Modelling the impacts of new diagnostic tools for tuberculosis in developing countries to enhance policy decisions

Abstract

The introduction and scale-up of new tools for the diagnosis of Tuberculosis (TB) in developing countries has the potential to make a huge difference to the lives of millions of people living in poverty. To achieve this, policy makers need the information to make the right decisions about which new tools to implement and where in the diagnostic algorithm to apply them most effectively. These decisions are difficult as the new tools are often expensive to implement and use, and the health system and patient impacts uncertain, particularly in developing countries where there is a high burden of TB. The authors demonstrate that a discrete event simulation model could play a significant part in improving and informing these decisions. The feasibility of linking the discrete event simulation to a dynamic epidemiology model is also explored in order to take account of longer term impacts on the incidence of TB. Results from two diagnostic districts in Tanzania are used to illustrate how the approach could be used to improve decisions.

Application of discrete event simulation in health care: a systematic review

“…The transparent presentation plays an important role in improving the understandability and acceptability of model outputs to health care stakeholders [5, 59, 60]. …”

Operations research in global health: a scoping review with a focus on the themes of health equity and impact

“…A DES model was developed by Langley et al [39] to evaluate the impact of automated nucleic amplification test (aNAAT), a new TB diagnostic test, compared to existing techniques in Tanzania. The model recommended several combinations of TB diagnostic options incorporating aNAAT testing that were cost-effective in both urban and rural settings.…”

“…The model recommended several combinations of TB diagnostic options incorporating aNAAT testing that were cost-effective in both urban and rural settings. At the time of publication, policymakers in Tanzania were considering specific sites for a trial of the new aNAAT technology, and results from the DES model were going to be used to inform the implementation plan for the trial [39]. …”

“…Thompson and Tebbens advise that “ modelers need to focus on working effectively with the people who need and can use the results ” [215]. Langley et al [39], who underwent a comprehensive review of the questions that policymakers need to address when assessing different TB diagnostic strategies, are a great example of this. Situating their work within this ‘Impact Assessment Framework’ [221] not only helped identify the questions that their model should answer, but also informed the appropriate choice of modelling methodology to achieve their goals.…”

“…Friedrich et al [72] also underwent an iterative design process, whereby their nurse scheduling DSS model was tested with users and continuously improved throughout development. Langley et al [39] worked with experts from Tanzania and Malawi to ensure the input parameters and control logic for their DES model were valid.…”