Cost-effectiveness of GeneXpert and LED-FM for diagnosis of pulmonary tuberculosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Early and accurate diagnosis of tuberculosis is a priority for TB programs globally to initiate treatment early and improve treatment outcomes. Currently, Ziehl-Neelsen (ZN) stain-based microscopy, GeneXpert and Light Emitting Diode-Fluorescence Microscopy (LED-FM) are used for diagnosing pulmonary drug sensitive tuberculosis. Published evidence synthesising the cost-effectiveness of these diagnostic tools is scarce. METHODOLOGY: PubMed, EMBASE and Cost-effectiveness analysis registry were searched for studies that reported on the cost-effectiveness of GeneXpert and LED-FM, compared to ZN microscopy for diagnosing pulmonary TB. Risk of bias was assessed independently by four authors using the Consensus Health Economic Criteria (CHEC) extended checklist. The data variables included the study settings, population, type of intervention, type of comparator, year of study, duration of study, type of study design, costs for the test and the comparator and effectiveness indicators. Incremental cost-effectiveness ratio (ICER) was used for assessing the relative cost-effectiveness in this review. RESULTS: Of the 496 studies identified by the search, thirteen studies were included after removing duplicates and studies that did not fulfil inclusion criteria. Four studies compared LED-FM with ZN and nine studies compared GeneXpert with ZN. Three studies used patient cohorts and eight were modelling studies with hypothetical cohorts used to evaluate cost-effectiveness. All these studies were conducted from a health system perspective, with four studies utilising cost utility analysis. There were considerable variations in costing parameters and effectiveness indicators that precluded meta-analysis. The key findings from the included studies suggest that LED-FM and GeneXpert may be cost effective for pulmonary TB diagnosis from a health system perspective. CONCLUSION: Our review identifies a consistent trend of the cost effectiveness of LED-FM and GeneXpert for pulmonary TB diagnosis in different countries with diverse context of socio-economic condition, HIV burden and geographical distribution. However, all the studies used different parameters to estimate the impact of these tools and this underscores the need for improving the methodological issues related to the conduct and reporting of cost-effectiveness studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".