Estimate of global human papillomavirus vaccination coverage: analysis of country-level indicators
Bibliographic record
Abstract
BACKGROUND: Mortality rates from cervical cancer demonstrate deep inequality in health between richer and poorer populations. Over 310 000 women died of this preventable disease in 2018, mostly in low-income and middle-income countries (LMICs) where screening and treatment are beyond the capacity of health systems. Immunisation against human papillomavirus (HPV) offers a primary prevention strategy, but rates of vaccination uptake are unclear. Understanding coverage levels and factors affecting uptake can inform immunisation strategies. OBJECTIVES: The aim of this study is to evaluate the status of HPV vaccination coverage from nationally reported indicators and to estimate global coverage in a single year cohort of vaccine-eligible girls. DESIGN: This study provides quantitative population-level estimates of important global health indicators. Using data from the Global Cancer Observatory and WHO/UNICEF, incidence of and mortality from cervical cancer and HPV vaccination coverage are described for countries, categorised by income group. Characteristics of LMICs achieving high coverage are explored using selected development indicators from World Bank sources. Global HPV immunisation coverage is calculated and its impact on cervical cancer mortality estimated. RESULTS: Incidence and mortality for cervical cancer correlate with poverty. Whilst all WHO member states report high infant measles vaccination rates, fewer than half report on HPV vaccination. Even amongst high-income countries, coverage varies widely. In upper-middle-income countries, there is a trend for higher coverage with increased health spending per capita. Four LMICs report good coverage levels, all associated with external funding. Global HPV immunisation coverage for 2018 is estimated at 12.2%. Of the global cohort of 61 million 15-year-old girls in 2018, 7000 are likely to die from cervical cancer, almost all in LMICs. CONCLUSIONS: Countries in all income groups must devise strategies to achieve and maintain higher levels of HPV immunisation. For all but the richest, affordability remains a barrier.
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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 | 0.002 |
| 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) | 0.010 | 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".