Vaccine-Preventable Disease Outbreaks Among Healthcare Workers: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Outbreaks of vaccine-preventable diseases (VPDs) in healthcare workers (HCWs) can result in morbidity and mortality and cause significant disruptions to healthcare services, patients, and visitors as well as an added burden on the healthcare system. This scoping review aimed to describe the epidemiology of VPD outbreaks in HCWs caused by diseases that are prevented by the 10 vaccines recommended by the World Health Organization for HCWs. METHODS: In April 2022, CINAHL, MEDLINE, Global Health, and EMBASE were searched for all articles reporting on VPD outbreaks in HCWs since the year 2000. Articles were included regardless of language and study type. Clinical and epidemiological characteristics of VPD outbreaks were described. RESULTS: Our search found 9363 articles, of which 216 met the inclusion criteria. Studies describing 6 of the 10 VPDs were found: influenza, measles, varicella, tuberculosis, pertussis, and rubella. Most articles (93%) were from high- and upper-middle-income countries. While most outbreaks occurred in hospitals, several influenza outbreaks were reported in long-term-care facilities. Based on available data, vaccination rates among HCWs were rarely reported. CONCLUSIONS: We describe several VPD outbreaks in HCWs from 2000 to April 2022. The review emphasizes the need to understand the factors influencing outbreaks in HCWs and highlights the importance of vaccination 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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".