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Research

Evaluating Human Papillomavirus Vaccination Programs

Al V. Taira,* Christopher P. Neukermans,† and Gillian D. Sanders†‡
*Stanford School of Medicine, Stanford, California, USA; †Stanford University, Stanford, California, USA; and ‡Duke University, Durham, North Carolina, USA

Appendix

To capture the effect of a male vaccination program on female human papillomavirus (HPV) infection rates and cervical cancer incidence, we needed to directly model the effect of vaccination on HPV disease-transmission dynamics. We developed disease-transmission models for HPV-16 and HVP-18, the HPV types associated with most cervical cancer cases and the most likely to be included in HPV vaccines. For both types, the transmission models estimated HPV prevalence and infection rates for the U.S. population overall, by age group, by level of sexual activity, and by sex. The models also enabled us to evaluate the effect of various vaccination scenarios on prevalence and infection rates.

For the status quo and for each vaccination scenario, infection rates by age group for vaccinated and unvaccinated women were estimated by the disease transmission models. Infection rates by age group for vaccinated and unvaccinated women were then incorporated into a probabilistic decision model. The decision model estimated the annual incidence of HPV-related precancerous lesions, lifetime cases of invasive cervical cancer, resulting cervical cancer deaths, and total cost of care for a given set of age-specific infection rates. By using the combination of the transmission and decision model, we were able to estimate the effectiveness (increase in life expectancy and reduction in cervical cancer incidence) and cost-effectiveness of alternative vaccine rollout strategies.

Transmission Model Structure

In our analysis, both sexes are either HPV infected or uninfected at the beginning of each period. In each period, uninfected persons can remain uninfected or become infected, based on infection rates by age category. G_a,r,t,v=0 represents unvaccinated women (v = 0) of a given age (a), of a given sexual activity subgroup (r), in time period (t) who are not HPV infected. In the next time period, as these women move into the next age category (a+1), they can remain uninfected, assuming the status of G_{a+1,r,t+1,v=0}, or they can become infected, assuming the status of infected women of age (a+1) and sexual activity subgroup (r), F_{a+1,r,t+1,v=0}. The rate of HPV infection is a function of p_a,r, the number of partners per year for age group (a) and sexual activity group (r); i_a, the infectivity per infected partner for persons of age (a); and Q_a,r, the prevalence of HPV infection in the pool of male partners for women of this age and sexual activity subgroup. Overall prevalence of HPV infection in their specific pool of male partners (Q_a,r,), in turn, depends on the preference of women in this age group for male partners in younger (l_f,a), older (h_f,a), and the same (1–l_f,a–h_f,a) age group and the prevalence of HPV infection in these respective male groups. The resulting rate of HPV infection differs by age group and differs for women in different sexual activity subgroup within the different age groups. Rate of HPV regression, d_a, when active infection clears and at which time a woman is no longer infective, is also age dependent.

	Appendix Figure 1
	Click to view enlarged image Appendix Figure 1. Prevalence of HPV-16 and HPV-18. A) HPV-16 prevalence in women. B) HPV-16 prevalence in men...
	Appendix Figure 2
	Click to view enlarged image Appendix Figure 2. Incidence of HPV-16 and HPV-18. A) HPV-16 incidence in women. B) HPV-16 incidence in men...

Women can enter the transmission model already having been vaccinated or can receive the vaccine at older ages, depending on vaccination scenario being evaluated. The rate of HPV infection for vaccinated women, G_a,r,t,v=1, is a function of p_a,r, the number of partners per year; i_a, the infectivity per infected partner for persons of age (a); Q_a,r, the prevalence of HPV infection in the pool of male partners for women of this age and sexual activity subgroup; and vaccine efficacy (e).

J_a,r,t,v represents men of a given age (a), of a given sexual activity subgroup (r), in time period (t), who are not HPV infected. K_a,r,t,v represents men of a given age (a), of a given sexual activity subgroup (r), in time period (t) who are HPV infected. Infection rates for men and the effect of a vaccine are determined in the same way as for women, though the values of variables differ between men and women of similar age groups (e.g., in most age categories, more men than women prefer partners in lower age groups).

Scenario Evaluation

During each period a new cohort of approximately 2 million male participants and 2 million female participants enters the model. This cohort is divided into vaccinated and unvaccinated persons. Vaccination rates for a given age cohort are 0%–100%, depending on which scenario is being evaluated. Vaccination rates can also vary by sex, and vaccination rates of persons the same age can differ according to sexual activity risk groups. This variation enables us to evaluate scenarios of differing vaccine penetration by risk category.

Vaccine efficacy (e) can vary from 0% to 100%. To capture the effect of alternative vaccines with different durations of effectiveness, we adjust the variable (e) for different age groups. For instance, to model a vaccination strategy for 12-year-old girls with a 90% effective vaccine that has a 10-year duration, the variable (e) would be set equal to 90% for ages 12–21. For ages 22 or older, efficacy can be set to equal 0%. To model a 10-year booster in the above scenario, the variable (e) can also be set equal to 90% for ages 22–31, to reflect the protection of the booster. In addition, gradually waning vaccine efficacy can be modeled by gradually decreasing variable (e) from 90% to 0% over a given number of age categories

Transmission Model Output

Using the transmission model, we estimated the prevalence of HPV-16 and HPV-18 infection by age group, in the absence of a vaccine (Appendix Figure 1). For both HPV-16 and HPV-18, prevalence increases rapidly after sexual debut, peaking in women and men in their early 20s. Prevalence drops off for persons in their late 20s and early 30s, which corresponds to a drop in the number of new sex partners per year in these age groups. For both men and women overall (including all sexual activity categories), population prevalence of HPV-16 peaks at ≈5% and population prevalence of HPV-18 peaks at ≈2%, Prevalence decreases to <1% for both HPV types for persons >40 years old. The predicted age-specific prevalence of HPV infection in our natural history arm has a shape and peak of similar magnitude to that seen in the literature (1–4).

We evaluated various vaccination program scenarios to assess how different vaccination program options would affect HPV transmission and prevalence. We used the transmission model to estimate the incidence and prevalence of HPV-16 and HPV-18 by age group for both vaccinated and unvaccinated women under two main vaccination scenarios. First, we evaluated the effect of vaccinating only female participants. We then compared that to the effect of vaccinating both sexes. Results are presented in Appendix Figures 1 and 2.

Decision Model

In a previous analysis (5), we developed a probabilistic Markov decision model to estimate the progression of high-risk oncogenic HPV types to different stages of cervical dysplasia or cancer and to evaluate different female-only vaccine strategies. In our original modeling approach, we assumed that vaccinated women would have reduced HPV infection rates that equaled the estimated vaccine efficacy. Also, in the original model, unvaccinated women had no decrease in HPV infection rates, despite the presence of a widespread vaccination program. In truth, the effect of a vaccine on HPV infection rates for vaccinated women depends on both the vaccine efficacy and the overall impact of the vaccination program at reducing population prevalence of HPV in both men and women. Additionally, a vaccine program will reduce infection rates for unvaccinated women by a herd immunity benefit. Our modeling approach includes these factors.

In our approach, we use the transmission model described above to estimate the reduction in HPV infection rates for both vaccinated and unvaccinated women under a female-only vaccination strategy. For each age group, for vaccinated and unvaccinated women, these infection rates are incorporated into the Markov model, which then estimates disease progression, cervical cancer incidence, cervical cancer deaths and associated costs, and health utilities under a female-only vaccine program. In this way, we capture both the direct vaccine effect and the indirect benefit of herd immunity to both vaccinated and unvaccinated women for a female-only vaccine program.

Next, we used the transmission model to estimate the reduction in HPV infection rates for both vaccinated and unvaccinated women when both sexes are vaccinated. These scenario-specific infection rates for each age group and for both vaccinated and unvaccinated women are included in the Markov model, and the costs and health benefits are evaluated.

Appendix References

1. Jacobs MV, Walboomers JM, Snijders PJ, Voorhorst FJ, Verheijen RH, Fransen-Daalmeijer N, et al. Distribution of 37 mucosotropic HPV types in women with cytologically normal cervical smears: the age-related patterns for high-risk and low-risk types. Int J Cancer. 2000;87:221–7.
2. Hildesheim A, Gravitt P, Schiffman MH, Kurman RJ, Barnes W, Jones S, et al. Determinants of genital human papillomavirus infection in low-income women in Washington, D.C. Sex Transm Dis. 1993;20:279–85.
3. Melkert PW, Hopman E, van den Brule AJ, Risse EK, van Diest PJ, Bleker OP, et al. Prevalence of HPV in cytomorphologically normal cervical smears, as determined by the polymerase chain reaction, is age-dependent. Int J Cancer. 1993;53:919–23.
4. Bauer HM, Hildesheim A, Schiffman MH, Glass AG, Rush BB, Scott DR, et al. Determinants of genital human papillomavirus infection in low-risk women in Portland, Oregon. Sex Transm Dis. 1993;20:274–8.
5. Sanders G, Taira A. Cost effectiveness of a potential vaccine for human papillomavirus. Emerg Infect Dis. 2003;9:37–48.

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This page posted October 6, 2004
This page last reviewed October 6, 2004

Emerging Infectious Diseases Journal
National Center for Infectious Diseases
Centers for Disease Control and Prevention