Cost-Effectiveness of Automated Digital Microscopy for Diagnosis of Active Tuberculosis.
BACKGROUND
Automated digital microscopy has the potential to improve the diagnosis of tuberculosis (TB), particularly in settings where molecular testing is too expensive to perform routinely. The cost-effectiveness of TB diagnostic algorithms using automated digital microscopy remains uncertain.


METHODS
Using data from a demonstration study of an automated digital microscopy system (TBDx, Applied Visual Systems, Inc.), we performed an economic evaluation of TB diagnosis in South Africa from the health system perspective. The primary outcome was the incremental cost per new TB diagnosis made. We considered costs and effectiveness of different algorithms for automated digital microscopy, including as a stand-alone test and with confirmation of positive results with Xpert MTB/RIF ('Xpert', Cepheid, Inc.). Results were compared against both manual microscopy and universal Xpert testing.


RESULTS
In settings willing to pay $2000 per incremental TB diagnosis, universal Xpert was the preferred strategy. However, where resources were not sufficient to support universal Xpert, and a testing volume of at least 30 specimens per day could be ensured, automated digital microscopy with Xpert confirmation of low-positive results could facilitate the diagnosis of 79-84% of all Xpert-positive TB cases, at 50-60% of the total cost. The cost-effectiveness of this strategy was $1280 per incremental TB diagnosis (95% uncertainty range, UR: $340-$3440) in the base case, but improved under conditions likely reflective of many settings in sub-Saharan Africa: $677 per diagnosis (95% UR: $450-$935) when sensitivity of manual smear microscopy was lowered to 0.5, and $956 per diagnosis (95% UR: $40-$2910) when the prevalence of multidrug-resistant TB was lowered to 1%.


CONCLUSIONS
Although universal Xpert testing is the preferred algorithm for TB diagnosis when resources are sufficient, automated digital microscopy can identify the majority of cases and halve the cost of diagnosis and treatment when resources are more scarce and multidrug-resistant TB is not common.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States of America.
Swati
S
Jha
Centre for Tuberculosis National Institute for Communicable Diseases, Sandringham, South Africa.|Department of Medical Microbiology, University of Pretoria, Pretoria, South Africa.
Nazir
N
Ismail
Aurum Institute, Johannesburg, South Africa.
David
D
Clark
MRC Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
James J
JJ
Lewis
Centre for Tuberculosis National Institute for Communicable Diseases, Sandringham, South Africa.
Shaheed
S
Omar
Centre for Tuberculosis National Institute for Communicable Diseases, Sandringham, South Africa.|School of Public Health, University of the Witswatersrand, Johannesburg, South Africa.
Andries
A
Dreyer
Aurum Institute, Johannesburg, South Africa.
Violet
V
Chihota
Aurum Institute, Johannesburg, South Africa.|MRC Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.|School of Public Health, University of the Witswatersrand, Johannesburg, South Africa.
Gavin
G
Churchyard
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States of America.
David W
DW
Dowdy
D001331:Automation; D003362:Cost-Benefit Analysis; D006801:Humans; D008853:Microscopy / Q000379:methods*; D012680:Sensitivity and Specificity; D013019:South Africa / Q000453:epidemiology; D014376:Tuberculosis / Q000175:diagnosis* / Q000191:economics* / Q000453:epidemiology; D018088:Tuberculosis, Multidrug-Resistant / Q000191:economics