Projected impact of Cervarix™ vaccination on oncogenic human papillomavirus infection and cervical cancer in the United Kingdom

Abstract

We developed a dynamic compartmental model to assess the impact of HPV Universal Mass Vaccination (UMV) with Cervarix™, which offers protection against HPV16/18 and cross-protection against other cancer-causing types, using up-to-date efficacy data. Analyses were performed in the UK because of the large amount of high quality epidemiological data available. For each HPV type/group of types considered, the model was calibrated to 14 epidemiological datasets (prevalence of HPV infection, cervical intraepithelial neoplasia (CIN): CIN1, CIN2, CIN3 pre-screening and cervical cancer (CC) incidence over 10 y post-screening). Impacts of cross-protection, female catch-up vaccination, and additional male vaccination on oncogenic infections, high-grade CIN (CIN2+) and CC were evaluated. Our results show that female UMV with 80% coverage and cross-protection against high-risk types resulted in 81% CIN2+ and 88% CC reductions vs. 57% and 75%, respectively, without cross-protection. Vaccinating 40% of males and 80% of females was equivalent to 90% female-only coverage regarding CIN2+ (87% and 87%, respectively) and CC (93% and 94%, respectively) reductions. Female-only coverage of 80% substantially reduced male HPV16 and 18 infection due to herd protection (74% and 89%, respectively). Increasing female coverage to 90% reduced HPV16 and HPV18 infections in males relatively similarly to 80% female combined with 40% male coverage. Model outcomes strengthen previous conclusions about the significant added value of Cervarix™ cross-protection for CC prevention, the primary HPV vaccination public health priority. Regarding female CC prevention and male HPV16/18 infection, small increases in female coverage induce similar benefits to those achieved by additionally vaccinating men with 40% coverage.

Keywords: Cervarix™; HPV; cervical cancer; cross-protection; universal mass vaccination.

Figure 1.

Calibration – model projections vs. observed for HPV16 – prevalence at steady state prior to screening. (A) Prevalence of HPV infection. Red circles: HPV infection normal in females observed; Red stars: HPV infection normal in females model; Blue circles: all HPV infection in females observed; Blue stars: all HPV infection in females model (i.e., both with normal and with abnormal cytology; Magenta triangles: all HPV infection in males model). (B) Prevalence of CIN1 (Red: observed; Black: model-projected). (C) Prevalence of CIN2 (Red: observed; Black: model-projected). (D) Prevalence of CIN3 (Red: observed; Black: model-projected). The age values for the points on all Figure 1 plots correspond to the midpoint of the corresponding age intervals, with the exception of the last point which corresponds to the age group of 55 y and older. Those age intervals here are: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, and 55+ y.

Figure 2.

Calibration – model projections vs. observed for CC incidence associated with HPV16 over time, with screening. Red: observed; Black: model-projected. The age values for the points on all Figure 2 plots correspond to the midpoint of the corresponding age intervals. Those age intervals here are: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, and 70–74 y.

Figure 3.

Impact of vaccination with cross-protection observed for Cervarix™ vs. no cross-protection. Black: Cervarix™ with observed cross-protection for HPV31/33/45 and further 9 oncogenic types pooled (- - - using 95% lower limit (LL) and upper limit (UL) estimates for the efficacy of Cervarix™, respectively); Red: without cross-protection for HPV31/33/45 and further 9 oncogenic types pooled (- - - using 95% LL and UL for the efficacy of Cervarix™ for HPV16 and HPV18, Table 2). The plot shows the pooled outcome across all the oncogenic HPV types considered in the model. (A) Percent reduction in CIN2+ incidence. (B) Percent reduction in CC incidence.

Figure 4.

Impact of vaccination with varying UMV coverage. Blue: 70%; Black: 80% (base-case); Green: 90%; Cyan: 100%. The plot shows the pooled outcome across all the oncogenic HPV types considered in the model. (A) Percent reduction in CIN2+ incidence. (B) Percent reduction in CC incidence.

Figure 5.

Impact of catch-up vaccination. Black: UMV 80% coverage and no catch-up (base-case); Blue: UMV 80% coverage and catch-up 50% coverage in 16–18 y-old; Green: UMV 80% coverage and catch-up 50% coverage in 16–25 y-old; Cyan: UMV 80% coverage and catch-up 50% 16–35 y-old. The plot shows the pooled outcome across all the oncogenic HPV types considered in the model. (A) Percent reduction in CIN2+ incidence. (B) Percent reduction in CC incidence.

Figure 6.

Impact of vaccinating males and females on CIN2+ and CC. Female vaccination with 80% coverage and varying levels of male vaccination coverage. Black: base-case (no male vaccination); Blue: 40% coverage in males; Green: 60% coverage in males; Cyan: 80% coverage in males. The plot shows the pooled outcome across all the oncogenic HPV types considered in the model. (A) Percent reduction in CIN2+ incidence. (B) Percent reduction in CC incidence.

Figure 7.

Impact of vaccinating males and females vs. higher UMV coverage in females on HPV16 and HPV18 infection in males. (A) and (C) Higher coverage in females, no male vaccination: Coverage in females: Blue: 70%; Black: 80% (base-case); Green: 90%; Cyan: 100%. (B) and (D) Female and male vaccination: Coverage 80% female UMV and Black: 0% in males (base-case); Blue: 40% in males; Green: 60% in males; Cyan: 80% in males. (A) and (B) Percent reduction in HPV16 infection in males; (C) and (D) Percent reduction in HPV18 infection in males.

Figure 8.

Disease states and flows (HPV type specific) in females for the different oncogenic types considered in the model, either individually or pooled.