WHO Centre for Tuberculosis Research and Innovation, Institute for Global Health, University College London, London, UK
Center for Global Public Health, Islamabad, Pakistan.
BMJ Open Respir Res. 2024 Jul 11;11(1):e002079. doi: 10.1136/bmjresp-2023-002079.
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.
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.
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.
NCT06017843.
巴基斯坦显著加强了结核病(TB)主动病例发现(ACF)的能力,目前正在全国范围内大规模实施。然而,ACF 的检出率低于预期,这引起了人们对其在规划环境中的有效性的关注。社区中结核病的分布可能存在空间异质性,在结核病患病率较高的地区开展 ACF 可能有助于提高检出率。SPOT-TB 的主要目的是调查一项政策变化是否可以提高巴基斯坦的检出率,该政策变化是使用人工智能(AI)软件 MATCH-AI 支持的针对地理区域的 ACF 方法。
SPOT-TB 将采用实用的、阶梯式楔形群随机设计。总共将对 30 个移动 X 射线单位及其现场团队进行随机分组,以接受干预。干预地区的 ACF 现场选择将主要通过使用 MATCH-AI 软件来指导,该软件可以模拟分区 TB 流行率并确定潜在的疾病热点。对照区将使用基于工作人员知识、经验和历史数据分析的现有方法进行现场选择。主要结局指标是干预区与对照区之间检测到的细菌学确诊的新发病例 TB 的差异。所有剩余的与 ACF 相关的程序和算法都不会受到该试验的影响。
该研究已获得巴基斯坦伊斯兰堡卫生服务学院(7-82/IERC-HSA/2022-52)和巴基斯坦卫生服务、监管和协调部 TB、HIV 和疟疾共同管理单位(26-IRB-CMU-2023)的伦理批准。该研究的结果将通过在同行评议期刊上发表文章以及在巴基斯坦与实施伙伴和公共部门官员举行利益相关者会议进行传播。研究结果还将在当地和国际医学和公共卫生会议上展示。
NCT06017843。