MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, Norfolk Place, London W2 1PG, UK.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.
Nature. 2015 Dec 3;528(7580):S60-7. doi: 10.1038/nature16045.
Tuberculosis is a major source of global mortality caused by infection, partly because of a tremendous ongoing burden of undiagnosed disease. Improved diagnostic technology may play an increasingly crucial part in global efforts to end tuberculosis, but the ability of diagnostic tests to curb tuberculosis transmission is dependent on multiple factors, including the time taken by a patient to seek health care, the patient's symptoms, and the patterns of transmission before diagnosis. Novel diagnostic assays for tuberculosis have conventionally been evaluated on the basis of characteristics such as sensitivity and specificity, using assumptions that probably overestimate the impact of diagnostic tests on transmission. We argue for a shift in focus to the evaluation of such tests' incremental value, defining outcomes that reflect each test's purpose (for example, transmissions averted) and comparing systems with the test against those without, in terms of those outcomes. Incremental value can also be measured in units of outcome per incremental unit of resource (for example, money or human capacity). Using a novel, simplified model of tuberculosis transmission that addresses some of the limitations of earlier tuberculosis diagnostic models, we demonstrate that the incremental value of any novel test depends not just on its accuracy, but also on elements such as patient behaviour, tuberculosis natural history and health systems. By integrating these factors into a single unified framework, we advance an approach to the evaluation of new diagnostic tests for tuberculosis that considers the incremental value at the population level and demonstrates how additional data could inform more-effective implementation of tuberculosis diagnostic tests under various conditions.
结核病是由感染引起的全球死亡的主要原因之一，部分原因是大量未确诊的疾病仍在持续存在。改进的诊断技术可能在全球终结结核病的努力中发挥越来越重要的作用，但诊断测试遏制结核病传播的能力取决于多个因素，包括患者寻求医疗保健的时间、患者的症状以及诊断前的传播模式。传统上，结核病的新型诊断检测方法是根据敏感性和特异性等特征进行评估的，这些特征的假设可能高估了诊断测试对传播的影响。我们主张将重点转移到评估此类测试的增量价值上，定义反映每个测试目的的结果（例如，避免的传播），并根据这些结果，比较带有测试的系统与没有测试的系统。增量价值也可以用每增加一个资源单位（例如，金钱或人力）的增量结果来衡量。我们使用一种新的简化结核病传播模型，该模型解决了早期结核病诊断模型的一些局限性，表明任何新型测试的增量价值不仅取决于其准确性，还取决于患者行为、结核病自然史和卫生系统等因素。通过将这些因素整合到一个单一的统一框架中，我们提出了一种评估结核病新型诊断测试的方法，该方法考虑了在人群层面上的增量价值，并展示了在各种条件下，如何利用更多的数据为结核病诊断测试的更有效实施提供信息。
