Clinical trial NCT06017843: Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding
Official title: Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding in Pakistan: a Pragmatic Stepped Wedge Cluster Randomized Trial
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Tuberculosis
Keywords: Active case finding
Artificial Intelligence
Hotspots
Lead sponsor: Centre for Global Public Health Pakistan
Sponsor class: OTHER
Collaborators: name: Mercy Corps Pakistan
First posted: 2023-08-30
Enrollment: 180000
Sex: ALL
Minimum age (years): 15
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Intervention model description: The MATCH-AI evaluation is designed as a stepped-wedge cluster randomized trial. In this study, clusters (mobile X-ray van teams) will successively switch over in groups of 3 to the intervention in a randomly assigned order until all clusters are eventually exposed to the intervention.

In the intervention arm, TB active case finding camps will be conducted primarily in locations guided by MATCH-AI a modelling software that predicts TB hotspots. In the control sites, field-teams will continue to utilize existing approaches such as local knowledge, historical data etc towards camp site-selection.
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: The aim of this pragmatic, stepped wedge cluster-randomized trial is to measure the comparative yield (number of incident TB cases diagnosed during active case-finding camps) using a site selection approach based on predictions generated via an artificial intelligence software called MATCH-AI (intervention group) versus the conventional approach of camp site selection using field-staff knowledge and experience (control group). The trial will help inform whether a targeted approach towards screening for TB using artificial-intelligence can improve yields of TB cases detected through community-based active case-finding.

Detailed description: Despite significant progress over the past decades, an estimated 10.6 million individuals fell ill with tuberculosis (TB) in 2021 and the disease caused 1.6 million deaths globally. Pakistan is ranked as the 5th highest TB burden country in the world and TB causes 42,000 deaths annually in the country. A key challenge in the Pakistan's response to TB is ensuring diagnosis and treatment of all individuals with TB. In 2020, out of the 573,000 cases, a total of 276,736 (48%) were notified. Bridging this case-detection gap is a critical objective for the National TB Program (NTP). Active case-finding (ACF), is a potential strategy to increase case-detection by systematic screening of communities for TB. Recent evidence, indicates that ACF can also reduce population-level TB incidence and prevalence through early detection. While ACF interventions have demonstrated effectiveness in community-trials and are now being conducted at scale in Pakistan, concerns remain regarding their yields and cost-effectiveness in programmatic settings.

The primary aim of this study is to investigate whether a targeted approach towards community-based screening using MATCH-AI, an artificial intelligence software that models sub-district TB prevalence, can improve the yield of ACF interventions in Pakistan. In the intervention arm, field-team will conduct community-based ACF activities (called chest camps) primarily in locations predicted by MATCH-AI to have a higher prevalence of TB. In the control arm, field-teams will continue to utilize existing approaches towards camp site-selection. The trial will be conducted in 65 districts of Pakistan in collaboration with implementation partners of the NTP.

Eligibility criteria: Inclusion Criteria:

* All individuals \>15 years of age presenting to camp sites
* Individuals with previous history of TB disease

Exclusion Criteria:

* Children and adolescents \<15 years of age
* Pregnant women

Primary outcomes:
measure: Camp positivity yield
timeFrame: 12 months
Secondary outcomes:
measure: Camp positivity rate
timeFrame: 12 months
measure: Camp All-Forms yield
timeFrame: 12 months
measure: Camp All-Forms TB rate
timeFrame: 12 months
Other outcomes:
Locations:
city: Islamabad
country: Pakistan
facility: Mercy Corps Pakistan
Study officials:
affiliation: Centre for Global Public Health Pakistan
name: Faran Emmanuel
role: PRINCIPAL_INVESTIGATOR
References: