Building resilient cervical cancer prevention through gender-neutral HPV vaccination

  1. Irene Man
  2. Damien Georges
  3. Rengaswamy Sankaranarayanan
  4. Partha Basu
  5. Iacopo Baussano  Is a corresponding author
  1. International Agency for Research on Cancer (IARC/WHO), Early Detection, Prevention and Infections Branch, France
  2. Karkinos Healthcare, India

Abstract

The COVID-19 pandemic has disrupted HPV vaccination programmes worldwide. Using an agent-based model, EpiMetHeos, recently calibrated to Indian data, we illustrate how shifting from a girls-only (GO) to a gender-neutral (GN) vaccination strategy could improve the resilience of cervical cancer prevention against disruption of HPV vaccination. In the base case of 5-year disruption with no coverage, shifting from GO to GN strategy under 60% coverage (before disruption) would increase the resilience, in terms of cervical cancer cases still prevented in the disrupted birth cohorts per 100,000 girls born, by 2.8-fold from 107 to 302 cases, and by 2.2-fold from 209 to 464 cases under 90% coverage. Furthermore, shifting to GN vaccination helped in reaching the World Health Organization (WHO) elimination threshold. Under GO vaccination with 60% coverage, the age-standardised incidence rate of cervical cancer in India in the long term with vaccination decreased from 11.0 to 4.7 cases per 100,000 woman-years (above threshold), as compared to 2.8 cases (below threshold) under GN with 60% coverage and 2.4 cases (below threshold) under GN with 90% coverage. In conclusion, GN HPV vaccination is an effective strategy to improve the resilience to disruption of cancer prevention programmes and to enhance the progress towards cervical cancer elimination.

Editor's evaluation

This study presents valuable findings on how gender-neutral vaccination against human papillomavirus can help improve program resilience in the case of vaccination disruptions. The evidence supporting the claims of the authors is convincing, although the results are only applicable to India and other countries with a similar HPV context; researchers can adapt the model for their local context and use it as a starting point for future research.

https://doi.org/10.7554/eLife.85735.sa0

Introduction

In August 2020, the World Health Assembly adopted the Global Strategy for cervical cancer elimination, with the overarching target of reducing the age-standardised incidence rate (ASIR) of cervical cancer to fewer than 4 cases per 100,000 woman-years (WHO, 2020a). To reach this target, the following actions have been recommended: to fully vaccinate 90% of all girls with the HPV vaccine by age 15 years, to screen 70% of women using a high-performance test by the age of 35, and again by the age of 45, and to treat 90% of women with pre-cancer and 90% of women with invasive cancer (WHO, 2020b). However, the delivery of these interventions has been severely disrupted worldwide by the outbreak of the COVID-19 pandemic, which was declared a global emergency in the same year by the World Health Organization (WHO) (WHO, 2020a). More specifically, HPV vaccination worldwide was affected at many different levels: (a) where population-based HPV vaccination programmes were already active, vaccine delivery slowed down or was interrupted (Muhoza et al., 2021), (b) the launch of HPV vaccination programmes was delayed in several countries, in particular in resource-limited settings (The Lancet Oncology, 2022), and (c) the production of HPV vaccines was also limited in favour of manufacturing other vaccines (WHO, 2022a).

Apart from the COVID-19 pandemic, which has resulted in extensive disruption of health-care provision at a global level, health-care systems or public-health programmes at a local level have also regularly suffered severe disruption caused by other factors such as changes in political commitment, financial constraints, scepticism of the civil society, geo-political unrest, and environmental disasters (Colón-López et al., 2021; Gallagher et al., 2017; Germani et al., 2022; Jawad et al., 2021; Larson, 2020; McGrath, 2022). Such circumstances may even lead key public health actors to reconsider their previous commitments. These partial or complete disruptions to organised disease prevention and control programmes may multiply public health crises (Sharpless, 2020), as well as provoke substantial, usually unquantified waste of human, logistic, and financial resources (Richards et al., 2020). Clearly, devising and implementing pre-emptive measures aimed at improving the resilience of public health programmes would help mitigate disruption.

In a previous study, conducted before the COVID-19 pandemic in a high-income country, we showed that the addition of vaccination in boys to a programme targeting girls only could increase the resilience of the vaccination programme (Elfström et al., 2016). In the present paper, we complement our previous work by assessing the potential effect of switching from girls-only (GO) to gender-neutral (GN) HPV vaccination on resilience and progress towards the elimination of cervical cancer in India, which in turn may help local health authorities in deciding on their vaccination strategy. Currently, India is the country with the largest burden of cervical cancer (Bonjour et al., 2021). Cervical cancer screening is still limitedly accessible in India, with less than 3% ever-in-life coverage in women aged 30–49 years (Sankaranarayanan et al., 2019; The Lancet Oncology, 2022). HPV vaccination (in girls) has been introduced in two Indian states with high vaccination coverage (Sankaranarayanan et al., 2019). Following the recent marketing authorisation granted to an indigenous vaccine (The Lancet Oncology, 2022), the prospect of introducing HPV vaccination into the national immunisation programme has also significantly improved.

Results

To estimate the impact of HPV vaccination on cervical cancer burden, we used the agent-based HPV transmission model, EpiMetHeos (Man et al., 2022), in combination with the cervical cancer progression model, Atlas (Bonjour et al., 2021). Routine HPV vaccination at age 10 under the GO or GN strategy was modelled with different vaccination coverages in boys and girls from the range between 0% and 100%, at 10% intervals, as we do not yet know what coverage a national HPV immunisation programme in India may reach. Estimates of the life-time number of cervical cancer cases prevented per 100,000 girls born were derived by cohort.

GN vaccination to improve resilience of cervical cancer prevention

To consider the impact of scaling up vaccination from such suboptimal coverage in girls, we highlighted the following four scenarios: (A) GO strategy with 60% coverage in girls, (B) GO strategy with 90% coverage in girls, (C) GN strategy with 60% coverage in both girls and boys, (D) GN strategy with 90% coverage in both girls and boys. As the base case, we simulated disruption in vaccination for 5 years, taking place 10 years after the introduction of vaccination. Life-time cervical cancer cases prevented in the birth cohorts vaccinated prior to the disruption period ranged between 562 (UI: 444, 676) and 807 (UI: 752, 853) cases per 100,000 girls born in the four highlighted scenarios. The highest impact was achieved by the GN strategy with 90% coverage, closely followed by GO with 90% coverage, then GN with 60% coverage, and then GO with 60% coverage (Figure 1).

Resilience against HPV vaccination disruption in the base case.

Predicted resilience, defined as life-time number of cervical cancer cases still prevented in the birth cohorts with disruption of vaccination per 100,000 girls born (blue arrow), and drop in cervical cancers prevented as compared to impact in the cohorts vaccinated prior to the disruption (black arrow), under the four highlighted scenarios. Disruption was simulated according to the base case with a period of disruption of 5 years and 0% coverage in girls and boys during the disruption period.

Among the four highlighted scenarios, the GO strategy with 60% coverage also had the lowest resilience, which we defined as the mean number of cases still prevented across the birth cohorts with disruption (Figure 1). We found that the life-time number of cervical cancer cases prevented would drop considerably from 562 (UI: 444, 676) cases in the undisrupted cohorts to 107 (UI: 7, 214) cases per 100,000 girls born in the disrupted cohorts (i.e., resilience of 107). Increasing the coverage under the GO strategy from 60% to 90% would lead to a small absolute gain in resilience, from 107 (UI: 7, 214) to 209 (UI: 81, 340) cases prevented per 100,000 girls born, and this despite the larger increase in impact in the undisrupted cohorts vaccinated prior to the disruption from 562 (UI: 444, 676) to 773 (UI: 701, 836) cases prevented. By contrast, switching from GO to GN vaccination, under 60% coverage, led to a moderate gain of impact in the undisrupted cohorts from 562 (UI: 444, 676) to 647 (UI: 539, 746) cases prevented, but a substantial increase in resilience from 107 (UI: 7, 214) to 302 (UI: 170, 437) cases prevented per 100,000 girls born. Finally, GN vaccination with 90% coverage led to the highest impact of 807 (UI: 752, 853) cases prevented per 100,000 girls born in the undisrupted cohorts as well as the highest resilience of 464 (UI: 328, 602) cases prevented per 100,000 girls born in the disrupted cohorts.

Considering the required number of vaccine doses, we found a trade-off between dose-efficiency and resilience. In general, GO vaccination favoured dose-efficiency and GN vaccination favoured resilience (Figure 1). For instance, increasing GO coverage from 60% to 90% would lead to a higher impact in undisrupted cohorts, while needing less additional vaccine doses than switching from GO to GN vaccination under 60% coverage. However, this switch from GO to GN vaccination would yield a higher gain in resilience than when increasing GO coverage from 60% to 90% (2.8-fold versus 2-fold increase). Similarly, switching from GO to GN vaccination under 90% coverage would require an additional doubling of the number of doses and only marginally improve the impact in the undisrupted cohorts, but would produce a 2.2-fold gain in resilience.

The same trade-off between dose-efficiency and resilience was found when stratifying by Indian state. Interestingly, in the states with high cervical cancer incidence, we found slightly lower resilience under the GO strategy and a slightly greater relative gain in resilience when switching from GO to GN vaccination than in states with low incidence (Supplementary file 3, Appendix 2—figures 13). In the base case, for example, the resilience ratio for switching from GO to GN strategy (under 60% coverage) was 3-fold in states with high cervical cancer incidence and 2.7-fold in states with low cervical cancer incidence.

Sensitivity analyses on less strong disruption in vaccination coverage or alternative durations of disruption also yielded higher resilience with GN than GO vaccination (Table 1, Appendix 2—figures 23). The gain in resilience by switching from GO to GN vaccination was most evident under complete disruption of vaccination (i.e., 0% coverage at disruption) but still consistently found under less strong disruption (i.e., 20% or 40% coverage at disruption) (Table 1 part II, Appendix 2—figure 2). As expected, resilience decreased with longer duration of disruption, with any vaccination strategy, but was always higher with the GN strategy (Figure 2). The gain in resilience by switching from GO to GN vaccination was consistently above 2.7-fold under 60% coverage and above 2.0-fold under 90% coverage (Table 1 part III). Finally, in the scenario with no restart of vaccination, GN vaccination could ensure some resilience of at least 125 cases prevented per 100,000 girls born for eight birth cohorts from the start of disruption (Figure 2).

Resilience against HPV vaccination disruption in sensitivity analyses on duration of disruption.

Predicted life-time cervical cancer cases prevented by birth cohorts (per 100,000 girls born) under different durations of disruption (panel) and in the four highlighted scenarios: girls-only strategy with 60% coverage (light red), girls-only strategy with 90% coverage (dark red), gender-neutral strategy with 60% coverage (light blue), and gender-neutral strategy with 90% coverage (dark blue). Vaccination coverage was fixed at 0% in girls and boys during the period of disruption. Birth cohort 0 corresponds to the first vaccinated cohort. Birth cohorts with disruption correspond to dashed lines and birth cohorts without disruption to solid lines.

Table 1
Sensitivity analyses on coverage at disruption and duration of disruption on resilience.

Life-time number of cervical cancer cases prevented per 100,000 girls born in birth cohorts vaccinated prior to disruption in part I. Sensitivity analyses on coverage at disruption in part II and on duration of disruption in part III on resilience (defined as the life-time number of cervical cancer cases still prevented in the birth cohorts with disruption of vaccination per 100,000 girls born) and resilience ratio (defined as fold change in resilience by switching from one scenario to another). Uncertainty intervals are reported in brackets. [Resilience ratio of scenario X to scenario Y] is defined as [resilience of scenario Y]/[resilience of scenario X]. For example, in the base case, [resilience ratio of GO 60% to GO 90%] = [resilience of GO 90%]/[resilience of GO 60%]=209/107=2.0.

I. Life-time number of cervical cancer cases prevented prior to disruption
ScenarioGO 60%GO 90%GN 60%GN 90%
No disruption562 (444, 676)773 (701, 836)647 (539, 746)807 (752, 853)
II. Sensitivity analyses on coverage at disruption (with duration of disruption fixed at 5 years)
Coverage at disruption in %Resilience by vaccination strategy and coverageResilience ratio
GO 60%GO 90%GN 60%GN 90%GO 60% to GO 90%GO 60% to GN 60%GO 90% to GN 90%
0 (base case)107 (7, 214)209 (81, 340)302 (170, 437)464 (328, 602)2.02.82.2
20271 (155, 391)355 (221, 490)425 (297, 559)550 (416, 680)1.31.61.6
40410 (277, 534)476 (343, 599)527 (401, 647)621 (500, 730)1.21.31.3
III. Sensitivity analyses on duration of disruption (with coverage at disruption fixed at 0%)
Duration of disruption in yearsResilience by vaccination strategy and coverageResilience ratio
GO 60%GO 90%GN 60%GN 90%GO 60% to GO 90%GO 60% to GN 60%GO 90% to GN 90%
1137 (26, 253)261 (125, 407)365 (215, 502)517 (372, 655)1.92.72.0
2125 (17, 233)240 (105, 375)344 (206, 480)500 (359, 642)1.92.72.1
5 (base case)107 (7, 214)209 (81, 340)302 (170, 437)464 (328, 602)2.02.82.2
1080 (0, 182)154 (33, 275)226 (96, 358)382 (240, 525)1.92.82.5

GN vaccination to enhance progress towards elimination of cervical cancer

Subsequently, we assessed the feasibility of reaching the WHO elimination threshold for ASIR of cervical cancer under highlighted scenarios. Introducing GO vaccination with 60% coverage would have the lowest impact, reducing the nationwide ASIR of cervical cancer from 11 cases, which is the present level before the introduction of vaccination, to 4.7 (UI: 4.3, 5.1) cases per 100,000 woman-years in 100 years, but this remained above the WHO elimination threshold (Figure 3). The other highlighted scenarios enabled elimination to be reached within 55–62 years. Introducing GN vaccination with 60% coverage in both girls and boys would reduce incidence down to 2.8 (UI: 2.5, 3.2) cases per 100,000 woman-years, hence below the elimination threshold. The GO strategy with 90% coverage, as recommended by WHO, would also decrease incidence below the threshold to 2.4 (UI: 2.3, 2.6) cases per 100,000 woman-years. Lastly, given the already high vaccination coverage of 90% in girls, switching to the GN strategy would only marginally reduce incidence to 2.3 (UI: 2.2, 2.3) cases per 100,000 woman-years.

Progress towards cervical cancer elimination over time.

Predicted cervical cancer age-standardised incidence (in cases per 100,000 woman-years) in the years since start of vaccination in India under no vaccination and in the four highlighted scenarios. The dashed line represents the World Health Organization (WHO) elimination threshold for cervical cancer elimination as a public health priority, that is, age-standardised incidence of 4 cases per 100,000 woman-years. Disruption was simulated according to the base case with a period of disruption of 5 years and 0% coverage in girls and boys during the disruption period.

Other combinations of coverage in boys and girls would also allow elimination. Without vaccination in boys, the WHO elimination threshold could be reached through a critical GO coverage of 70% (UI: 65, 73) (Figure 4). With vaccination in boys, a lower critical coverage in girls might be sufficient for elimination. For instance, coverage between 50% and 70% in girls might also be sufficient for elimination when combined with moderate coverage (30%) in boys (Figure 4).

Attainment of cervical cancer elimination in the long term.

Heatmap of the predicted cervical cancer age-standardised incidence rate (in cases per 100,000 woman-years) in the long term (i.e., at 100 years after the start of vaccination) in India by vaccination coverage in girls and boys (under no disruption of vaccination). The dashed curve represents the World Health Organization (WHO) elimination threshold for cervical cancer elimination, that is, age-standardised incidence of 4 cases per 100,000 woman-years. The five black circles correspond to no vaccination and the four highlighted scenarios: girls-only strategy with 60% coverage, girls-only strategy with 90% coverage, gender-neutral strategy with 60% coverage, and gender-neutral strategy with 90% coverage. The horizontal arrows represent scale-up of vaccination by increasing coverage in girls, and the vertical arrows represent switching to gender-neutral vaccination.

Finally, while disruption of vaccination does not prevent countries from reaching the elimination threshold eventually (provided vaccination resumes after disruption), it can prolong the number of years taken to attain this goal (Appendix 2—figure 4). Under the GO strategy with 90% coverage, for instance, disruption of vaccination for 5 years (as in the base case) would delay reaching elimination from 57 to 65 years since the start of vaccination. By switching to GN strategy (under 90% coverage), elimination could be reached in 58 years since the start of vaccination, even with disruption.

Discussion

As observed worldwide during the COVID-19 pandemic, public health programmes can be dramatically impacted by the occurrence of sudden societal and infrastructural crises (WHO, 2021). The repercussions of disruption of health-care systems may subsequently translate into increased morbidity and mortality (Sharpless, 2020), and economic burden (Richards et al., 2020), at a population level. In the present paper, using India as an example, we illustrate how the introduction of GN HPV vaccination could mitigate the negative impact of a sudden HPV vaccine delivery disruption. More specifically, using a validated agent-based model, calibrated to context-specific data, we have simulated a range of plausible scenarios of disruption of a national HPV vaccination programme and assessed the gain in resilience by shifting from GO to GN vaccination in terms of the number of cervical cancer cases still prevented in the birth cohort with vaccination disruption.

Our model-based exercise showed that in the case of HPV vaccination disruption for 5 years (base case), resilience increased by 2.8-fold (from 107 to 302 cases per 100,000 girls born) and 2.2-fold (from 209 to 464 cases per 100,000 girls born) when shifting from GO to GN vaccination under 60% and 90% coverage, respectively. GN vaccination was shown to improve resilience irrespective of the duration of disruption. However, the absolute magnitude of resilience and of resilience gain resulting from adding boys to routine vaccination of girls steadily decreased with increasing duration of disruption, under all the vaccination strategies assessed, indicating that measures to restore HPV vaccination should be implemented as soon as feasible.

An important determinant underlying the gain in resilience by switching from GO to GN vaccination is likely the age difference between sexual partners. In general, men are on average older than women within sexual partnerships almost everywhere worldwide (Wellings et al., 2006). In cases of vaccination disruption, the birth cohorts of boys vaccinated before the disruption would indirectly protect the cohorts of younger girls who missed out on vaccination during the disruption period. Since sexual behaviour is regulated by population-specific social norms, the magnitude of the resilience attributable to GN HPV vaccination is also expected to be different across populations. In India, for example, the age difference between male and female partners is on average 7 years (USAID, 2017), similar to the number of birth cohorts still retaining some protection by GN vaccination when we simulated no restart of vaccination after disruption. As shown by our results, increasing coverage in girls also helps to increase resilience. However, vaccinating boys is a more direct way to reduce the force of infection in men than vaccinating girls, which is the likely mechanism underlying the higher resilience achieved by GN strategy than GO strategy.

In the present paper, we have also illustrated how the introduction of GN vaccination across India would enhance progress towards the elimination of cervical cancer. In principle, elimination across India could be reached with a critical level of GO coverage of 70%, which falls between the average coverage observed among girls aged 9–15 years living in countries with an active HPV vaccination programme, that is, 53% (Bruni et al., 2021), and the coverage recommended by the WHO, that is, 90% (WHO, 2020b). On the other hand, our model-based estimates suggest that cervical cancer elimination could be reached with 50–70% coverage in girls when combined with moderate coverage in boys, even if coverage in boys were lower than that of girls. In cases where GO coverage is already above 70%, GN vaccination would allow even more ambitious cervical cancer control targets to be reached, which might eventually make cervical screening targeted to vaccinated birth cohorts redundant (Tota et al., 2020). Moreover, as previously reported (Man et al., 2022), reaching elimination might be challenging in the Indian states with the highest baseline cervical cancer incidence before vaccination. In these cases, additional measures such as GN vaccination would be even more relevant.

The availability and limitations of data on sexual behaviour and cervical cancer incidence across Indian states were discussed in detail in our previous publication in which we presented the calibrated model for the first time (Man et al., 2022). A limitation of this study is that the critical levels of HPV vaccination coverage found for India are not necessarily generalisable to other populations. Baseline cervical cancer incidence and the magnitude of herd effect, that is, the indirect protection offered by the vaccinated to the unvaccinated individuals of a population against HPV infection (Malagón et al., 2018) should be considered. These are, in turn, governed by local sexual behaviour (Bosch et al., 1994; Schulte-Frohlinde et al., 2022). As mentioned above for resilience, the critical level of vaccination coverage to reach cervical cancer elimination will be context-specific (Baussano et al., 2018; Lehtinen et al., 2022). Extrapolation of the results of this study to other populations will be limited to those sharing similar patterns of demography, social norms, and cervical cancer epidemiology as India. Nevertheless, we expect the principle of improving the resilience of cervical cancer prevention through a shift to GN vaccination to be widely applicable, and this is supported by the consistency of our results in India when stratifying by state-specific cervical cancer incidence.

Another limitation is the finite number of scenarios considered in which disruption of a health system might occur. For example, we did not explore changes in sexual behaviour in the population due to disruption, which occurs in dire circumstances like a pandemic. However, we did account for a large variation in the duration of disruption and in vaccination coverage at disruption through our sensitivity analyses. In addition, we did not consider the influence of choice of HPV vaccine, as a quadrivalent vaccine (either indigenously produced or Gardasil) would be the likely choice for India due to the price advantage. In cases of using a nonavalent vaccine, or a vaccine with higher levels of cross-protection, we expect similar qualitative results for resilience, and elimination of cervical cancer could be expected to be more easily achieved. We also did not consider changes in cervical cancer screening, as it is difficult to predict how much coverage of cervical cancer screening will increase in India in the coming years.

Although a formal health-economic assessment of the introduction of GN HPV vaccination in India is beyond the scope of this paper, clearly adding vaccination of boys to the routine coverage of girls would approximately double the required number of vaccine doses and would increase the financial effort to be made by the national government. It has been demonstrated that GN HPV vaccination schedules are economically attractive in high-income tender-based settings, in particular where GO vaccine uptake is below 80% (Qendri et al., 2020). GN vaccination is likely to be even more attractive in India where population-based screening programmes have not yet been widely implemented. Of course, the actual determinants of a trade-off between redundancy and efficiency of resource allocation depends on context-specific planning and assessment. In our example, to achieve greater resilience, GN vaccination with 60% coverage would require more doses of vaccine while leading to fewer prevented cases of cervical cancer in vaccinated cohorts without interruption than GO vaccination with 90% coverage (Figure 2). However, extending vaccination to boys could be easier to put in place than improving coverage in girls from 60% to 90% in some contexts. Finally, shifting from a two-dose to a single-dose HPV vaccination schedule could also improve the affordability of GN vaccination. More research is needed to investigate the cost-effectiveness and affordability of GN vaccination in low-resource settings and how resilience should be accounted for in deciding the vaccination strategy.

Having clearly illustrated the merits of GN vaccination considering different aspects across different settings here and in earlier studies (Chow et al., 2021; Elfström et al., 2016; Lehtinen et al., 2022; Lehtinen et al., 2018; Qendri et al., 2020), the next step is to establish a feasible pathway for implementation (de Sanjose and Bruni, 2020). In high-income countries, where affordability was less of an issue, most vaccination programmes were initiated with the GO strategy, often combined with catch-up in girls/women, and are now increasingly shifting towards GN vaccination. In resource-limited settings, once routine vaccination in girls has been implemented, countries may need to make a choice between boys’ vaccination or female catch-up vaccination. The limited global vaccine supply will likely remain an important factor influencing this decision-making process in the coming years (WHO, 2022b). In addition, the choice at the country level should also account for the health-economic objectives, the type of health infrastructure already in place as well as socio-cultural context. In some contexts, important arguments for choosing GN strategy could be the fact that it helps to overcome the stigma associated with a vaccine that targets only girls and aims to prevent a sexually transmitted disease (Cernasev et al., 2023), and the direct health benefits in men who have sex with men.

In conclusion, within the limitations of our model, the model-based estimates show that shifting from GO to GN vaccination may improve the resilience of the Indian HPV vaccination programme while also enhancing progress towards the elimination of cervical cancer. Our resilience estimates go beyond health-care disruption due to the COVID-19 pandemic. Indeed, any societal events undermining the routine activities of a health-care system can produce a comparable effect. Over the years, disease prevention programmes have been disrupted by the occurrence of pandemics (Osterholm, 2017; WHO, 2021), armed conflicts (Jawad et al., 2021; McGrath, 2022), economic sanctions (Germani et al., 2022), or widespread vaccine hesitancy (Larson, 2020). Therefore, we argue that such societal crises, which are unpredictable but expected to occur, should be anticipated through careful planning of disease prevention programmes.

Materials and methods

Model

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EpiMetHeos was used to simulate a dynamic sexual contact network, which allows long-term and overlapping partnerships, through which HPV infections were transmitted. Based on the impact of vaccination on high-risk HPV incidence derived from EpiMetHeos, Atlas was then used to derive the impact on cervical cancer burden.

The models were previously calibrated to sexual behaviour, HPV prevalence, and/or (delete as appropriate) cervical cancer incidence in India to assess the impact of single-dose vaccination in India, while accounting for the uncertainty in the duration of vaccine protection (Man et al., 2022). Since the high-quality data on HPV prevalence and cervical cancer incidence data needed to calibrate the models to each Indian state were not available, we used a Footprinting framework to approximate the missing data and extrapolate the impact by state. In short, we first identified clusters of states with similar patterns of cervical cancer incidence. We then calibrated EpiMetHeos to the state of Tamil Nadu to represent the states in the high cancer incidence cluster, and the state of West Bengal to represent the states in the low cancer incidence cluster, using the available sexual behaviour and HPV prevalence data of these two states. Finally, we estimated the impacts for Tamil Nadu and West Bengal and extrapolated these to other states within each cluster. The model was calibrated for previous publications (Man et al., 2022; Man et al., 2023). See more details of the model in Appendix 1 sections ‘HPV transmission model’, ‘Model calibration’, and ‘Computation of the model outcomes’. This study adheres to HPV-FRAME, a quality framework for modelled evaluations of HPV-related cancer control (Reporting standard 1) (Canfell et al., 2019).

Simulation scenarios

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Recently, marketing authorisation has been awarded to a locally produced quadrivalent HPV vaccine in India based on successful immunobridging between the new vaccine and the existing quadrivalent vaccine (Gardasil, MSD) (The Lancet Oncology, 2022). While it is still uncertain which HPV vaccine will be used in the Indian immunisation programme, we simulated vaccination with a quadrivalent vaccine targeting two high-risk types, HPV16 and HPV18 (and two low-risk types, HPV6 and HPV11). The efficacy estimates of the locally produced HPV vaccine were not available to us; hence, we based our model assumptions on the efficacy estimates of the IARC India vaccine trial, which considered Gardasil (Basu et al., 2021; Joshi et al., 2023). In this trial, no difference was found between the efficacy estimates of the single-dose and two-dose schedules, which were moreover stable after up to 10 years’ follow-up. Hence, we based the model on pooled efficacy estimates (95% efficacy for HPV16 and HPV18, 9% cross-protection for HPV 31, HPV33, and HPV45, and 0% efficacy for the remaining high-risk HPV types) and assumed no waning of vaccine immunity over time. In doing so, the modelled efficacy could represent vaccination under either a single-dose or two-dose schedule. Vaccine protection was modelled through an all-or-nothing working mechanism, that is, if the efficacy is X%, then X% of the vaccinated individuals in the model are fully protected and 100-X% fully not protected.

To assess the resilience of different vaccination strategies on cervical cancer prevention, we simulated the above scenarios first without disruption of the HPV vaccination programme, and then with disruption occurring 10 years after the start of vaccination. As the base case, we simulated a disruption period of 5 years with total interruption of HPV vaccine delivery, that is, 0% vaccination coverage in both boys and girls. As sensitivity analyses, we considered less severe disruption with still 20% and 40% vaccination coverage in girls (but still 0% in boys), as well as shorter or longer durations of disruption of 1, 2, and 10 years, and no restart of vaccination. These sensitivity analyses were considered to represent different types of disruption attributable to a COVID-19 pandemic or to any other interference of the HPV vaccination programme.

Model outcomes

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Estimates of the life-time number of cervical cancer cases prevented per 100,000 girls born were derived by cohort. The first 40 birth cohorts following the introduction of vaccination were considered to have estimates for both cohorts before and after the disruption period. The mean number of cases prevented across the first 10 vaccinated birth cohorts was used to evaluate the impact prior to the disruption. As a measure of resilience, we used the mean number of cases still prevented, as a result of previous HPV vaccination, across the birth cohorts with disruption. Considering such a metric of resilience allows us to evaluate whether any given strategy provides a sufficient level of protection retained during disruption as compared to a possible prefixed target level of protection. Estimates of resilience were compared between the highlighted scenarios A–D based on their ratio.

To assess progress towards cervical cancer elimination, we also derived the ASIR of cervical cancer up to 100 years after the start of vaccination. The long-term impact of vaccination was defined as the impact at 100 years after the start of vaccination. The Segi world standard population was used for the standardisation (Segi, 1960). Model estimates of ASIR were compared to the threshold for elimination defined by the WHO of 4 cases per 100,000 woman-years (WHO, 2020b).

Model outcomes were reported as the mean and the 10th and 90th percentiles, that is, uncertainty interval (UI), of the simulations using the 100 parameter sets best fitting the sexual behaviour and HPV prevalence data obtained through calibration.

Given the extremely low coverage of the existing screening programme in India, we have not considered the impact of screening in our study (Bruni et al., 2022).

Appendix 1

HPV transmission model

We adapted a previously published HPV transmission model (Baussano et al., 2013) into a stochastic agent-based dynamic model, EpiMetHeos. EpiMetHeos is an extension of EpiModel (Jenness et al., 2018) an open-source statistical framework that allows simulation of infectious disease transmission on dynamic contact networks.

Demography

The model considers an open population in which individuals enter at the age of 10 years and exit due to death with maximum age of 100 years. Each individual is characterised by the two demographic characteristics, age and sex, denoted by a[10,100] and g{W,M}, respectively. The entire age range is further stratified into the following age groups: ageg{10-14,15-19,20-24,25-29,30-49,50-59,60-99}.

Individuals die according to sex- and age-specific probabilities mg,age . The number of new individuals born into the population per time step is given by b, which is set to a value that keeps the total population constant over time and with 50%–50% of female and male newborns. A constant age-specific population size distribution corresponding to mg,age is achieved through the simulation of a burn-in period.

Sexual contact behaviour

Two dynamic contact networks of sexual partnerships are modelled among the model individuals: one for stable partnerships and one for one-off partnerships. Stable partnerships represent marital partnerships and one-off partnerships outside marriage. The networks are constructed using EpiModel’s implementation of the Separable Temporal Exponential-family Random Graph Models (STERGM) (Jenness et al., 2018). An STERGM is characterised by formation and dissolution probabilities of partnerships. At each time step, partnerships that were present at the previous time step can be dissolved, and new partnership can be formed between individuals that were not connected at the previous time step.

Formation of partnerships is only allowed between opposite sex individuals to model heterosexual networks. Formation probabilities of stable as well as one-off partnerships depend on the individual’s sex, age group, and the assigned risk group of sexual activity. Each individual is assigned to one of the five risk groups of sexual activity, denoted by riskg{1,2,3,4,5}, which determines which type of partnerships he/she is allowed to form as follows:

  • riskg=1: no stable, no one-off,

  • riskg=2: only stable,

  • riskg=3: both stable and one-off,

  • riskg=4: only one-off,

  • riskg= 5: only one-off; in women, this risk group represents female sex workers.

The risk group of an individual is assigned randomly at birth according to a sex-specific multinomial distribution pg,riskg and remains unchanged for the rest of the individual’s lifespan. The dissolution probability differs between stable and one-off partnerships but is the same for all individuals.

Sexual behaviour is further characterised based on parameters regarding the number of sex acts within established partnerships. At each time step, sex acts may occur within a stable partnership. The number of sex acts is randomly generated according to a Poisson distribution with mean rmain . Within a one-off partnership, the number of sex acts rone-off is exactly one. The parameter values regarding sexual contact behaviour were fixed or obtained through a calibration step described in Section ‘Calibration to sexual behaviour data’.

HPV natural history

HR HPV types are assumed to be transmitted independently, governed by type-specific natural history parameters, including the probability of transmission, duration of infection, and natural immunity. Transmission of all HR HPV types are assumed to follow and the ‘Susceptible-Infected-Removed/Immune-Susceptible’ dynamics in women (Appendix 1—figure 1). In men, it has generally been observed that the HPV prevalence and the rate of acquiring new HPV infection remain constant across age, and that the seroconversion rate is low after natural infection (Giuliano et al., 2008; Giuliano et al., 2015; Schiffman et al., 2016). Hence, we assumed the ‘Susceptible-Infected-Susceptible’ dynamics in men. Effects of HIV, smoking, and use of contraceptives, as risk factors of HPV transmission and cervical cancer progression, were not included in the model. We chose not to include them in the model. For HIV infection, this choice was based on the low HIV prevalence in India (Stelzle et al., 2021). As for smoking and the use of contraceptives, the choice was based on the lack of data.

Appendix 1—figure 1
Structure of HPV natural history in EpiMetHeos.

All individuals enter the population being susceptible. At each time step, an unvaccinated susceptible individual having l sex acts with an individual infected with type i has a probability of 1-1-βil becoming infected, where βi is the probability of transmission per sex act. If vaccinated and vaccine protection is successfully induced, the probability of transmission for a vaccine-targeted type, either a vaccine type or a cross-protective type, becomes zero. In other words, we assumed an all-or-nothing working mechanism for vaccine protection. When vaccine-induced protection is lost, the probability of transmission returns to 1-1-βil.

The duration of infection follows a type-specific distribution with six parameters γi, ηi, δi,1, δi,2, νi,1, νi,2 . This distribution describes the process through CIN0, CIN1, regressive CIN2/3, and non-regressive CIN2/3 stages as modelled in an extensively validated cervical cancer progression model (Berkhof et al., 2013; Bogaards et al., 2010). In particular, non-regressive CIN2/3 here represents the part of HPV infections that will persist and progress to cancer. For those HPV infections that do not persist, upon clearance, men become susceptible and women become removed/immune. Note that while the clearance distribution of HPV infection in the model was constructed by reproducing the transitions through different intermediate CIN stages, we do not explicitly simulating the prevalence or incidence of these intermediate stages to reduce the computational load of simulation. The duration of natural immunity in women follows an exponential distribution type-specific rate μi , after which women also become susceptible again. The values of the natural history parameters were fixed or obtained through a calibration step described in the section 'Calibration to HPV prevalence data’ and are reported in Supplementary file 1.

Model calibration

Footprinting framework

Since high-quality data about HPV prevalence and cervical cancer incidence data essential to project the impact of HPV vaccination are not available for each Indian state, the Footprinting framework was used to approximate missing data and extrapolate impact projections. The Footprinting framework is described in more details in a separate manuscript (Man et al., 2023). The framework consists of three steps: clustering, classification, and projection. These steps are briefly described below.

Clustering step

In the clustering step, Indian states with available cervical cancer incidence data were clustered based on their similarity in patterns of age-specific cervical cancer incidence. Sources for cervical cancer incidence data were volume XI of Cancer Incidence in Five Continent (CI5) and the 2012–2016 Report of National Cancer Registry Programme by National Centre for Disease Informatics and Research (NCDIR) (Bray et al., 2017; National Centre for Disease Informatics and Research, 2020). See Supplementary file 2A for an overview of Indian states (or groups of states) with available cancer incidence data from local registries. Of the 25 Indian states, 14 states have local registries and 11 do not. Whenever a registry was both present in CI5 and NCDIR, only the data by CI5 were used. See Supplementary file 2B and Appendix 1—figure 4 for the extracted age-specific cervical cancer incidence data.

A Poisson-regression-based CEM clustering algorithm was used to cluster the age-specific cervical cancer incidence curve. Details of the clustering algorithm described in Appendix 1 of the separate manuscript are reported in the main text of the separate manuscript (Man et al., 2023). We identified one cluster with high cancer incidence and one with low cancer incidence. See also column ‘cluster’ of Supplementary file 2B for the assigned cluster of each Indian state. In this table, the Indian states with cervical cancer incidence, hence involved in this clustering step, are indicated by ‘extracted’ in the column ‘source’.

Classification step

The remaining Indian states without cervical cancer incidence data were classified to the identified clusters of Indian states with similar patterns of cervical cancer incidence based on similarity in sexual behaviour data from the behaviour surveillance survey by the National AIDS Control Organization of India (National AIDS Control Organisation, 2006).

Random forest was used for classification. Details of the classification method are reported in the main text of the separate manuscript (Man et al., 2023). See also column ‘cluster’ of Supplementary file 2B for the assigned cluster of each Indian state. In this table, the Indian states without cervical cancer incidence, hence involved in this classification step, are indicated by ‘inferred’ in the column ‘source’.

Projection step

In the projection step, baseline (i.e., in the scenario without vaccination) HPV prevalence and cervical cancer incidence were approximated, based on the available data within each cluster. For HPV prevalence, high-quality type- and age-specific HPV prevalence data were only available for Tamil Nadu and West Bengal, the former being in the ‘high’ and the latter in the ‘low’ incidence cluster (Dutta et al., 2012; Franceschi et al., 2005). As for cervical cancer incidence data, approximation was based on the mean age-specific incidence within each cluster. See Appendix 1—figure 5 and Supplementary file 2B for mean values. From the approximated Indian state-specific baseline cervical cancer incidence, we then derived cervical cancer risk in terms of three indicators, life-time risk (LTR), and ASIR, according to the methodology described in the sections LTR of cervical cancer and ASIR of cervical cancer, respectively. See Supplementary file 2G for baseline cervical cancer risk by LTR and ASIR.

HPV incidence and cervical cancer risk under vaccination scenarios were obtained as follows. The HPV transmission model, EpiMetHeos, was calibrated to these two representative Indian states (Tamil Nadu and West Bengal) according to the procedure described in the section Calibration to sexual behaviour data. Using the two obtained models, we then simulated different vaccination scenarios and obtained estimates of relative reduction in HPV infection risk according to the methodology described in the section Cumulative risk of HPV infection. Finally, these estimates of relative reduction were applied to the previously obtained baseline values of cervical cancer risk to derive the values under vaccination scenarios.

Calibration to sexual behaviour data

The model was calibrated to the two representation states: West Bengal and Tamil Nadu. Model calibration consists of two steps. In the first step, the component of the model simulating the dynamic contact networks was calibrated to the sexual behaviour data without considering the component concerning the natural history of HPV yet. Four sources of sexual behaviour data were used:

  • The Demographic and Health Survey (DHS) programme, to provide information on stable partnerships in West Bengal and Tamil Nadu (USAID, 2017).

  • National Behavioural Surveillance Survey: General Population 2006 by The National AIDS Control Organisation (NACO), to provide information on one-off partnerships in West Bengal and Tamil Nadu (National AIDS Control Organisation, 2006).

  • Publication by Gaffey et al., ‘Male use of female sex work in India: a nationally representative behavioural survey’, to provide information on proportion of men with stable partnership that also have one-off partnerships in West Bengal and Tamil Nadu (Gaffey et al., 2011).

  • Publication by Vandepitte et al., ‘Estimates of the number of female sex workers in different regions of the world’, to provide information on one-off partnerships across India (Vandepitte et al., 2006).

First, the probabilities of being assigned to different risk groups of sexual activity were fixed. See Supplementary file 1 for the fixed values and the justification. Subsequently, formation and dissolution probabilities of the dynamic sexual contact networks were fitted to a set of target statistics using the netest function of EpiModel. The set of target statistics was:

  • Stable partnerships:

    ◦ Sex- and age-group-specific population proportion with stable partnerships dg,agegstable

    ◦ Sex- and risk-group-specific population proportion with stable partnerships dg,riskgstable

    ◦ Overall population proportion with stable partnerships dstable

    ◦ Mean age difference in stable partnerships κstable

    ◦ Mean spread of absolute age difference in stable partnerships ωmain

    ◦ Mean duration of stable partnerships sstable

  • One-off partnerships:

    ◦ Sex- and age-group-specific mean degree of one-off partnerships dg,agegone-off

    ◦ Sex- and risk-group-specific mean degree of one-off partnerships dg,riskgone-off

    ◦ Overall mean degree of one-off partnerships done-off

    ◦ Mean duration of one-off partnerships sone-off

See Supplementary file 1 for the values of the target statistics and the corresponding data sources used. See Supplementary file 1 also for the fixed parameters and the corresponding data sources used. See Appendix 1—figure 2 for the fit to the target statistics. Note that assortativeness of sexual contact by age and risk groups are results of the fitting process.

Appendix 1—figure 2
Model fit of the target statistics of sexual contact behaviour.

Left column: West Bengal. Right column: Tamil Nadu. Circle: targets derived from data. Triangle: fit by model.

Calibration to HPV prevalence data

In the second step, the entire model was calibrated, including the component concerning the natural history of HPV. This was done by fitting the model HPV prevalence among sexually active women to HPV prevalence data from two surveys among married women in Tamil Nadu and West Bengal (Dutta et al., 2012; Franceschi et al., 2005). For the study in Tamil Nadu (Franceschi et al., 2005), we could extract the age-specific prevalence data for all HR HPV types from the database at IARC. For the study in West Bengal (Dutta et al., 2012), we had only the data reported in the publication, which include the age-specific prevalence of HPV 16 and 18 but not that of other HR HPV types. To derive the prevalence of other HR HPV types in West Bengal and ensure consistent type-specific HPV natural history parameters between Tamil Nadu and West Bengal, we scaled the relative type-specific prevalence of Tamil Nadu to West Bengal. The relative prevalence of HPV 16/18 between Tamil Nadu and West Bengal was used as the scaling factor.

Due to the low prevalence of some HR HPV types, we used the average prevalence of the following three groups of HR HPV types as target prevalence:

  • HPV 16

  • HPV 18

  • HPV 31/33/45/35/39/51/52/56/58/59/68

Note that for the subsequent simulation of the vaccination scenarios, the calibrated parameters based on average prevalence of HPV 31/33/45/35/39/51/52/56/58/59/68 were then used to model the cross-protective types HPV 31/33/45 and the other HR HPV types HPV 35/39/51/52/56/58/59/68, which are referred to as ‘cross’ and ‘other’.

The parameter values regarding the type-specific progression, clearance rates, and waning rates of natural immunity were fixed to those estimated for the extensively validated cervical cancer progression model (Berkhof et al., 2013; Bogaards et al., 2010). For HPV 31/33/45/35/39/51/52/56/58/59/68, we derived the mean values of the type-specific estimates.

The parameter values obtained in this calibration step regard type-specific transmission probabilities and one-off partnership underreporting rate. Using 2500 parameter sets that were uniformly generated from the range (Supplementary file 1), we selected 100 best-fitting parameter sets. Model fit was evaluated based on log-likelihood of the observed HPV prevalence data given the simulated HPV prevalence under a binomial distribution. For each parameter set, log-likelihood was computed at each year in the last 50 years of 250 years of simulation. The maximum and mean log-likelihood across these years were used as the two summary statistics. See Appendix 1—figure 3 for the fit to the HPV target prevalence.

Appendix 1—figure 3
Model fit of the type-specific HPV prevalence data.

Panel A: West Bengal. Panel B: Tamil Nadu. Model estimates of HPV prevalence of each of the 100 best-fitting parameter sets are given by a separate line. The confidence intervals of the observed HPV prevalence under binomial distribution are given by the dashed lines. The confidence intervals for West Bengal follow the same shape as those for Tamil Nadu, as the type-specific prevalence for West Bengal was derived by rescaling that of Tamil Nadu (the prevalence for West Bengal being approximately half that of Tamil Nadu).

Computation of model outcomes

Cumulative risk of HPV infection

For each of the four representative HPV type i and each birth cohort, model estimates on half-year incidence ri,a up to age a=40 years were combined to derive the cumulative risk of HPV infection Yi , as follows: Yi=a=15450.5ri,asa. Here, sa=exp(-a`=1040.5da`) is the survival probability up to age a derived from the mortality rates da` of the UN life table of India in 2015–2020 (Supplementary file 2C).

The cumulative risk of any HR HPV infection was a weighted average of the relative reduction in type-specific cumulative risk of HPV infection. The weights were based on the type-specific contribution to cervical cancer as observed in India (Basu et al., 2011). The contributions by HPV 16, 18, and 31/33/45 were 62%, 16%, and 6%, respectively (Supplementary file 2D). These proportions were derived by normalising the type-specific contributions.

As for outcome of HPV prevalence, we assumed the model estimates for cumulative risk of HPV infection for West Bengal and Tamil Nadu to apply to all states in the low- and high-cancer-incidence clusters, respectively.

LTR of cervical cancer

To obtain the LTR of cervical cancer in the scenario without vaccination, we extracted age-specific cervical cancer incidence data from volume XI of Cancer Incidence in Five Continent (CI5) and the 2012–2016 Report of National Cancer Registry Programme by National Centre for Disease Informatics and Research (NCDIR) (Bray et al., 2017; National Centre for Disease Informatics and Research, 2020) Of the 25 Indian states, 14 states have local registries and 11 do not. Whenever a registry was present in both CI5 and NCDIR, only the data corresponding to CI5 were included. As described in the section Footprinting framework, each state without a local cancer registry was classified to either the cluster of states with high or low cervical cancer incidence. The missing incidence were inferred based on the cluster mean of the classified cluster. See Supplementary file 2B, Appendix 1—figure 4, and Appendix 1—figure 5 for the extracted or inferred age-specific cervical cancer incidence data by Indian state.

Appendix 1—figure 4
Age-specific cervical cancer incidence data by Indian state.
Appendix 1—figure 5
Mean of age-specific cervical cancer incidence of the cluster of Indian states.

A recently published method (Bonjour et al., 2021) was used to derive LTR of cervical cancer from age-specific incidence. This method converts age-specific cancer incidence into LTR of cervical cancer (in cases per 100,000 girls born) while accounting for the competing risk of dying from other causes before possible occurrence of cervical cancer. For LTR, we used the UN data on female mortality rates for 2015–2020 in India (Supplementary file 2C, United Nations Department of Economic Social Affairs Population Dynamics, 2022). See Supplementary file 2G for the baseline LTR without vaccination by Indian state.

To derive the LTR of cervical cancer in the scenarios with vaccination, we approximated the relative reduction in risk of cervical cancer by model estimates of the relative reduction in the cumulative risk of any HR HPV infection. Model estimates of West Bengal and Tamil Nadu were used for the states in the low- and high-cancer-incidence clusters, respectively.

ASIR of cervical cancer

ASIR of cervical cancer (in cases per 100,000 woman-years) was obtained based on the world standard population (Supplementary file 2E, Segi, 1960). See Supplementary file 2G for the baseline ASIR without vaccination by state. As for the LTR, the ASIR in the scenarios with vaccination was derived based on model estimates of the relative reduction in the cumulative risk of any HR HPV infection.

Aggregating model outcomes

To obtain outcomes for India as a whole and by low- and high-incidence states, state-specific outcomes were weighted based on the state-specific female population size according to the table C-13 by the Indian Census of 2011 (Supplementary file 2F, Office of the Registrar, General Census Commissioner India, 2011).

Model outcomes corresponding to the simulation of the 100 best-fitting parameter sets were used to derive the mean and the 10th and 90th percentiles, that is, UI, of the model outcomes. For the nationwide model outcomes, the 100 best-fitting parameter sets of West Bengal and Tamil Nadu were paired for derived 100×100 outcomes, which were subsequently used to derive the mean and the 10th and 90th percentiles. Furthermore, nationwide outcomes and outcomes across all high- and low-cancer-incidence states were obtained by weighting the state-specific outcomes by the corresponding population sizes.

Appendix 2

Please see Supplementary file 3

Supplementary results in figures

Appendix 2—figure 1
Resilience against HPV vaccination disruption in the base case by Indian state.

Predicted HPV vaccination resilience, defined as life-time number of cervical cancer cases still prevented in the birth cohorts with disruption of vaccination per 100,000 girls born (blue arrow), and drop in cervical cancers prevented as compared to impact in the undisrupted cohorts (black arrow), under the four highlighted scenarios in Indian states with (A) high and (B) low cervical cancer incidence. Disruption was simulated according to the base case with a period of disruption of 5 years and 0% coverage in girls and boys during the disruption period. Figure 1 in the main text corresponds to the results for all Indian states.

Appendix 2—figure 2
Resilience against HPV vaccination disruption in sensitivity analyses on coverage at disruption by Indian state.

Predicted HPV vaccination resilience, defined as life-time number of cervical cancer cases still prevented in the birth cohorts with disruption of vaccination per 100,000 girls born (blue arrow), and drop in cervical cancers prevented as compared to impact in the undisrupted cohorts (black arrow), under different vaccination strategies and coverage (rows) in the sensitivity analyses of the coverage of disruption in (A) all Indian states, (B) Indian states with high cervical cancer incidence, and (C) Indian states with low cervical cancer incidence. Disruption was simulated for 0% (base case), 20%, or 40% coverage in girls at disruption. The vaccination coverage was fixed at 0% in boys, and the duration of disruption was fixed at 5 years.

Appendix 2—figure 3
Resilience against HPV vaccination disruption in sensitivity analyses on duration of disruption by Indian state.

Predicted HPV vaccination resilience, defined as life-time number of cervical cancer cases still prevented in the birth cohorts with disruption of vaccination per 100,000 girls born (blue arrow), and drop in cervical cancers prevented as compared to impact in the undisrupted cohorts (black arrow), under different vaccination strategies and coverage (rows) in the sensitivity analyses of the duration of disruption in (A) all Indian states, (B) Indian states with high cervical cancer incidence, and (C) Indian states with low cervical cancer incidence. Disruption was simulated for 1, 5 (base case), or 10 years indicated by different empty shapes. Vaccination coverage was fixed at 0% in girls and boys during the period of disruption.

Appendix 2—figure 4
Progress towards cervical cancer elimination over time with and without disruption.

Predicted cervical cancer age-standardised incidence (in cases per 100,000 woman-years) in the years since start of vaccination in the four highlighted scenarios with (dashed curves) and without (solid curves) disruption. The dashed horizontal line represents the World Health Organization (WHO) elimination threshold for cervical cancer elimination, that is, age-standardised incidence of 4 cases per 100,000 woman-years. Disruption was simulated according to the base case with a period of disruption of 5 years and 0% coverage in girls and boys during the disruption period.

Data availability

All data used in the present study were openly available and extracted from http://ci5.iarc.fr for the cervical cancer incidence data published by the International Agency for Research on Cancer, from https://www.ncdirindia.org/All_Reports/Report_2020/resources/NCRP_2020_2012_16.pdf for the cervical cancer incidence data published by the National Centre for Disease Informatics and Research of India, from https://doi.org/10.1038/sj.bjc.6602348 and https://doi.org/10.1097/pgp.0b013e3182399391 for the HPV prevalence data, from https://www.aidsdatahub.org/sites/default/files/resource/national-bss-general-population-india-2006.pdf for the sexual behaviour data published by the National AIDS Control Organisation Ministry of Health and Family Welfare Government of India, from https://doi.org/10.1128/jcm.00354-11 for the data on type-specific contribution of HPV types in cervical cancer, from https://population.un.org/wpp/Download/ for the mortality rates data published by the United Nations, from https://censusindia.gov.in/census.website/data/census-tables for the Indian state-specific census data published by the General Census Commissioner India, from https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/gpe_discussion_paper_series_paper31_2001_age_standardization_rates.pdf for the Segi standard world population data. The computer code regarding the HPV transmission model EpiMetHeos is a part of the METHIS (ModElling Tools for HPV Infection-related cancers) project, which will gradually make publicly available a set of open-source models on a platform at the webpage https://iarc-miarc.gitlab.io/methis/methis.website/about/ from now to 2025. Before 2025, the code of EpiMetHeos can be made available upon request to the authors. Requests will be assessed on a case-by-case basis in consultation with lead investigators and co-investigators. The computer code regarding the cervical cancer progression model Atlas, also a part of METHIS, is already publicly available on the platform webpage.

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Decision letter

  1. Talía Malagón
    Reviewing Editor; McGill University, Canada
  2. Diane M Harper
    Senior Editor; University of Michigan, United States
  3. Romina Tejada
    Reviewer; McGill University, Canada

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Building resilient cervical cancer prevention through gender-neutral HPV vaccination" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Diane Harper as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Romina Tejada (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

– Provide more context on HPV vaccination programs and screening in India.

– Reviewers questioned the calculation methods used for resilience ratios. Consider providing more rationale for these ratios and/or using other metrics for measuring resilience.

– Address requests for clarifications made by reviewers.

Reviewer #1 (Recommendations for the authors):

I found the manuscript well-written and easy to read. Nevertheless, I have some questions:

– Why did the authors use an agent-based model? Stochastic models (individual-based models) are mostly used for studying outbreaks and not in cases of disseminated diseases such as HPV. Do the authors consider that using a deterministic model could have provided different results?

– The authors mention in the Appendix that they considered an "all-or-nothing working mechanism for vaccine protection" for those HPV types included in the vaccine as well as for cross-protection. This should be added to the main article as it would be important for decision-makers who might not read the Appendix.

– Why did the authors consider that men did not develop natural immunity?

– Results, Pag 11 lines 152-153 "Among the four highlighted scenarios, the GO strategy with 60% coverage was also the least resilient". It would be better to start with the most resilient scenario.

– Presenting the prevented cases in the disrupted cohort (resilience) as a percentage of the cases prevented in the undisrupted scenarios would be more informative for the reader when comparing the four scenarios.

– The resilience ratios were constructed with the absolute number of cases prevented; they should have been constructed with the relative number of cases prevented because the values changed greatly between scenarios for the undisrupted cohorts. For example, the resilience ratio for GO 60% to GN 60% in the base case analysis for all states reported by the authors was 2.8 when using absolute numbers but it would be 2.4 when using relative numbers.

– Figure B2: The description says "with (A) high and (B) low cervical cancer incidence" but the figures are (A) all case (B) high incidence and (C) low incidence. Please correct.

Reviewer #2 (Recommendations for the authors):

1. Please describe the current status of HPV vaccination in India in the introduction, including when foreign HPV vaccines were available on the market in India, whether a routine HPV vaccination program has been / will be implemented in India (and when if yes), the current (estimated) HPV vaccine uptake, although I may miss the corresponding text.

2. Used an agent-based model (EpiMetHeos) to simulate the dynamic transmission of HPV infection of high-risk HPV types and a cervical cancer progression model (Altas) to estimate the impact on cervical cancer burden. The authors are suggested to include the advantage (and limitations) or the difference of using the agent-based model when compared with other models.

3. Model validity.

a. Please show the uncertainties (e.g., confidence interval) of the model calibration/fitting.

b. Please explicitly state the table(s)/figure(s) that contain(s) the values and assumptions used in the model/study in the manuscript, especially when referring to table(s)/figure(s) in the supplementary materials.

c. Percentile intervals (10% to 90%) were presented based on the simulations using the 100 parameter sets best fitting the sexual behaviour and HPV prevalence data obtained through calibration. Please state the criteria that were used to determine the "best fitting" (e.g., minimal difference, log-likelihood, or other metrics)

4. Vaccine characteristics

a. The study considered a locally developed quadrivalent HPV vaccine by referring the vaccine efficacy to Gardasil quadrivalent. The authors are advised to present the numerical values of the efficacy of the locally developed HPV vaccine explicitly in addition to claiming the similarity (although a reference was cited).

b. A sensitivity analysis on the below items may be useful when addressing the uncertainties of the assumption.

(i). shorter vaccine-induced protection, in addition to assuming no waning of protection over time.

(ii). vaccine efficacy, perhaps at the lower bound of the reported estimated efficacy, in addition to the point estimates.

c. Please discuss the latest estimate of vaccine uptake in India among girls and preferably among boys. That would be the comparator when evaluating the impact of a vaccination program. Please also provide the rationale/information for the four "highlighted" scenarios on vaccination coverage (lines 113-115).

d. Please state the year that the vaccination program would start in the model simulation.

5. Screening uptake in India

"Given the extremely low coverage of the existing screening programme in India, we have not considered the impact of screening in our study (Bruni et al., 2022)" (lines 142-143)

Please state the screening coverage explicitly.

6. Results

For each scenario, the authors are advised to include the relative changes in the number of cervical cancer cases eliminated with and without disruption.

7. There are likely some potential typos and grammar issues.

Reviewer #3 (Recommendations for the authors):

Overall

1. This study will benefit from a better selection of realistic vaccine trade-offs in moving from the base strategy (i.e GO 60%) to alternate strategies. This 'trade-off' is briefly mentioned in Lines 166-168: "Considering the required number of vaccine doses, we found a trade-off between dose-efficiency and resilience. In general, GO vaccination favoured dose efficiency and GN vaccination favoured resilience (Figure 1)." However, the trade-off is not easily interpreted when the alternate strategies use a different quantity of vaccines (as seen in Figure 1). If the alternate strategies used a similar quantity of vaccines, then the results of the study would be better understood by decision-makers/readers regarding the potential implications of various vaccination strategies with limited vaccine supplies.

2. The study will benefit from a lengthier discussion on the mechanisms through which gender-neutral vaccination can increase the HPV vaccine programme 'resilience' in preventing cervical cancer:

a) The mechanisms through which a gain in 'resilience' is achieved in moving to a GN strategy have been indirectly described in previous publications. During a period of vaccine disruption, my interpretation of the reason cervical cancer incidence increases is due to the reduction in vaccine coverage amongst all girls over time – and it has been shown in previous modelling studies that a gender-neutral vaccination programme is more beneficial when coverage amongst girls is lower. Therefore are the findings of this study a manifestation of this mechanism? – this would be worth discussing.

b) The authors describe the primary factor influencing resilience in Lines 241-243: "The main determinant underlying the gain in resilience by switching from GO to GN vaccination is the age difference between sexual partners, with men being on average older than women within sexual partnerships almost everywhere worldwide (Wellings et al., 2006)." However, it is not clear from the results of this paper why this is described as the 'main determinant'. Possibly providing evidence through sensitivity analyses to show how changes in age difference between sexual partners change the resilience of GN strategies may help prove the point to readers that this is the main determinant.

Methodology

3. The outcome of 'resilience' is calculated as the [in lines 128-131]: "mean number of cases still prevented across the birth cohorts with disruption, as a result of previous HPV vaccination,…, which was compared to the mean number of cases prevented across the birth cohorts before the disruption." I had a few concerns about this outcome measure:

a) My initial impression of the use of the term 'resilience' would be the ability of the vaccine programme to retain its population protective effect in times of disruption. Therefore the use of the 'ratio of the mean number of cases prevented' did not reflect resilience to me. Instead, I felt the description of the definition of 'resilience' in your Elfstrom et al. 2016 paper [referenced in lines 66-67] is clearer to readers as a measure of resilience: "the difference in percentage attributable to vaccination estimated with and without temporary coverage reduction". Would the team be able to reconcile whether the definitions/formulas used in this study and Elfstrom et al.'s 2016 paper are identical?

b) The calculation of the 'resilience ratio' in Appendix B1 is not immediately clear to readers, and an example/formula would help readers understand how it is calculated better.

c) The comparison of resilience ratios (seen in Appendix B1) would be better interpreted if the shift from GO 60% to alternative scenarios all involved the same quantity of vaccine supply. For e.g. resilience ratio in a (i) GO 60% to GO 90% scenario being compared with a (ii) GO 60% to Girl-60%/Male-30% scenario [assuming males & females are equal in each cohort, this would mean the same quantity of vaccines]. Such a comparison would be easier to interpret and would help decision-makers understand the benefits & drawbacks of either course of action.

Results

4. In lines 211-212 it is mentioned that: "coverage between 50% and 70% in girls might also be sufficient for elimination when combined with moderate coverage (30%) in boys." A table/graph to illustrate this scenario and its associated results would help readers understand the conclusion made here. If the data is presented in the appendix, then a reference to the location of the information in the appendix would be helpful.

Discussion

5. In lines 141-142 it is mentioned that: "we have not considered the impact of screening in our study". It might be useful to discuss the implications of this assumption in the discussion, bringing in the foreseeable changes in screening uptake & screening technology in India and other low-income countries.

6. The discussion would benefit from challenging the assumptions made in the model. Such as the assumptions that:

a) The total population in the model is kept constant over time

b) Only modelling heterosexual partnerships – this would not account for the fact that vaccinating 10-year-old boys who become predominantly men-who-have-sex-with-men (MSM) when sexually active, would likely have minimal to no impact on reducing cervical cancer incidence.

c) Sexual behaviours remain unchanged throughout the person's lifespan.

d) Susceptible-Infected-Susceptible (SIS) model used in men, which does have a period of immunity incorporated.

e) Having limited data on sexual behaviours and cervical cancer incidence in other Indian states.

https://doi.org/10.7554/eLife.85735.sa1

Author response

Essential revisions:

– Provide more context on HPV vaccination programs and screening in India.

India has not yet introduced HPV vaccination into the national immunization programme. Two Indian states have introduced state-wide HPV immunization programme under a girls-only strategy with high coverage (Sankaranarayanan et al., Lancet Oncol, 2019). Following the recent marketing authorisation granted to an indigenous vaccine (The Lancet, Editorial “HPV vaccination in south Asia: new progress, old challenges”, 2022), the prospect of introducing HPV vaccination into the national immunisation programme (NIP) has significantly improved. However, it is still unclear when the national introduction will take place and which HPV vaccine will be used. As for cervical cancer screening, the coverage of the existing screening programme in India is low, with an ever-in-life coverage of lower than 3% for women aged 30-49-years (Bruni et al., 2022).

We acknowledge that the manuscript would benefit from a more detailed description of the current status of HPV vaccination and cervical cancer screening in India. We have revised the manuscript in various places, as follow:

  • Page 4 lines 74-79: “HPV vaccination (in girls) has been introduced in two

Indian states with high vaccination coverage (Sankaranarayanan et al., 2019). Following the recent marketing authorisation granted to an indigenous vaccine (The Lancet, 2022), the prospect of introducing HPV vaccination into the national immunisation programme (NIP) has also significantly improved.”

  • Page 21 lines 389-392: “Recently, marketing authorisation has been awarded to a locally produced quadrivalent HPV vaccine in India … (The Lancet, 2022). … there is still uncertainty in which HPV vaccine will be used in the Indian immunisation programme, …”

  • Page 4 lines 72-74: “Cervical cancer screening is still limitedly accessible in

India, with less than 3% ever-in-life coverage in women aged 30-49 years

(Sankaranarayanan et al. 2019, Bruni et al., 2022)”.

– Reviewers questioned the calculation methods used for resilience ratios. Consider providing more rationale for these ratios and/or using other metrics for measuring resilience.

In general, we think that resilience is a complex multifaceted phenomenon. Using different metrics may help revealing different aspects of the phenomenon.

In our study, the aspect we wanted to focus on was the absolute burden, i.e., how many cervical cancer cases we can prevent more during disruption when switching from one strategy/scenario to another. In our opinion, the absolute metric (the number of cervical cancer cases still prevented) is a straightforward metric for studying this aspect. Furthermore, considering this absolute metric would allow the evaluation of strategies in terms of whether it provides a minimum level of protection during disruption period.

To clarify why we have chosen the absolute metric, we have revised the manuscript as follows (page 22 lines 420-424): “As a measure of resilience, we used the mean number of cases still prevented, as a result of previous HPV vaccination, across the birth cohorts with disruption. Considering such a metric of resilience allows us to evaluate whether any given strategy provides a sufficient level of protection retained during disruption as compared to a possible prefixed target level of protection.”

Nevertheless, we agree with the reviewers that a relative metric of resilience, e.g., the proportion of cervical cancer cases still prevented as compared to the number of cases prevented before/without the disruption, could also be interesting. It provides an idea of how efficient the resource invested before disruption can be retained at disruption. However, in our opinion, it might be less straightforward to compare the “relative resilience” between strategy/scenario; an increase in relative resilience does not necessarily mean more cancer cases prevented during disruption.

Furthermore, we are unsure whether we should consider efficiency when dealing crises.

At the end, to avoid using too many different metrics, we chose to consider only the absolute resilience and the ratio thereof. Furthermore, the relative resilience can be easily read off from Figure 1 and calculated from Table 1.

Reviewer #1 (Recommendations for the authors):

I found the manuscript well-written and easy to read. Nevertheless, I have some questions:

– Why did the authors use an agent-based model? Stochastic models (individual-based models) are mostly used for studying outbreaks and not in cases of disseminated diseases such as HPV. Do the authors consider that using a deterministic model could have provided different results?

A similar question has also been posed by Reviewer #2. To our knowledge, the use of agent-based models is not limited to studying outbreaks. In fact, many calibrated models for studying HPV are also agent-based, e.g., HPVADVISE (http://www.marc-brisson.net/HPVadvise-LMIC.pdf) and HavardHPV (Burger et al., 2018, Vaccine). In general, agent-based models have the advantage of allowing long-term and overlapping partnerships in a more straightforward manner than in deterministic models. This aspect of modelling long-term and overlapping partnerships is particularly important for the Indian setting, where a considerable part of HPV transmission to married women is believed to occur through their marital partners. To clarify this, we have revised the manuscript as follows (page 20 lines 367368): “EpiMetHeos was used to simulate a dynamic sexual contact network, which allows long-term and overlapping partnerships, through which HPV infections were transmitted.”

– The authors mention in the Appendix that they considered an "all-or-nothing working mechanism for vaccine protection" for those HPV types included in the vaccine as well as for cross-protection. This should be added to the main article as it would be important for decision-makers who might not read the Appendix.

We have added the mention of the “all-or-nothing working mechanism for vaccine protection” in the main text, as follows (page 21 lines 402-404): “Vaccine protection was modelled through an all-or-nothing working mechanism, i.e., if the efficacy is X%, then X% of the vaccinated individuals are fully protected and 100-X% fully not protected.”

– Why did the authors consider that men did not develop natural immunity?

In men, it has generally been observed that the HPV prevalence and the rate of acquiring new HPV infection remain constant across age, and that the seroconversion rate is low after natural infection (Giuliano et al., Cancer Epidemiol Biomarkers Prev, 2008; Giuliano et al., Papillomavirus Res, 2015; Schiffman et al., Nat Rev Dis Primers, 2016). Hence, we assume no development of natural immunity in men.

We have revised the following sentence in the first paragraph of Appendix 1.1.3. to clarify this point: “Transmission of all HR HPV types are assumed to follow the “Susceptible-Infected-Removed/Immune-Susceptible” dynamics in women (Appendix 1-figure 1). In men, it has generally been observed that the HPV prevalence and the rate of acquiring new HPV infection remain constant across age, and that the seroconversion rate is low after natural infection (Giuliano et al., Cancer Epidemiol Biomarkers Prev, 2008; Giuliano et al., Papillomavirus Res, 2015; Schiffman et al., Nat Rev Dis Primers, 2016). Hence, we assumed the “Susceptible-Infected-Susceptible” dynamics in men.”

– Results, Pag 11 lines 152-153 "Among the four highlighted scenarios, the GO strategy with 60% coverage was also the least resilient". It would be better to start with the most resilient scenario.

We understand the reasoning of the reviewer’s suggestion. However, our main aim here is to illustrate and emphasize the gain in resilience by switching to GN strategy, therefore we prefer starting with the GO strategy.

– Presenting the prevented cases in the disrupted cohort (resilience) as a percentage of the cases prevented in the undisrupted scenarios would be more informative for the reader when comparing the four scenarios.

A similar point has been also raised by the Editor, please see our reply for the second point under Essential revision.

– The resilience ratios were constructed with the absolute number of cases prevented; they should have been constructed with the relative number of cases prevented because the values changed greatly between scenarios for the undisrupted cohorts. For example, the resilience ratio for GO 60% to GN 60% in the base case analysis for all states reported by the authors was 2.8 when using absolute numbers but it would be 2.4 when using relative numbers.

A similar point has been also raised by the Editor, please see our reply for the second point under Essential revision.

– Figure B2: The description says "with (A) high and (B) low cervical cancer incidence" but the figures are (A) all case (B) high incidence and (C) low incidence. Please correct.

We thank the reviewer for noticing the wrong labels. We have revised the text accordingly.

Reviewer #2 (Recommendations for the authors):

1. Please describe the current status of HPV vaccination in India in the introduction, including when foreign HPV vaccines were available on the market in India, whether a routine HPV vaccination program has been / will be implemented in India (and when if yes), the current (estimated) HPV vaccine uptake, although I may miss the corresponding text.

A similar point has been also raised by the Editor, please see our reply for the first point under Essential revision.

2. Used an agent-based model (EpiMetHeos) to simulate the dynamic transmission of HPV infection of high-risk HPV types and a cervical cancer progression model (Altas) to estimate the impact on cervical cancer burden. The authors are suggested to include the advantage (and limitations) or the difference of using the agent-based model when compared with other models.

A similar question has also been posed by Reviewer #1. To our knowledge, the use of agent-based models is not limited to studying outbreaks. In fact, many calibrated models for studying HPV are also agent-based, e.g., HPVADVISE (http://www.marc-brisson.net/HPVadvise-LMIC.pdf) and HavardHPV (Burger et al., 2018, Vaccine). In general, agent-based models have the advantage of allowing long-term and overlapping partnerships in a more straightforward manner than in deterministic models. This aspect of modelling long-term and overlapping partnerships is particularly important for the Indian setting, where a considerable part of HPV transmission to married women is believed to occur through their marital partners. To clarify this, we have revised the manuscript as follows (page 20 lines 367368): “EpiMetHeos was used to simulate a dynamic sexual contact network, which allows long-term and overlapping partnerships, through which HPV infections were transmitted.”

3. Model validity.

a. Please show the uncertainties (e.g., confidence interval) of the model calibration/fitting.

The uncertainties of the model calibration/fitting are shown in Appendix 1-figure 2 and Appendix 1-figure 3.

b. Please explicitly state the table(s)/figure(s) that contain(s) the values and assumptions used in the model/study in the manuscript, especially when referring to table(s)/figure(s) in the supplementary materials.

As stated in the Materials and methods section (page 20 lines 383-385), details on model calibration can be found in Appendices 1.1-3. To clarify which information can be found in which appendix section, we added the following specification: “See Appendix 1.1 for details on the structure of the HPV transmission model, 1.2 on the model calibration process and model fit, and 1.3 on the computation of the model outcomes.”

c. Percentile intervals (10% to 90%) were presented based on the simulations using the 100 parameter sets best fitting the sexual behaviour and HPV prevalence data obtained through calibration. Please state the criteria that were used to determine the "best fitting" (e.g., minimal difference, log-likelihood, or other metrics)

Calibration for determining the “best-fitting” parameters was based on log-likelihood as stated in the last paragraph of Appendix 1.2.3.

4. Vaccine characteristics

a. The study considered a locally developed quadrivalent HPV vaccine by referring the vaccine efficacy to Gardasil quadrivalent. The authors are advised to present the numerical values of the efficacy of the locally developed HPV vaccine explicitly in addition to claiming the similarity (although a reference was cited).

As stated in the Materials and methods section (page 21 line 389-391), the marketing authorisation was awarded to the locally produced vaccine based on successful immune-bridging between the new vaccine and the existing quadrivalent vaccine (Gardasil, MSD). The manuscript reporting these results is currently under review. Regarding vaccine efficacy, there are no data available yet.

b. A sensitivity analysis on the below items may be useful when addressing the uncertainties of the assumption.

(i) Shorter vaccine-induced protection, in addition to assuming no waning of protection over time.

(ii) Vaccine efficacy, perhaps at the lower bound of the reported estimated efficacy, in addition to the point estimates.

In this study, the aim is to explore the gain in resilience when switching from girls-only to gender-neutral strategy. Therefore, we focussed on sensitivity analyses regarding the variation in how disruption may occur. Furthermore, we have explored in depth the uncertainty in the duration of vaccine protection and lower efficacy in a previous publication, of which this study is the next (Man et al., Lancet Oncol, 2022). To clarify this, we have added the following to the revised manuscript (page 20 lines 371-373): “The models were previously calibrated to sexual behaviour, HPV prevalence, cervical cancer incidence in India to assess the impact of single-dose vaccination in India, while accounting for the uncertainty in the duration of vaccine protection (Man, Georges, de Carvalho, et al., 2022).”

c. Please discuss the latest estimate of vaccine uptake in India among girls and preferably among boys. That would be the comparator when evaluating the impact of a vaccination program. Please also provide the rationale/information for the four "highlighted" scenarios on vaccination coverage (lines 113-115).

As explained in the reply to the reviewer’s comment 1., India has not yet introduced a national immunization programme.

d. Please state the year that the vaccination program would start in the model simulation.

As it is not yet clear when India would start a national vaccination programme, we think it is more suitable to report the results in “years since the start of vaccination” instead of arbitrarily choosing a year of introduction.

5. Screening uptake in India

"Given the extremely low coverage of the existing screening programme in India, we have not considered the impact of screening in our study (Bruni et al., 2022)" (lines 142-143)

Please state the screening coverage explicitly.

We have revised the text to state the screening coverage explicitly, as follows (Page 4 lines 72-74): “Cervical cancer screening is still limitedly accessible in India, with less than 3% ever-in-life coverage in women aged 30-49 years (Sankaranarayanan et al. 2019, Bruni et al., 2022)”

6. Results

For each scenario, the authors are advised to include the relative changes in the number of cervical cancer cases eliminated with and without disruption.

A similar point has been also raised by the Editor, please see above our reply for the second point under Essential Revision.

7. There are likely some potential typos and grammar issues.

We have done our best to correct for typos and grammar issues throughout the manuscript.

Reviewer #3 (Recommendations for the authors):

Overall

1. This study will benefit from a better selection of realistic vaccine trade-offs in moving from the base strategy (i.e GO 60%) to alternate strategies. This 'trade-off' is briefly mentioned in Lines 166-168: "Considering the required number of vaccine doses, we found a trade-off between dose-efficiency and resilience. In general, GO vaccination favoured dose efficiency and GN vaccination favoured resilience (Figure 1)." However, the trade-off is not easily interpreted when the alternate strategies use a different quantity of vaccines (as seen in Figure 1). If the alternate strategies used a similar quantity of vaccines, then the results of the study would be better understood by decision-makers/readers regarding the potential implications of various vaccination strategies with limited vaccine supplies.

We agree with the reviewer that the efficiency of HPV vaccine dose allocation is a key operational issue and understand that the suggested strategies allow comparison of scenarios in terms of how to best allocate resources. However, we still decided to focus on comparing strategies of girls-only and gender-neutral that do not necessarily correspond to the same amount of vaccine doses. The reason was that while the policymakers have control to decide on the vaccination strategy (either girls-only or gender-neutral), they usually do not have control over the coverage eventually attained in the population. For example, the scenario of girls-only vaccination with 90% coverage requires the same number of vaccines as the scenario of gender-neutral vaccination with 60% in girls and 30% in boys. However, it would not be realistic to stop vaccinating at 30% coverage in boys if more boys would like to be vaccinated. While data on uptake in boys is still scarce, there are indication that uptake in boys could be expected to be similar to that in girls (Wähner et al., 2023, Infection). This was the rationale behind the chosen scenarios. In our opinion, it is enough to demonstrate the trade-off between dose-efficiency and resilience with the part of the Results section mentioned by the reviewer.

2. The study will benefit from a lengthier discussion on the mechanisms through which gender-neutral vaccination can increase the HPV vaccine programme 'resilience' in preventing cervical cancer:

a) The mechanisms through which a gain in 'resilience' is achieved in moving to a GN strategy have been indirectly described in previous publications. During a period of vaccine disruption, my interpretation of the reason cervical cancer incidence increases is due to the reduction in vaccine coverage amongst all girls over time – and it has been shown in previous modelling studies that a gender-neutral vaccination programme is more beneficial when coverage amongst girls is lower. Therefore are the findings of this study a manifestation of this mechanism? – this would be worth discussing.

Resilience in cervical cancer prevention is the herd effect in women unvaccinated due to disruption because the reduced population force of infection from men that have contact with these unvaccinated women. We agree with the reviewer that vaccinating more girls could also decrease the force of infection in men, and so increasing resilience. However, vaccinating boys is a more direct way to reduce the force of infection in men than vaccinating girls. This is likely the mechanism underlying the higher resilience achieved by gender-neutral strategy than girls-only strategy.

To better explain this mechanism, we have added the following sentences to the Discussion section (page 15 lines 275-278): “As showed by our results, increasing coverage in girls also helps to increase resilience. However, vaccinating men is a more direct way to reduce the force of infection in men than vaccinating girls, which is likely the mechanism underlying the higher resilience achieved by gender-neutral strategy than girls-only strategy.”

b) The authors describe the primary factor influencing resilience in Lines 241-243: "The main determinant underlying the gain in resilience by switching from GO to GN vaccination is the age difference between sexual partners, with men being on average older than women within sexual partnerships almost everywhere worldwide (Wellings et al., 2006)." However, it is not clear from the results of this paper why this is described as the 'main determinant'. Possibly providing evidence through sensitivity analyses to show how changes in age difference between sexual partners change the resilience of GN strategies may help prove the point to readers that this is the main determinant.

We acknowledge that the wording “main determinant” was not appropriate. We have rephrased it as follows: “An important determinant underlying the gain in resilience by switching from GO to GN vaccination is likely the age difference between sexual partners.”

Methodology

3. The outcome of 'resilience' is calculated as the [in lines 128-131]: "mean number of cases still prevented across the birth cohorts with disruption, as a result of previous HPV vaccination,.., which was compared to the mean number of cases prevented across the birth cohorts before the disruption." I had a few concerns about this outcome measure:

a) My initial impression of the use of the term 'resilience' would be the ability of the vaccine programme to retain its population protective effect in times of disruption. Therefore the use of the 'ratio of the mean number of cases prevented' did not reflect resilience to me. Instead, I felt the description of the definition of 'resilience' in your Elfstrom et al. 2016 paper [referenced in lines 66-67] is clearer to readers as a measure of resilience: "the difference in percentage attributable to vaccination estimated with and without temporary coverage reduction". Would the team be able to reconcile whether the definitions/formulas used in this study and Elfstrom et al.'s 2016 paper are identical?

We think that the definition in this study is in line with the initial impression the reviewer describes as the definition in Elfström et al. What the reviewer refers to as “protective effect retained” is what we refer to as “cervical cancer cases still prevented”, which is what we define as “resilience” in this study.

The ratio of the number of cervical cases still prevented, or resilience ratio, is then used to evaluate the increase in resilience between scenarios. We have now clarified the definition of the resilience both in the Materials and methods section and in the caption of Table 1 and Appendix 2-Table 1. – Page 22 lines 424-425: “Estimates of resilience were compared between the highlighted scenarios A-D based on their ratio.”

Page 36 lines 637-639: “[Resilience ratio of scenario X to scenario Y] is defined is [resilience of scenario Y] / [resilience of scenario X]. For example, in the base case, [resilience ratio of GO 60% to GO 90%] = [resilience of GO 90%] / [resilience of GO 60%] = 209 / 107 = 2.0.”

Finally, what we define as resilience in Elfström et al. corresponds to the “drop in cervical cancer prevented” in Figure 1. In this study, we defined resilience to focus on the protective effect still retained during disruption.

b) The calculation of the 'resilience ratio' in Appendix B1 is not immediately clear to readers, and an example/formula would help readers understand how it is calculated better.

The following definition of the resilience ratio is now added to the caption of Table 1 and Appendix 2-Table 1: “[Resilience ratio of scenario X to scenario Y] is defined is [resilience of scenario Y] / [resilience of scenario X].

For example, in the base case, [resilience ratio of GO 60% to GO 90%] = [resilience of GO 90%] / [resilience of GO 60%] = 209 / 107 = 2.0.”.

c) The comparison of resilience ratios (seen in Appendix B1) would be better interpreted if the shift from GO 60% to alternative scenarios all involved the same quantity of vaccine supply. For e.g. resilience ratio in a (i) GO 60% to GO 90% scenario being compared with a (ii) GO 60% to Girl-60%/Male-30% scenario [assuming males & females are equal in each cohort, this would mean the same quantity of vaccines]. Such a comparison would be easier to interpret and would help decision-makers understand the benefits & drawbacks of either course of action.

See above for our reply to Reviewer 3, comment 1.

Results

4. In lines 211-212 it is mentioned that: "coverage between 50% and 70% in girls might also be sufficient for elimination when combined with moderate coverage (30%) in boys." A table/graph to illustrate this scenario and its associated results would help readers understand the conclusion made here. If the data is presented in the appendix, then a reference to the location of the information in the appendix would be helpful.

Figure 4 is the figure illustrating this scenario and the associated results. This figure has been referred to in the line right before the lines the reviewer has mentioned. To help the reader understand we are still considering the results in Figure 4, we have added the reference to this figure at the end of these lines, as follows: “For instance, coverage between 50% and 70% in girls might also be sufficient for elimination when combined with moderate coverage (30%) in boys (Figure 4).”

Discussion

5. In lines 141-142 it is mentioned that: "we have not considered the impact of screening in our study". It might be useful to discuss the implications of this assumption in the discussion, bringing in the foreseeable changes in screening uptake & screening technology in India and other low-income countries.

To our opinion, it is still difficult to forecast how much coverage of cervical cancer screening would increase in India. Therefore, we assumed no change in screening uptake in our analysis. To account for this point, we added the following in the Discussion (page 17 lines 319-320): “We also did not consider changes in cervical cancer screening, as it is difficult to predict how much coverage of cervical cancer screening will increase in India in the coming years.”

6. The discussion would benefit from challenging the assumptions made in the model. Such as the assumptions that:

a) The total population in the model is kept constant over time

Keeping the total population constant is a common practice in this type of HPV transmission model, which is essential to ensure stable model impact estimates, such as reduction in HPV prevalence and cervical cancer risk.

b) Only modelling heterosexual partnerships – this would not account for the fact that vaccinating 10-year-old boys who become predominantly men-who-have-sex-with-men (MSM) when sexually active, would likely have minimal to no impact on reducing cervical cancer incidence.

While it is true that our model does not consider homosexual partnerships among MSM, we do not think the impact on cervical cancer incidence would be much affected by this assumption. This is because of the likely small proportion of MSM in India (estimated as 3% in the 2006 NACO report of sexual behaviour), and vaccination in 10-year-old boys is expected to result in similar in boys who later become heterosexual or MSM. However, we would be fitting to discuss the direct benefit in MSM for HPV related cancer in men. We have now added this point to the Discussion section, as follows (page 18 lines 349-352): “In some context, important arguments for choosing GN strategy could be … the direct health benefits in men who have sex with men.”

c) Sexual behaviours remain unchanged throughout the person's lifespan.

We did allow sexual behaviours to change throughout the person’s lifespan. This was explained in the second paragraph of Appendix 1.1.2. (“Formation probabilities of stable as well as one-off partnerships depend on the individual’s sex, age group and the assigned risk group of sexual activity.”) and showed in Appendix 1-figure 2.

d) Susceptible-Infected-Susceptible (SIS) model used in men, which does have a period of immunity incorporated.

In men, it has generally been observed that the HPV prevalence and the rate of acquiring new HPV infection remain constant across age, and that the seroconversion rate is low after natural infection (Giuliano et al., Cancer Epidemiol Biomarkers Prev, 2008; Giuliano et al., Papillomavirus Res, 2015; Schiffman et al., Nat Rev Dis Primers, 2016). Hence, we assume no development of natural immunity in men.

We have revised the following sentence in the first paragraph of Appendix 1.1.3. to clarify this point: “Transmission of all HR HPV types are assumed to follow the “Susceptible-Infected-Removed/Immune-Susceptible” dynamics in women (Appendix 1-figure 1). In men, it has generally been observed that the HPV prevalence and the rate of acquiring new HPV infection remain constant across age, and that the seroconversion rate is low after natural infection (Giuliano et al., Cancer Epidemiol Biomarkers Prev, 2008; Giuliano et al., Papillomavirus Res, 2015; Schiffman et al., Nat Rev Dis Primers, 2016). Hence, we assumed the “Susceptible-Infected-Susceptible” dynamics in men.”

e) Having limited data on sexual behaviours and cervical cancer incidence in other Indian states.

The availability and limitations of data on sexual behaviours and cervical cancer incidence across Indian states were discussed in detail in our previous publication in which we presented the calibrated model for the first time (Man et al., Lancet Oncol, 2022). To account for this, we have added the following sentence to the Discussion (page 16 lines 294-296): “The availability and limitations of data on sexual behaviours and cervical cancer incidence across Indian states were discussed in detail in our previous publication in which we presented the calibrated model for the first time (Man et al., Lancet Oncol, 2022).”

https://doi.org/10.7554/eLife.85735.sa2

Article and author information

Author details

  1. Irene Man

    International Agency for Research on Cancer (IARC/WHO), Early Detection, Prevention and Infections Branch, Lyon, France
    Contribution
    Conceptualization, Software, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3177-6904
  2. Damien Georges

    International Agency for Research on Cancer (IARC/WHO), Early Detection, Prevention and Infections Branch, Lyon, France
    Contribution
    Software, Validation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2425-7591
  3. Rengaswamy Sankaranarayanan

    Karkinos Healthcare, Ernakulam, India
    Contribution
    Supervision, Funding acquisition, Validation, Writing – review and editing
    Competing interests
    Rengaswamy Sankaranarayanan is affiliated with Karkinos Healthcare. The author has no financial interests to declare
  4. Partha Basu

    International Agency for Research on Cancer (IARC/WHO), Early Detection, Prevention and Infections Branch, Lyon, France
    Contribution
    Supervision, Funding acquisition, Validation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0124-4050
  5. Iacopo Baussano

    International Agency for Research on Cancer (IARC/WHO), Early Detection, Prevention and Infections Branch, Lyon, France
    Contribution
    Conceptualization, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Writing – review and editing
    For correspondence
    baussanoi@iarc.who.int
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7322-1862

Funding

Bill and Melinda Gates Foundation (INV-039876)

  • Iacopo Baussano
  • Damien Georges
  • Irene Man

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This study was funded by the Bill & Melinda Gates Foundation (grant number: INV-039876). For the authors identified as personnel of the International Agency for Research on Cancer (IARC) or WHO, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the IARC or WHO. The designations used and the presentation of the material in this article do not imply the expression of any opinion whatsoever on the part of WHO and the IARC about the legal status of any country, territory, city, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries.

Senior Editor

  1. Diane M Harper, University of Michigan, United States

Reviewing Editor

  1. Talía Malagón, McGill University, Canada

Reviewer

  1. Romina Tejada, McGill University, Canada

Version history

  1. Received: December 21, 2022
  2. Preprint posted: January 18, 2023 (view preprint)
  3. Accepted: June 8, 2023
  4. Version of Record published: July 24, 2023 (version 1)

Copyright

© 2023, Man et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Irene Man
  2. Damien Georges
  3. Rengaswamy Sankaranarayanan
  4. Partha Basu
  5. Iacopo Baussano
(2023)
Building resilient cervical cancer prevention through gender-neutral HPV vaccination
eLife 12:e85735.
https://doi.org/10.7554/eLife.85735

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