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Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding

NCT06017843 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 180000

Last updated 2024-07-25

No results posted yet for this study

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.

Conditions

Interventions

OTHER

Camps site selection for active case finding for TB using MATCH-AI

The primary intervention in this study is the roll-out of MATCH-AI, an artificial intelligence software that models sub-district TB prevalence, to guide site selection of ACF camps. The MATCH-AI tool uses a Bayesian modelling approach to predict TB prevalence to a resolution of 10,000 population that are mapped as polygons. The model integrates data from a range of sources including historical TB facility notification data, previous ACF data as well as contextual factors such as demographics, income, population density, health indicators such as vaccination coverage and climate related variables to predict localized TB prevalence. In the intervention arm, camps will be conducted primarily in locations guided by MATCH-AI.

Sponsors & Collaborators

  • Mercy Corps Pakistan

    collaborator OTHER

  • Centre for Global Public Health Pakistan

    lead OTHER

Principal Investigators

  • Faran Emmanuel · Centre for Global Public Health Pakistan

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Model
CROSSOVER

Eligibility

Min Age
15 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-09-01
Primary Completion
2024-10-31
Completion
2025-06-30

Countries

  • Pakistan

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06017843 on ClinicalTrials.gov