Nursing and Community Rates of Mycobacterium tuberculosis Infection among Students in Harare, Zimbabwe
Bibliographic record
Abstract
BACKGROUND: African hospitals have experienced major increases in admissions for tuberculosis, but they are ill-equipped to prevent institutional transmission. We compared institutional rates and community rates of tuberculin skin test (TST) conversion in Harare, Zimbabwe. METHODS: We conducted a cohort study of TST conversion 6, 12, and 18 months into training among 159 nursing and 195 polytechnic school students in Harare. Students had negative TST results (induration diameter, < or =9 mm) with 2-step testing at the start of training. RESULTS: Nursing students experienced 19.3 TST conversions (increase in induration diameter, > or =10 mm) per 100 person-years (95% confidence interval [CI], 14.2-26.2 conversions per 100 person-years), and polytechnic school students experienced 6.0 (95% CI, 3.5-10.4) conversions per 100 person-years. The rate of difference was 13.2 conversions (95% CI, 6.5-20.0) per 100 person-years. With a more stringent definition of conversion (increase in the induration diameter of > or =10 mm to at least 15 mm), which is likely to increase specificity but decrease sensitivity, conversion rates were 12.5 and 2.8 conversions per 100 person-years in nursing and polytechnic school students, respectively (rate difference, 9.7 conversions per 100 person-years; 95% CI, 4.5-14.8 conversions per 100 person-years). Nursing students reportedly nursed 20,868 inpatients with tuberculosis during 315 person-years of training. CONCLUSIONS: Both groups had high TST conversion rates, but the extremely high rates among nursing students imply high occupational exposure to Mycobacterium tuberculosis. Intense exposure to inpatients with tuberculosis was reported during training. Better prevention, surveillance, and management of institutional M. tuberculosis transmission need to be supported as part of the international response to the severe human immunodeficiency virus infection epidemic and health care worker crisis in Africa.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".