The effectiveness of isoniazid preventive treatment among contacts of multidrug-resistant tuberculosis: a systematic review and individual-participant meta-analysis
Bibliographic record
Abstract
RATIONALE: Recent empirical research suggests that isoniazid may lead to a risk reduction of incident tuberculosis (TB) among close TB contacts of someone with multidrug-resistant TB (MDR-TB). OBJECTIVES: To evaluate the association between isoniazid TB preventive treatment (TPT), compared to no treatment, upon incident TB in household contacts of MDR-TB cases using a large global consortium of TB contact tracing studies. METHODS: We conducted a systematic review and individual-participant meta-analysis among observational studies of household contact tracing studies. Participants were included if they were exposed to someone with MDR-TB and were given either 6 months of isoniazid TPT or no TPT. Our primary outcome was incident TB in contacts exposed to TB. We derived adjusted hazard ratios (aHRs) using mixed-effects, multivariable survival regression models with study-level random effects. The effectiveness of isoniazid TPT against incident TB was estimated through propensity score matching. MEASUREMENTS AND MAIN RESULTS: We included participant-level data from 6668 contacts exposed to MDR-TB from 17 countries. The effectiveness of isoniazid TPT against incident TB in contacts of MDR-TB was 57% (aHR, 0.43 [95% CI, 0.26-0.71]) and did not appreciably change with adjustment for additional potential confounders. The reduction in incident TB was marginally greater among child (<20 years old) contacts (aHR, 0.51 [95% CI, 0.28-0.92) compared to adult contacts (aHR, 0.69 [95% CI, 0.22-2.20]). The reduction in incidence was 73% (aHR, 0.27 [95% CI, 0.11-0.70]) in the first year of follow-up; effectiveness dropped to 60% (aHR, 0.40 [95% CI, 0.15-1.06]) from 12 to 23 months of follow-up and was nonsignificant after 2 years (28% effectiveness; aHR, 0.72 [95% CI, 0.33-1.54]). CONCLUSIONS: Among >6500 contacts of MDR-TB, isoniazid TPT was highly effective in preventing incident TB. The reduction was greatest in high-burden countries and waned after 2 years of follow-up.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.002 | 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".