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Capturing multiple-type interactions into practical predictors of type replacement following human papillomavirus
Irene Man1,2, Kari Auranen3,4, Jacco Wallinga1,2
11 Centre for Infectious Diseases Control, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven , The Netherlands.
Summary
Current human papillomavirus (HPV) vaccines may lead to non-targeted HPV types replacing targeted ones. This study develops new predictors for HPV type replacement risk using prevalence data, improving upon existing methods.
Area of Science:
- Epidemiology
- Mathematical Biology
- Vaccinology
Background:
- Current human papillomavirus (HPV) vaccines target only a subset of oncogenic HPV types.
- Vaccination may inadvertently promote the emergence of non-targeted HPV types due to competitive interactions.
- Existing methods for assessing replacement risk are limited to pairwise interactions and lack predictive power.
Purpose of the Study:
- To develop practical predictors for HPV type replacement in a multi-type setting.
- To enable estimation of replacement risk using pre-vaccination prevalence data (longitudinal or cross-sectional).
- To provide methods for predicting both individual non-targeted type replacement and overall non-vaccine-type prevalence increases.
Main Methods:
- Development of predictors based on weighted cross-hazard ratios of acquisition versus clearance.
- Development of predictors based on aggregate odds ratios of coinfection with vaccine types.
- Utilizing pre-vaccination longitudinal or cross-sectional prevalence data for estimation.
- Simulation studies to evaluate predictor performance under various interaction structures.
Main Results:
- Proposed hazard-based predictors effectively incorporate direct and indirect type interactions.
- Demonstrated equivalence between hazard-based and odds-based predictors under certain conditions.
- Validated the utility of pooling type-specific predictors for forecasting overall non-vaccine-type prevalence.
- Simulation results show good performance of the developed predictors across different interaction scenarios.
Conclusions:
- The developed predictors offer a practical approach to assess HPV type replacement risk in multi-type settings.
- These methods are estimable from readily available prevalence data, offering an improvement over existing techniques.
- The methodology has potential applications in informing public health strategies related to HPV vaccination programs.
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