Population-Based Tuberculin Skin Testing and Prevalence of Tuberculosis Infection in Afghanistan
Bibliographic record
Abstract
OBJECTIVE: A tuberculin skin-test survey was conducted in eight provinces of Afghanistan to estimate the prevalence and annual risk of tuberculosis infection among the Afghan population. METHODS: A cluster survey in eight Afghan provinces, chosen based on population density and geographic distribution, was carried out between October and February 2006. Interviews were conducted and tuberculin skin tests were administered and read. FINDINGS: 11,413 individuals participated in the study. Using the international standard cut-off of >or= 10 mm, tuberculosis prevalence and annual risk of infection in the population were 15% (CI: 14.4-15.7) and 0.80 (CI: 0.76-0.84), respectively. Tuberculosis prevalence was higher in rural than in urban areas. Other risk factors included age, prior tuberculosis treatment or contact, productive cough or cough >3 weeks, no prior bacille Calmette Guérin (BCG) vaccination and a cooking fire in the sleeping room. CONCLUSIONS: The survey documented a lower prevalence and risk of tuberculosis infection than the 1978 national survey and a substantially lower estimate of incidence of new smear-positive tuberculosis cases than World Health Organization estimates. However, other findings suggest that active tuberculosis may remain widespread and undiagnosed, and indicate a need for both additional research and continued investment in tuberculosis treatment and prevention, and in health infrastructure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".