Comparative Effectiveness of Rapid TB Diagnostics in Uganda

描述(由申请人提供)：活动性结核病(TB)诊断测试不足是导致全世界三分之一以上的结核病病例未得到诊断、每年有170万人死亡的关键原因，尽管有治疗方法可用。在过去5年中，推荐的活动性结核病诊断测试的数量急剧增加，其他诊断测试正在开发中，这使得当地政策制定者不确定如何最好地实施或扩大新的结核病诊断战略。大多数现有的结核病诊断影响和成本效益模型侧重于诊断准确性(例如，敏感性和特异性)，但诊断测试控制疾病的能力还取决于患者、提供者、操作基础设施和使用这些测试的卫生系统的特征。拟议的研究将通过在乌干达直接测量这些特征并使用这些数据来填充旨在描述不同诊断策略的比较有效性和成本效益的模型，从而帮助弥合这一知识差距。然后，我们将把这一模式传播给全球的决策者，使他们能够以符合当地情况的方式，为扩大新型结核病诊断做出基于证据的决策。在具体目标1中，我们将从乌干达收集关于新型分子结核病诊断检测(Xpert MTB/RIF)成本的初步数据，将成本描述为患者、提供者、操作和卫生系统因素的函数。为了加强这一成本计算工作，我们将衡量Xpert MTB/RIF在不同实施策略下的覆盖范围、患者和提供商对准确性和效率的权衡偏好，以及将覆盖新测试的现有诊断模式。在具体目标2中，我们将使用这些数据创建Xpert MTB/RIF流行病影响和成本效益的两个独立模型：队列(决策分析)模型和动态传播模型。这两个模型都将结合在目标1中收集的数据，以演示环境因素如何影响在不同环境中部署的Xpert的影响和成本效益。在具体目标3中，我们将把目标2中开发的模型推广到乌干达以外的环境，并推广到Xpert MTB/RIF以外的诊断测试。我们还将创建一个向全球决策者传播这一工具的平台。在这个项目成功完成后，我们将开发一个通用的框架，将与当地相关的患者、提供者、操作和卫生系统变量纳入到关于在不同环境中适当扩大结核病诊断的决策中。该项目不仅将具体回答有关结核病诊断的紧迫政策问题，而且还将更广泛地推进传染病控制领域，作为一个范例，将运筹学、实施科学、经济评估、流行病学和传播建模整合到一个协调框架中，以执行将在当地和世界各地拯救生命的决策。

DESCRIPTION (provided by applicant): 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.