Research on Key Interventional Technologies for Controlling the Epidemic in High-prevalence Areas of Tuberculosis in Guangxi, China

This study evaluates the effectiveness and feasibility of a novel active case finding (ACF) strategy for tuberculosis (TB) in rural Guangxi, China. The intervention involves the use of mobile vans equipped with artificial intelligence (AI)-aided radiology, and rapid diagnostic testing (GeneXpert) to identify TB cases among high-risk populations. TB is a significant public health issue in the proposed research areas, particularly among older adults, individuals with a history of TB, close contacts of TB patients, and those with underlying conditions such as diabetes or HIV. By addressing the gaps in routine care, this study aims to reduce TB prevalence and provide insights for implementing similar approaches in other high-burden settings.

The study is designed as a pragmatic, parallel, cluster-randomized controlled trial conducted in two counties with high TB prevalence. A total of 23 townships are randomized into intervention and control groups in a 1:1 ratio. In the intervention group, a one-time ACF campaign will be conducted during Year 1. This campaign integrates AI-supported digital radiography (DR) for chest X-rays, symptom screening, and sputum collection for laboratory-based TB testing. The control group will continue receiving routine care, primarily relying on passive case finding. TB treatment in both groups will follow standard national guidelines.

Participants are individuals aged 15 years and older who are at high risk for TB. This includes older adults, individuals previously treated for TB or with close contact with TB patients diagnosed in the last three years, and those clinically diagnosed with conditions such as diabetes or HIV or exposed to occupational hazards like mining. In the intervention group, mobile vans equipped with DR machines and refrigerated storage will visit villages to perform on-site screenings. Eligible individuals will undergo chest X-rays and provide sputum samples if TB-related symptoms or abnormalities on X-rays are detected. Sputum samples will be transported to county hospitals for diagnostic testing using smear microscopy, culture, and GeneXpert technologies. Diagnosed TB cases will be promptly notified and referred for treatment per national guidelines.

The primary outcome of this study is the prevalence of bacteriologically confirmed TB among high-risk populations in Year 3. Data collection includes demographic, clinical, laboratory, and cost information from patient, health system, and societal perspectives. The analysis will employ mixed-effect logistic regression models to evaluate the impact of the intervention on primary and secondary outcomes. Cost-effectiveness analysis will calculate the incremental cost required for a percentage reduction in TB prevalence. In addition, a process evaluation will assess the intervention's feasibility, acceptability, and fidelity using qualitative and quantitative methods, including interviews with healthcare workers, community members, and participants, as well as analysis of participation rates.

This trial addresses the challenges of TB detection in resource-limited rural settings by integrating innovative technologies such as AI and mobile health solutions. It has the potential to contribute significantly to achieving the World Health Organization's (WHO) End TB Strategy, which aims to eliminate TB by 2035. The study has received ethical approval from the Guangxi Institutional Review Board, and informed consent will be obtained from all participants. Findings from this study will be disseminated through academic publications, policy briefs, and conference presentations to inform global TB control strategies.

Inclusion criteria

• all residents who are elderly (i.e., aged 65 and above)
• all residents who are aged 15 to 64 with one of the following conditions: being patients previously treated for TB or close contacts of a patient with a TB patient diagnosed within the last three years; having been clinically diagnosed with diabetes, HIV positive, or worked as a miner
• Have signed consent form

Exclusion criteria

• Residents who refuse participation.