A pragmatic stepped-wedge cluster randomized trial to evaluate the effectiveness and cost-effectiveness of active case finding for household contacts within a routine tuberculosis program, San Juan de Lurigancho, Lima, Peru
Bibliographic record
Abstract
BACKGROUND: Active case finding (ACF) in household contacts of tuberculosis (TB) patients is now recommended for National TB Programs (NTP) in low- and middle-income countries. However, evidence supporting these recommendations remains limited. This study evaluates the effectiveness and cost-effectiveness of ACF for household contacts of TB cases in a large TB endemic district of Lima, Peru. METHODS: A pragmatic stepped-wedge cluster randomized controlled trial was conducted in 34 health centers of San Juan de Lurigancho district. Centers were stratified by TB rate and randomly allocated to initiate ACF in groups of eight or nine centers at four-month intervals. In the intervention arm, NTP providers visited households of index patients to screen contacts for active TB. The control arm was routine passive case finding (PCF) of symptomatic TB cases. The primary outcomes were the crude and adjusted active TB case rates among household contacts. Program costs were directly measured, and the cost-effectiveness of the ACF intervention was determined. FINDINGS: 3222 index TB cases and 12,566 household contacts were included in the study. ACF identified more household contact TB cases than PCF, 199.29/10,000 contacts/year vs. 132.13 (incidence rate ratio of 1.51 (95% CI 1.21-1.88)). ACF was associated with an incremental cost-effectiveness ratio of US $16,400 per disability-adjusted life year averted and not cost-effective assuming a willingness-to-pay threshold for Peru of US $6360. CONCLUSION: ACF of TB case household contacts detected significantly more secondary TB cases than PCF alone, but was not cost-effective in this setting. In threshold analyses, ACF becomes cost-effective if associated with case detection rates 2.5 times higher than existing PCF programs.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".