A modelled analysis of the impact of COVID-19-related disruptions to HPV vaccination

  1. Louiza S Velentzis
  2. Megan A Smith
  3. James Killen
  4. Julia ML Brotherton
  5. Rebecca Guy
  6. Karen Canfell

  • Curated by eLife

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    eLife assessment

    The study presents important findings for public health authorities and policymakers to enable them to make evidence-based decisions when deciding on how to manage the effect of HPV vaccination disruptions. This study is particularly relevant in light of the efforts of the WHO to achieve global elimination of cervical cancers. The findings are convincing and the model used is appropriate.

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Abstract

COVID-19 disrupted school attendance in many countries, delaying routine adolescent vaccination against human papillomavirus (HPV) in some settings. We used Policy1-Cervix , a dynamic model simulating HPV transmission, natural history, vaccination, cervical screening, and diagnosis of HPV-related cancers, to estimate the impact on HPV-related cancers from disruptions to HPV vaccination in a high-income setting. A baseline scenario of no disruption to HPV vaccination was modelled, which assumed uptake of the nonavalent vaccine at the age of 12 by 82.4% of females and 75.5% of males, as is the coverage in Australia. Additional lifetime HPV-related cancer cases were calculated for three disruption scenarios affecting one birth cohort (2008; aged 12 in 2020) compared to the baseline scenario: (1) 1-year delay (no doses missed); (2) 1- to 7-year delay (slow catch-up); (3) no catch-up (herd effects only). A fourth scenario assumed no catch-up HPV vaccination for two birth cohorts, that is all individuals born in 2008 and in 2009 missed vaccination (worst-case scenario). Compared to 1532 HPV-related cancer cases estimated for the baseline no disruption scenario, we found a 1-year delay could result in ≤0.3% more HPV-related cancers ( n = 4) but the increase would be greater if catch-up was slower (5%; n = 70), and especially if there was no catch-up (49%; n = 750). Additional cancers for a single missed cohort were most commonly cervical (23% of the additional cases) and anal cancers (16%) in females and oropharyngeal cancers in males (20%). In the worst-case scenario of two birth cohorts missing vaccination, ≤62% more HPV-related cancers would be diagnosed ( n = 1892). In conclusion, providing catch-up of missed HPV vaccines is conducted, short-term delays in vaccinating adolescents are unlikely to have substantial long-term effects on cancer.

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  1. Version published to 10.7554/elife.85720 on eLife
  2. eLife

    Author Response

    Reviewer #2 (Public Review):

    1. It could benefit from fleshing out concepts instead of using parentheses, particularly in the abstract.

    We agree and have amended the abstract and methods (please refer to responses provided to the editor’s comments 1a-1e)

    1. There is space to expand on the results presented in Table 1, including an explanation of Affected cohorts 2008 vs Affected cohorts 2008-2009. It may also be useful to explain this analysis in the methods section.

    Please refer to response provided to editor on the same question (comment 5).

    1. Given that Australia is a best-case scenario and other countries have not had the same success in HPV vaccination coverage, in the discussion would it be possible to give a comparison of how these three scenarios would look different in a population with school-based vaccination but lower coverage volume, such that readers could understand how much of the success / failures of each of the three catch-up scenarios? It would be particularly helpful for readers who are not familiar with the modelling tool used in this analysis.

    We have added some commentary in the discussion in response to the reviewer’s comment. In future, further similar work in countries with lower base coverage would be informative.

    “Australia is a relatively high HPV vaccination coverage setting. Outcomes may be less favourable in a lower coverage setting, as there would be less protection from herd effects; however, the impact of disruptions might also be smaller in a setting with lower coverage, since a lower coverage program would be less effective. Nevertheless, the finding that if catch-up is performed expeditiously then it mitigates much of the effect from vaccination delays, is likely to hold in other settings. In a previous study (Simms et al, Lancet Public Health. 2020 Apr;5(4):e223-e234) modelling the health impacts of HPV vaccination hesitancy in Japan from 2013 to 2019 and the potential effects of restoring coverage to 70% with catch-up vaccination in 2020 is informative as it demonstrates that multi-age HPV catch-up vaccination, after catastrophic falls in coverage in Japan, would be effective in mitigating the effects. “

  3. eLife

    eLife assessment

    The study presents important findings for public health authorities and policymakers to enable them to make evidence-based decisions when deciding on how to manage the effect of HPV vaccination disruptions. This study is particularly relevant in light of the efforts of the WHO to achieve global elimination of cervical cancers. The findings are convincing and the model used is appropriate.

  4. eLife

    Reviewer #1 (Public Review):

    A modelling study was conducted to estimate how disruption of a school-based HPV vaccination program due to COVID-19 restrictive measures might affect lifetime HPV-related cancers in women and men in Australia. The authors used the Policy 1-Cervix model, which has been validated and widely used for modelling and evaluation of interventions to prevent HPV- related disease. The study shows that a large part of the negative effect of disrupting the vaccination program (in terms of HPV-related cancers) can be overcome by a catch-up campaign, if this is undertaken rapidly. Delays in the catch-up campaign or no catch-up lead, according to the model, to a significant increase in the number of cases, of which a proportion could be prevented by cervical cancer screening, provided that this is carried out for cohorts that receive HPV 9 without alterations.

    1. Strengths:
    Well-designed modelling study, comparing several scenarios: baseline (no interruption of vaccination), catch-up with two modalities, and no catch-up vaccination, with a comparator (no vaccination at all), aiming to predict the number of HPV-related cancers for the various scenarios. Indeed, the study investigates the potential impact of disruptions, broken down into types of cancers, in both men and women. However, as always in modelling studies, the strength of the findings depends on the appropriateness and completeness of the assumptions used.

    2. Weaknesses:
    Although the authors claim to have considered sexual behaviour they fail to show how they exactly did this. It is very likely that restrictive measures have had an impact on sexual behaviour (i.e. transmission of HPV), but the authors did not consider this in the model. Furthermore, the baseline scenario uses a higher 2-dose vaccination uptake than the figures they present for 2020, which might have overestimated the impact of HPV vaccination in that year.

  5. eLife

    Reviewer #2 (Public Review):

    This is a modeled analysis of the impact of disruptions in school-based HPV vaccination due to the COVID-19 pandemic. Different catch-up scenarios were considered, ranging from a rapid catch-up period to no catch-up vaccination, and the impact of these on future HPV-related malignancies was approximated. The approach in this study could shed light on strategies for catch-up vaccination due to disruptions caused by the COVID-19 pandemic.

    Strengths:
    - Using the context of Australia, which has led the world in vaccination, allows us to consider a best-case scenario for the impact of disruptions in a well-running HPV vaccination program with good population coverage.
    - The model accounts for multiple factors, including HPV transmission dynamics, mitigation of disease development by screening

    Suggested clarifications:
    - It could benefit from fleshing out concepts instead of using parentheses, particularly in the abstract.
    - There is space to expand on the results presented in Table 1, including an explanation of Affected cohorts 2008 vs Affected cohorts 2008-2009. It may also be useful to explain this analysis in the methods section.
    - Given that Australia is a best-case scenario and other countries have not had the same success in HPV vaccination coverage, in the discussion would it be possible to give a comparison of how these three scenarios would look different in a population with school-based vaccination but lower coverage volume, such that readers could understand how much of the success / failures of each of the three catch-up scenarios? It would be particularly helpful for readers who are not familiar with the modeling tool used in this analysis.

  6. Version published to 10.1101/2023.03.07.23286911v1 on medRxiv