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Table 2 Modelled outcomes between 2017 and 2025 for each scenario

From: Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling

Scenario Additional notifications between 2017 and 2025 (absolute numbers, × 1000) Number of cases averted between 2017 and 2025 (× 1000) Percent reduction in incidence by 2025 compared to 2017 Absolute change in PPV by 2025 compared to baseline Number of additional FP notifications per additional TP notification Number of additional notifications needed to avert one case
Total TP FP
Scenario 1 Algorithm A 4.0 6.4 −2.4 14.7 8.4% + 2 −0.4 0.3
Algorithm B 13.8 7.6 6.2 24.2 12.2% −5 0.8 0.6
Scenario 2 ICF with microscopy 24.3 1.3 23.0 3.6 3.6% −11 17.7 6.7
ICF with GeneXpert −25.1 6.6 − 31.7 5.0 4.3% + 36 −4.8 −5.0
  1. TP True positive notification; FP False positive notification; PPV Positive predictive value; Scenario 1 comparing the impact of two different diagnostic algorithms in a defined population; Algorithm A Prolonged cough & GeneXpert; Algorithm B Any symptom & microscopy/clinical diagnosis; Scenario 2 Examining the impact of expanding case detection towards population of lower disease
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