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Record W2533661589 · doi:10.1016/j.vaccine.2016.09.042

Manufacturing costs of HPV vaccines for developing countries

2016· article· en· W2533661589 on OpenAlexaff
Chaevia Clendinen, Yapei Zhang, Rebecca Warburton, Donald W. Light

Bibliographic record

VenueVaccine · 2016
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversity of Victoria
FundersDoctors Without Borders
KeywordsDeveloping countryVirologyBusinessMedicineEconomic growthEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Nearly all of the 500,000 new cases of cervical cancer and 270,000 deaths occur in middle or lower income countries. Yet the two most prevalent HPV vaccines are unaffordable to most. Even prices to Gavi, the Vaccine Alliance, are unaffordable to graduating countries, once they lose Gavi subsidies. Merck and Glaxosmithkline (GSK) claim their prices to Gavi equal their manufacturing costs; but these costs remain undisclosed. We undertook this investigation to estimate those costs. METHODS: Searches in published and commercial literature for information about the manufacturing of these vaccines. Interviews with experts in vaccine manufacturing. FINDINGS: This detailed sensitivity analysis, based on the best available evidence, finds that after a first set of batches for affluent markets, manufacturing costs of Gardasil for developing countries range between $0.48 and $0.59 a dose, a fraction of its alleged costs of $4.50. Because volume of Cervarix is low, its per unit costs are much higher, though at comparable volumes, its costs would be similar. INTERPRETATION: Given the recovery of fixed and annual costs from sales in affluent markets, Merck's break-even price to Gavi could be $0.50-$0.60, not $4.50. These savings could support Gavi programs to strengthen delivery and increase coverage. Outside Gavi, prices to lower- and middle-income countries, with profit, could also be lowered and made available to millions more adolescents at risk. These estimates and their policy implications deserve further discussion.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.337
Teacher spread0.305 · 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.

Study designOther design
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

Citations60
Published2016
Admission routes1
Has abstractyes

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