Prioritization strategies for pandemic influenza vaccine in 27 countries of the European Union and the Global Health Security Action Group: a review
Bibliographic record
Abstract
BACKGROUND: Although there is rapid progress in vaccine research regarding influenza pandemic vaccines it is expected that pandemic influenza vaccine production can only start once the pandemic virus has been recognized. Therefore, pandemic vaccine capacity will be limited at least during the first phase of an influenza pandemic, requiring vaccine prioritization strategies. WHO recommends developing preliminary priorities for pandemic vaccine use. The goal of this review is to provide a thorough overview of pandemic vaccine prioritization concepts in the 27 European Union (EU) member states and the four non-EU countries of the Global Health Security Action Group. METHODS: Between September and December 2006 data was collected for each country through two data sources: (i) the national influenza pandemic plan; (ii) contacting key persons involved in pandemic planning by email and/or phone and/or fax. RESULTS: Twenty-six (84%) countries had established at least one vaccine priority group. Most common reported vaccine priority groups were health care workers (HCW) (100%), essential service providers (ESP) (92%) and high risk individuals (HRI) (92%). Ranking of at least one vaccine priority group was done by 17 (65%) of 26 countries. Fifteen (88%) of these 17 countries including a ranking strategy, decided that HCW with close contact to influenza patients should be vaccinated first; in most countries followed and/or ranked equally by ESP and subsequently HRI. Rationales for prioritization were provided by 22 (85%) of 26 countries that established vaccine priority groups. There was large variation in the phrasing and level of detailed specification of rationales. Seven (32%) of 22 countries providing rationales clearly associated each vaccine priority group with the specific rationale. Ten (32% of the 31 countries studied) countries have consulted and involved ethical experts to guide decisions related to vaccine prioritization. CONCLUSION: In the majority of the countries the establishment of vaccine priority groups, ranking and underlying rationales are in line with WHO recommendations. In most public plans the criteria by which prioritized groups are identified are not easily recognizable. Clarity however, may be necessary to assure public acceptability of the prioritization. Ethical experts, results of modelling exercises could play an increasing role in the future decision making process.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".