Quality of Life Research

, Volume 24, Issue 6, pp 1337–1349 | Cite as

The impact of tuberculosis on health utility: a longitudinal cohort study

  • Melissa Bauer
  • Sara Ahmed
  • Andrea Benedetti
  • Christina Greenaway
  • Marek Lalli
  • Allison Leavens
  • Dick Menzies
  • Claudia Vadeboncoeur
  • Bilkis Vissandjée
  • Ashley Wynne
  • Kevin SchwartzmanEmail author



To estimate health utility derived from the Short Form-36 (SF-36) questionnaire and Standard Gamble instrument for persons diagnosed and treated for tuberculosis (TB) disease, those diagnosed and treated for latent TB infection (LTBI), and those screened but not treated for TB disease or LTBI over the year following their diagnosis/initial assessment.


Participants were recruited at two Montreal hospitals (2008–2011) and completed the SF-36 and Standard Gamble at baseline and at follow-up visits 1, 2, 4, 6, 9, and 12 months thereafter. SF-6D health utility scores were derived from SF-36 responses. Linear mixed models were used to compare mean health utility at each evaluation and changes in health utility between participants treated for TB disease, those treated for LTBI, and those in the control group.


Of the 263 participants, 48 were treated for TB disease, 105 for LTBI, and 110 were control participants. Fifty-four percent were women, mean age was 35 years, and 90 % were foreign-born. Participants treated for TB disease reported worse health utility compared with control participants at the baseline visit (mean SF-6D: 0.69 vs. 0.81; mean Standard Gamble: 0.64 vs. 0.96). They reported successive improvement at months 1 and 2 that was then sustained throughout follow-up. Health utility reported by participants treated for LTBI and control participants was comparable throughout the study.


Treatment for TB disease had a substantial negative impact on health utility, particularly during the first 2 months of treatment. However, treatment for LTBI did not have a substantial impact.


Tuberculosis Health utility SF-6D Standard Gamble Linear mixed model regression 



This research was funded by the Canadian Institutes of Health Research (CIHR). M. Bauer was supported by the CIHR-Quebec Respiratory Health Training Program, the Research Institute of the McGill University Health Centre, and the Faculty of Medicine, McGill University.

Conflict of interest

The authors do not have any competing interests to declare.

Supplementary material

11136_2014_858_MOESM1_ESM.doc (82 kb)
Supplementary material 1 (DOC 82 kb)
11136_2014_858_MOESM2_ESM.pdf (199 kb)
Supplementary material 2 (PDF 199 kb)
11136_2014_858_MOESM3_ESM.doc (206 kb)
Supplementary material 3 (DOC 206 kb)


  1. 1.
    Bauer, M., Leavens, A., & Schwartzman, K. (2012). A systematic review and meta-analysis of the impact of tuberculosis on health-related quality of life. Quality of Life Research, 21(10), 1727–1730.CrossRefGoogle Scholar
  2. 2.
    Tuberculosis Coalition for Technical Assistance. (2006). International standards for tuberculosis care (ISTC). The Hague: Tuberculosis Coalition for Technical Assistance.Google Scholar
  3. 3.
    Public Health Agency of Canada, Canadian Thoracic Society, and the Canadian Lung Association. (2013). Canadian tuberculosis standards (7th ed.)
  4. 4.
    Menzies, D., Long, R., Trajman, A., Dion, M. J., Yang, J., Al Jahdali, H., et al. (2008). Adverse events with 4 months rifampin or 9 months isoniazid as therapy for latent TB infection: Results of a randomized trial. Annals of Internal Medicine, 149, 689–697.PubMedCrossRefGoogle Scholar
  5. 5.
    Linas, B. P., Wong, A. Y., Freedberg, K. A., & Horsburgh, C. R, Jr. (2011). Priorities for screening and treatment of latent Tuberculosis infection in the United States. American Journal of Respiratory and Critical Care Medicine, 184(5), 590–601.PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Vissandjée, B., Bates, K., Vialla, F., & Kuntz, J. (2013). Expérience d’immigration et droit à la santé, à des soins et à des services de qualité: une question de justice sociale. Revue Internationale de la Recherche Interculturelle Alterstice, 3(1), 67–83.Google Scholar
  7. 7.
    Ware, J. E., Kosinski, M., & Keller, S. D. (1994). SF-36 Physical and Mental Health Summary Scales: A user’s manual. Boston: The Health Institute.Google Scholar
  8. 8.
    Rossi, C., Zwerling, A., Thibert, L., Rivest, P., McIntosh, F., Behr, M. A., et al. (2012). Mycobacterium tuberculosis transmission over an 11-year period in a low-incidence, urban setting. International Journal of Tuberculosis and Lung Disease, 16, 312–318.PubMedCrossRefGoogle Scholar
  9. 9.
    Brazier, J., & Roberts, J. R. (2004). The estimation of a preference-based index from the SF-12. Medical Care, 42(9), 851–859.PubMedCrossRefGoogle Scholar
  10. 10.
    Brazier, J., Roberts, J. R., & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21, 271–292.PubMedCrossRefGoogle Scholar
  11. 11.
    Dion, M. J., Tousignant, P., Bourbeau, J., Menzies, D., & Schwartzman, K. (2004). Feasibility and reliability of health-related quality of life measurements among tuberculosis patients. Quality of Life Research, 13, 653–665.PubMedCrossRefGoogle Scholar
  12. 12.
    Drummond, M. F., Sculpher, M. J., Torrance, G. W., O’Brien, B. J., & Stoddard, G. L. (2005). Methods for the economic evaluation of health care programmes (3rd ed.). New York: Oxford University Press.Google Scholar
  13. 13.
    Dion, M. J., Tousignant, P., Bourbeau, J., Menzies, D., & Schwartzman, K. (2002). Measurement of health preferences among patients with tuberculosis infection and disease. Medical Decision Making, 22(Suppl), S102–S114.PubMedCrossRefGoogle Scholar
  14. 14.
    Fisher, R. A. (1925). Statistical methods for research workers. Edinburgh: Oliver & Boyd.Google Scholar
  15. 15.
    Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical magazine series 5. doi:  10.1080/14786440009463897
  16. 16.
    Student, (1908). The probable error of a mean. Biometrika, 6(1), 1–25.CrossRefGoogle Scholar
  17. 17.
    Sloane, J., & Symonds, T. (2001). Health related quality of life: When does a statistically significant change become clinically significant?. Washington, DC: ISOQOL Educational Workshop.Google Scholar
  18. 18.
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillside, NJ, USA: Academic Press.Google Scholar
  19. 19.
    Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.CrossRefGoogle Scholar
  20. 20.
    SAS Institute Inc., SAS statistical software v. 9.3. (2011). Cary, NC, USA.Google Scholar
  21. 21.
    Microsoft. Microsoft Excel. (2010). Washington, USA: Redmond.Google Scholar
  22. 22.
    Guo, N., Marra, C. A., Marra, F., Moadebi, S., Elwood, R. K., & Fitzgerald, J. M. (2008). Health state utilities in latent and active tuberculosis. Value in Health, 11(7), 1154–1161.PubMedCrossRefGoogle Scholar
  23. 23.
    Kruijshaar, M. E., Lipman, M., Essink-Bot, M.-L., Lozewicz, S., Créer, D., Dart, S., et al. (2010). Health status of UK patients with active tuberculosis. The International Journal of Tuberculosis and Lung Disease, 14(3), 296–302.PubMedGoogle Scholar
  24. 24.
    Hays, R. D., Reeve, B. B., Smith, A. W., & Clauser, S. B. (2013). Associations of cancer and other chronic medical conditions with SF-6D preference-based scores in Medicare beneficiaries. Quality of Life Research.  10.1007/s11136-013-0503-9
  25. 25.
    Simich, L. (2009). Health literacy and immigrant populations (policy brief).
  26. 26.
    Menzies, D., Oxlade, O., & Lewis, M. (2006). Costs for tuberculosis care in Canada. Public Health Agency of Canada publications and educational materials. 1–28.Google Scholar
  27. 27.
    Khanna, D., Yan, X., Tashkin, D. P., Furst, D. E., Elashoff, R., Roth, M. D., et al. (2007). Impact of oral cyclophosphamide on health-related quality of life in patients with active scleroderma lung disease: Results from the Scleroderma Lung Study. Arthritis and Rheumatism, 56, 1676–1684.PubMedCrossRefGoogle Scholar
  28. 28.
    McDonald, J. F., & Moffitt, R. A. (1980). The uses of tobit analysis. The Review of Economics and Statistics,. doi: 10.2307/1924766.Google Scholar
  29. 29.
    Twisk, J., & Rijmen, F. (2009). Longitudinal tobit regression: A new approach to analyze outcome variables with floor or ceiling effects. Journal of Clinical Epidemiology, 62, 953–958.PubMedCrossRefGoogle Scholar
  30. 30.
    Von Hippel, P. T. (2007). Regression with missing ys: An improved strategy for analyzing multiple imputed data. Sociological Methodology,. doi: 10.1111/j.1467-9531.2007.00180.x.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Melissa Bauer
    • 1
    • 2
  • Sara Ahmed
    • 3
  • Andrea Benedetti
    • 1
    • 2
  • Christina Greenaway
    • 4
  • Marek Lalli
    • 1
  • Allison Leavens
    • 1
  • Dick Menzies
    • 1
  • Claudia Vadeboncoeur
    • 1
  • Bilkis Vissandjée
    • 5
  • Ashley Wynne
    • 1
  • Kevin Schwartzman
    • 1
    Email author
  1. 1.Respiratory Epidemiology and Clinical Research UnitMcGill UniversityMontrealCanada
  2. 2.Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealCanada
  3. 3.Division of Clinical EpidemiologyMcGill University Health CenterMontrealCanada
  4. 4.Division of Infectious Diseases and Clinical EpidemiologySir Mortimer B. Davis – Jewish General HospitalMontrealCanada
  5. 5.Faculté des sciences infirmières – School of NursingUniversité de MontréalMontrealCanada

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