ArticlePloS one2016
Cost-Effectiveness of Automated Digital Microscopy for Diagnosis of Active Tuberculosis.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Economic Analyses of Respiratory Tract Infection Diagnostics: A Systematic Review.PharmacoEconomics · 2021Pooled it
- Cost-effectiveness of GeneXpert and LED-FM for diagnosis of pulmonary tuberculosis: A systematic review.PloS one · 2018Pooled it
- Cost-effectiveness of Low-complexity Screening Tests in Community-based Case-finding for Tuberculosis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Article
- Health-Economic Analyses of Diagnostics: Guidance on Design and Reporting.PharmacoEconomics · 2021Article
- Article
- Feasibility of the TBDx automated digital microscopy system for the diagnosis of pulmonary tuberculosis.PloS one · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
Abstract
backgroundAutomated 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.
methodsUsing 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.
resultsIn 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%.
conclusionsAlthough 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.