Cost-effectiveness analysis of MMR immunization in health care workers
Bibliographic record
Abstract
BACKGROUND: Assessment of measles, mumps and rubella (MMR) status is an essential part of occupational health clearance for new health care workers (HCWs). At the time of this study the policy at Sheffield Occupational Health Service (SOHS) was to perform serological testing of HCWs without evidence of previous immunization prior to MMR vaccination. AIMS: To identify the cost implications of changing policy to offer vaccination without prior serological testing to HCWs without evidence of previous immunization. METHODS: A retrospective cohort analysis of all MMR serological results from individuals attending SOHS for pre-placement assessment between 1 April 2010 and 31 March 2012. RESULTS: Seven thousand five hundred and sixty-nine individuals attended SOHS for pre-placement screening. Of these, 52% (3921) had no evidence of prior vaccination to at least one MMR disease and underwent serological testing. Thirty-three per cent (1204) of these HCWs were sero-negative to at least one condition requiring vaccination. With the suggested change in policy, our data indicate a cost-saving of over £105 000 per year may currently be achieved at SOHS. CONCLUSIONS: Our findings highlight significant savings through offering vaccination without prior serology for HCWs with no evidence of prior immunization to MMR. An awareness of costs associated with serology, vaccination and staff clinics, as well as the wider impact of population vaccination campaigns, are important factors determining the most cost-effective strategy in this area.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".