Comparative Effectiveness of Rapid Diagnostics for Tuberculosis in Uganda
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. Most existing models of TB diagnostics' impact and cost- effectiveness focus on diagnostic accuracy (e.g., sensitivity and specificity), but the ability of diagnostic tests to control disease depends also on characteristic of the patients, providers, operational infrastructure, and health systems that utilize those tests The proposed research will help to bridge this knowledge gap by directly measuring these characteristics in Uganda and using those data to populate a model that aims to describe the comparative effectiveness and cost-effectiveness of different diagnostic strategies. We will then disseminate this model to decision-makers throughout the globe, enabling them to make evidence-based decisions for scaling-up novel TB diagnostics in a fashion that makes sense in their local context. In Specific Aim 1, we will collect primary data from Uganda as to the cost of novel molecular TB diagnostic test (Xpert MTB/RIF), describing that cost as a function of patient, provider, operational, and health system factors. To augment this costing effort, we will measure the reach of Xpert MTB/RIF under different implementation strategies, patient and provider preferences for tradeoffs of accuracy and efficiency, and existing patterns of diagnosis onto which novel tests would be overlaid. In Specific Aim 2, we will use those data to create two separate models of Xpert MTB/RIF's epidemiological impact and cost-effectiveness: a cohort (decision analysis) model and a dynamic transmission model. Both of these models will incorporate the data collected in Aim 1 to demonstrate how contextual factors influence both the impact and cost-effectiveness of Xpert as deployed in diverse settings. In Specific Aim 3, we will generalize the model developed in Aim 2 to settings beyond Uganda, and to diagnostic tests other than Xpert MTB/RIF. We will also create a platform for dissemination of this tool to decision-makers across the globe. Upon successful completion of this project, we will have developed a generalizable framework for incorporating locally-relevant patient, provider, operational, and health system variables into decisions about the appropriate scale-up of TB diagnostics across a diverse array of settings. Not only will this project answer urgent policy questions regarding TB diagnostics specifically, but it will also advance the field of infectious disease control more generally, serving as a paradigm for integrating operational research, implementation science, economic evaluation, epidemiology, and transmission modeling into a coordinated framework for executing decisions that will save lives both locally and throughout the world.
Older tests for diagnosing tuberculosis (TB) are inadequate - often missing over 50% of all cases - but a wide variety of new rapid tests is rapidly becoming available. The ability of these tests to help control TB depends not just on their accuracy, but also on characteristics of the patients, providers, clinics/labs, and health systems that use them. We will study how these characteristics affect TB diagnosis in Uganda and use that data to develop a generalizable model, incorporating these contextual factors into more realistic and locally-relevant estimates of TB diagnostics' impact and cost-effectiveness.