Trade-offs in diagnostic algorithm, population coverage, and duration of community screening for tuberculosis: A modelling study

06 Apr 20264 min read0 viewsJournal Feed

GIST

by Katherine C. Horton, Alexandra S.

Richards, Alvaro Schwalb, Rein M. G.

J. Houben Background Current tuberculosis (TB) prevention and care strategies have failed to reduce disease burden at the pace required to meet global targets.

Community screening may enable more rapid declines in TB burden, but evidence is limited. We used mathematical modelling to evaluate approaches using different diagnostic algorithms, population coverage, and duration of screening.

Methods and findings We used a deterministic compartmental TB model, which recognised symptomatic and asymptomatic infectious TB (defined by whether an individual reported symptoms at screening), as well as noninfectious TB. We simulated diagnostic algorithms targeting symptomatic infectious TB (prolonged cough with confirmatory Xpert Ultra), infectious TB (Xpert Ultra), or all TB (chest X-ray), and we varied population coverage and duration of screening.

Clinical Editorial

Scope and design rationale

A deterministic compartmental TB model was employed to explore how differing diagnostic algorithms, population coverage, and screening duration influence TB outcomes.
The model distinguishes symptomatic infectious TB (defined by reported symptoms at screening), asymptomatic infectious TB, and noninfectious TB, framing potential detection pathways accordingly.
The study focuses on reductions in symptomatic TB incidence and TB mortality over a 10-year horizon, without presenting new trial data.

Diagnostic strategies and their implied performance

Three algorithmic approaches were simulated:
Targeting symptomatic infectious TB: screen individuals with prolonged cough and use confirmatory Xpert Ultra.
Targeting infectious TB: apply Xpert Ultra to the screened cohort.
Targeting all TB: utilize chest X-ray across the screening population, capturing broader disease definitions.
Across scenarios, diagnostic performance and subsequent treatment implications were assumed based on available data and were source-dependent, with explicit notes on limitations where data were incomplete.

Population reach and temporal deployment

Scenarios varied both the fraction of the population screened (coverage) and the number of annual screening rounds (duration).
High coverage (100%) for five annual rounds served as the reference for maximum sustained reach, while alternative designs examined reduced coverage and/or shorter or longer durations.

Key quantitative signals and their interpretation

With full coverage and five rounds:
Targeting symptomatic TB yielded a 26.9% reduction in symptomatic TB incidence (uncertainty interval 22.8–31.5%).
Targeting infectious TB achieved a 74.0% reduction (UI 68.5–79.1%).
In both cases, incidence tended to rebound after screening ended, erasing 9.8% and 15.9% of the initial reductions within five years.
Targeting all TB (chest X-ray) produced the highest potential for rapid incidence reductions (exceeding 98%), yet was constrained by substantial overdiagnosis because current tools had limited diagnostic accuracy, yielding about 7.2 false positives per true positive in a single round.
Broader disease definitions (infectious or all TB) generally generated greater epidemiologic impact at lower coverage and/or shorter durations, indicating efficiency gains in certain design choices.
Mortality trends mirrored the symptomatic/incidence patterns, with broadly similar results across the evaluated strategies.

Uncertainties, limitations, and practical implications

The authors note a homogeneous population assumption and acknowledge the need to make several data-driven assumptions around algorithm performance, treatment acceptance, and completion across disease states due to gaps in available data.
As a modelling exercise, real-world heterogeneity, operational constraints, and health system capacity could influence observed effects.