Projected Tuberculosis Incidence Reductions from Screening Algorithms

Projections detail reductions in symptomatic tuberculosis incidence achievable over a 10-year period following screening interventions. Katherine C. Horton modeled outcomes based on varying diagnostic algorithms, population coverage percentages, and program durations for a population with a baseline prevalence of 500 per 100,000. The dataset was last updated in April 2026.

Use Cases

• Compare projected incidence reductions across different diagnostic_algorithm types (e.g., targeting symptomatic infectious TB vs. infectious TB).
• Analyze trade-offs between population_coverage percentages and program_duration to achieve equivalent impact targets.
• Assess uncertainty in projections using the provided 95% uncertainty_interval ranges for each scenario.
• Identify coverage and duration combinations (marked as italic or bold values) that match the impact of maximum intervention scenarios.

Strengths

• Includes 95% uncertainty intervals for all projections, quantifying model confidence.
• Explicitly compares impact equivalence across multiple intervention parameters (algorithm, coverage, duration).
• Released under a permissive CC-BY-4.0 license for reuse.

Limitations

• Dataset is very small at 9.5 KB, indicating limited scope and likely a single summary table.
• No raw patient or clinical data is included; only aggregated model outputs are provided.
• Projections are based on a single baseline_prevalence scenario of 500 per 100,000, limiting generalizability.

Provenance

Source

Katherine C. Horton via the figshare repository.

Collection Method

Results from an epidemiological modeling study projecting TB incidence.

Time Range

Projections cover a 10-year period following the start of screening.

Freshness

Last updated in April 2026, indicating forward-looking projections rather than historical data.

Geography

Modeled for a generic population with a specified baseline prevalence; specific geographic region is not defined.