Ranking the most efficient human papillomavirus vaccination strategies in low-income and lower-middle income countries: a mathematical modelling analysis
Bibliographic record
Abstract
BACKGROUND: One-dose vaccination and increased vaccine supply provide the opportunity for low-income countries (LICs) and lower-middle-income countries (LMICs) to extend human papillomavirus (HPV) vaccination to populations other than girls aged 9-14 years targeted by routine vaccination. The aim of our study was to use mathematical modelling to identify and rank HPV vaccination strategies according to their efficiency at reducing cervical cancer in LICs and LMICs. METHODS: In this mathematical modelling analysis, we used HPV-ADVISE to model the efficiency of 162 vaccination strategies in 67 LICs and LMICs varying the following parameters: targeted populations (girls and women or gender neutral); age cohorts (routine vaccination at age 9 years or multi-age cohorts up to age 14 years, 20 years, 25 years, 30 years, and 35 years); number of doses (one or two); and vaccination coverage (40-90%), under different one-dose vaccine efficacy and duration assumptions. We measured efficiency using the number of doses needed to prevent one cervical cancer (ie, number needed to vaccinate [NNV]), obtained by dividing the incremental number of doses given by the incremental number of cervical cancers averted over 100 years. We ranked, incrementally, all strategies from most to least efficient. FINDINGS: Globally, under our base-case assumptions (80% vaccination coverage, non-inferior one-dose vaccine, and unfeasibility of increasing vaccination coverage), the model projects that, following routine vaccination of girls aged 9 years with one dose, the most efficient strategies (efficiency frontier) would be, in order: multi-age cohort vaccination of girls aged 10-14 years with one dose (NNV 48); multi-age cohort vaccination of girls aged 15-20 years with one dose (NNV 64); multi-age cohort vaccination of women aged 21-25 years with two doses (NNV 369); routine and multi-age cohort vaccination of boys aged 9-20 years with one dose (NNV 512); multi-age cohort vaccination of women aged 26-30 years (NNV 640) and 31-35 years (NNV 771) with two doses. Under all scenarios investigated (varying vaccination coverage, one-dose vaccine assumptions, and country characteristics), the model projects that the most efficient strategies would be to vaccinate girls up to age 20 years with one dose. The next most efficient strategies depend on the vaccination coverage that can be achieved and the cervical cancer incidence in a given country. INTERPRETATION: Our study suggests that the most efficient vaccination strategy to prevent cervical cancer in LICs and LMICs is to vaccinate girls aged up to 20 years with a single-dose vaccine with high vaccination coverage, before adding boys or providing a second dose to girls. The choice of additional populations to vaccinate will depend on the characteristics and prioritisation goals of a country. FUNDING: WHO, Canadian Institute of Health Research, and the Gates Foundation.
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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".