Using community health workers for facility and community based TB case finding: An evaluation in central Mozambique
Bibliographic record
Abstract
BACKGROUND: Mozambique has one of the highest incidence rates of both TB and HIV in the world and an estimated tuberculosis (TB) treatment coverage of only 57% in 2018. Numerous approaches are being tested to reduce existing gaps in coverage and the estimated number of missing cases. METHODS: Thirty Community Healthcare Workers (CHWs) were tasked with increasing TB notifications by performing verbal facility-based TB screening of all people presenting for care and TB contact tracing in the community. Using routine National TB Program data, we analyzed trends in TB notifications in five intervention districts and seven control districts in Manica province the year before this project and during a one-year intervention period. RESULTS: In the four quarters before the study, the intervention districts notified 5,219 individuals with all forms of TB, and the control districts notified 2,248 TB cases. During the study 5,982 all forms of people with TB were notified in the intervention area, an increase of 763 (14.6%) over the baseline, whereas the control districts notified 1,877 persons with TB, a decrease of -371 (-16.5%). The CHW screening activities yielded 1,502 notified and treated individuals with TB. CONCLUSIONS: Employing CHWs to promote facility-based TB screening and household contact tracing may lead to an overall increase in TB notification.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".