How much does TB screening cost? A systematic review of economic evaluations
Bibliographic record
Abstract
BACKGROUND: Systematic screening for TB has been recommended as a method to control TB on a global level; however, this involves significant costs that place a burden on the health system.METHODS: We conducted a systematic review of the existing economic literature on systematic screening for TB to summarise costs, cost-effectiveness and affordability, and the key factors that influence costs and cost-effectiveness. Specific populations of interest included the general population, children and close contacts of TB patients.RESULTS: We identified 21 studies that provided both cost and outcome data on TB screening among the populations of interest. All were from low- and middle-income settings. Studies were heterogenous in the intervention, and included costs and reported outcomes. The incremental cost-effectiveness ratio (ICER) estimates ranged from USD281 to USD698 per disability-adjusted life-year (DALY) averted among the general population, USD619/DALY averted among children and USD372–3,718/DALY averted among close contacts.CONCLUSION: Prevalence of TB among targeted high-risk groups was identified across the majority of studies as a driver of cost-effectiveness. The heterogeneity of the included costs and outcomes across the economic literature for systematic screening suggests a need for standardisation of included cost components and key economic evaluation methods to improve comparability and generalisability of results.
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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 | 0.031 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".