Use of Models to Identify Cost-effective Interventions: Pertussis Vaccination for Pediatric Health Care Workers
Bibliographic record
Abstract
OBJECTIVE: Acellular pertussis vaccine is safe and effective in adults. An explicit recommendation for pertussis booster vaccination in pediatric health care workers is based on the importance of health care workers as a potential source of infection for patients. However, limited information is available on the economic attractiveness of this intervention. We sought to evaluate the health-economic attractiveness of a diphtheria-tetanus-acellular pertussis booster vaccination program for health care workers in a pediatric intensive care setting. METHODS: We developed a Markov model to calculate the cost-effectiveness of vaccinating NICU health care workers in different proportions ranging from the current strategy of no pertussis booster vaccination program to a vaccination program that achieves between 25% and 95% vaccine coverage. RESULTS: Implementation of a vaccination program that achieves 25% coverage was projected to be cost-saving compared with no vaccine program. At all coverage levels the intervention reduced costs, increased life expectancy, and was cost-effective. Projections were most sensitive to the risk of a pertussis introduction via an infected health care worker. Once the monthly risk of an introduction exceeded ∼0.3%, implementation of an immunization program with at least 25% coverage provided both greater health and greater economic benefits than having no vaccine program. CONCLUSIONS: The implementation of a hospital-based and funded diphtheria-tetanus-acellular pertussis vaccine program administered through an occupational health program is cost-effective or cost-saving in the context of pediatric health care facilities in which many of the patients are at risk of serious morbidity and mortality should they acquire pertussis while hospitalized.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".