Optimal human papillomavirus vaccination strategies to prevent cervical cancer in low-income and middle-income countries in the context of limited resources: a mathematical modelling analysis
Bibliographic record
Abstract
BACKGROUND: Introduction of human papillomavirus (HPV) vaccination has been slow in low-income and middle-income countries (LMICs) because of resource constraints and worldwide shortage of vaccine supplies. To help inform WHO recommendations, we modelled various HPV vaccination strategies to examine the optimal use of limited vaccine supplies and best allocation of scarce resources in LMICs in the context of the WHO global call to eliminate cervical cancer as a public health problem. METHODS: In this mathematical modelling analysis, we developed HPV-ADVISE LMIC, a transmission-dynamic model of HPV infection and diseases calibrated to four LMICs: India, Vietnam, Uganda, and Nigeria. For different vaccination strategies that encompassed use of a nine-valent vaccine (or a two-valent or four-valent vaccine assuming high cross-protection), we estimated three outcomes: reduction in the age-standardised rate of cervical cancer, number of doses needed to prevent one case of cervical cancer (NNV; as a measure of efficiency), and the incremental cost-effectiveness ratio (ICER; in 2017 international $ per disability-adjusted life-year [DALY] averted). We examined different vaccination strategies by varying the ages of routine HPV vaccination and number of age cohorts vaccinated, the population targeted, and the number of doses used. In our base case, we assumed 100% lifetime protection against HPV-16, HPV-18, HPV-31, HPV-33, HPV-45, HPV-52, and HPV-58; vaccination coverage of 80%; and a time horizon of 100 years. For the cost-effectiveness analysis, we used a 3% discount rate. Elimination of cervical cancer was defined as an age-standardised incidence of less than four cases per 100 000 woman-years. FINDINGS: We predicted that HPV vaccination could lead to cervical cancer elimination in Vietnam, India, and Nigeria, but not in Uganda. Compared with no vaccination, strategies that involved vaccinating girls aged 9-14 years with two doses were predicted to be the most efficient and cost-effective in all four LMICs. NNV ranged from 78 to 381 and ICER ranged from $28 per DALY averted to $1406 per DALY averted depending on the country. The most efficient and cost-effective strategies were routine vaccination of girls aged 14 years, with or without a later switch to routine vaccination of girls aged 9 years, and routine vaccination of girls aged 9 years with a 5-year extended interval between doses and a catch-up programme at age 14 years. Vaccinating boys (aged 9-14 years) or women aged 18 years or older resulted in substantially higher NNVs and ICERs. INTERPRETATION: We identified two strategies that could maximise efforts to prevent cervical cancer in LMICs given constraints on vaccine supplies and costs and that would allow a maximum of LMICs to introduce HPV vaccination. FUNDING: World Health Organization, Canadian Institute of Health Research, Fonds de recherche du Québec-Santé, Compute Canada, PATH, and The Bill & Melinda Gates Foundation. TRANSLATIONS: For the French and Spanish translations of the abstract see Supplementary Materials section.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".