Inadequate diagnostic tests for active tuberculosis (TB) are a key reason that over one-third of all TB cases worldwide go undiagnosed and 1.7 million people die annually, despite the availability of curative treatment. The number of recommended diagnostic tests for active TB has increased dramatically in the last 5 years, and other diagnostic tests are in development, leaving local policymakers uncertain about how best to implement or scale up new diagnostic strategies for TB. Mathematical models can project the epidemiological impact and cost-effectiveness of alternative implementation strategies, and thus empower local decision-makers to scale up TB diagnostics in an evidence-based fashion. However, no model of TB diagnostic interventions is currently accessible on a broad scale to decision-makers in the field. As such, unless decision-makers have access to a TB modeling expert, they must operate in the absence of modeling data. We have assembled a team of mathematical modelers, TB epidemiologists, and implementation/dissemination experts to develop and disseminate a flexible epidemic-economic model of the TB diagnostic process. We aim to make this model: "accessible to anyone with an internet connection, "flexible in its abiliy to model tests with different performance characteristics under local conditions," simple for use by decision-makers without modeling expertise, and " widely disseminated for maximum impact. In Specific Aim 1, we will develop a combined epidemic-economic model of TB diagnosis in a user-friendly, open-source coding language (Python), creating a parameterization routine whereby users can input estimates of local TB epidemiology (incidence, prevalence, mortality, HIV prevalence, etc.) and proposed diagnostic interventions, in order to obtain customized model outputs. In Specific Aim 2, we will generate estimates of impact and cost-effectiveness for commonly-considered strategies to scale up TB diagnostics, comparing our model's results against those of other models that are tailored to specific countries or diagnostic strategies. In Specific Aim 3, we will actively disseminate our model, including development of a Web-based portal for local decision-makers and delivery of a training workshop for TB experts from high-burden countries who wish to adapt the model to decisions of local importance. Upon successful completion of this project, we will have generated an innovative tool (a dynamic economic-epidemic model that is flexible, accessible, open-source, and broadly useful) for disseminating scientific knowledge into evidence-based decisions at the local level. Not only will this project answer urgent policy questions about the scale-up of TB diagnostics, but it will also greatly advance the field of implementation and dissemination science, serving as a novel mechanism for translating scientific data on the transmission of infectious diseases into appropriate, locally-tailored policies for their prevention and control.
Older tests for diagnosing tuberculosis (TB) are inadequate - often missing over 50% of all cases - but a wide variety of new tests is rapidly becoming available. Policy-makers throughout the world need tools that will tell them how these new tests might change the TB epidemic in their local region (and how much they will cost), so they can choose which tools to implement. We will create this kind of tool (a mathematical model) and put it in the hands of local policymakers so that they can make decisions that are guided by scientific evidence.