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Abstract：
BACKGROUND: Pakistan has significantly strengthened its capacity for active case finding (ACF) for tuberculosis (TB) that is being implemented at scale in the country. However, yields of ACF have been lower than expected, raising concerns on its effectiveness in the programmatic setting. Distribution of TB in communities is likely to be spatially heterogeneous and targeting of ACF in areas with higher TB prevalence may help improve yields. The primary aim of SPOT-TB is to investigate whether a policy change to use a geographically targeted approach towards ACF supported by an artificial intelligence (AI) software, MATCH-AI, can improve yields in Pakistan.
METHODS: SPOT-TB will use a pragmatic, stepped wedge cluster randomised design. A total of 30 mobile X-ray units and their field teams will be randomised to receive the intervention. Site selection for ACF in the intervention areas will be guided primarily through the use of MATCH-AI software that models subdistrict TB prevalence and identifies potential disease hotspots. Control areas will use existing approaches towards site selection that are based on staff knowledge, experience and analysis of historical data. The primary outcome measure is the difference in bacteriologically confirmed incident TB detected in the intervention relative to control areas. All remaining ACF-related procedures and algorithms will remain unaffected by this trial.
BACKGROUND: Ethical approval has been obtained from the Health Services Academy, Islamabad, Pakistan (7-82/IERC-HSA/2022-52) and from the Common Management Unit for TB, HIV and Malaria, Ministry of Health Services, Regulation and Coordination, Islamabad, Pakistan (26-IRB-CMU-2023). Findings from this study will be disseminated through publications in peer-reviewed journals and stakeholder meetings in Pakistan with the implementing partners and public-sector officials. Findings will also be presented at local and international medical and public health conferences.
BACKGROUND: NCT06017843.
METHODS: SPOT-TB will use a pragmatic, stepped wedge cluster randomised design. A total of 30 mobile X-ray units and their field teams will be randomised to receive the intervention. Site selection for ACF in the intervention areas will be guided primarily through the use of MATCH-AI software that models subdistrict TB prevalence and identifies potential disease hotspots. Control areas will use existing approaches towards site selection that are based on staff knowledge, experience and analysis of historical data. The primary outcome measure is the difference in bacteriologically confirmed incident TB detected in the intervention relative to control areas. All remaining ACF-related procedures and algorithms will remain unaffected by this trial.
BACKGROUND: Ethical approval has been obtained from the Health Services Academy, Islamabad, Pakistan (7-82/IERC-HSA/2022-52) and from the Common Management Unit for TB, HIV and Malaria, Ministry of Health Services, Regulation and Coordination, Islamabad, Pakistan (26-IRB-CMU-2023). Findings from this study will be disseminated through publications in peer-reviewed journals and stakeholder meetings in Pakistan with the implementing partners and public-sector officials. Findings will also be presented at local and international medical and public health conferences.
BACKGROUND: NCT06017843.
摘要：
背景：巴基斯坦已大大加强了其结核病（TB）主动发现病例（ACF）的能力，该能力正在该国大规模实施。然而,ACF的产量低于预期，对其在方案环境中的有效性表示关注。结核病在社区中的分布可能在空间上是异质的，在结核病患病率较高的地区靶向ACF可能有助于提高产量。SPOT-TB的主要目的是调查政策是否改变，以使用人工智能（AI）软件支持的针对ACF的地理针对性方法。MATCH-AI,可以提高巴基斯坦的产量。
方法：SPOT-TB将使用实用的，阶梯式楔形簇随机设计。共有30个移动X射线单位及其现场团队将被随机分配以接受干预。在干预地区选择ACF的地点将主要通过使用MATCH-AI软件进行指导，该软件对分区结核病患病率进行建模并确定潜在的疾病热点。控制区将使用基于员工知识的现有选址方法，经验和历史数据的分析。主要结果指标是干预措施中检测到的细菌学证实的事件结核病相对于对照地区的差异。所有剩余的ACF相关程序和算法将不受该试验的影响。
背景：已获得卫生服务学院的道德批准，伊斯兰堡,巴基斯坦(7-82/IERC-HSA/2022-52)和结核病共同管理股，艾滋病毒和疟疾，卫生部,监管和协调，伊斯兰堡,巴基斯坦（26-IRB-CMU-2023）。这项研究的结果将通过同行评审期刊上的出版物以及在巴基斯坦与执行伙伴和公共部门官员举行的利益相关者会议进行传播。研究结果还将在当地和国际医疗和公共卫生会议上发表。
背景：NCT06017843。
方法：SPOT-TB将使用实用的，阶梯式楔形簇随机设计。共有30个移动X射线单位及其现场团队将被随机分配以接受干预。在干预地区选择ACF的地点将主要通过使用MATCH-AI软件进行指导，该软件对分区结核病患病率进行建模并确定潜在的疾病热点。控制区将使用基于员工知识的现有选址方法，经验和历史数据的分析。主要结果指标是干预措施中检测到的细菌学证实的事件结核病相对于对照地区的差异。所有剩余的ACF相关程序和算法将不受该试验的影响。
背景：已获得卫生服务学院的道德批准，伊斯兰堡,巴基斯坦(7-82/IERC-HSA/2022-52)和结核病共同管理股，艾滋病毒和疟疾，卫生部,监管和协调，伊斯兰堡,巴基斯坦（26-IRB-CMU-2023）。这项研究的结果将通过同行评审期刊上的出版物以及在巴基斯坦与执行伙伴和公共部门官员举行的利益相关者会议进行传播。研究结果还将在当地和国际医疗和公共卫生会议上发表。
背景：NCT06017843。
