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

University of Toronto

Status: Enrolling

Conditions: Tuberculosis (TB)

Treatments: Diagnostic Test: Active case finding

Study type: Interventional

Funder types: Other

Identifiers

NCT06702774

GXTB202108

Details and patient eligibility

About

The goal of this study is to find out if using mobile vans with advanced technology can help reduce tuberculosis (TB) in rural Guangxi, China. The study will also examine how practical and cost-effective this approach is. The main questions it aims to answer are: 1) Does this new screening method lower the number of TB cases among high-risk groups? and 2) Is this method practical and acceptable for communities and healthcare workers? Participants in the study will: 1) undergo TB screening with mobile vans that use artificial intelligence (AI) to read chest X-rays, 2) answer a short questionnaire about their symptoms and health history, and 3) provide sputum samples for GeneXpert testing if needed.

Some communities will receive the new screening method, while others will continue with usual care. Researchers will compare TB rates in the two groups over three years to see if the new approach works better for TB control. If successful, this method could be used to improve TB control in other areas.

Full description

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.

Enrollment: 72,000 estimated patients

Sex: All

Ages: 15+ years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

Primary purpose: Screening

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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72,000 participants in 2 patient groups

Intervention

Experimental group

Description:

A single active case finding campaign for Tuberculosis will occur in Year 1 alongside the usual care.

Treatment:

Diagnostic Test: Active case finding

Control

No Intervention group

Description:

Usual care will be provided and no active case finding activities will be implemented.