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Different population-level vaccination effectiveness for HPV types 16, 18, 6 and 11
  1. Marc Brisson1,2,
  2. Nicolas Van de Velde1,2,
  3. Marie-Claude Boily1,3
  1. 1Département de médecine sociale et préventive, Université Laval, Québec, Canada
  2. 2URESP, Centre de recherche FRSQ du CHA universitaire de Québec, Québec, Canada
  3. 3Department of Infectious Disease Epidemiology, Imperial College, London, UK
  1. Correspondence to Dr Marc Brisson, Unité de recherche en santé des populations, Centre hospitalier affilié universitaire de Québec, Hôpital Saint-Sacrement, 1050 Chemin Sainte-Foy, Québec, Canada; marc.brisson{at}uresp.ulaval.ca

Abstract

Background Given that the human papillomavirus (HPV) vaccine types have different durations of infectiousness and infectivity, the population-level vaccine effectiveness of these types may differ even if vaccine efficacy is identical.

Objective To compare the type-specific effectiveness of vaccination against HPV types 16, 18, 6 and 11.

Methods An individual-based stochastic model of HPV transmission (18 HPV-types) in a population stratified by age, gender and sexual activity was developed. Multiple parameter sets were identified by fitting the model to sexual behaviour data and age- and type-specific HPV prevalence.

Results Under base case assumptions (70% coverage, 99% vaccine efficacy per act and 20 years' duration of protection), vaccinating 12-year-old girls is predicted to reduce HPV-16, HPV-18 and HPV-6/11 prevalence by 61% (80% uncertainty interval (UI) 53–77), 92% (80% UI 65–100) and 100% (80% UI 97–100), respectively, 50 years after the start of the vaccination programme. Differences in type-specific vaccine effectiveness increased over time, and decreased with improved vaccine efficacy characteristics.

Conclusions For the same vaccine efficacy, the population-level impact of HPV vaccination will most likely be different, with HPV-16, the most oncogenic type, having the lowest effectiveness. These results should be taken into account when designing and interpreting post-vaccination surveillance studies.

  • Human papillomavirus (HPV)
  • vaccine
  • mathematical model
  • cervical cancer
  • genital warts

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Footnotes

  • Funding This work was supported by the Canada Research Chairs program (support for MB).

  • Competing interests MB was an employee of Merck Frosst Canada Ltd from 2003 to 2006. Since 2006, he has held a Canada Research Chair in Mathematical Modeling and Health Economics of Infectious Disease and is assistant professor at Laval University. He has consulted for Merck Frosst, and has received reimbursement for travel expenses from Merck Frosst and GlaxoSmithKline. NVdeV has no conflicts of interest to declare. M-CB has an unrestricted grant from Merck Frosst Canada Ltd.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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