Health Care Worker Exposures to Pertussis: Missed Opportunities for Prevention
Bibliographic record
Abstract
OBJECTIVE: Pediatric health care workers (HCWs) are at particular risk for pertussis exposure, infection, and subsequent disease transmission to susceptible patients. This cross-sectional study describes the epidemiology of occupational exposures to pertussis and identifies factors that may inform interventions to promote effective implementation of infection prevention and control (IPC) guidelines. METHODS: We abstracted data from occupational health (OH) and IPC records for pertussis cases that resulted in an exposure investigation in a large quaternary pediatric care network, January 1, 2002 to July 18, 2011. We calculated the frequency of occupational exposures and measured associated characteristics. To assess the frequency of potential missed exposures, we reviewed electronic health record (EHR) data identifying laboratory-confirmed pertussis cases not documented in OH or IPC records. RESULTS: A total of 1193 confirmed HCW pertussis exposures were associated with 219 index cases during the study period. Of these, 38.8% were infants <6 months old and 7 were HCWs. Most (77.5%) of exposures occurred in the emergency department or an ambulatory site; 27.0% of exposures occurred after documented initiation of IPC precautions. We identified 450 laboratory-confirmed pertussis cases through EHR review, of which 49.8% (N = 224) had no OH or IPC investigation. The majority of uninvestigated cases (77.2%) were from ambulatory sites. CONCLUSIONS: Occupational exposures to pertussis occur frequently in pediatric health care settings despite appropriate IPC guidelines. Interventions are needed to ensure consistent implementation of IPC practices and timely identification and reporting of pertussis index cases to prevent HCW exposures and potential transmission to patients.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".