Implementation of tuberculosis prevention for exposed children, Burkina Faso
Bibliographic record
Abstract
OBJECTIVE: To develop and test a simple system for recording and reporting the diagnosis and treatment of latent tuberculosis infection and to compare the effects of passive and active tracing of child contacts on indicators of such infection. METHODS: We revised Burkina Faso's latent tuberculosis infection register and quarterly tuberculosis reporting form. Subsequently, coverage of the routine screening of contacts, who were younger than five years, for active tuberculosis and the corresponding percentages of such contacts who, if eligible, initiated preventive therapy were measured, nationwide, between 1 April 2016 and 31 March 2017. In 2016, we evaluated indicators of latent tuberculosis infection in the Hauts-Bassins region before and after community health workers had begun the active tracing of contacts who were younger than five years. FINDINGS: In Burkina Faso, during our study period, 3717 cases of pulmonary tuberculosis and 1166 corresponding contacts who were younger than five years were reported as the result of routine screening and passive contact tracing. The overall contact:index ratio was 0.31 and corresponding screening coverage was 82.0% (956/1166) and proportion of children starting on preventive treatment was 90.5% (852/941). Active tracing in Hauts-Bassins led to a substantially higher contact/index ratio (1.83) and screening coverage (99.3%; 145/146). CONCLUSION: The newly established recording and reporting system proved feasible and user-friendly and allowed measurement of global indicators of latent tuberculosis infection. Compared with active tracing, passive tracing led to much lower estimates of the numbers of child contacts.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".