<i>Mycobacterium tuberculosis</i> infection and disease in healthcare workers in a tertiary referral hospital in Bandung, Indonesia
Bibliographic record
Abstract
Background Healthcare workers (HCWs), especially in high tuberculosis (TB) incidence countries, are at risk of Mycobacterium tuberculosis infection and TB disease, likely due to greater exposure to TB cases and variable implementation of infection control measures. Aim We aimed to estimate the prevalence of tuberculin skin test (TST) positivity, history of TB and to identify associated risk factors in HCWs employed at a tertiary referral hospital in Bandung, Indonesia. Methods A cross-sectional study was conducted from April to August 2018. A stratified sample of the HCWs were recruited, screened by TST, assessed for TB symptoms, history of TB disease and possible risk factors. Prevalence of positive TST included diagnosis with TB after starting work. HCWs with TB disease diagnosed earlier were excluded. Survey weights were used for all analyses. Possible risk factors were examined using logistic regression; adjusted odds ratios and 95% confidence intervals (CI) are presented. Results Of 455 HCWs recruited, 42 reported a history of TB disease (25 after starting work) and 395 had a TST result. The prevalence of positive TST was 76.9% (95% CI 72.6–80.8%). The odds increased by 7% per year at work (95% CI 3–11%) on average, with a rapid rise in TST positivity up to 10 years of work and then a plateau with around 80% positive. Discussion A high proportion of HCWs had a history of TB or were TST positive, increasing with longer duration of work. A package of TB infection control measures is needed to protect HCWs from Mycobacterium tuberculosis infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".