Using System Dynamics to Assess the Role of Socio-economic Status in Tuberculosis Incidence

  • Marisa Analía Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7041)


Tuberculosis is one of the diseases that generate more mortality in recent years. Recent research on the impact of DOTS programs for tuberculosis control suggest that, after several years of successful implementation, the incidence is not decreasing as expected. Globally and in most regions, the prevalence and mortality decay, but not quickly enough to achieve the Millennium goals set by the WHO. Many socio-economic determinants and the exposure of the population to risk factors have a major impact on the incidence of tuberculosis. The aim of this paper is to develop a conceptual model based on the dynamics of the tuberculosis epidemiology and its relationship to socio-economic determinants and risk factors. The model will aid in understanding the causes of undesired behavior and designing new policies to eliminate or mitigate them. The work includes results of simulations and projections for Jujuy region of Argentina.


tuberculosis social determinants risk factors system dynamics simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Leischow, S., Best, A., Trochim, W., Clark, P., Gallagher, R., Marcus, S., Matthews, E.: Systems Thinking to Improve the Public’s Health. American Journal of Preventive Medicine 35(2S), 196–203 (2008)CrossRefGoogle Scholar
  2. 2.
    Stokols, D., Hall, K., Taylor, B., Moser, R.: The science of team science: overview of the field and introduction to the supplement. American Journal of Preventive Medicine 35(2S), 77–89 (2008)CrossRefGoogle Scholar
  3. 3.
    Barlas, Y.: System dynamics: Systemic Feedback Moeling for Policy Analysis. In: Knowledge for Sustainable Development - An Insight into the Encyclopedia of Life Support Systems, pp. 1131–1175. UNESCO-Eolss Publishers, Paris (2002)Google Scholar
  4. 4.
    Forrester, J.: Industrial Dynamics. Pegasus Communications, Massachutses (1961) Google Scholar
  5. 5.
    Senge, P.: The fifth discipline: the art and practice of the learning organization. Doubleday/Curency, New York (1990)Google Scholar
  6. 6.
    World Health Organization: Report of the Meeting of the DOTS Expansion Working Group. Engaging professional Associations in TB Control., Geneve (2009) Google Scholar
  7. 7.
    Lönnroth, K., Jaramillo, E., Williams, B., Dye, C., Raviglione, M.: Drivers of tuberculosis epidemics: The role of risk factors and social determinants. Social Science & Medicine 68, 2240–2246 (2009)CrossRefGoogle Scholar
  8. 8.
    Horner, J., Hirsch, G.: American Journal of Public Health (96), 452–458 (March 2006)Google Scholar
  9. 9.
    Thompson, K., Duintjer Tebbens, R.: Using system dynamics to develop policies that matter: global management of poliomyelitis and beyond. System Dynamics Review 24(4), 433–449 (2008)CrossRefGoogle Scholar
  10. 10.
    Zagonel, A., Rohrbaugh, J., Andersen, D.: Using simulation models to address “What if” questions about welfare reform. Journal of Policy Analysis and Management 23(4), 890–901 (2004)CrossRefGoogle Scholar
  11. 11.
    Dudley, R.: A basis for understanding fishery management dynamics. System Dynamics Review 24(1), 1–29 (2008)CrossRefGoogle Scholar
  12. 12.
    Bontkes, T.: Dynamics of rural development in southern Sudan. System Dynamics Review 9(1), 1–21 (1993)CrossRefGoogle Scholar
  13. 13.
    Andersen, D., Richardson, G., Vennix, J.: Group model building: adding more science to the craft. System Dynamics Review 13(2), 187–201 (1997)CrossRefGoogle Scholar
  14. 14.
    Ghaffarzadegan, N., Lyneis, J., Richardson, G.: Why and How Small Systems Dynamics Models Can Help Plicymakers: A Review of Two Public Policy Models. In: 26th International Conference of the System Dynamics Society, Athens (2008) Google Scholar
  15. 15.
    Chubb, M., Jacobsen, K.: Mathematical modeling and the epidemiological research process. European Journal Epidemiology 25, 13–19 (2010)CrossRefGoogle Scholar
  16. 16.
    Colijn, C., Cohen, T., Murray, M.: Mathematical models of tuberculosis: accomplishments and future challenges. In: International Symposium on Mathematical and Computational Biology (2006) Google Scholar
  17. 17.
    Dye, C., Garnett, G., Sleeman, K., Williams, B.: Prospects for worldwide tuberculosis control under the WHO DOTS strategy Directly Observed Shortcourse therapy. The Lancet 367(9514), 938–940 (2006)CrossRefGoogle Scholar
  18. 18.
    Blower, S., McLean, A., Porco, T., Small, P., Hopewell, P., Sanchez, M., Ross, A.: The intrinsic transmission dynamics of tuberculosis epidemics. Nat. Med. 1(8), 815–821 (1995)CrossRefGoogle Scholar
  19. 19.
    Vynnycky, E., Fine, P.: Lifetime risks, incubation period, and serial interval of tuberculosis. Am. J. Epidemiol. 152(3), 247–263 (2000)CrossRefGoogle Scholar
  20. 20.
    Cohen, T., Colijn, C., Finklea, B., Murray, M.: Exogenous Re-Infection and the Dynamics of Tuberculosis Epidemics: Local Effects in a Network Model of Transmission. J. R. Soc. Interface 4(14), 523–531 (2007)CrossRefGoogle Scholar
  21. 21.
    Feng, Z., Castillo, C., Capurro, A.: A Model for Tuberculosis with Exogenous Reinfection. Theoretical Population Biology (57), 235–247 (2000) Google Scholar
  22. 22.
    Blower, S., Small, P., Hopewell, P.: Control Strategies for Tuberculosis Epidemics: New Models for Old Problems. Science 273(5274), 497–500 (1996)CrossRefGoogle Scholar
  23. 23.
    Castillo-Chavez, C., Feng, Z.: Global stability of an age-structure model for tuberculosis and its applications to optimal vaccination strategies. Math. Biosci. 151(2), 135–154 (1998)zbMATHCrossRefGoogle Scholar
  24. 24.
    Gomes, M., Franco, A., Medley, G.: The Reinfection Threshold Promotes Variability in tuberculosis Epidemiology and Vaccine Efficacy. Proc. Biol. Sci., 617–623 (2004)Google Scholar
  25. 25.
    Vinnicky, E., Fine, P.: The Natural History of Tuberculosis: The Implications of Age-dependent Risks of Disease and The Role of Infection. Epidemiol. Infect. (119), 183–201 (1997) Google Scholar
  26. 26.
    Cohen, T., Sommers, B., Murray, M.: The effect of drug resistance on the fitness of mycobacterium tuberculosis. Lancet Infect. Dis. 3(1), 13–21 (2003)CrossRefGoogle Scholar
  27. 27.
    Cohen, T., Murray, M.: Modeling Epidemics of Multidrug-resistant Tuberculosis of Heterogeneous Fitness. Nat. Med. 10(10), 1117–1121 (2004)CrossRefGoogle Scholar
  28. 28.
    Blower, S., Chou, T.: Modeling the emergence of the “hot zones”: tuberculosis and the amplification dynamics of drug resistance. Nat. Med. 10(10), 1111–1116 (2004)CrossRefGoogle Scholar
  29. 29.
    Currie, C., Williams, B., Cheng, R., Dye, C.: Tuberculosis epidemics driven by HIV: is prevention better than cure? AIDS 17(17), 2501–2508 (2003)CrossRefGoogle Scholar
  30. 30.
    World Health Organization: Report of the Meeting of the DOTS Expansion Working Group. Engaging professional Associations in TB Control., Geneve (2009) Google Scholar
  31. 31.
    Lönnroth, K., Raviglione, M.: Global Epidemiology of Tuberculosis: Prospects for Control. Semin. Respir. Crit. Care Med. (29), 481–491 (2008) Google Scholar
  32. 32.
    World Health Organization. Regional Office for the Eastern Mediterranean: Diagnostic and treatment delay in tuberculosis, Cairo (2006) Google Scholar
  33. 33.
    Instituto Nacional de Enfermedades Respiratorias: Notificación de Casos de Tuberculosis en la República Argentina. Período 1980-2006. PRO.TB.Doc.Tec. 07/07 (2007) Google Scholar
  34. 34.
    World Health Organization: The Global Plan to Stop TB 2011-2015, Geneve (2010) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marisa Analía Sánchez
    • 1
  1. 1.Dpto. de Ciencias de la AdministraciónUniversidad Nacional del SurBahía BlancaArgentina

Personalised recommendations