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Record W2138193970 · doi:10.1093/occmed/kqt086

Cost-effectiveness analysis of MMR immunization in health care workers

2013· article· en· W2138193970 on OpenAlexaff
Prosenjit Giri, Subhashis Basu, Diana C. Farrow, Anil Adisesh

Bibliographic record

VenueOccupational Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsMedicineVaccinationSerologyMeaslesHealth careImmunizationPopulationCohortRubellaFamily medicineEnvironmental healthPediatricsImmunologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.046
GPT teacher head0.397
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2013
Admission routes1
Has abstractyes

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