Screening for Tuberculosis Among Adults Newly Diagnosed With HIV in Sub-Saharan Africa
Bibliographic record
Abstract
OBJECTIVE: New tools, including light-emitting diode (LED) fluorescence microscopy and the molecular assay Xpert MTB/RIF, offer increased sensitivity for tuberculosis (TB) in persons with HIV but come with higher costs. Using operational data from rural Malawi, we explored the potential cost-effectiveness of on-demand screening for TB in low-income countries of Sub-Saharan Africa. DESIGN AND METHODS: Costs were empirically collected in 4 clinics and in 1 hospital using a microcosting approach, through direct interview and observation from the national TB program perspective. Using decision analysis, newly diagnosed persons with HIV were modeled as being screened by 1 of the 3 strategies: Xpert, LED, or standard of care (ie, at the discretion of the treating physician). RESULTS: Cost-effectiveness of TB screening among persons newly diagnosed with HIV was largely determined by 2 factors: prevalence of active TB among patients newly diagnosed with HIV and volume of testing. In facilities screening at least 50 people with a 6.5% prevalence of TB, or at least 500 people with a 2.5% TB prevalence, Xpert is likely to be cost-effective. At lower prevalence-including that observed in Malawi-LED microscopy may be the preferred strategy, whereas in settings of lower TB prevalence or small numbers of eligible patients, no screening may be reasonable (such that resources can be deployed elsewhere). CONCLUSIONS: TB screening at the point of HIV diagnosis may be cost-effective in low-income countries of Sub-Saharan Africa, but only if a relatively large population with high prevalence of TB can be identified for screening.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".