Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding
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
About This Trial
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.
Who May Be Eligible (Plain English)
Original Eligibility Criteria
View original clinical language
Treatments Being Tested
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.