Policy1-Cervix (USYD) The University of Sydney, Australia

Model Overview

The Policy1-Cervix (USYD) model includes a dynamic simulation of human papillomavirus (HPV) transmission and vaccination linked to a multi-cohort microsimulation model of cervical carcinogenesis, cervical screening, diagnosis, pre-cancer treatment, post-treatment surveillance, and invasive cancer survival.

HPV Transmission

Heterosexual behavior is modeled by stratifying the population by sex, age, and level of sexual activity (classifying into four sexual activity groups) using data from national behavioral surveys of sexual behavior. The model has been extended to include semi-assortative and age-and sex-specific mixing parameters, a revised sexual mixing matrix, the capacity to vary the annual per-partner transmission probability according to HPV type, sex and sexual activity group, and ability to capture the effects of more rapid change in behavior (by single year of age) during adolescence and early adulthood. There is capacity to simulate alternative assumptions for the duration of naturally-conferred type-specific immunity against HPV infection and its waning. A multi-type structure is used; with the effects of nonavalent (9v) vaccines simulated through modelling of the 9v included types individually, and the other non-9v included types grouped. (i.e., type-specific modelling is performed for as HPV16, HPV18, HPV31, HPV33, HPV45, HPV52, HPV58, and grouped other non-nonavalent-vaccine-included high risk types).

Cervical Carcinogenesis

This component takes cohort- and type-specific HPV incidence from the dynamic model as input and involves a microsimulation implementation of the natural history of cervical pre-cancer. Progression and regression between states representing productive HPV infection (i.e., HPV infection with and without cervical intraepithelial neoplasia (CIN)1) and then states representing CIN 2 and 3 (modelled as attributable to particular HPV types as above) are modeled, as is progression from CIN3 to invasive cervical cancer, as well as progression of undiagnosed invasive cervical cancer. The model accounts for age-specific hysterectomy rates (for causes other than cervical cancer treatment) in the population. Each HPV type (or type-group) and associated CIN and invasive cancer outcomes is modelled simultaneously and the natural history for each type is assumed to act independently of other HPV types.

Vaccination

The dynamic model simulates vaccination uptake by single year of age, sex, and time.^1-3 Vaccination of older females (and males) in catch-up programs, if applicable, is modeled by single year of age, taking into account the potential for prior HPV type-specific exposure and its impact on type-specific vaccination efficacy at different ages, and modelling the impact of herd immunity for unvaccinated females due to other females vaccinated. Male vaccination uptake is also modeled as required, to account for incremental herd immunity effects in females. The model allows varying vaccine properties (e.g., efficacy, waning). Herd immunity is a phenomenon where vaccination of a significant proportion of the population can reduce the prevalence of the vaccine-targeted HPV types in the population, thereby providing some protection for individuals who are not vaccinated.

Screening, Diagnosis, and Treatment of Pre-cancer

The sensitivity and specificity of cytology are setting-specific and fitted to data on the distribution of cytology test results (e.g., cytology-histology correlations) in a particular setting.^3-6 Fitted test characteristics are constrained to be consistent with findings from international meta-analyses^7,8 that report the absolute and relative sensitivity and specificity of cytology and HPV testing. Empirical data from the appropriate setting is used to inform the ages and rates at which screening initiation is performed, as well as the rates of return to screening or follow-up management, according to last screening test result, the follow-up recommendation, and age group. Following treatment for CIN, post-treatment natural history and surveillance for recurrent disease are based on meta-analysis of the literature on outcomes after pre-cancer treatment.⁹

Cancer Treatment and Survival

Cancer staging and progression is modeled, accounting for both screen-detection and symptomatic detection, and the possibility of downstaging at diagnosis due to screening. Predictions for age-specific cervical cancer incidence and mortality have been calibrated to observed rates in unscreened populations. The model is then additionally validated against country-specific registry data for incidence and mortality, when run with an overlay of screening according to country-specific guidelines. The stage and interval-specific cancer survival parameters are based on analysis of data from cancer registries and validated against observed data.

Calibration and Validation

Directly unobservable parameters, such as the duration of immunity following natural infection and progression rates from productive HPV/CIN1 to CIN2 and CIN3 and invasive cancer, were calibrated using a multi-parameter Bayesian approach to the age-specific HPV prevalence prior to vaccination² and the proportion of women positive for HPV-16, -18 and other high-risk types. Natural history progression and regression rates for cervical carcinogenesis have been calibrated across a large number of targets from national screening programs in Australia, England, Ireland and New Zealand, after incorporating setting-specific screening behavior and clinical management algorithms. Calibration targets from each country include age-and type-specific HPV prevalence, screen-detected high-grade abnormalities, CIN 2/3 treatment ratio, numbers of screening, number of diagnostic examinations, and age-and type-specific rates of cancer incidence and mortality. The model produces accurate predictions of the relative contribution of specific HPV types to HPV infections in the population, screen-detected histologically-confirmed high-grade abnormalities, and invasive cervical cancer rates and case numbers.^3,10 A close correspondence between prior model predictions of HPV vaccine impact and the observed post-vaccination data on HPV prevalence¹¹ in Australia has been demonstrated post-hoc. Ongoing validation will use findings from the Compass trial, a major Australian trial assessing primary HPV testing.

Settings in which the platform has been applied and relevant policy evaluations

The Policy1-Cervix model was one of three models used by the WHO Cervical Cancer Elimination Modeling Consortium (CCEMC) to evaluate the impact of cervical cancer elimination targets in 78 LMIC and was reviewed and endorsed by the WHO Advisory Committee on Immunization and Vaccines related Implementation Research (IVIR-AC) for the use in CCEMC modelling of elimination for WHO.^12,13 Policy1-Cervix was also used to predict the timeline to elimination of cervical cancer for 181 countries,¹⁴ for USA,¹⁵ and Australia.¹⁶ It has been used for a range of government-commissioned studies on behalf of national cervical screening programs in Australia, New Zealand and England. Some specific examples of this include: the effectiveness modelling and economic evaluation of cervical screening for both unvaccinated cohorts and cohorts offered vaccination, as part of the Renewal of the cervical screening program in Australia,¹⁷ as well as similar screening policy evaluations for New-Zealand¹⁸ and England¹⁰. It has also been used to provide estimates of resource utilization and disease impacts during the transition from cytology to HPV screening in Australia and New Zealand,^19-21 to inform clinical management guidelines in Australia²² and evaluate the impact of adopting self-collected HPV testing in Australia.²³ It has previously been extensively validated and used to evaluate changes to the screening interval in Australia and the United Kingdom,^10,24 the role of alternative technologies for screening in Australia, New Zealand and England,^4,10,25,26 the role of HPV testing for the follow-up management of women treated for cervical abnormalities,⁹ the cost-effectiveness of alternative screening strategies and combined screening and vaccination approaches in China,^27,28 the impact of HPV vaccine hesitancy in Japan²⁹ and the cost-effectiveness of primary HPV testing and the potential for elimination in Malaysia.³⁰ The model has also been used to evaluate the impact of HPV vaccination1 and the incremental impact of vaccinating males in Australia,^2,31 the impact of the nonavalent HPV vaccine in four developed countries31 and to assess the cost-effectiveness of the nonavalent HPV vaccine in Australia.³² Predictions from the dynamic HPV transmission and vaccination model have also been validated against observed declines in HPV prevalence in women aged 18-24 years after the introduction of the quadrivalent vaccine.¹¹

More recently, the Policy1-Cervix model was used to predict outcomes for different screening strategies across all 78 LMICs to support the 2021 update of WHO cervical cancer screening guidelines for the general population,³³ as well as for women living with HIV.³⁴

Policy1-Cervix has been used for several analyses in the USA. Some examples include assessing the cost-effectiveness of HPV vaccination for adults aged 30-45 years,³⁵ estimating cancer risk in females eligible to exit screening,³⁶ and examining disparities in cervical cancer elimination timing.³⁷

Policy1-Cervix was used to estimate the impact of COVID-19 related disruptions to screening and diagnosis on cervical cancer incidence,³⁹ as well as disruptions to HPV vaccination.⁴⁰

Acknowledgement

Policy1-Cervix has been developed over a 23+ year period since 2001 by the team led by Prof Karen Canfell, now at the University of Sydney. Many hundreds of individuals and collaborators have been involved in the epidemiological program of work which has informed model development, and we gratefully thank them all. At various times, model development was supported at the following institutions: The University of Oxford UK, The University of New South Wales (UNSW) Australia, The University of Melbourne, Cancer Council NSW, and The University of Sydney. Model development has been funded by a large number of competitive grants and contracts from agencies including (but not limited to) the following: National Health and Medical Research Council Australia, National Cancer Institute USA (multiple grants), the World Health Organisation (multiple grants), the Australian government Department of Health and Aged Care (multiple grants), the Department of Foreign Affairs and Trade Australia, Ministries of Health in Ireland, New Zealand, UK (multiple grants from many of these agencies), and Cancer Research UK.

References

1. Smith MA, Canfell K, Brotherton JM, Lew JB, Barnabas RV. The predicted impact of vaccination on human papillomavirus infections in Australia. Int J Cancer. 2008;123(8):1854–63.
2. Smith MA, Lew JB, Walker RJ, Brotherton JM, Nickson C, Canfell K. The predicted impact of HPV vaccination on male infections and male HPV-related cancers in Australia. Vaccine. 2011;29(48):9112–22.
3. Jie Bin Lew, Kate Simms, Megan Smith, Yoon-Jung Kang, Xiangming Xu, Michael Caruana, et al. National Cervical Screening Program Renewal: Effectiveness modelling and economic evaluation in the Australian setting (Assessment Report). MSAC Application No. 1276. 2013.
4. Medical Services Advisory Committee. Automation Assisted and Liquid Based Cytology for Cervical Cancer Screening. MSAC reference 1122, Assessment report. Canberra: Australian Government Department of Health; 2009.
5. Creighton P, Lew JB, Clements M, Smith M, Howard K, Dyer S, et al. Cervical cancer screening in Australia: modelled evaluation of the impact of changing the recommended interval from two to three years. BMC Public Health. 2010;10:734.
6. Lew JB, Howard K, Gertig D, Smith M, Clements M, Nickson C, et al. Expenditure and resource utilisation for cervical screening in Australia. BMC Health Serv Res. 2012;12(1):446.
7. Arbyn M, Bergeron C, Klinkhamer P, Martin-Hirsch P, Siebers AG, Bulten J. Liquid compared with conventional cervical cytology: a systematic review and meta-analysis. Obstet Gynecol. 2008;111(1):167–77.
8. Arbyn M, Ronco G, Anttila A, Meijer CJ, Poljak M, Ogilvie G, et al. Evidence regarding human papillomavirus testing in secondary prevention of cervical cancer. Vaccine. 2012 Nov 20;30 Suppl 5:F88-99.
9. Rosa Legood, Megan Smith, Jie-Bin Lew, Robert Walker, Sue Moss, Henry Kitchener, et al. Cost effectiveness of human papillomavirus test of cure after treatment for cervical intraepithelial neoplasia in England: economic analysis from NHS Sentinel Sites Study. BMJ. 2012 Nov 1;345.
10. Kitchener HC, Canfell K, Gilham C, Sargent A, Roberts C, Desai M, et al. The clinical effectiveness and cost-effectiveness of primary human papillomavirus cervical screening in England: extended follow-up of the ARTISTIC randomised trial cohort through three screening rounds. Health Technol Assess. 2014;18(23):1–196.
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13. Brisson M, Kim JJ, Canfell K, Drolet M, Gingras G, Burger EA, et al. Impact of HPV vaccination and cervical screening on cervical cancer elimination: a comparative modelling analysis in 78 low-income and lower-middle-income countries. Lancet Lond Engl. 2020 Feb 22;395(10224):575–90.
14. Simms KT, Steinberg J, Caruana M, Smith MA, Lew JB, Soerjomataram I, et al. Impact of scaled up human papillomavirus vaccination and cervical screening and the potential for global elimination of cervical cancer in 181 countries, 2020-99: a modelling study. Lancet Oncol. 2019 Mar;20(3):394–407.
15. Burger EA, Smith MA, Killen J, Sy S, Simms KT, Canfell K, et al. Projected time to elimination of cervical cancer in the USA: a comparative modelling study. Lancet Public Health. 2020 Apr;5(4):e213–22.
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17. Lew JB, Simms KT, Smith MA, Hall M, Kang YJ, Xu XM, et al. Primary HPV testing versus cytology-based cervical screening in women in Australia vaccinated for HPV and unvaccinated: effectiveness and economic assessment for the National Cervical Screening Program. Lancet Public Health. 2017 Feb;2(2):e96–107.
18. Lew JB, Simms K, Smith M, Lewis H, Neal H, Canfell K. Effectiveness Modelling and Economic Evaluation of Primary HPV Screening for Cervical Cancer Prevention in New Zealand. PLOS ONE. 2016 May 17;11(5):e0151619.
19. Smith MA, Gertig D, Hall M, Simms K, Lew JB, Malloy M, et al. Transitioning from cytology-based screening to HPV-based screening at longer intervals: implications for resource use. BMC Health Serv Res. 2016 Apr 26;16(1):147.
20. Hall MT, Smith MA, Lew JB, O’Hallahan J, Fentiman G, Neal H, et al. The combined impact of implementing HPV immunisation and primary HPV screening in New Zealand: Transitional and long-term benefits, costs and resource utilisation implications. Gynecol Oncol. 2019 Mar;152(3):472–9.
21. Hall MT, Simms KT, Lew JB, Smith MA, Saville M, Canfell K. Projected future impact of HPV vaccination and primary HPV screening on cervical cancer rates from 2017–2035: Example from Australia. PLoS ONE. 2018 Feb 14;13(2):e0185332.
22. Simms KT, Hall M, Smith MA, Lew JB, Hughes S, Yuill S, et al. Optimal Management Strategies for Primary HPV Testing for Cervical Screening: Cost-Effectiveness Evaluation for the National Cervical Screening Program in Australia. PLoS ONE. 2017 Jan 17;12(1):e0163509.
23. Smith MA, Hall M, Saville M. Could HPV Testing on Self-collected Samples Be Routinely Used in an Organized Cervical Screening Program? A Modeled Analysis. Cancer Epidemiol Biomarkers Prev. 2021;30(2):268–77.
24. Medical Services Advisory Committee. Human Papillomavirus Triage Test For Women With Possible or Definite Low-Grade Squamous Intraepithelial Lesions. MSAC reference 39, Assessment report. Canberra: Australian Government Department of Health; 2009.
25. Canfell K, Barnabas R, Patnick J, Beral V. The predicted effect of changes in cervical screening practice in the UK: results from a modelling study. Br J Cancer. 2004;91(3):530–6.
26. Canfell K, Lew JB, Smith M, Walker R. Cost-effectiveness modelling beyond MAVARIC study end-points. In: Kitchener HC, Blanks R, Cubie H, Desai M, Dunn G, Legood R, et al., editors. MAVARIC - a comparison of automation-assisted and manual cervical screening: a randomised controlled trial Health Technology Assessment 2011; Vol 15: No 3. 2011.
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28. Shi JF, Canfell K, Lew JB, Zhao FH, Legood R, Ning Y, et al. Evaluation of primary HPV-DNA testing in relation to visual inspection methods for cervical cancer screening in rural China: an epidemiologic and cost-effectiveness modelling study. BMC Cancer. 2011;11(1):239.
29. Simms KT, Hanley SJB, Smith MA, Keane A, Canfell K. Impact of HPV vaccine hesitancy on cervical cancer in Japan: a modelling study. Lancet Public Health. 2020 Apr;5(4):e223–34.
30. Keane A, Ng CW, Simms KT, Nguyen D, Woo YL, Saville M, et al. The road to cervical cancer elimination in Malaysia: Evaluation of the impact and cost-effectiveness of human papillomavirus screening with self-collection and digital registry support. Int J Cancer. 2021 Dec 15;149(12):1997–2009.
31. Simms KT, Smith MA, Lew JB, Kitchener HC, Castle PE, Canfell K. Will cervical screening remain cost-effective in women offered the next generation nonavalent HPV vaccine? Results for four developed countries. Int J Cancer. 2016 Dec 15;139(12):2771–80.
32. Simms KT, Laprise JF, Smith MA, Lew JB, Caruana M, Brisson M, et al. Cost-effectiveness of the next generation nonavalent human papillomavirus vaccine in the context of primary human papillomavirus screening in Australia: a comparative modelling analysis. Lancet Public Health. 2016 Dec 1;1(2):e66–75.
33. Simms KT, Keane A, Nguyen DTN, Caruana M, Hall MT, Lui G, et al. Benefits, harms and cost-effectiveness of cervical screening, triage and treatment strategies for women in the general population. Nat Med. 2023 Dec;29(12):3050–8.
34. Hall MT, Simms KT, Murray JM, Keane A, Nguyen DTN, Caruana M, et al. Benefits and harms of cervical screening, triage and treatment strategies in women living with HIV. Nat Med. 2023 Dec 1;29(12):3059–66.
35. Kim J, Simms K, Killen J. Human papillomavirus vaccination for adults aged 30 to 45 years in the United States: A cost-effectiveness analysis. PLoS Med. 2021;18(3):e1003534.
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39. Burger EA, Jansen EE, Killen J, Kok IM de, Smith MA, Sy S, et al. Impact of COVID-19-related care disruptions on cervical cancer screening in the United States. J Med Screen. 2021 Jun 1;28(2):213–6.
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