Screening for tuberculosis infection and effectiveness of preventive treatment among people with HIV in low-incidence settings
Bibliographic record
Abstract
OBJECTIVE: To determine the yield of screening for latent tuberculosis infection (LTBI) among people with HIV (PWH) in low tuberculosis (TB) incidence countries (<10 TB cases per 100 000 persons). DESIGN: A systematic review and meta-analysis were performed to assess prevalence and predictive factors of LTBI, rate of TB progression, effect of TB preventive treatment (TPT), and numbers needed to screen (NNS). METHODS: PubMed and Cochrane Library were searched for studies reporting primary data, excluding studies on active or paediatric TB. We extracted LTBI cases, odds ratios, and TB incidences; pooled estimates using a random-effects model; and used the Newcastle-Ottawa scale for bias. RESULTS: In 51 studies with 65 930 PWH, 12% [95% confidence interval (CI) 10-14] had a positive LTBI test, which was strongly associated with origin from a TB-endemic country [odds ratio (OR) 4.7] and exposure to TB (OR 2.9). Without TPT (10 629 PWH), TB incidence was 28/1000 person-years (PY; 95% CI 12-45) for LTBI-test positive versus 4/1000 PY (95% CI 0-7) for LTBI-test-negative individuals. Among 625 PWH (1644 PY) receiving TPT, 15 developed TB (6/1000 PY). An estimated 20 LTBI-positive individuals would need TPT to prevent one case of TB, and numbers NNS to detect LTBI or prevent active TB varied according to a-priori risk of LTBI. CONCLUSION: The relatively high prevalence of LTBI among PWH and the strong correlation with origin from a TB-endemic country support risk-stratified LTBI screening strategies for PWH in low-incidence countries and treating those who test positive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".