ScreenTB

ScreenTB models tuberculosis screening strategies to quantify case yield (true- and false-positives), diagnostic costs, and cost-effectiveness to inform National TB Programmes (NTPs) planning.

Key Features:

• Contextual Tailoring: Adapts screening strategies to local epidemiology and population contexts to align expected yield and resource use with local conditions.
• Risk Group Prioritization: Identifies and ranks populations most likely to benefit from screening to guide targeted case-finding.
• Algorithm Selection: Evaluates diagnostic algorithms with respect to sensitivity and specificity to balance true-positive detection and minimize false-positive diagnoses.
• Outcome Modeling: Simulates yields of TB cases (true- and false-positives), associated costs, and cost-effectiveness for specified populations and diagnostic tools.
• Strategic Planning Support: Assesses feasibility, impacts on TB transmission, vulnerability of risk groups, human rights implications, and equity in access to inform programmatic decisions.

Scientific Applications:

• Enhanced Case Detection: Informs strategies to estimate and increase detection of active tuberculosis cases through modeled screening scenarios.
• Resource Optimization: Guides allocation of diagnostic resources by prioritizing high-risk groups and selecting algorithms that reduce false positives and costs.
• Policy Formulation: Provides quantitative evidence on yield, costs, and equity considerations to support policy decisions for TB screening programs.

Methodology:

Employs a data-driven modeling approach that integrates epidemiological data with diagnostic accuracy metrics to simulate outcomes of TB screening scenarios.