Tuberculosis (TB)NCT06702774
Research on Key Interventional Technologies for Controlling the Epidemic in High-prevalence Areas of Tuberculosis in Guangxi, China
A study testing Active case finding in people with Tuberculosis (TB), run by University of Toronto at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 15 years and olderHealthy volunteers can take part
- Conditions the study is for
- Tuberculosis (TB)
- What is being tested?
- Active case finding
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 43,067 participants
- Where
- 1 siteChina
- Study dates
- 20 Nov 2021to 31 Jul 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 15 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- 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
Who cannot join
- Residents who refuse participation.
Trial locations and contacts
1 site in 1 country.
China1 site
Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention
Nanning, Guangxi, 530000
About this study
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
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.
Read the 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.
- Conditions
- Tuberculosis (TB)
- Being tested
- Active case finding
- Keywords
- Tuberculosis, active case finding, mobile van, artificial intelligence, cluster randomized controlled trial, China
- Medical subject headings
- Tuberculosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Active case findingDiagnostic test
Villagers will be informed through public announcements and social workers. Before the campaign, social workers and village doctors will recruit participants and obtain consent through door-to-door visits. A mobile van equipped with an AI-assisted digital radiography (DR) machine and a refrigerator will visit villages on agreed dates. Participants will complete a TB symptom questionnaire and undergo DR screening. Those with TB symptoms or abnormal DR results will provide on-site sputum samples and collect additional morning and night samples. Trained staff will ensure proper collection and offer nebulizer support if needed. Samples will be transported daily to hospitals for testing using smear, culture, and GeneXpert. Participants with negative bacteriological results but abnormal findings will be referred for further clinical assessment.
| Study group | Role | What they receive |
|---|---|---|
| Intervention | Experimental | A single active case finding campaign for Tuberculosis will occur in Year 1 alongside the usual care. Receives: Diagnostic Test: Active case finding |
| Control | No intervention | Usual care will be provided and no active case finding activities will be implemented. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Prevalence rate of bacteriologically positive TB
Prevalence rate of bacteriologically positive TB in Year 3 among the high-risk populations , including those of 65 and older, those who are under 65 but have a history of tuberculosis treatment or have been in close contact with a person diagnosed of TB within the past three years, have been clinically diagnosed with diabetes, HIV, or have a background of working as a miner.
Measured: In year 3 after recruitment
Secondary outcomes (3)
Prevalence rate of active TB
Prevalence rate of active TB, including both bacteriologically positive and negative cases, among the high-risk populations in Year 3
Measured: In Year 3 after recruitment
Notification rates of bacteriologically positive TB
Notification rates of bacteriologically positive TB cases among all populations in Year 3
Measured: In Year 3 after recruitment
Notification rates of active TB cases
Notification rates of active TB cases, including both bacteriologically positive and negative cases among all populations in Year 3
Measured: In Year 3 after recruitment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Nov 2024
- Study start20 Nov 2021
- Primary completion31 Jul 2025
- Study completion31 Jul 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Tuberculosis (TB)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 43,067 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Toronto
- Sponsor type
- University, hospital or other
- Collaborators
- Guangxi Zhuang Autonomous Region Center for Disease Prevention and Control
- Responsible party
- Principal investigator, Xiaolin Wei, University of Toronto
- Investigators
- Dabin Liang, PhD, Principal investigator, Guangxi Zhuang Autonomous Region Center for Disease Prevention and ControlXiaolin Wei, PhD, Principal investigator, University of Toronto
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06702774
- Sponsor's protocol ID
- GXTB202108
- Status last verified
- Oct 2026
- First submitted
- 20 Nov 2024
- Last updated
- 21 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data won't be publicly available due to local policy. Clustered data can be shared upon reasonable request to lxy530028@163.com for research purpose only after January 1, 2027.
Publications
Wei X, Liang D, Zhang Z, Thorpe KE, Zhou L, Zhao J, Qin H, Liang X, Cui Z, Huang Y, Huang L, Lin M. Active case finding using mobile vans with artificial intelligence aided radiology tests and sputum collection for rapid diagnostic tests to reduce tuberculosis prevalence among high-risk population in rural China: Protocol for a pragmatic trial. PLoS One. 2025 Apr 11;20(4):e0316073. doi: 10.1371/journal.pone.0316073. eCollection 2025.
PubMed 40215230
Study at a glance
From the registry record, last updated by the sponsor on 21 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06702774
- Acronym / sponsor ID
- GXTB202108
- Conditions
- Tuberculosis (TB)
- Keywords
- Tuberculosis, active case finding, mobile van, artificial intelligence, cluster randomized controlled trial, China
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 43,067 participants (actual)
- Lead sponsor
- University of TorontoUniversity, hospital or other
- Collaborators
- Guangxi Zhuang Autonomous Region Center for Disease Prevention and Control
- Study start
- 20 Nov 2021 (actual)
- Primary completion
- 31 Jul 2025 (actual)
- Study completion
- 31 Jul 2025 (actual)
- First posted
- 25 Nov 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention | Experimental | Diagnostic Test: Active case findingA single active case finding campaign for Tuberculosis will occur in Year 1 alongside the usual care. |
| Control | No intervention | –Usual care will be provided and no active case finding activities will be implemented. |
Who may qualify
- Ages
- 15 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (1 listed)
- Residents who refuse participation.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Active case findingVillagers will be informed through public announcements and social workers. Before the campaign, social workers and village doctors will recruit participants and obtain consent through door-to-door visits. A mobile van equipped with an AI-assisted digital radiography (DR) machine and a refrigerator will visit villages on agreed dates. Participants will complete a TB symptom questionnaire and undergo DR screening. Those with TB symptoms or abnormal DR results will provide on-site sputum samples and collect additional morning and night samples. Trained staff will ensure proper collection and offer nebulizer support if needed. Samples will be transported daily to hospitals for testing using smear, culture, and GeneXpert. Participants with negative bacteriological results but abnormal findings will be referred for further clinical assessment. | Diagnostic test | Intervention |
Primary outcome
Prevalence rate of bacteriologically positive TB
Time frame: In year 3 after recruitment
Prevalence rate of bacteriologically positive TB in Year 3 among the high-risk populations , including those of 65 and older, those who are under 65 but have a history of tuberculosis treatment or have been in close contact with a person diagnosed of TB within the past three years, have been clinically diagnosed with diabetes, HIV, or have a background of working as a miner.
3 secondary outcomes
Prevalence rate of active TB
Time frame: In Year 3 after recruitment
Notification rates of bacteriologically positive TB
Time frame: In Year 3 after recruitment
Notification rates of active TB cases
Time frame: In Year 3 after recruitment
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Oct 2026.
- Derived from this study: PubMed 40215230Wei X, Liang D, Zhang Z, Thorpe KE, Zhou L, Zhao J, Qin H, Liang X, Cui Z, Huang Y, Huang L, Lin M. Active case finding using mobile vans with artificial intelligence aided radiology tests and sputum collection for rapid diagnostic tests to reduce tuberculosis prevalence among high-risk population in rural China: Protocol for a pragmatic trial. PLoS One. 2025 Apr 11;20(4):e0316073. doi: 10.1371/journal.pone.0316073. eCollection 2025.
- Individual participant dataSharing planned: No. Individual participant data won't be publicly available due to local policy. Clustered data can be shared upon reasonable request to lxy530028@163.com for research purpose only after January 1, 2027.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Dabin Liang, PhD principal investigator
Guangxi Zhuang Autonomous Region Center for Disease Prevention and Control
Xiaolin Wei, PhD principal investigator
University of Toronto
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Active case finding in people with Tuberculosis (TB), run by University of Toronto at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 21 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prevalence rate of bacteriologically positive TB (time frame: In year 3 after recruitment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06702774, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Oct 2026.