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

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health Interventions

Background:

• Current tuberculosis (TB) control strategies are insufficient to meet global targets.
• Community screening shows potential for accelerated TB burden reduction, but evidence is limited.
• Mathematical modeling is used to assess various screening approaches.

Purpose of the Study:

• To evaluate the impact of different community screening strategies on TB incidence and mortality.
• To compare diagnostic algorithms targeting symptomatic, infectious, or all TB cases.
• To analyze the influence of population coverage and screening duration.

Main Methods:

• A deterministic compartmental TB model was employed.
• Simulations included algorithms for symptomatic infectious TB, infectious TB, and all TB.
• Population coverage and screening duration were varied to assess outcomes.

Main Results:

• Targeting infectious TB with maximum coverage and duration reduced incidence by 74.0%, with a 15.9% rebound.
• Screening all TB cases showed >98% reduction potential but faced overdiagnosis issues.
• Broader screening definitions generally yielded greater impact with less coverage/duration.

Conclusions:

• Community screening can achieve substantial TB morbidity and mortality reductions.
• Symptom-agnostic algorithms and balancing coverage/duration are crucial for epidemiological impact.
• Accurate diagnostics and treatment for noninfectious TB are essential for sustained control.