Algorithm-driven HPV vaccine allocation paradigm for cervical cancer elimination targeting the Chinese population

Area of Science:

• Public Health
• Health Economics
• Epidemiology

Background:

• Cervical cancer remains a significant public health concern globally.
• Effective Human Papillomavirus (HPV) vaccination strategies are crucial for prevention.
• China faces challenges in implementing widespread and sustainable HPV vaccination programs.

Purpose of the Study:

• To identify optimal and fiscally sustainable HPV vaccination strategies for China.
• To model the impact of different vaccination coverages on cervical cancer burden.
• To analyze the financial requirements and propose funding mechanisms for HPV vaccination.

Main Methods:

• Utilized Bayesian optimization and transmission dynamics modeling.
• Integrated a demographic-based HPV transmission model with a decision-analysis framework.
• Conducted a budget impact analysis using willingness-to-pay data from a survey of 787 parents.

Main Results:

• Eliminating cervical cancer within 40 years requires 23.97% HPV vaccination coverage in 15-17-year-old girls.
• A 30-year elimination target necessitates 76.66% coverage.
• A funding gap of ¥35.93 billion exists for the 40-year target at current prices, with autonomous demand being insufficient.

Conclusions:

• A tripartite financing mechanism (consumers 45.90%, government 26.19%, medical insurance 27.91%) is proposed to ensure fiscal sustainability.
• Algorithm-driven insights offer an evidence-based framework for optimizing health financing.
• The findings support accelerating cervical cancer elimination in China through strategic HPV vaccination and financing.