PREDICTING THE OUTCOME OF TUBERCULOSIS TREATMENT COURSE IN FRAME OF DOTS - From Demographic Data to Logistic Regression Model

Sharareh R. Niakan Kalhori, Xiao-Jun Zeng

2009

Abstract

About fifteen years after the start of WHO’s DOTS strategy, tuberculosis remains a major global health threat. Patients vary considerably in their performance in completing treatment course of tuberculosis. Defect in treatment completion have serious undesirable consequences. Although several studies have predicted outcome of treatment for pulmonary tuberculosis, few tools are available to identify high risk patients in finishing treatment course and getting cure prospectively. A logistic regression model proposed to predict the given outcome applying patient demographic characteristics related to just less than 10,000 tuberculosis patients diagnosed by Iranian health surveillance system in 2005. Several tests validate the developed model, X2 (6) = 351.902, P < 0.0001. Also, the model confirmed the significant role of considered factors, calculating the odds ratio of outcome occurring based on each category of variables and explaining the possibility of using the model in other similar patient population. In brief, to support the decision of how intensive the carrying out of DOTS should be for each patient, the predictive models like logistic regression could be useful.

References

  1. Abu-Hanna, A., Lucas, P.J.F., 2001. Prognostic Models in Medicine, AI and Statistical Approaches. Methods of Information in Medicine, 40 ,1-5.
  2. Buskin, S.E., Gale, J.L., Weiss, N.S., Nolan, C.M., 1994. Tuberculosis Risk Factors in Adults in King County, Washington, 1988 through 1990. American Journal of Public Health, 84(11), 1750-1756.
  3. Davidow, A.L., Mangura, B.T., Napolitano, E.C., Reichman, L.B., 2003. Rethinking the Socioeconomics and Geography of Tuberculosis among Foreign-born Residents of New Jersey, 1994- 1999. American Journal of Public Health, 93(6), 1007- 1012.
  4. Dye,C., Garnett G.p., Sleeman K., Williams B.G., 1998. Prospects for Worldwide Tuberculosis Control under the WHO DOTS Strategy. The Lancet, 352(12), 1886- 1891.
  5. Field A., 2005. Discovering Statistics Using SPSS. SAGE Publication LTD, London, 2nd edition.
  6. Floyd, K., Blanc, L., Raviglione, M., Lee, J. 2002. Resource Required for Global Tuberculosis Control. Science, 295, 2040- 2041.
  7. Harries, A.D., Dye, C., 2006. Tuberculosis. Annals of Tropical Medicine & Parasitology, 100(5, 6), 415-30.
  8. Juzar, A., 2005. The Many Faces of Tuberculosis Control and the Challenges Faced. Business Briefing: US Respiratory Care, 1-4.
  9. Murray C. J. L., Salmon J.A. 1998. Modelling the Impact of Global Tuberculosis Control Strategies. Proceedings of the National Academy of Sciences of the United States of America, 95(11)13881-13886.
  10. Obermeyer, Z., Abbott-Klafter, J., Murray, C.J.L., 2008. Has the DOTS Strategy Improved Case Finding or Treatment Success? An Empirical Assessment. PloS ONE, 3 (3), e1721.
  11. Picon, P.D., Bassanesi, S. L., Caramori, M. L. A., Ferriera, R. L. T., Jarczewski, C. A., Vieira, P. R. B., 2007. Risk factors for recurrence of tuberculosis. J. Bras Pneumol, 33(5): 72-578.
  12. Tanguis, H.G., Cayla, J. A., Garcia de Olalla, P., Jansa J.M., Brugal, M.T., 2000. Factors predicting noncompletion of tuberculosis treatment among HIVinfected patients in Barcelona (1987-1996). International Journal of Tuberculosis and Lung Disease, 4(1), 55-60.
  13. Wallis R.S., Perkins M.D., Phillips M., Joloba M., Namale A., Johnson J.L., Whalen C.C., Teixeira L., Demchuk B., Dietze R., Mugerwa R.D., Eisenach K., Ellner J.J., 2000. Predicting the Outcome of Therapy for Pulmonary Tuberculosis. American Journal of Respiratory and Critical Care Medicine, 161, 1076- 1080.
  14. World Health Organization, 2006. The Stop TB Strategy, Document WHO/HTM/TB/2006.35. Geneva: WHO.
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Paper Citation


in Harvard Style

R. Niakan Kalhori S. and Zeng X. (2009). PREDICTING THE OUTCOME OF TUBERCULOSIS TREATMENT COURSE IN FRAME OF DOTS - From Demographic Data to Logistic Regression Model . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2009) ISBN 978-989-8111-63-0, pages 129-134. DOI: 10.5220/0001431401290134


in Bibtex Style

@conference{healthinf09,
author={Sharareh R. Niakan Kalhori and Xiao-Jun Zeng},
title={PREDICTING THE OUTCOME OF TUBERCULOSIS TREATMENT COURSE IN FRAME OF DOTS - From Demographic Data to Logistic Regression Model},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2009)},
year={2009},
pages={129-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001431401290134},
isbn={978-989-8111-63-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2009)
TI - PREDICTING THE OUTCOME OF TUBERCULOSIS TREATMENT COURSE IN FRAME OF DOTS - From Demographic Data to Logistic Regression Model
SN - 978-989-8111-63-0
AU - R. Niakan Kalhori S.
AU - Zeng X.
PY - 2009
SP - 129
EP - 134
DO - 10.5220/0001431401290134