Risk of tuberculosis infection and disease associated with work in health care settings.
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) in health care workers (HCWs) was not considered a serious problem following the advent of effective antibiotic therapy. Interest was re-stimulated by the occurrence of several major nosocomial outbreaks. METHODS: We have reviewed the available published literature regarding prevalence and incidence of TB infection and disease among HCWs in countries categorised by mean income. We included studies published in English since 1960 from low- and middle-income countries (LMICs) and since 1990 from high-income countries (HICs). We excluded outbreak reports and studies based only on questionnaires. RESULTS: The median prevalence of latent TB infection (LTBI) in HCWs was 63% (range 33-79%) in LMICs and 24% in HICs (4-46%). Among HCWs from LMICs, LTBI was consistently associated with markers of occupational exposure, but in HICs it was more often associated with non-occupational factors. The median annual incidence of TB infection attributable to health care work was 5.8% (range 0-11%) in LMICs and 1.1% (0.2-12%) in HICs. Rates of active TB in HCWs were consistently higher than in the general population in all countries, although findings were variable in HICs. Administrative infection control measures had a modest impact in LMICs, yet seemed the most effective in HICs. CONCLUSIONS: TB remains a very important occupational risk for HCWs in LMICs and for workers in some institutions in HICs. Risk appears particularly high when there is increased exposure combined with inadequate infection control measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".