Prevalence and determinants of latent tuberculosis infection among frontline tuberculosis healthcare workers in southeastern China: A multilevel analysis by individuals and health facilities
Bibliographic record
Abstract
OBJECTIVES: Healthcare workers (HCWs) are at high risk of latent tuberculosis infection (LTBI), and the baseline prevalence of LTBI among frontline TB HCWs in southeastern China remains unknown. The aim of this study was to assess the prevalence of LTBI among TB HCWs and to analyze factors associated with LTBI at both the individual and institutional level. METHODS: Based on a cross-sectional study design, 31 out of 89 TB-designated hospitals in Zhejiang Province of China were selected. Information on TB infection control measures was collected through field visits to each of the selected hospitals. All TB HCWs from the selected hospitals were recruited to answer a questionnaire and to undergo LTBI testing by TB interferon gamma release assay. Univariate analyses and a generalized linear mixed model were applied to analyze factors associated with LTBI at both the individual and hospital level. RESULTS: A total of 487 TB HCWs were recruited at the 31 TB-designated hospitals; 33.9% of them tested positive for LTBI. At the institutional level, a low TB epidemic level, regular infection control training for HCWs, and regular maintenance of ultraviolet disinfection equipment were found to be significantly associated with a lower LTBI rate among HCWs. At the individual level, alcohol use, a greater number of years working on TB, and a longer weekly duration of contact with TB patients were identified as associated factors for LTBI among HCWs. CONCLUSIONS: The LTBI rate among frontline TB HCWs was found to be high in southeastern China. Factors at the institutional and individual level could both affect the prevalence of LTBI among HCWs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".