the Wilson and Cleary (J Am Med Assoc 59–65, 2) model was substantiated, and the utility of BRFSS data for operationalization of HRQoL concepts was demonstrated successfully. This study has: (1) expanded the role of epidemiological research to include whole theory testing; and (2) successfully operationalized the Wilson and Cleary (J Am Med Assoc 59–65, 2) model using available, non-clinical data, which represents a major methodological contribution to the study of HRQoL.
vorige artikel Visual analogue scales: scale recalibration by patients with dementia and their proxies
volgende artikel Non-severe nocturnal hypoglycemic events: experience and impacts on patient functioning and well-being
Voetnoten
1
Referred to as “The Model” in this paper.
 
Literatuur
1.
Remington, P., Simoes, E., Brownson, R., & Siegel, P. (2003). The role of epidemiology in chronic disease prevention and health promotion programs. Journal of Public Health Management Practice, 9(4), 258–265.
2.
Wilson, I., Cleary, P. (1995). Linking critical variables with health-related quality of life. Journal of the American Medical Association, 273(1), 59–65.
3.
Sousa, K. H., & Kwok, O.-M. (2006). Putting Wilson and Cleary to the test: Analysis of a HRQOL conceptual model using structural equation modeling. Quality of Life Research, 15, 725–737.PubMedCrossRef
4.
Vidrine, D., Amick, B., I. I. I., Gritz, E. R., & Arduino, R. C. (2005). Assessing a conceptual framework of health-related quality of life in a HIV/AIDS population. Quality of Life Research, 14, 923–933.PubMedCrossRef
5.
Sousa, K. H., & Chen, F. F. (2002). A theoretical approach to measuring quality of life. Journal of Nursing Measurement, 10(1), 47–58.PubMedCrossRef
6.
Wettergren, L., Bjorkholm, M., Axdorph, U., & Langius-Eklof, A. (2004). Determinants of health-related quality of life in long-term survivors of Hodgkin’s lymphoma. Quality of Life Research, 13, 1369–1379.PubMedCrossRef
7.
Sousa, K., & Williamson, A. (2003). Symptom status and health-related quality of life: Clinical relevance. Journal of Advanced Nursing, 42(6), 571–577.PubMedCrossRef
8.
Baker, S. R., Pankhurst, C. L., & Robinson, P. G. (2007). Testing relationships between clinical and non-clinical variables in xerostomia: A structural equation model of oral health-related quality of life. Quality of Life Research, 16, 297–308.PubMedCrossRef
9.
Heo, S., Moser, D. K., Riegel, B., Hall, L. A., & Christman, N. (2005). Testing a published model of health-related quality of life in heart failure. Journal of Cardiac Failure, 11(5), 372–379.PubMedCrossRef
10.
Register, M. E., & Herman, J. (2006). A middle range theory for generative quality of life for the elderly. Advances in Nursing Science, 29(4), 340–350.PubMed
11.
Schalock, R. L., Bonham, G., & Marchand, C. B. (2000). Consumer based quality of life assessment: A path model of perceived satisfaction. Evaluation and Program Planning, 23, 77–87.CrossRef
12.
Centers for Disease Control and Prevention. (2008). Racial/ethnic disparities in self-rated health status among adults with and without disabilities—United States. Morbidity and Mortality Weekly Report, 57, 1069–1073.
13.
Nelson, D. E., Holtzman, D., Bolen, J., Stanwyck, C. A., & Mack, K. A. (2001). Reliability and validity of measures from the behavioral risk factor surveillance system (BRFSS). Social and Prevent Medicine, 46(Suppl 1), S03–S42.
14.
Nelson, D. E., Powell-Griner, E., Town, M., & Kovar, M. G. (2003). A comparison of national estimates from the national health interview survey and the behavior risk factor surveillance system. American Journal of Public Health, 93(8), 1335–1341.PubMedCrossRef
15.
16.
Greenlund, K.J., Denny, C.H., Mokdad, A.H., Watkins, N., Crost, J.B., & Mensah, G.A. (2005). Using behavioral risk factor surveillance data for heart disease and stroke prevention. American Journal of Preventive Medicine, 29 (5S1), 81–87.
17.
Hayes, D. D. (2006). Racial/ethnic and socioeconomic differences in multiple risk factors for heart disease and stroke in women: Behavioral risk factor surveillance system, 2003. Journal of Women’s Health, 15(9), 1000–1008.PubMedCrossRef
18.
Campostrini, S., Holtzman, D., McQueen, D., & Boaretto, E. (2206). Evaluating the effectiveness of health promotion policy: Changes in the law on drinking and driving in California. Health Promotion International, 21 (2), 130–135.
19.
Moriarty, D., Zack, M., & Kobau, R. (2003). The centers for disease control and prevention’s health days measures–population tracking of perceived physical and mental health over time. Resource document. http://​www.​hqlo.​com/​content/​1/​1/​37. Accessed April 20, 2008.
20.
21.
Sloan, J. A. (2005). Statistical issues in the application of cancer outcome studies. In J. Lipscomb, C. C. Gotay, & C. Snyder (Eds.), Outcomes assessment in cancer: Measures, methods, and applications (pp. 362–385). Cambridge, UK: Cambridge University Press.
22.
Hui, C. H., & Triandis, H. C. (1985). Measurement in cross-cultural psychology: A review and comparison of strategies. Journal of Cross-Cultural Psychology, 16(2), 131–152.CrossRef
23.
Gotay, C. C., Lipscomb, J., & Synder, C. F. (2005). Reflections on findings of the cancer outcomes measurement working group: Moving to the next phase. Journal of the National Cancer Institute, 97(21), 1568–1574.PubMedCrossRef
24.
Schmidt, P., Clouth, J., Haggenmuller, L., Naber, D., & Reitberger, U. (2006). Constructing an index for the subjective well-being under neuroleptics scale (SWN), short form: Applying structural equation modeling for testing reliability and validity of the index. Quality of Life Research, 15, 1191–1202.PubMedCrossRef
25.
Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York City: The Guilford Press.
26.
27.
Keith, T. (2005). Multiple regression and beyond. Boston, MA: Pearson.
28.
Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum Associates.
29.
King, C. (1998). Overview of quality of life and controversial issues. In C. King & P. Hinds (Eds.), Quality of life from nursing and patient perspectives: Theory, research, practice (pp. 23–34). Sudbury, MA: Jones and Bartlett Publishers.
30.
Fawcett, J., & Downs, F. S. (1986). The relationship of theory and research. Norwalk, CT: Appleton-Century-Crofts.
Metagegevens
Titel
The utility of the behavioral risk factor surveillance system (BRFSS) in testing quality of life theory: an evaluation using structural equation modeling
Auteurs
Victoria M. Rizzo
Evelyn Kintner
Publicatiedatum
01-06-2013
Uitgeverij
Springer Netherlands
Gepubliceerd in
Quality of Life Research / Uitgave 5/2013
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-012-0228-1

Andere artikelen Uitgave 5/2013

Mapping of the PDQ-39 to EQ-5D scores in patients with Parkinson’s disease

Discriminant and convergent validity of a subjective quality-of-life instrument aimed at high content validity for schizophrenic persons

Evaluation of a role functioning computer adaptive test (RF-CAT)

Usefulness of EQ-5D for evaluation of health-related quality of life in young adults with first-episode psychosis

A comparison of the ICECAP-O with EQ-5D in a falls prevention clinical setting: are they complements or substitutes?

The psychometric properties of the Migraine-Specific Quality of Life Questionnaire version 2.1 (MSQ) in chronic migraine patients

  • Open Access