A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools

Int J Tuberc Lung Dis. 2011 Aug;15(8):996-1004. doi: 10.5588/ijtld.11.0062.


Efforts to stimulate technological innovation in the diagnosis of tuberculosis (TB) have resulted in the recent introduction of several novel diagnostic tools. As these products come to market, policy makers must make difficult decisions about which of the available tools to implement. This choice should depend not only on the test characteristics (e.g., sensitivity and specificity) of the tools, but also on how they will be used within the existing health care infrastructure. Accordingly, policy makers choosing between diagnostic strategies must decide: 1) What is the best combination of tools to select? 2)Who should be tested with the new tools? and 3)Will these tools complement or replace existing diagnostics? The best choice of diagnostic strategy will likely vary between settings with different epidemiology (e.g., levels of TB incidence, human immunodeficiency virus co-infection and drug-resistant TB) and structural and resource constraints (e.g., existing diagnostic pathways, human resources and laboratory capacity). We propose a joint modelling framework that includes a tuberculosis (TB) transmission component (a dynamic epidemiological model) and a health system component (an operational systems model) to support diagnostic strategy decisions. This modelling approach captures the complex feedback loops in this system: new diagnostic strategies alter the demands on and performance of health systems that impact TB transmission dynamics which, in turn, result in further changes to demands on the health system. We demonstrate the use of a simplified model to support the rational choice of a diagnostic strategy based on health systems requirements, patient outcomes and population-level TB impact.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antitubercular Agents / therapeutic use
  • Bacteriological Techniques* / economics
  • Computer Simulation
  • Decision Support Techniques*
  • Feedback
  • Health Care Costs
  • Health Policy
  • Health Resources / economics
  • Health Resources / statistics & numerical data
  • Humans
  • Policy Making
  • Predictive Value of Tests
  • Prognosis
  • Sensitivity and Specificity
  • Sputum / microbiology
  • Time Factors
  • Tuberculosis / diagnosis*
  • Tuberculosis / drug therapy
  • Tuberculosis / economics
  • Tuberculosis / epidemiology
  • Tuberculosis / transmission


  • Antitubercular Agents