Clinical trial NCT06702774: Research on Key Interventional Technologies for Controlling the Epidemic in High-prevalence Areas of Tuberculosis in Guangxi, China
Official title: Active Case Finding Using Mobile Vans Equipped With Artificial Intelligence Aided Radiology Tests and Sputum Collection for Rapid Diagnostic Tests to Reduce Tuberculosis Prevalence Among High-risk Populations in Rural China: a Pragmatic Cluster Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Tuberculosis (TB)
Keywords: Tuberculosis
active case finding
mobile van
artificial intelligence
cluster randomized controlled trial
China
Lead sponsor: University of Toronto
Sponsor class: OTHER
Collaborators: name: Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention
First posted: 2024-11-25
Enrollment: 72000
Sex: ALL
Minimum age (years): 15
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SCREENING

Brief summary: 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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Prevalence rate of bacteriologically positive TB
timeFrame: In year 3 after recruitment
Secondary outcomes:
measure: Prevalence rate of active TB
timeFrame: In Year 3 after recruitment
measure: Notification rates of bacteriologically positive TB
timeFrame: In Year 3 after recruitment
measure: Notification rates of active TB cases
timeFrame: In Year 3 after recruitment
Other outcomes:
Locations:
city: Nanning
country: China
facility: Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention
state: Guangxi
Study officials:
affiliation: Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention
name: Dabin Liang, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: University of Toronto
name: Xiaolin Wei, PhD
role: PRINCIPAL_INVESTIGATOR
References: