Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
London School of Hygiene & Tropical Medicine, London, UK.
Int J Tuberc Lung Dis. 2021 Mar 1;25(3):171-181. doi: 10.5588/ijtld.20.0565.
Active case-finding (ACF) is an important component of the End TB Strategy. However, ACF is resource-intensive, and the economics of ACF are not well-understood. Data on the costs of ACF are limited, with little consistency in the units and methods used to estimate and report costs. Mathematical models to forecast the long-term effects of ACF require empirical measurements of the yield, timing and costs of case detection. Pragmatic trials offer an opportunity to assess the cost-effectiveness of ACF interventions within a 'real-world´ context. However, such analyses generally require early introduction of economic evaluations to enable prospective data collection on resource requirements. Closing the global case-detection gap will require substantial additional resources, including continued investment in innovative technologies. Research is essential to the optimal implementation, cost-effectiveness, and affordability of ACF in high-burden settings. To assess the value of ACF, we must prioritize the collection of high-quality data regarding costs and effectiveness, and link those data to analytical models that are adapted to local settings.
主动病例发现（ACF）是终结结核病策略的一个重要组成部分。然而，ACF 是一项资源密集型工作，其经济学原理尚未被充分理解。有关 ACF 成本的数据有限，用于估计和报告成本的单位和方法缺乏一致性。预测 ACF 长期效果的数学模型需要对病例发现的收益、时间和成本进行实证测量。实用临床试验提供了一个机会，可以在“真实世界”背景下评估 ACF 干预措施的成本效益。然而，此类分析通常需要尽早引入经济评估，以便能够对资源需求进行前瞻性数据收集。要缩小全球病例发现差距，将需要大量额外资源，包括对创新技术的持续投资。研究对于在高负担国家实现 ACF 的最佳实施、成本效益和可负担性至关重要。为了评估 ACF 的价值，我们必须优先收集有关成本和效果的高质量数据，并将这些数据与适用于当地情况的分析模型联系起来。
