Potential overestimation of HPV vaccine impact due to unmasking of non-vaccine types: quantification using a multi-type mathematical model.

Introduction: Estimates of human papillomavirus (HPV) vaccine impact in clinical trials and modelling studies rely on DNA tests of cytology or biopsy specimens to determine the HPV type responsible for a cervical lesion. DNA of several oncogenic HPV types may be detectable in a specimen. However, only one type may be responsible for a particular cervical lesion. Misattribution of the causal HPV type for a particular abnormality may give rise to an apparent increase in disease due to non-vaccine HPV types following vaccination ("unmasking").

Methods: To investigate the existence and magnitude of unmasking, we analysed data from residual cytology and biopsy specimens in English women aged 20-64 years old using a stochastic type-specific individual-based model of HPV infection, progression and disease. The model parameters were calibrated to data on the prevalence of HPV DNA and cytological lesion of different grades, and used to assign causal HPV types to cervical lesions. The difference between the prevalence of all disease due to non-vaccine HPV types, and disease due to non-vaccine HPV types in the absence of vaccine HPV types, was then estimated.

Results: There could be an apparent maximum increase of 3-10% in long-term cervical cancer incidence due to non-vaccine HPV types following vaccination.

Conclusion: Unmasking may be an important phenomenon in HPV post-vaccination epidemiology, in the same way that has been observed following pneumococcal conjugate vaccination.