Peer-led active tuberculosis case-finding among people living with HIV: lessons from Nepal
Bibliographic record
Abstract
PROBLEM: People living with a human immunodeficiency virus (HIV) infection have a high risk of tuberculosis and should undergo regular screening. However, they can be difficult to reach because they are stigmatized and discriminated against. APPROACH: In Nepal, the nongovernmental organization Naya Goreto implemented a peer-led tuberculosis screening project in which people living with HIV volunteered to contact others in this high-risk population. Volunteers took part in a short training course, after which they attempted to contact people living with HIV through existing networks and self-help groups. Tuberculosis screening and testing were carried out in accordance with national guidelines. LOCAL SETTING: In Nepal, the prevalence of HIV infection is 0.3% in the general population but is much higher, at 6%, in people in Kathmandu who inject drugs. To date, the health system has not been able to implement systematic tuberculosis screening in people living with HIV. RELEVANT CHANGES: Between May 2014 and mid-September 2015, 30 volunteers screened 6642 people in 10 districts, 5430 (82%) of whom were living with HIV. Of the 6642, 6046 (91%) were tested for tuberculosis and 287 (4.3%) were diagnosed with the disease, 240 of whom were HIV-positive. Of those with tuberculosis, 270 (94%) initiated treatment. LESSONS LEARNT: Using peers to contact people living with HIV for tuberculosis screening resulted in a high participation rate and the identification of a considerable number of HIV-positive tuberculosis patients. Follow-up during treatment was difficult in this highly mobile group and needs more attention in future interventions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".