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

Table 1 Net sensitivity and specificity of diagnostic algorithms reflected in each scenario, by smear type. Green cells represent large increase in value compared to baseline algorithm

Scenario		Definition	Net sensitivity		Net specificity
Scenario		Definition	Smear positive	Smear negative	Net specificity
Baseline		Prolonged cough & microscopy/clinical diagnosis	50.0%	20.9%	94.9%
Scenario 1	Algorithm A	Prolonged cough & GeneXpert	49.1%	27.8%*	99.9%*
Scenario 1	Algorithm B	Any symptom & microscopy/clinical diagnosis	77.0%*	20.9%	94.3%
Scenario 2	Microscopy (baseline)	Prolonged cough & microscopy/clinical diagnosis	50.0%	20.9%	94.9%
Scenario 2	GeneXpert	Prolonged cough & GeneXpert	49.1%	27.8%*	99.9%*

*Asterisk signifies large increase in value compared to baseline algorithm; Scenario 1 comparing the impact of two different diagnostic algorithms in a defined population; Scenario 2 examining the impact of expanding case detection towards population of lower disease

ISSN: 1471-2334