Elsevier

Vaccine

Volume 28, Issue 24, 28 May 2010, Pages 4091-4102
Vaccine

Transmission dynamic modelling of the impact of human papillomavirus vaccination in the United Kingdom

https://doi.org/10.1016/j.vaccine.2009.09.125Get rights and content

Abstract

Many countries are considering vaccination against human papillomavirus (HPV). However, the long-term impact of vaccination is difficult to predict due to uncertainty about the prevalence of HPV infection, pattern of sexual partnerships, progression of cervical neoplasias, accuracy of screening as well as the duration of infectiousness and immunity. Dynamic models of human papillomavirus (HPV) transmission were developed to describe the infection spread and development of cervical neoplasia, cervical cancer (squamous cell and adenocarcinoma) and anogenital warts. Using different combinations of assumptions, 9900 scenarios were created. Each scenario was then fitted to epidemiological data and the best-fitting scenarios used to predict the impact of vaccination. Results suggest that vaccinating 12-year-old girls at 80% coverage will result in a 38–82% reduction in cervical cancer incidence and 44–100% reduction in anogenital warts incidence after 60 years of an ongoing vaccination programme if vaccine protection lasts 20 years on average. The marginal benefit of vaccinating boys depends on the degree of protection achieved by vaccinating girls.

Introduction

Human papillomavirus (HPV) infection is necessary for the development of cervical cancer in women as well as anogenital warts in both men and women. Most diagnosed cervical cancers are squamous cell carcinomas; however, the incidence of adenocarcinomas has been increasing rapidly [1] and now accounts for up to 20% of all cancer diagnoses in the United Kingdom (UK). Two HPV types (16 and 18) are responsible for about 70% of squamous cell carcinomas [2] and 85% of adenocarcinomas [3], while another two types (6 and 11) cause over 90% of cases of anogenital warts [4].

Two prophylactic vaccines against HPV have been developed: a bivalent vaccine (Cervarix™) against types 16 and 18, and a quadrivalent vaccine (Gardasil™) that also includes types 6 and 11. In clinical trials, use of either vaccine in HPV-naive females resulted in at least 90% reduction in persistent infection and associated disease during 30 months of follow-up [5], [6]. The quadrivalent vaccine has also been shown to be highly effective at preventing anogenital warts [7].

The vaccines have the potential to reduce the substantial burden of HPV-related disease. However, they are likely to be priced at levels significantly higher than other vaccines in national vaccination schedules, so their epidemiological and economic impact needs to be carefully considered. Because of the complexity of HPV infection and pathogenesis, as well as the long delays between infection and the most serious disease endpoints (cervical cancer), mathematical models are required to estimate the impact of vaccination.

The majority of existing studies (reviewed in Ref. [8]) use static models, which explore the natural history of HPV infection on an individual level, but underestimate the population-wide impact of vaccination. Studies using dynamical models are rarer because of their greater demands in terms of both model complexity and data requirements in order to represent sexual contact patterns. However, they are required to take into account herd immunity and hence estimate the wider impact of vaccination. To date, one theoretical HPV transmission model has been published [9], and transmission models have been applied to assess HPV vaccination in the United States [10], Finland [11], [12], Brazil [13] and the UK [14]. Most of these models assume a fixed structure to represent HPV transmission and progression between stages of disease. However, there are still significant gaps in our knowledge of HPV epidemiology and natural history that make it problematic to assume a single structure for a model of HPV disease and vaccination. In order to capture this uncertainty, we develop here a series of transmission models that represent different possibilities about sexual behaviour, natural immunity, vaccine characteristics, disease progression and screening accuracy. These models are then fitted to available data and used to project the impact of vaccination, providing robust estimates of the possible impact of alternative immunisation programmes. The use of the outcomes of the models to parameterise a cost effectiveness model of HPV vaccination has been described elsewhere [15].

Section snippets

Model structure

We developed a set of compartmental Markov models to represent acquisition and heterosexual transmission of infection, with an imbedded progression model to represent the subsequent development of HPV-related disease (different stages of pre-cancerous cervical neoplasias, squamous cell carcinomas, adenocarcinomas and anogenital warts). The models are stratified by HPV type, age, sex and sexual activity-based risk group. The model time step was 1 month [16].

HPV types in the model are divided

Base-case vaccination programme

Fig. 4 shows the estimated impact of vaccinating 12-year-old girls at 80% coverage on diagnosed HPV 6/11/16/18-related disease for different assumptions about the duration of vaccine protection. Large reductions in incidence of cervical dysplasia, cervical cancer and anogenital warts cases are expected, provided vaccine-induced immunity lasts for 10 years or more, although the reduction in cervical cancer takes much longer to become apparent (note the change of scale).

One of the key biological

Discussion

We present here a dynamical model of heterosexual HPV transmission and the development of cervical dysplasia, cervical cancer and anogenital warts in the UK. Model results indicate that vaccinating 12-year-old girls at 80% coverage over 60 years will result in substantial reductions in cervical cancer and anogenital warts incidence. The magnitude of the reduction depends on a number of parameters, particularly the duration of vaccine protection. Catch-up campaigns reduce short-term incidence

Acknowledgments

We thank Kate Soldan and Liz Miller for insightful comments and help throughout this process, and the panel of referees who commented on an earlier version of this work. This work was funded by grants from the Department of Health Policy Research Programme reference numbers DOH 039/0030 and DOH 0039/031. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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