Back

Determining context-specific economically feasible age ranges for female HPV catch-up vaccination in LMICs: a model-based health economic assessment

Wondimu, A.; Georges, D.; Macacu, A.; Wittenauer, R.; Fuady, A.; Gini, A.; Baussano, I.; Man, I.

2026-03-27 health economics
10.64898/2026.03.26.26348394 medRxiv
Show abstract

Background Catch-up vaccination will be pivotal for achieving WHOs cervical cancer elimination goals in low- and middle-income countries (LMICs). We assessed the health-economic impact of catch-up HPV vaccination for females in LMICs. Methods Using IARCs METHIS modelling platform and data from 132 LMICs, we simulated HPV catch-up vaccination beyond the primary target age, varying the maximum age up to 30 years. Budget impact was expressed as a share of national five-year immunization budgets and current health expenditure. We conducted cost-effectiveness analyses for a smaller subset of countries for which high-quality cervical cancer treatment costs were available. Findings Catch-up HPV vaccination up to age 30 in LMICs could prevent 9.2 million cervical cancer cases over the lifetime among females aged 9-30 years. Across countries, budget impact ranged from 0.007%-2.24% of five-year health expenditure and 0.002%-236.65% of immunization budgets, with vaccine procurement comprising about 70% of costs. Gavi support could reduce costs by nearly 70% for catch-up up to age 18. Catch-up vaccination up to age 30 was cost-effective in almost all evaluated countries, except in one where cost-effectiveness was achieved up to age 21. Interpretation In LMICs, after achieving adequate coverage in the primary target group (9-14 years), expanding HPV catch-up vaccination would be impactful and cost-effective. Sustainable financing, Gavi support, and cost-minimization strategies are crucial for successful catch-up programmes and progress toward cervical cancer elimination.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.