Related Experiment Video
Updated: Jul 3, 2026

Diagnosing Pulmonary Tuberculosis with the Xpert MTB/RIF Test
Published on: April 9, 2012
Community-based "X-ray+Xpert® MTB/RIF ultra pooling test" case-finding strategy among high-risk groups in rural
Zhengwei Liu1, Ruiqi Chen1, Bing Li2
1Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, People's Republic of China.
None:
Active case-finding (ACF) strategies for tuberculosis (TB) in high-incidence rural areas require cost-effective solutions. We evaluated a novel community-based ACF strategy integrating pooled sputum molecular testing via Xpert® MTB/RIF Ultra. A prospective study was conducted in three high-TB-incidence rural townships in Zhejiang, China (2024). Residents aged ≥65 years underwent centralized health check-ups, including chest X-rays (CXR). Sputum samples from eight consecutive individuals with abnormal CXR findings were pooled for Xpert® MTB/RIF Ultra testing. Positive pools prompted individual retesting. Participants with positive individual test results were referred to designated hospitals for standardized treatment. Among 16,558 eligible residents, 6,960 (42.0%) participated. CXR abnormalities were detected in 1,912 participants (27.5%), with 1,883 providing sputum samples. Pooled testing identified 32 bacteriologically confirmed TB cases among 1,883 participants with CXR abnormalities, yielding a detection rate of 1.7%. The number needed to screen (NNS) to identify one case was 218, which was 177 fewer than that required by the chest X-ray-based alone active case finding strategy. Screening yields of newly detected TB through ACF was 0.460%. The innovative strategy reduced per-case screening costs to US$4.37 (vs. US$54.78 for individual testing) and per-confirmed-case cost to US$949.49. Integrating a pooled Xpert® MTB/RIF Ultra testing with routine health check-ups provides a high-yield, cost-effective ACF strategy for detecting active TB in high-risk elderly populations in rural settings. This approach addresses key barriers to scalable community-based screening.

