Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.
PLoS One. 2021 Nov 19;16(11):e0257242. doi: 10.1371/journal.pone.0257242. eCollection 2021.
In the last decade, active case finding (ACF) strategies for tuberculosis (TB) have been implemented in many diverse settings, with some showing large increases in case detection and reporting at the sub-national level. There have also been several studies which seek to provide evidence for the benefits of ACF to individuals and communities in the broader context. However, there remains no quantification of the impact of ACF with regards to reducing the burden of transmission. We sought to address this knowledge gap and quantify the potential impact of active case finding on reducing transmission of TB at the national scale and further, to determine the intensification of intervention efforts required to bring the reproduction number (R0) below 1 for TB.
We adopt a dynamic transmission model that incorporates heterogeneity in risk to TB to assess the impact of an ACF programme (IMPACT TB) on reducing TB incidence in Vietnam and Nepal. We fit the models to country-level incidence data using a Bayesian Markov Chain Monte Carlo approach. We assess the impact of ACF using a parameter in our model, which we term the treatment success rate. Using programmatic data, we estimate how much this parameter has increased as a result of IMPACT TB in the implementation districts of Vietnam and Nepal and quantify additional efforts needed to eliminate transmission of TB in these countries by 2035.
Extending the IMPACT TB programme to national coverage would lead to moderate decreases in TB incidence and would not be enough to interrupt transmission by 2035. Decreasing transmission sufficiently to bring the reproduction number (R0) below 1, would require a further intensification of current efforts, even at the sub-national level.
Active case finding programmes are effective in reducing TB in the short term. However, interruption of transmission in high-burden countries, like Vietnam and Nepal, will require comprehensive incremental efforts. Complementary measures to reduce progression from infection to disease, and reactivation of latent infection, are needed to meet the WHO End TB incidence targets.
在过去的十年中，许多不同环境中实施了针对结核病（TB）的主动病例发现（ACF）策略，其中一些在国家以下各级的病例发现和报告方面取得了较大的增长。也有一些研究旨在为更广泛背景下的个人和社区提供 ACF 益处的证据。然而，对于 ACF 降低传播负担的影响，仍然没有量化。我们试图解决这一知识空白，并量化主动病例发现对降低国家范围内结核病传播的潜在影响，此外，还确定了为使结核病的繁殖数（R0）降至 1 以下而需要加强干预力度的程度。
我们采用一种动态传播模型，该模型结合了对结核病风险的异质性，以评估在越南和尼泊尔实施的主动病例发现计划（IMPACT TB）对降低结核病发病率的影响。我们使用贝叶斯马尔可夫链蒙特卡罗方法，根据国家层面的发病率数据对模型进行拟合。我们使用模型中的一个参数来评估 ACF 的影响，我们称之为治疗成功率。利用规划数据，我们估计由于 IMPACT TB 在越南和尼泊尔实施地区的实施，该参数增加了多少，并量化了在这些国家消除结核病传播所需的额外努力。
将 IMPACT TB 计划扩大到全国范围将导致结核病发病率适度下降，并且到 2035 年还不足以中断传播。要使繁殖数（R0）降至 1 以下，从而充分降低传播，即使在国家以下一级，也需要进一步加强当前的努力。
主动病例发现计划在短期内有效降低结核病的发病率。然而，像越南和尼泊尔这样的高负担国家要想中断传播，就需要全面地增加努力。需要采取补充措施来减少从感染到发病的进展，以及潜伏感染的再激活，以实现世界卫生组织终止结核病发病率目标。
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