A randomised controlled trial (RCT) to evaluate a scalable active case finding primary care-based intervention for tuberculosis using a point-of-care

结核病仍然是全球最主要的传染病杀手。令人吃惊的统计数据是，全球每5例结核病病例中就有2例（40%）未得到诊断和治疗。这些“漏诊”或未确诊的病例不成比例地集中在非洲和亚洲大城市的大型城郊“贫民窟”和非正规住区（它们往往症状轻微，但仍具有传染性）。除非消灭这个巨大的传播源，否则结核病永远不会得到控制。与患者“自我寻求”护理（被动病例发现）的当前模式相比，缺乏敏感的低成本当天检测是主动基于社区的病例发现（ACF）的主要挑战。最近，被称为基因Xpert（Xpert）的敏感结核病DNA检测测试已经上市。这些测试的小型便携式电池供电版本现在可用（OMNI）。我们在南非和津巴布韦进行了一项大型研究（发表于2016年），该研究表明，在配备发电机的小型卡车上使用旧的非便携式Xpert版本对ACF是可行的，并且非常有效。在随后由美国政府资助的一项研究（XACT II）中，我们表明，在小型低成本可扩展面板货车（实际上是一个移动的迷你诊所）的背面使用便携式Xpert是可行的，并且在城市周边社区具有非常高的结核病检出率（约10%的人正在接受靶向筛查;见初步数据）。在拟议的研究（XACT III）中，我们将使用相同的方法，但我们需要确保这种策略在挑战和条件不同的不同环境中是可扩展和可行的。更重要的是，我们需要在方法上优化ACF模型。因此，我们需要从物理位置的角度来确定Xpert（诊断检测）的最佳放置位置，即我们是否真的需要将其安装在移动的微型诊所上，或者是否可以将其放置在集中实验室中（就像现在一样），并将样本发送到这些实验室？这是一个非常重要的问题：我们知道，将收集的痰液样本发送到集中实验室会容易得多，因为它使用现有的基础设施，然而，缺点是20%至40%的患者无法返回收集结果（治疗前失访; PTLF）。在移动的小型货车（在护理点; POC）中使用诊断显著地减少该PTLF，从而实现快速诊断和中断传输。为了明确解决这个问题，我们需要使用2种不同的策略进行研究，以找出哪种策略最具成本效益，但可以快速获得最多病例并最大限度地减少传播。该研究还将回答另外2个重要的子问题。胸部X光片可以识别结核病高危人群，现在可以通过计算机算法自动读取（称为计算机辅助结核病诊断; CAD-TB）。了解使用CAD-TB的大规模筛查是否可以对个体进行分类，即缩小网络，以便我们将ACF仅针对那些具有结核病高风险的人，这将是非常重要的。这可以节省更多的钱，但同样有效。第二，一个基本的未回答的问题是，为什么只有轻微症状或没有症状的个体可以具有高度传染性（传播疾病）？我们需要更详细地研究这种现象，使用咳嗽气雾剂读数，胸部X光片，并观察结核菌株。这可能为医学科学提供设计诊断或治疗干预措施以解决这一重要问题所需的信息。然而，现在的关键优先事项是证明XACT方法在不同环境中是可行的，并澄清分子诊断应该如何最佳定位。了解这些问题将使许多国家能够开始实施艾滋病防治基金方案，并将为决策者提供关键数据，以便传播和实施艾滋病防治基金准则。

TB remains the foremost infectious disease killer globally. A starling statistic is that 2 out of every 5 TB cases globally (40%) remain undiagnosed and untreated. These 'missed' or undiagnosed cases are disproportionately concentrated in large peri-urban 'slums' and informal settlements of large cities in Africa and Asia (they are frequently minimally symptomatic but remain infectious). TB will never be brought under control unless this large reservoir of transmission is wiped out. The lack of a sensitive low cost same-day test represented a major challenge to active community-based case finding (ACF) compared to the current model where patients 'self-seek' care (passive case finding). More recently, sensitive TB DNA-detection tests called Gene Xpert (Xpert) have become available. Small portable battery-operated versions of these tests are now available (OMNI). We conducted a large study in South Africa and Zimbabwe (published in 2016) that showed that using the old non-portable version of Xpert on a mini-truck equipped with a generator was feasible and highly effective for ACF. In a subsequent study funded by the American government (XACT II) we showed that using the portable version of Xpert on the back of a small low-cost scalable panel van (in effect a mobile mini clinic) was feasible and had a very high pick-up rate of TB in peri-urban communities (~10% of those undergoing targeted screening; see preliminary data). In the proposed study (XACT III) we will use the same approach, but we need to be sure that such a strategy is scalable and feasible in different settings where the challenges and conditions vary. More importantly, we need to methodologically optimise the ACF model. Thus, we need to determine where Xpert (the diagnostic test) should be optimally placed from a physical location point-of-view, i.e. do we really need to install it on the mobile mini clinic, or, can it be located in centralised laboratories (as it is now) with samples being sent to these laboratories? This is a very important question: we know that sending collected sputum samples to centralised laboratories will be much easier as it uses existing infrastructure, however, the downside is between 20 and 40% of patients fail to come back to collect their results (pre-treatment loss to follow-up; PTLF). Using the diagnostic in the mobile mini van (at point-of-care; POC) dramatically reduces this PTLF enabling quick diagnosis and interrupting transmission. To definitively settle the question we need a study using the 2 different strategies to find out which strategy is most cost-effective yet can rapidly pick up the most cases and minimise transmission.There are 2 other important sub-questions that the study will answer. Chest x-rays, which can identify people at high risk of having TB, can now be automatically read by a computer algorithm (called computer-assisted diagnosis of TB; CAD-TB). It will be very important to know whether mass screening using CAD-TB can triage individuals i.e. narrow the net so that we target the ACF only to those at high risk of having TB. This could save even more money yet be just as effective. Secondly, a fundamental unanswered question is why individuals with minimal or no symptoms can be highly infectious (transmit disease)? We need to study this phenomenon in greater detail using cough aerosol readouts, chest x rays, and looking at the TB strains. This might provide medical science with the information it needs to design diagnostic or therapeutic interventions to address this important problem. However, the key priority now is to show that the XACT approach is feasible in different settings and to clarify how the molecular diagnostics should be optimally located. Answering these questions will allow the initiation of ACF programmes in many countries and will contribute critical data to policy makers so that guidelines on ACF can be disseminated and implemented.