MétaCan
Menu
← Back to cohort
Record W4399518692 · doi:10.1101/2024.06.10.24308663

Optimization of an adult immunization program in Canada

2024· preprint· en· W4399518692 on OpenAlexaffabout
Nathan Locke, Mike Paulden, Shannon E. MacDonald, Stephanie Montesanti, Ashleigh R. Tuite, Karsten Hempel, Wade McDonald, Ellen Rafferty

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsPublic Health OntarioUniversity of TorontoPublic Health Agency of CanadaUniversity of SaskatchewanUniversity of AlbertaInstitute of Health Economics
Fundersnot available
KeywordsImmunizationPortfolioPopulationBudget constraintHealth careMedicineActuarial scienceBusinessEconomicsEnvironmental healthFinanceImmunologyEconomic growthMicroeconomics

Abstract

fetched live from OpenAlex

Abstract Background Provincial decisions to fund a new immunization program are generally made on a case-by-case basis, without systematic consideration of how the new immunization program may fit within the larger provincial immunization portfolio. Aim The goal of this study was to develop evidence and tools to guide policy-makers in making fiscally and ethically responsible decisions on which adult immunization programs to include in their portfolio under various constrained budgetary scenarios. Methods Using previously published infectious disease models, cost-utility data was estimated for adult pneumococcal, influenza, pertussis, and shingles immunization programs. This data was then inputted into a newly developed constrained optimization model to determine portfolios of immunization programs that maximize either population health or incremental net monetary benefit, subject to a budget constraint. Sensitivity analyses were conducted on model parameters such as vaccine costs, cost-effectiveness thresholds, and the budget constraint. Results Optimized solutions changed dramatically based on the number of immunization programs included, total budget, what was optimized for (i.e., population health or incremental net monetary benefit), the cost-effectiveness threshold and the assumed vaccine prices. Maximal health gains and budget spending was achieved when optimizing based on population health. Reductions in health gains and budget spending were observed at a CAN$50,000 cost-effectiveness threshold, and at a CAN$30,000 threshold, the budget was significantly underutilized and health gains were noticeably reduced. Conclusion If budgets for the adult immunization portfolio are fixed, then shifting to more expensive programs that offer large health benefits may be preferable. However, if budgets can be spread across various public health programs (i.e., childhood immunization, well-baby programs), it may make more sense to optimize based on cost-effectiveness. Constrained optimization tools could improve goals-based decision-making and allow for transparent and effective methods to make allocation decisions. Highlights Optimized solutions changed dramatically based on the number of immunization programs available, total budget, and the cost-effectiveness threshold. If budgets for the adult immunization portfolio are fixed, then shifting to more expensive programs that offer large health benefits may be preferable. If budgets can be spread across various public health programs, it may make more sense to optimize based on cost-effectiveness. Constrained optimization tools could improve goals-based decision-making and allow for transparent and effective methods to make allocation decisions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuemedRxiv→Same topicVaccine Coverage and Hesitancy→French-language works237,207→