Browse the corpus

p (S3; the WHO triple-intervention strategy). We also considered two supplementary vaccination scenarios: girls-only vaccination with initial extended multi-age cohort catch-up to age 25 years (S4), and vaccination of girls and boys at age 9 years with multi-age cohort catch-up at ages 10–14 years (S5; appendix 57–59). Vaccination was assumed to scale up to 90% coverage from 2020 with 100% lifetime broad spectrum protection against HPV oncogenic types 16, 18, 31, 33, 45, 52, and 58 in individuals susceptible to the relevant type; the analysis thus applies to a broad-spectrum vaccine that protects against these types either by direct protection (as per a second-generation 9-valent vaccine) or via cross-protection for non-vaccine-included types. We assumed that full efficacy against vaccine types was achieved with two doses for vaccine recipients aged younger than 15 years, and with three doses for older vaccine recipients (although dose delivery was not explicitly modelled). Cervical screening was assumed to involve HPV testing once or twice per lifetime at age 35 years, or at ages 35 years and 45 years, with increasing uptake from 45% in 2023, 70% in 2030, to 90% in 2045 onwards. Sensitivity of HPV testing was assumed to be 90% for CIN2 and 94% for CIN3 or worse, independent of age. We assumed that 90% of HPV screen-positive women received visual assessment and appropriate treatment as required for precancer or cancer (triaging was not explicitly modelled). For successfully delivered precancer treatment, treatment success was assumed to be 100%; CCEMC groups differed in their modelling of post-treatment natural history for whether an elevated risk of recurrence was simulated (appendix pp 50–56). We assumed that 50% of women with invasive cervical cancers would have access to high quality surgery, radiotherapy, and chemotherapy by 2023, and this would increase to 90% by 2030. Once treatment access was scaled up to 90% in 2030, 10-year survival was assumed to increase to 78% for women diagnosed at FIGO Stage 1, 69% at FIGO Stage 2, 52% at FIGO Stages 3–4A, and 8% at FIGO Stage 4B (appendix p 71).

quality surgery, radiotherapy, and chemotherapy by 2023, and this would increase to 90% by 2030. Once treatment access was scaled up to 90% in 2030, 10-year survival was assumed to increase to 78% for women diagnosed at FIGO Stage 1, 69% at FIGO Stage 2, 52% at FIGO Stages 3–4A, and 8% at FIGO Stage 4B (appendix p 71). For this analysis we considered two types of intervention packages—vaccination alone or vaccination combined with cervical screening and treatment for precancer and screen-detected cancer, delivered in conjunction with scaled-up treatment services for clinically detected cancer. This approach took into account the feasibility and acceptability of whether interventions could be considered in isolation from each other. Although vaccination can be considered in isolation since it is prophylactic, population-wide implementation of cervical screening leads to screening-related detection of precancer and invasive cervical cancer (with favourable effects on stage-shifting). Referral pathways should be organised so that women with screen-detected invasive cancer are offered prompt and effective treatment (with treatment capacity scaling up as screening expands), since this approach then leads to improved survival outcomes.

Introduction In 2018, an estimated 570 000 cases of cervical cancer were diagnosed, and 311 000 women died from the disease.1 Although cervical cancer has been relatively well controlled for several decades in many high-income countries, mainly because of cervical screening initiatives and effective cancer treatment services, it remains the most common cause of cancer-related death among women in 42 countries, most of which are low-income and lower-middle-income countries (LMICs).2

vely well controlled for several decades in many high-income countries, mainly because of cervical screening initiatives and effective cancer treatment services, it remains the most common cause of cancer-related death among women in 42 countries, most of which are low-income and lower-middle-income countries (LMICs).2 Prophylactic vaccines against oncogenic human papillomavirus (HPV) have been available in most high-income countries from 2006 onwards. First-generation vaccines directly protect against oncogenic HPV types 16 and 18 in individuals naive for those types, and these HPV types are responsible for approximately 70% of invasive cervical cancers.3, 4 More recently, broader-spectrum protection against the types responsible for up to 90% of cervical cancers has been shown either via direct protection against a larger proportion of types (second-generation 9-valent vaccine) or via cross-protection against non-vaccine included types (bivalent vaccine).5, 6 However, because vaccines are primarily targeted at pre-adolescents or young adolescents, it is expected to take several decades after deployment in a population before their full benefits in terms of cancer prevention are realised, and a substantial impact of vaccines on cervical cancer incidence or mortality outcomes is yet to be observed. To date, vaccine coverage in LMICs has been low overall, with an estimated 3% of the primary targeted population of young girls in less developed regions vaccinated by 2014.7 By 2016, only 14% of LMICs had established vaccination programmes.8

nes on cervical cancer incidence or mortality outcomes is yet to be observed. To date, vaccine coverage in LMICs has been low overall, with an estimated 3% of the primary targeted population of young girls in less developed regions vaccinated by 2014.7 By 2016, only 14% of LMICs had established vaccination programmes.8 Research in context Evidence before this study

nes on cervical cancer incidence or mortality outcomes is yet to be observed. To date, vaccine coverage in LMICs has been low overall, with an estimated 3% of the primary targeted population of young girls in less developed regions vaccinated by 2014.7 By 2016, only 14% of LMICs had established vaccination programmes.8 Research in context Evidence before this study Most low-income and lower-middle-income countries (LMICs) do not have access to human papillomavirus (HPV) vaccination, cervical screening programmes are unavailable or poorly implemented, and population-level access to cancer treatment services is variable. WHO, with its partners, is developing a global strategy towards the elimination of cervical cancer as a public health problem. The draft strategy involves triple-intervention targets for scale-up of vaccination, screening, and precancer treatment and invasive cancer treatment and palliative care in all countries; these targets, known as the 90–70–90 WHO triple-intervention strategy, specify 90% coverage of HPV vaccination, 70% coverage of twice-lifetime screening with HPV testing (or a similarly high sensitivity test), and 90% of women having access to cervical precancer and cancer treatment and palliative care services, by 2030. In the accompanying Article published in The Lancet, the WHO Cervical Cancer Elimination Modelling Consortium (CCEMC) predicted the impact of various HPV vaccination and screening and precancer treatment strategies on cervical cancer incidence in 78 LMICs. The analysis found that cervical cancer elimination by 2120 at a threshold of four cases per 100 000 women-years was possible in all 78 LMICs if girls-only vaccination was combined with twice-lifetime screening. The results suggested that elimination was consistently achievable, and the number of cervical cancer cases averted maximised, only if vaccination was combined with twice-lifetime cervical screening and with appropriate treatment for women found to have cervical precancer. The CCEMC harnesses three independent, extensively peer-reviewed models: Policy1-Cervix (Cancer Council NSW, Sydney, NSW Australia), Harvard (Harvard University, Boston, MA, USA), and HPV-ADVISE (Laval University, Quebec, QC, Canada). In this analysis, the models projected the reductions in cervical cancer mortality over time by use of standardised scenarios determined via consultations at various WHO technical expert, advisory group, and global stakeholder meetings.

niversity, Boston, MA, USA), and HPV-ADVISE (Laval University, Quebec, QC, Canada). In this analysis, the models projected the reductions in cervical cancer mortality over time by use of standardised scenarios determined via consultations at various WHO technical expert, advisory group, and global stakeholder meetings. Added value of this study This analysis of the impact of the WHO triple-intervention cervical cancer elimination strategy on mortality outcomes shows that, in the next 10 years, achieving substantial reductions in mortality will require successful scale-up of cancer diagnostic and treatment services in LMICs, including pathology, surgery, radiotherapy, and chemotherapy; supportive and palliative care services will also need to be scaled up. If this is done, the 2030 UN Sustainable Development Goal of achieving a greater than one-third reduction in premature mortality from non-communicable diseases could be realised for cervical cancer. In the next 50 years, cervical screening and vaccination will both have an important role. The triple-intervention strategy would result in mortality rate reductions of 92% by 2070, increasing to almost 99% over the course of the next century as the full benefits of vaccination of young cohorts are realised over time. Implications of all the available evidence

This analysis of the impact of the WHO triple-intervention cervical cancer elimination strategy on mortality outcomes shows that, in the next 10 years, achieving substantial reductions in mortality will require successful scale-up of cancer diagnostic and treatment services in LMICs, including pathology, surgery, radiotherapy, and chemotherapy; supportive and palliative care services will also need to be scaled up. If this is done, the 2030 UN Sustainable Development Goal of achieving a greater than one-third reduction in premature mortality from non-communicable diseases could be realised for cervical cancer. In the next 50 years, cervical screening and vaccination will both have an important role. The triple-intervention strategy would result in mortality rate reductions of 92% by 2070, increasing to almost 99% over the course of the next century as the full benefits of vaccination of young cohorts are realised over time. Implications of all the available evidence Implementing the 90–70–90 WHO triple-intervention strategy to achieve cervical cancer elimination will result in more than 74 million cervical cancer cases averted and more than 62 million women's lives saved over the course of the next century. These findings have informed the draft WHO global strategy for cervical cancer elimination, which will be presented to the WHO Executive Board in February, 2020, and thereafter considered at the World Health Assembly in May, 2020.

averted and more than 62 million women's lives saved over the course of the next century. These findings have informed the draft WHO global strategy for cervical cancer elimination, which will be presented to the WHO Executive Board in February, 2020, and thereafter considered at the World Health Assembly in May, 2020. Many high-income countries are transitioning, or considering transitioning, from cervical cytology to primary HPV testing for cervical screening, which is generally a more effective and cost-effective approach to screening.9, 10, 11 Initiatives for both HPV vaccination and screening have been introduced in the context of broad access to diagnostic, precancer treatment, cancer treatment, and supportive and palliative care services in high-income countries, and the combination of early detection via screening and effective treatment with surgery, chemotherapy, and radiotherapy has meant that net 5-year survival for cervical cancer is around 60–70% or greater in several high-income countries.12 However, in LMICs, uptake of cervical screening has been low and inconsistent, and population-level access to cancer care is generally poor. As a consequence of these differentials in access to cervical screening and treatment, the majority of deaths (91%) from cervical cancer currently occur in LMICs and upper-middle-income countries, and 60% of deaths are in LMICs.1 Access to supportive and palliative care services for people in LMICs is poor,13 and thus the majority of women dying from cervical cancer do so with little or no supportive care or pain relief.

eaths (91%) from cervical cancer currently occur in LMICs and upper-middle-income countries, and 60% of deaths are in LMICs.1 Access to supportive and palliative care services for people in LMICs is poor,13 and thus the majority of women dying from cervical cancer do so with little or no supportive care or pain relief. In May, 2018, the Director-General of WHO announced a call to action to eliminate cervical cancer as a public health problem, and in January, 2019, the WHO Executive Board requested that a draft global strategy to achieve elimination be developed. The draft global strategy being developed by WHO, with its partners, includes triple-intervention targets for scale-up of vaccination, screening, precancer treatment, and invasive cancer treatment in all countries; these targets specify 90% coverage of HPV vaccination, 70% coverage of twice-lifetime screening, and 90% access to cervical precancer and cancer treatment services and palliative care, by 2030.14 To inform the strategic planning process, the WHO Cervical Cancer Elimination Modelling Consortium (CCEMC) was formed and has done comparative modelling of potential intervention scenarios in all 78 LMICs. In the accompanying Article published in The Lancet,15 CCEMC predictions of the impact of HPV vaccination, screening, and precancer treatment strategies on cervical cancer incidence and cases averted are presented; the analysis found that elimination by 2120 at a threshold of four cases per 100 000 women was possible in all 78 LMICs if girls-only vaccination was combined with twice-lifetime screening. This strategy was predicted to reduce age-standardised incidence across 78 LMICs by 97% and to avert more than 74 million cervical cancer cases over the next century.15 The analysis concluded that adding screening with high uptake to vaccination will expedite reductions in cervical cancer incidence and the number of cases averted, and will be necessary to eliminate cervical cancer in countries with the highest burden.

more than 74 million cervical cancer cases over the next century.15 The analysis concluded that adding screening with high uptake to vaccination will expedite reductions in cervical cancer incidence and the number of cases averted, and will be necessary to eliminate cervical cancer in countries with the highest burden. The aims of the current analysis were to model cancer treatment scale-up in addition to HPV vaccination and cervical screening and to assess the impact of achieving the 90–70–90 triple-intervention targets on cervical cancer mortality and deaths averted over the next century on the path to elimination. The cervical cancer elimination initiative has been framed within the context of the UN Sustainable Development Goals (SDGs) to support the realisation of SDG target 3.4—a one-third reduction in premature mortality from non-communicable diseases by 2030.16 Therefore, we also assessed the potential for the cervical cancer elimination strategy to deliver a one-third reduction in premature mortality from cervical cancer by 2030. Methods Countries included in the analysis The 78 LMICs considered were located in six regions according to World Bank definitions: east Asia and Pacific, Europe and central Asia, Latin America and Caribbean, north Africa and the Middle East, South Asia, and sub-Saharan Africa (see the appendix pp 44–45 for the full list of countries within each region and the grouping of countries by income level).

six regions according to World Bank definitions: east Asia and Pacific, Europe and central Asia, Latin America and Caribbean, north Africa and the Middle East, South Asia, and sub-Saharan Africa (see the appendix pp 44–45 for the full list of countries within each region and the grouping of countries by income level). Description of the WHO CCEMC models The WHO CCEMC comprised three modelling groups collaborating with WHO and the International Agency for Research on Cancer (IARC). The platforms were independent dynamic models, identified by WHO by use of predefined criteria. The modelling methods have been previously described.15 In brief, the selected models for the analysis explicitly considered the dynamic transmission of HPV infection (and could thus capture the effects of herd immunity); were capable of projecting the impact of HPV vaccination, cervical screening, and precancer treatment and clinical and screen-detected cancer treatment scale-up at a country level for all 78 LMICs considered; and were independently developed and have been extensively validated and peer reviewed. Three models were selected: Policy1-Cervix (Cancer Council NSW, Sydney, NSW, Australia), Harvard (Harvard University, Boston, MA, USA), and HPV-ADVISE (Laval University, Quebec, QC, Canada). The individual CCEMC models have been previously used to inform national policy on cervical screening and HPV vaccination in Australia, Canada, the UK, and the USA, and at the global level.10, 17, 18, 19, 20, 21, 22 The structure of the CCEMC models and the comparative modelling approach were endorsed by the WHO Advisory Committee on Immunization and Vaccines related Implementation Research (IVIR-AC).23

vical screening and HPV vaccination in Australia, Canada, the UK, and the USA, and at the global level.10, 17, 18, 19, 20, 21, 22 The structure of the CCEMC models and the comparative modelling approach were endorsed by the WHO Advisory Committee on Immunization and Vaccines related Implementation Research (IVIR-AC).23 HPV transmission and cervical carcinogenesis are modelled for the oncogenic HPV types included in second-generation vaccines (HPV types 16, 18, 31, 33, 45, 52, and 58) and other oncogenic types, and each model simulates the type-specific natural history of cervical cancer from persistent HPV infection to cervical cancer via high-grade precancerous cervical lesions (cervical intraepithelial neoplasia grades 2 [CIN2] and 3 [CIN3]). All models can simulate complex cervical screening and treatment algorithms, and for the current analysis these models were adapted to incorporate country-level assumptions about the proportion of women receiving cervical cancer treatment and the consequent survival outcomes. Reporting was done according to a consensus-based framework for modelled evaluations of HPV prevention and cervical cancer control: HPV-FRAME.24 See the appendix (pp 50–56, 74–76) for a detailed description of the model platforms and HPV-FRAME reporting.

g cervical cancer treatment and the consequent survival outcomes. Reporting was done according to a consensus-based framework for modelled evaluations of HPV prevention and cervical cancer control: HPV-FRAME.24 See the appendix (pp 50–56, 74–76) for a detailed description of the model platforms and HPV-FRAME reporting. Status quo assumptions The comparator (status quo) S0 scenario assumed no scale-up of vaccination, cervical screening, or cancer treatment. Under the status quo, it was assumed that none of the 78 LMICs had achieved substantial vaccination coverage by 2020, although in practice a few countries, such as Rwanda, have initiated high-coverage vaccination initiatives within the past few years. Thus, our analysis only captures the effect of scaled-up vaccination from 2020 onwards. For cervical screening, modelling groups made different assumptions about whether the impact of limited existing screening coverage was considered in the status quo (see the appendix pp 50–56 for further details).

he past few years. Thus, our analysis only captures the effect of scaled-up vaccination from 2020 onwards. For cervical screening, modelling groups made different assumptions about whether the impact of limited existing screening coverage was considered in the status quo (see the appendix pp 50–56 for further details). Treatment for cervical cancer involves stage-appropriate multimodality therapies with radiotherapy and chemotherapy, with surgery (partial or total hysterectomy) being an important option for early-stage disease. Cervical cancer clinical staging was based on the International Federation of Gynaecology and Obstetrics (FIGO) system. Institute for Health Metrics and Evaluation (IHME) sub-regional-level estimates for the stage distribution of invasive cervical cancer at diagnosis, and data on 5-year and 10-year survival rates were derived from systematic reviews done by WHO based on peer-reviewed publications and national reports including cancer control plans, cross-referenced to data from IARC cancer registries. Radiotherapy access, estimated as machine density per 1000 patients with cancer, was used as a surrogate for multimodal treatment delivery. We used 2018 data for radiotherapy access and availability of external beam radiation therapy and personnel (radiation oncologists, medical physicists, and radiation therapy technologists) provided by the International Atomic Energy Agency's Directory of Radiotherapy Centres (DIRAC). Ranges of treatment access rates in each World Bank region encompassed the lowest and the highest treatment access rates of the countries in each region and represented the percentage of the population that could potentially be served with the equipment and workforce available (table 1). These data were then used to derive initial estimates of country-level current status quo stage distributions, treatment access rates, and survival rates (appendix pp 63–70). We used these data as an initial (pre-calibration) input to the models.Table 1 Summary of treatment assumptions by region for status quo scenario: FIGO stage distributions, stage-specific survival rates, and treatment access rates

quo stage distributions, treatment access rates, and survival rates (appendix pp 63–70). We used these data as an initial (pre-calibration) input to the models.Table 1 Summary of treatment assumptions by region for status quo scenario: FIGO stage distributions, stage-specific survival rates, and treatment access rates Stage distribution at diagnosis Overall 5-year (and 10-year) survival rates Treatment access rate (range)* Stage 1 Stage 2 Stage 3–4A Stage 4B Stage 1 Stage 2 Stage 3–4A Stage 4B East Asia and Pacific 23% 39% 27% 11% 65% (15%) 51% (13%) 15% (10%) 2% (2%) 17% (0–37) Europe and central Asia 34% 19% 28% 19% 74% (42%) 62% (37%) 34% (28%) 6% (4%) 48% (18–100) Latin America and Caribbean 23% 26% 46% 5% 73% (39%) 61% (34%) 32% (26%) 6% (4%) 44% (0–77) North Africa and Middle East 13% 43% 31% 13% 80% (59%) 69% (52%) 46% (39%) 9% (6%) 67% (0–100) South Asia 13% 36% 40% 11% 74% (42%) 62% (37%) 34% (28%) 6% (4%) 48% (0–55) Sub-Saharan Africa 8% 36% 48% 8% 62% (6%) 47% (5%) 9% (4%) 1% (1%) 7% (0–37) This table provides a regional summary of the data used as an initial (pre-calibration) input to the models; however, each modelling group also applied a quality factor to further adjust survival in the status quo to fit to Global Cancer Observatory (GLOBOCAN) 2018 estimates for cervical cancer mortality by 5-year age group (appendix pp 3–7, 63–70). Detailed country-specific estimates for status quo treatment access rates are provided in the appendix (pp 63–70). Staging is according to International Federation of Gynaecology and Obstetrics (FIGO) staging for carcinoma of cervix (2009 version) and TNM, 7th edition. Data based on a systematic review done by WHO, which obtained information from 43 countries, prioritising countries with population-based cancer registries. Results were derived by the Institute for Health Metrics and Evaluation (IHME) subregions. Regional results shown are weighted on the basis of each country's cancer case burden.

a systematic review done by WHO, which obtained information from 43 countries, prioritising countries with population-based cancer registries. Results were derived by the Institute for Health Metrics and Evaluation (IHME) subregions. Regional results shown are weighted on the basis of each country's cancer case burden. * Treatment access rates were estimated on the basis of radiotherapy access and on the most recent availability of external beam radiation therapy and personnel (radiation oncologists, medical physicists, and radiation therapy technologists), which were provided by the Directory of Radiotherapy Centres (DIRAC). Ranges of treatment access rates in each region encompass the lowest and the highest treatment access rates of the countries in each region and represent the percentage of the population that could potentially be serviced on the basis of the equipment and workforce available.

Directory of Radiotherapy Centres (DIRAC). Ranges of treatment access rates in each region encompass the lowest and the highest treatment access rates of the countries in each region and represent the percentage of the population that could potentially be serviced on the basis of the equipment and workforce available. Calibration to GLOBOCAN 2018 Global Cancer Observatory (GLOBOCAN) 2018 estimates are based on IARC-certified cancer registry information where available in a country, or on a series of estimation methods if verified registry data are not available.1, 2 Each group incorporated initial country-level stage-specific 5-year and 10-year survival rates, and models were then calibrated to country-specific and age-specific mortality rates from GLOBOCAN 2018 by incorporating a quality factor into the final estimated country-specific and stage-specific survival assumptions. This approach encompasses limitations in the available data on staging, treatment access, uncertainties in actual delivery of treatment, variations in treatment delivery from established protocols and recommendations, equipment and infrastructure maintenance and logistics, and treatment abandonment. The calibrated results for incidence and mortality are shown for each model in the appendix (pp 3–7), summarised as the results across all 78 LMICs and at the regional level. Calibration results were comparable for all three models and generally demonstrated good fit with GLOBOCAN 2018.

tics, and treatment abandonment. The calibrated results for incidence and mortality are shown for each model in the appendix (pp 3–7), summarised as the results across all 78 LMICs and at the regional level. Calibration results were comparable for all three models and generally demonstrated good fit with GLOBOCAN 2018. Modelled scenarios Models projected age-standardised cervical cancer mortality and deaths over time in 78 LMICs for standardised scenarios. The selection of core scenarios was determined after consultation at several WHO technical expert, advisory group, and global stakeholder meetings in 2018 and was based on a multi-step process, as previously described.15, 23 The scenarios were aligned with the scale-up targets articulated in the WHO draft global strategy for elimination.14 The final fully articulated core scenarios for the mortality impact analysis were ongoing girls-only vaccination at age 9 years with multi-age cohort catch-up in the first year for ages 10–14 years (S1); girls-only vaccination, once-lifetime screening at around age 35 years with precancer treatment, and invasive cancer treatment scale-up (S2); and girls-only vaccination, twice-lifetime screening at around ages 35 years and 45 years with precancer treatment, and invasive cancer treatment scale-up (S3; the WHO triple-intervention strategy). We also considered two supplementary vaccination scenarios: girls-only vaccination with initial extended multi-age cohort catch-up to age 25 years (S4), and vaccination of girls and boys at age 9 years with multi-age cohort catch-up at ages 10–14 years (S5; appendix 57–59).

vasive cervical cancer (with favourable effects on stage-shifting). Referral pathways should be organised so that women with screen-detected invasive cancer are offered prompt and effective treatment (with treatment capacity scaling up as screening expands), since this approach then leads to improved survival outcomes. Comparative modelling approach and outcomes Each single-model analysis was done independently at a country level. The coordinating centre for the analysis (Cancer Council NSW, Australia) aggregated all results, applied standard populations and population projections, and estimated the median and range of results. Results are presented across all 78 LMICs, regionally, and by country. Rates were age-standardised by applying the age structure of the 2015 World Female Population aged 0–99 years. Premature mortality from cervical cancer was estimated by applying the 2015 World Female Population for ages 30–69 years, and in sensitivity analysis it was based on the probability of death from cervical cancer from age 30 years to 70 years.16 For calculation of deaths averted, country-specific and age-specific population projections were based on the UN World Population Prospects: 2017 Revision.25 Relative reductions over time were compared to the status quo. We summarised results for mortality reductions, and deaths averted were calculated from the beginning of 2020 to the end of 2030, 2070, and 2120, with the median (range) of model predictions for each result. See the appendix (pp 46–49) for more details.

.25 Relative reductions over time were compared to the status quo. We summarised results for mortality reductions, and deaths averted were calculated from the beginning of 2020 to the end of 2030, 2070, and 2120, with the median (range) of model predictions for each result. See the appendix (pp 46–49) for more details. Sensitivity analysis The analysis was a comparative exercise based on three models with different structural and parameterisation assumptions and a form of sensitivity analysis is built into the reported ranges of results. We reported on key model-specific findings for calibration outcomes and for age-specific mortality rates (appendix pp 3–7, 11–25). We also ran explanatory (but counterfactual) scenarios to understand the sensitivity of the model results to underlying aspects of the impact modelling, including an extreme sensitivity analysis on the impact of cancer treatment scale-up. We also assessed the impact of using alternative population structures for age standardisation on the predicted age-standardised rate and the impact of different underlying fertility assumptions for population projections on the cumulative number of cervical cancer deaths averted. Role of the funding source This research was partly funded by WHO, which contributed to study design, data analysis, data interpretation, and writing of the report. Other funders had no role in the design of this analysis or the decision to submit for publication. KC, JJK, and MB had full access to all the data in the study and had final responsibility for the decision to submit for publication.

ibuted to study design, data analysis, data interpretation, and writing of the report. Other funders had no role in the design of this analysis or the decision to submit for publication. KC, JJK, and MB had full access to all the data in the study and had final responsibility for the decision to submit for publication. Results Predictions from the three models were broadly consistent for all scenarios. Figure 1 shows the summary results across the models for the reduction in age-standardised mortality from 2020 to 2120, table 2 depicts these findings as numerical snapshots of the rates and relative reductions compared to the status quo scenario over time, and the reductions in premature mortality in women aged 30–69 years. Snapshots of the age-specific findings in 2020, 2070, and 2120 for each of the three CCEMC models are shown in the appendix (pp 11–25).Figure 1 Age-standardised cervical cancer mortality over time for all 78 LMICs

to the status quo scenario over time, and the reductions in premature mortality in women aged 30–69 years. Snapshots of the age-specific findings in 2020, 2070, and 2120 for each of the three CCEMC models are shown in the appendix (pp 11–25).Figure 1 Age-standardised cervical cancer mortality over time for all 78 LMICs The solid lines represent the median outcome of the three models; the shading represents the range of model outputs. HPV=human papillomavirus. LMICs=low-income and lower-middle-income countries. S0=status quo (no scale-up of vaccination, screening or treatment). S1=female-only vaccination at 9 years with multi-age cohort catch-up to age 14 years in 2020. S2=female-only vaccination and once-lifetime HPV testing at age 35 years with cancer treatment scale-up. S3=female-only vaccination and twice-lifetime HPV testing at age 35 years and 45 years with cancer treatment scale-up. Supplementary S4=female-only vaccination at 9 years with extended multi-age cohort catch-up to age 25 years in 2020. Supplementary S5=female and male vaccination at age 9 years with multi-age cohort catch-up to age 14 years in 2020. All scenarios assume the use of a broad-spectrum HPV vaccine with protection against seven oncogenic types. Table 2 Projected cervical cancer mortality rates over time, across all 78 low-income and lower-middle-income countries

The solid lines represent the median outcome of the three models; the shading represents the range of model outputs. HPV=human papillomavirus. LMICs=low-income and lower-middle-income countries. S0=status quo (no scale-up of vaccination, screening or treatment). S1=female-only vaccination at 9 years with multi-age cohort catch-up to age 14 years in 2020. S2=female-only vaccination and once-lifetime HPV testing at age 35 years with cancer treatment scale-up. S3=female-only vaccination and twice-lifetime HPV testing at age 35 years and 45 years with cancer treatment scale-up. Supplementary S4=female-only vaccination at 9 years with extended multi-age cohort catch-up to age 25 years in 2020. Supplementary S5=female and male vaccination at age 9 years with multi-age cohort catch-up to age 14 years in 2020. All scenarios assume the use of a broad-spectrum HPV vaccine with protection against seven oncogenic types. Table 2 Projected cervical cancer mortality rates over time, across all 78 low-income and lower-middle-income countries S1: girls-only vaccination S2: girls-only vaccination, once-lifetime screening, and cancer treatment scale-up S3: girls-only vaccination, twice-lifetime screening, and cancer treatment scale-up Supplementary S4: girls-only vaccination plus multi-age catch-up to age 25 years Supplementary S5: vaccination of girls and boys Age-standardised rate Reduction vs S0 (%) Age-standardised rate Reduction vs S0 (%) Age-standardised rate Reduction vs S0 (%) Age-standardised rate Reduction vs S0 (%) Age-standardised rate Reduction vs S0 (%) Women aged 0–99 years 2030 13·2 (12·9 to 14·0) 0·1% (0·1 to 0·5) 8·5 (8·2 to 11·1) 34·3% (21·4 to 37·4) 8·5 (8·2 to 10·8) 34·2% (23·3 to 37·8) 13·1 (12·9 to 13·9) 0·2% (−0·3 to 1·5) 13·2 (13·0 to 14·1) 0·1% (−0·7 to 0·2) 2070 5·0 (4·5 to 5·4) 61·7% (61·4 to 66·1) 1·4 (1·4 to 2·2) 88·9% (84·0 to 89·3) 1·0 (0·9 to 1·6) 92·3% (88·4 to 93·0) 3·2 (2·7 to 3·8) 77·5% (70·8 to 79·7) 4·5 (4·5 to 5·0) 65·3% (64·3 to 65·6) 2120 1·3 (1·3 to 1·9) 89·5% (86·6 to 89·9) 0·3 (0·3 to 0·7) 97·9% (95·0 to 98·0) 0·2 (0·2 to 0·5) 98·6% (96·5 to 98·6) 1·3 (1·3 to 1·8) 89·7% (86·9 to 89·9) 1·3 (0·7 to 1·5) 89·9% (89·2 to 94·6) Women aged 30–69 years*(premature mortality) 2030 23·7 (23·0 to 25·5) 0·2% (0·0 to 0·5) 15·2 (14·8 to 20·0) 34·2% (22·1 to 37·4) 15·2 (14·7 to 19·4) 33·9% (24·4 to 37·9) 23·6 (23·1 to 25·3) 0·1% (−0·2 to 1·4) 23·7 (23·3 to 25·6) 0·0% (−0·8 to 0·1) 2070 5·5 (5·1 to 6·2) 76·1% (75·7 to 78·5) 1·3 (1·2 to 2·3) 94·4% (91·1 to 94·6) 0·9 (0·8 to 1·4) 96·2% (94·3 to 96·8) 3·3 (3·1 to 3·9) 85·9% (84·9 to 86·8) 5·2 (4·4 to 5·4) 78·9% (77·9 to 81·0) 2120 2·4 (2·1 to 3·4) 89·9% (86·6 to 91·1) 0·5 (0·4 to 1·2) 98·0% (95·5 to 98·3) 0·3 (0·3 to 0·8) 98·6% (96·9 to 98·8) 2·4 (2·0 to 3·4) 89·9% (86·8 to 91·2) 2·4 (0·9 to 2·8) 89·9% (89·2 to 96·2) Results shown represent age-standardised rates per 100 000 women for a given year, and relative reductions are compared to the status quo (S0) in that year. Results represent the median (range) of estimates across all three models. Detailed results for each decade are provided in the appendix (pp 8–10). S0=status quo (no scale-up of vaccination, screening, or treatment). S1=female-only vaccination.

, and relative reductions are compared to the status quo (S0) in that year. Results represent the median (range) of estimates across all three models. Detailed results for each decade are provided in the appendix (pp 8–10). S0=status quo (no scale-up of vaccination, screening, or treatment). S1=female-only vaccination. S2=female-only vaccination and once-lifetime HPV testing at age 35 years and treatment scale-up. S3=female-only vaccination and twice-lifetime HPV testing at age 35 years and 45 years and treatment scale-up. Supplementary S4=female-only vaccination with multi-age cohort catch-up to 25 years in 2020. Supplementary S5=vaccination of girls and boys at age 9 years. All vaccination strategies assume the use of a broad-spectrum HPV vaccine with protection against the seven oncogenic types: 16, 18, 31, 33, 45, 52, and 58. Population projections were obtained from the UN and further projected out to 2120 (appendix pp 46–49). Model methods incorporate randomness and heterogeneity in estimates, which can occasionally, over shorter term timeframes, lead to relative increases rather than decreases in rates compared to the status quo, shown here as negative values. Randomness and heterogeneity can also lead to slight decreases in the percentage reduction in predicted rates even in the first year modelled (2020) and small differences from the expected relative ordering of the impact of different scenarios or the expected relative reductions over time. Caution should be applied in interpreting comparative differences between the values in this table, which represent the median and range across models; any individual median result could represent the findings of any one of the WHO Cervical Cancer Elimination Modelling Consortium (CCEMC) models.

e reductions over time. Caution should be applied in interpreting comparative differences between the values in this table, which represent the median and range across models; any individual median result could represent the findings of any one of the WHO Cervical Cancer Elimination Modelling Consortium (CCEMC) models. * Note that relative reductions in premature mortality are very similar if using the probability of dying between the ages of 30 and 70 years as a measure (appendix pp 8–10). Figure 2A depicts annual cervical cancer deaths over time and figure 2B provides information about the cumulative cervical cancer deaths averted. Table 3 summarises these findings for the cumulative deaths and deaths averted over the periods 2020–2030, 2020–2070, and 2020–2120, for all core and supplementary scenarios.Figure 2 Projected cervical cancer deaths across all 78 low-income and lower-middle-income countries

he cumulative cervical cancer deaths averted. Table 3 summarises these findings for the cumulative deaths and deaths averted over the periods 2020–2030, 2020–2070, and 2020–2120, for all core and supplementary scenarios.Figure 2 Projected cervical cancer deaths across all 78 low-income and lower-middle-income countries (A) Annual cervical cancer deaths. (B) Cumulative cervical cancer deaths averted. The solid lines in panel A represent the median of the three models and the shading represents the range of the model outputs. In panel B the column height represents the median of the three models and the error bars represent the range of the three models. HPV=human papillomavirus. S0=status quo (no scale-up of vaccination, screening, or treatment). S1=female-only vaccination at age 9 years with multi-age cohort catch-up to age 14 years in 2020. S2=female-only vaccination and once-lifetime HPV testing at age 35 years with cancer treatment scale-up. S3=female-only vaccination and twice-lifetime HPV testing at age 35 years and 45 years with cancer treatment scale-up. Supplementary S4=female-only vaccination at age 9 years with extended multi-age cohort catch-up to age 25 years in 2020. Supplementary S5=female and male vaccination at age 9 years with multi-age cohort catch-up to age 14 years in 2020. All scenarios assume the use of a broad-spectrum HPV vaccine with protection against seven oncogenic types. Table 3 Estimated cervical cancer deaths and deaths averted (in millions) from 2020 to 2030, 2020 to 2070, and 2020 to 2120

(A) Annual cervical cancer deaths. (B) Cumulative cervical cancer deaths averted. The solid lines in panel A represent the median of the three models and the shading represents the range of the model outputs. In panel B the column height represents the median of the three models and the error bars represent the range of the three models. HPV=human papillomavirus. S0=status quo (no scale-up of vaccination, screening, or treatment). S1=female-only vaccination at age 9 years with multi-age cohort catch-up to age 14 years in 2020. S2=female-only vaccination and once-lifetime HPV testing at age 35 years with cancer treatment scale-up. S3=female-only vaccination and twice-lifetime HPV testing at age 35 years and 45 years with cancer treatment scale-up. Supplementary S4=female-only vaccination at age 9 years with extended multi-age cohort catch-up to age 25 years in 2020. Supplementary S5=female and male vaccination at age 9 years with multi-age cohort catch-up to age 14 years in 2020. All scenarios assume the use of a broad-spectrum HPV vaccine with protection against seven oncogenic types. Table 3 Estimated cervical cancer deaths and deaths averted (in millions) from 2020 to 2030, 2020 to 2070, and 2020 to 2120 S0: status quo S1: girls-only vaccination S2: girls-only vaccination, once-lifetime screening, and cancer treatment scale-up S3: girls-only vaccination, twice-lifetime screening, and cancer treatment scale-up Supplementary S4: girls-only vaccination plus multi-age catch-up to age 25 years Supplementary S5: vaccination of girls and boys Cumulative deaths by 2030 (2020–2030) 2·5 (2·5–2·7) 2·5 (2·5–2·7) 2·2 (2·2–2·4) 2·2 (2·2–2·4) 2·5 (2·5–2·7) 2·5 (2·5–2·7) Deaths averted .. 0·0 (0·0–0·0)* 0·3 (0·3–0·3) 0·3 (0·3–0·4) 0·0 (0·0–0·0) 0·0 (0·0–0·0) Reduction vs S0 (%) .. 0% (0–0)* 12% (11–12) 12% (10–13) 0% (0–1) 0% (0–0) Cumulative deaths by 2070 (2020–2070) 20·7 (20·4–22·0) 16·3 (15·9–17·1) 7·1 (7·1–8·8) 6·4 (6·1–7·4) 13·5 (13·4–14·8) 16·0 (15·9–16·9) Deaths averted .. 4·8 (4·1–4·8) 13·3 (13·1–13·6) 14·6 (14·1–14·6) 7·3 (5·6–8·5) 4·8 (4·4–5·1) Reduction vs S0 (%) .. 22% (20–23) 65% (60–66) 69% (66–71) 35% (27–39) 23% (22–23) Cumulative deaths by 2120 (2020–2120) 70·1 (69·7–73·0) 25·1 (23·7–27·1) 8·9 (8·9–12·8) 7·6 (7·3–10·3) 21·5 (19·7–22·5) 23·8 (22·4–25·5) Deaths averted .. 45·8 (44·7–46·4) 60·8 (60·2–61·2) 62·6 (62·1–62·8) 50·5 (47·2–51·4) 47·3 (46·3–47·5) Reduction vs S0 (%) .. 64% (63–66) 87% (82–87) 89% (86–90) 70% (68–72) 66% (65–68) Cumulative cervical cancer deaths (in millions) across all 78 low-income and lower-middle-income countries over three time periods are shown. The values show the median (range) of three model outputs. All relative reductions are compared to the status quo (S0) predictions in the same year. HPV=human papillomavirus. S0=status quo (no scale-up of vaccination, screening, or treatment). S1=female-only vaccination at 9 years with multi-age cohort catch-up to 14 years in 2020. S2=female-only vaccination and once-lifetime HPV testing at age 35 years and treatment scale-up. S3=female-only vaccination and twice-lifetime HPV testing at age 35 years and 45 years and treatment scale-up. Supplementary S4=female-only vaccination with multi-age cohort catch-up to 25 years in 2020. Supplementary S5=vaccination of girls and boys at age 9 years, with multi-age catch-up to 14 years in 2020.

cale-up. S3=female-only vaccination and twice-lifetime HPV testing at age 35 years and 45 years and treatment scale-up. Supplementary S4=female-only vaccination with multi-age cohort catch-up to 25 years in 2020. Supplementary S5=vaccination of girls and boys at age 9 years, with multi-age catch-up to 14 years in 2020. All vaccination strategies assume the use of a broad-spectrum HPV vaccine with protection against the seven oncogenic types: 16, 18, 31, 33, 45, 52, and 58. Population projections were obtained from the UN and further projected out to 2120 (appendix pp 48–49). The median for deaths is the median of three possible model outputs for a given time period, and might use results from different models at different periods; similarly, the median for deaths averted and percentage reduction versus S0 is the median model for these metrics independently, and might be different to the median model selected for total deaths metric, and might also be different across the different periods. Caution should be applied in interpreting comparative differences between the values in this table, which represent the median and range across models; any individual median result could represent the findings of any one of the Cervical Cancer Elimination Modelling Consortium models. Note that the sum of averted cases and cases predicted for a given strategy might also not be identical to cases predicted for S0 because of rounding.

the median and range across models; any individual median result could represent the findings of any one of the Cervical Cancer Elimination Modelling Consortium models. Note that the sum of averted cases and cases predicted for a given strategy might also not be identical to cases predicted for S0 because of rounding. * Note that table entry is zero due to rounding. Actual median and range of estimates for deaths averted: 620 (−1100 to 3600) deaths (model methods incorporate randomness and heterogeneity in estimates, which can occasionally, over shorter-term timeframes, lead to relative increases rather than decreases in rates compared to the status quo, shown here as a negative value).

median and range of estimates for deaths averted: 620 (−1100 to 3600) deaths (model methods incorporate randomness and heterogeneity in estimates, which can occasionally, over shorter-term timeframes, lead to relative increases rather than decreases in rates compared to the status quo, shown here as a negative value). In 2020, the predicted age-standardised rate for cervical cancer mortality across all 78 LMICs was 13·2 (range 12·9–14·1) per 100 000 women. By 2030, vaccine-only strategies would have minimal impact on cervical cancer mortality, which would remain at 13·2 (12·9–14·0) deaths per 100 000 women, corresponding to a 0·1% (0·1–0·5) reduction, averting a median of 620 deaths across all 78 LMICs by 2030 (rounded to 0·0 million in table 3). However, scaling up twice-lifetime cancer screening and treatment in addition to vaccination would result in a mortality rate of 8·5 (8·2–10·8) by 2030, corresponding to a 34·2% (23·3–37·8) reduction, averting 300 000 (300 000–400 000) deaths, mainly due to the impact of improved access to cancer treatment. In this 10-year timeframe, vaccination plus once-lifetime screening or twice-lifetime screening and treatment scale-up would lead to similar mortality reductions. For further information about the relative contribution of the interventions, see the appendix (pp 33–40).

the impact of improved access to cancer treatment. In this 10-year timeframe, vaccination plus once-lifetime screening or twice-lifetime screening and treatment scale-up would lead to similar mortality reductions. For further information about the relative contribution of the interventions, see the appendix (pp 33–40). By 2070, girls-only vaccination would lead to a mortality rate of 5·0 (range 4·5–5·4) per 100 000 women, corresponding to a reduction of 61·7% (61·4–66·1), averting 4·8 million (4·1–4·8) deaths, but scaling up once-lifetime screening and treatment in addition to vaccination would result in a rate of 1·4 (1·4–2·2) per 100 000 women, corresponding to a reduction of 88·9% (84·0–89·3), averting 13·3 million (13·1–13·6) deaths. By 2070, girls-only vaccination, twice-lifetime screening, and treatment would result in a mortality rate of 1·0 (0·9–1·6) per 100 000 women, corresponding to a reduction of 92·3% (88·4–93·0), averting 14·6 million (14·1–14·6) deaths. Compared to girls-only vaccination with catch-up to age 14 years (S1), extended-multi-age cohort vaccination to 25 years (S4) would result in increased intermediate-term mortality benefits, bringing forward the benefits of vaccination by about a decade (figure 1). At the high levels of vaccination coverage for girls assumed in the analysis, additional vaccination of boys at age 9 years (S5) would have minimal additional impact on cervical cancer mortality in women over the next 50 years and would have similar intermediate-term benefits to girls-only vaccination by 2070 (figure 1, figure 3, table 2).Figure 3 Age-standardised cervical cancer mortality over time for LMICs in each region

s at age 9 years (S5) would have minimal additional impact on cervical cancer mortality in women over the next 50 years and would have similar intermediate-term benefits to girls-only vaccination by 2070 (figure 1, figure 3, table 2).Figure 3 Age-standardised cervical cancer mortality over time for LMICs in each region The solid lines represent the median outcome of the three models; the shading represents the range of model outputs. HPV=human papillomavirus. LMICs=low-income and lower-middle-income countries. S0=status quo (no scale-up of vaccination, screening or treatment). S1=female-only vaccination at 9 years with multi-age cohort catch-up to age 14 years in 2020. S2=female-only vaccination and once-lifetime HPV testing at age 35 years with cancer treatment scale-up. S3=female-only vaccination and twice-lifetime HPV testing at age 35 years and 45 years with cancer treatment scale-up. Supplementary S4=female-only vaccination at 9 years with extended multi-age cohort catch-up to age 25 years in 2020. Supplementary S5=female and male vaccination at age 9 years with multi-age cohort catch-up to age 14 years in 2020. All scenarios assume the use of a broad-spectrum HPV vaccine with protection against seven oncogenic types.

ale-only vaccination at 9 years with extended multi-age cohort catch-up to age 25 years in 2020. Supplementary S5=female and male vaccination at age 9 years with multi-age cohort catch-up to age 14 years in 2020. All scenarios assume the use of a broad-spectrum HPV vaccine with protection against seven oncogenic types. By 2120, girls-only vaccination would result in a mortality rate of 1·3 (range 1·3–1·9) per 100 000 women, corresponding to a mortality reduction of 89·5% (86·6–89·9), averting 45·8 million (44·7–46·4) deaths. By 2120, a mortality rate of 0·2 (0·2–0·5) per 100 000 women, corresponding to a reduction of 98·6% (96·5–98·6), would be achievable with the WHO triple-intervention strategy, averting 62·6 million (62·1–62·8) deaths. If screening were done once per lifetime instead of twice, 60·8 million (60·2–61·2) deaths would be averted over the same period. The specific estimate for the incremental benefit of the twice-lifetime versus once-lifetime screening package over this period was 1·6 million (1·3–2·5) additional deaths averted, with most of these additional deaths averted before 2070. Compared to girls-only vaccination alone, 16·8 million (16·4–17·4) additional deaths would be averted via the triple-intervention strategy by 2120. In terms of premature mortality outcomes (deaths at age 30–69 years), the triple-intervention strategy would result in rate reductions of 33·9% (range 24·4–37·9) by 2030, 96·2% (94·3–96·8) by 2070, and 98·6% (96·9–98·8) by 2120 (table 2).

By 2120, girls-only vaccination would result in a mortality rate of 1·3 (range 1·3–1·9) per 100 000 women, corresponding to a mortality reduction of 89·5% (86·6–89·9), averting 45·8 million (44·7–46·4) deaths. By 2120, a mortality rate of 0·2 (0·2–0·5) per 100 000 women, corresponding to a reduction of 98·6% (96·5–98·6), would be achievable with the WHO triple-intervention strategy, averting 62·6 million (62·1–62·8) deaths. If screening were done once per lifetime instead of twice, 60·8 million (60·2–61·2) deaths would be averted over the same period. The specific estimate for the incremental benefit of the twice-lifetime versus once-lifetime screening package over this period was 1·6 million (1·3–2·5) additional deaths averted, with most of these additional deaths averted before 2070. Compared to girls-only vaccination alone, 16·8 million (16·4–17·4) additional deaths would be averted via the triple-intervention strategy by 2120. In terms of premature mortality outcomes (deaths at age 30–69 years), the triple-intervention strategy would result in rate reductions of 33·9% (range 24·4–37·9) by 2030, 96·2% (94·3–96·8) by 2070, and 98·6% (96·9–98·8) by 2120 (table 2). Figure 3 shows the regional results across the models for the reduction in age-standardised mortality from 2020 to 2120. The highest mortality rates in 2020, at approximately 30 per 100 000 women, are in sub-Saharan Africa, followed by Latin America and the Caribbean (approximately 16 per 100 000 women). These regions are predicted to have the greatest absolute reductions in mortality rates over the next two decades if the triple-intervention strategy can be successfully scaled up; by 2040, cervical cancer mortality in sub-Saharan Africa could be reduced by more than two-thirds to less than ten per 100 000 women, and in Latin America and the Caribbean it could be reduced to approximately six per 100 000 women. Details about the age-specific cervical cancer incidence and mortality rates in 2020, 2070, and 2120 for each region are provided in the appendix (pp 11–25).

ced by more than two-thirds to less than ten per 100 000 women, and in Latin America and the Caribbean it could be reduced to approximately six per 100 000 women. Details about the age-specific cervical cancer incidence and mortality rates in 2020, 2070, and 2120 for each region are provided in the appendix (pp 11–25). With the WHO triple-intervention strategy, over the next 10 years, about half (48% [range 45–55]) of deaths averted would be in sub-Saharan Africa and almost a third (32% [29–34]) would be in South Asia (including India); over the next century, almost 90% of deaths averted would be in these regions (appendix p 26). The appendix (pp 27–32) provides information at the country level for the predicted impact of the WHO triple-intervention strategy. In all countries, the median estimates of mortality rates by 2120 approach 1 per 100 000 women or lower.

With the WHO triple-intervention strategy, over the next 10 years, about half (48% [range 45–55]) of deaths averted would be in sub-Saharan Africa and almost a third (32% [29–34]) would be in South Asia (including India); over the next century, almost 90% of deaths averted would be in these regions (appendix p 26). The appendix (pp 27–32) provides information at the country level for the predicted impact of the WHO triple-intervention strategy. In all countries, the median estimates of mortality rates by 2120 approach 1 per 100 000 women or lower. The findings for model-specific, explanatory, and sensitivity analyses are provided in the appendix (pp 11–25, 33–43). Overall, the findings were concordant between models. The only notable difference was in the level of herd immunity predicted at older ages for unvaccinated individuals, which probably relate to underlying differences in assumptions around assortative sexual mixing among different age groups and different behaviour groups; we consider that the model variation in this area provides a useful reflection of true uncertainty in outcomes. The explanatory results demonstrated that the main benefits by 2030 were via cancer treatment scale-up, and that screening would lead to substantial mortality reductions beyond those conferred by vaccination and cancer treatment scale-up from 2030 to 2070–80. The results of the sensitivity analysis show that the choice of standard population is an important driver for rate estimates and also showed that, for deaths averted, differences between individual model estimates were much smaller than the unavoidable uncertainties in future population projections over the next century.

results of the sensitivity analysis show that the choice of standard population is an important driver for rate estimates and also showed that, for deaths averted, differences between individual model estimates were much smaller than the unavoidable uncertainties in future population projections over the next century. Discussion In this analysis, we have quantified, for the first time, the number of women's lives that could be saved by the successful implementation of the WHO global strategy for cervical cancer elimination. This report complements our parallel analysis on cervical cancer incidence.15 Importantly, by extending the analysis to encompass mortality outcomes, we have quantified the impact of scaling up cancer treatment. Taken together, these two modelling analyses show that successful implementation of the WHO 90–70–90 triple-intervention strategy by 2030 would reduce cervical cancer incidence to 0·7 (0·6–1·6) per 100 000 women15 and mortality to 0·2 (0·2–0·5) per 100 000 women across all 78 LMICs by 2120. This outcome, which is only achievable through a multi-sectoral and integrated approach across the continuum of cancer care, would represent extraordinary reductions in cervical cancer incidence (97% reduction) and mortality (99% reduction). Consequently, around 74·1 million cervical cancer cases and 62·6 million deaths would be averted, representing an enormous gain in terms of both quality of life and lives saved.

continuum of cancer care, would represent extraordinary reductions in cervical cancer incidence (97% reduction) and mortality (99% reduction). Consequently, around 74·1 million cervical cancer cases and 62·6 million deaths would be averted, representing an enormous gain in terms of both quality of life and lives saved. A major strength of this study is that we used a comparative approach involving well established model platforms that have been previously validated with data from multiple countries and that have jointly informed many national vaccination and cervical screening policy decisions. Predictions from the three models were broadly consistent for all scenarios, even over a century-long projection period. Our results for vaccination-only strategies are generally consistent with a recent analysis of the shorter-term impact on likely radiotherapy demand in LMICs,26 which estimated that bivalent HPV vaccination of girls aged 12 years would only result in a 3·9% reduction in incident cervical cancer cases from 2015 to 2035. In line with our findings, the analysis found that incremental scale-up of radiotherapy in LMICs in the shorter term (up to 2035) would yield substantial health gains. Our sensitivity analysis demonstrated that for deaths averted, the variations generated by the differences in models were much smaller than uncertainties due to population size and structure over the next century. The sensitivity analysis also demonstrated that rates are somewhat sensitive to the choice of standard population used; this emphasises the importance of using the 2015 World Female Population for calculating cervical cancer incidence and mortality rates for comparability with our findings and across countries.

entury. The sensitivity analysis also demonstrated that rates are somewhat sensitive to the choice of standard population used; this emphasises the importance of using the 2015 World Female Population for calculating cervical cancer incidence and mortality rates for comparability with our findings and across countries. There were also some limitations to our analysis. The quality and availability of data about access to cancer treatment services, effective delivery of treatment, stage-distribution at diagnosis, and survival are variable for LMICs. Our modelling of survival was based on the latest data from major WHO reviews and we used updated DIRAC radiotherapy machine density as a surrogate for radiotherapy capacity and treatment access; this approach is reflective of the importance of radiotherapy as a cornerstone of effective treatment for cervical cancer and in line with the approach used by recently published models and the 2015 Lancet Oncology Commission on expanding global access to radiotherapy.26, 27 Furthermore, each modelling group independently did country-level model calibration of stage-specific survival to the best available mortality estimates from GLOBOCAN 2018. We incorporated a calibrated quality factor into the final estimated country-specific and stage-specific survival assumptions, which encompasses data limitations in treatment delivery information as well as variations in treatment delivery from established protocols and recommendations, equipment and infrastructure maintenance and logistics, and treatment abandonment due to financial stress or for other reasons. We did not take into account treatment improvements over time, assuming that mortality benefits resulting from cancer treatment scale-up by 2030 will be only due to the delivery of existing, effective treatment modalities, and not to emerging or hypothetical improvements in treatment beyond what is proven to be effective on a large scale in health services in high-income countries today.

g that mortality benefits resulting from cancer treatment scale-up by 2030 will be only due to the delivery of existing, effective treatment modalities, and not to emerging or hypothetical improvements in treatment beyond what is proven to be effective on a large scale in health services in high-income countries today. Another limitation is that we did not explicitly model HPV infection, precancer and cervical cancer in women living with HIV. Increased progression to precancer and invasive cancer and reduced clearance of HPV is known to occur in women living with HIV, and this group is at increased risk of developing invasive cervical cancer, although this risk might now be partly or largely countered by the beneficial effects of antiretroviral therapy in many settings.28, 29 A separate collaborative group sponsored and coordinated by WHO is analysing the effects of HIV burden on estimates of cervical cancer elimination timing in selected countries. Current WHO cervical screening recommendations specify more frequent screening in women living with HIV,30 and thus the mortality benefits we predicted are likely to depend on successful implementation of more intensive strategies for screening in high HIV-burden settings.

ncer elimination timing in selected countries. Current WHO cervical screening recommendations specify more frequent screening in women living with HIV,30 and thus the mortality benefits we predicted are likely to depend on successful implementation of more intensive strategies for screening in high HIV-burden settings. We did not include vaccination of boys or adult women in our core scenarios, because neither strategy has been found to be universally cost-effective even in high-income countries, and neither approach is recommended as part of the draft WHO elimination strategy. WHO's Strategic Advisory Group of Experts on Immunisation (SAGE) has recommended that vaccinating boys or older women should be delayed until current vaccine supply constraints are alleviated.31 Priority should be given to vaccination of young girls since this strategy will generate the greatest health benefits overall; boys will derive protection via herd immunity if high-coverage vaccination can be achieved in girls, and older women will be offered protection via scale-up of screening and treatment services. In this analysis, we did not explicitly consider cost-effectiveness, although previous work has shown the cost-effectiveness of combined vaccination and cervical screening approaches in various upper-middle-income countries and LMICs.32, 33 Cost-effectiveness will be required to weigh the trade-offs of the different strategies assessed here, including the incremental costs and benefits of vaccinating boys and doing two cervical screening tests instead of one in a lifetime. We found that the additional benefit of twice-lifetime versus once-lifetime screening was 1·6 million more deaths averted over a century, but the differences in cases averted is much higher.15 Thus, the incremental improvement in quality of life from including a second screen is likely to be substantial. Furthermore, our findings for screening are in the context of rapid and effective scale-up of cancer treatment. If cancer treatment is not as broadly available as we assumed, the incremental benefits of additional cancer prevention via increasing screening to two tests in a lifetime would be larger. Finally, the incremental benefits of a second screen are higher when considered over the next 50 years rather than 100 years, because if vaccination is scaled up successfully then screening will provide the most benefit in the next 50–60 years.

ention via increasing screening to two tests in a lifetime would be larger. Finally, the incremental benefits of a second screen are higher when considered over the next 50 years rather than 100 years, because if vaccination is scaled up successfully then screening will provide the most benefit in the next 50–60 years. In the future, it will be important to assess the potential for future de-intensification of cervical screening, since our findings suggest that this could be considered in some countries after about 2070–80, when the full benefits of vaccination for mortality outcomes are becoming realised. The ongoing work of the CCEMC is focused on more detailed analysis of the incremental benefits of the strategies and on quantifying cost-effectiveness for the 78 LMICs; we are also analysing a larger number of more nuanced alternative scenarios at a country level, including optimal triage policy. In general terms, more detailed country-level analyses, taking into account specific local factors important for the effective delivery of vaccination and screening interventions, will continue to be required, and should be viewed as an important complement to the current large-scale analysis.

ng optimal triage policy. In general terms, more detailed country-level analyses, taking into account specific local factors important for the effective delivery of vaccination and screening interventions, will continue to be required, and should be viewed as an important complement to the current large-scale analysis. The WHO scale-up targets for elimination can be considered aspirational. Many challenges will need to be overcome, including vaccine and screening test supply and delivery challenges, and the infrastructure challenges associated with scale-up of invasive cancer diagnostics, treatment, and supportive and palliative care services. If scale-up is achieved more slowly than we have assumed, then reductions in mortality will be correspondingly delayed. With respect to HPV vaccination, the assumed scaled up 90% coverage rate is broadly in line with data suggesting that global coverage of other vaccines in LMICs (including measles, poliomyelitis, hepatitis B and diphtheria-tetanus-pertussis) is 84–90%.34 Our analysis for screening broadly applies to a wide range of clinically validated HPV tests that can achieve benchmark sensitivity and specificity. Testing could be done either at a central laboratory or in a point of care environment, with clinician-collected or self-collected samples; the sensitivity of PCR-based self-collected tests has been shown to be comparable to that of clinician-collected samples.35 In principle, our findings also apply to any future screening test with similar performance to that of primary HPV testing. For example, machine learning approaches for analysing digitised cervical images hold promise in some settings.36 Our modelling of screening assumed that the majority (90%) of HPV-positive women were treated, with visual assessment for treatment done only to exclude the possibility of a frank cancer or a large precancerous lesion (which would require referral). Therefore, our findings for the impact of the cervical screening and referral pathway are likely to represent the maximum attainable benefit. In practice, resource-stratified guidelines recommend different approaches in different settings and, where possible, women are triaged to treatment to minimise the potential harms, which include psychosocial impact, potential overtreatment, and a possible impact on obstetric outcomes.

t the maximum attainable benefit. In practice, resource-stratified guidelines recommend different approaches in different settings and, where possible, women are triaged to treatment to minimise the potential harms, which include psychosocial impact, potential overtreatment, and a possible impact on obstetric outcomes. WHO is revising its guidelines for cervical screening and has already revised its guidelines for precancer treatment to take into account the latest evidence and the elimination strategy.30, 37

t the maximum attainable benefit. In practice, resource-stratified guidelines recommend different approaches in different settings and, where possible, women are triaged to treatment to minimise the potential harms, which include psychosocial impact, potential overtreatment, and a possible impact on obstetric outcomes. WHO is revising its guidelines for cervical screening and has already revised its guidelines for precancer treatment to take into account the latest evidence and the elimination strategy.30, 37 One of our main findings is that although achieving cervical cancer elimination per se will take many decades, the benefits of scaling up to the WHO elimination coverage targets will start to be realised within a decade. Key to this insight is an understanding of the timing of the effects of each intervention. Over the next 10–20 years, scaling up cancer treatment services will have the greatest impact because thousands of women in LMICs are being diagnosed every year with cervical cancer but have no access to adequate treatment. With appropriate treatment, survival prospects for early-stage and locally advanced cervical cancer are high. As a linked issue, offering appropriate palliative care to women who require it is an ethical and moral imperative. Over the intermediate term (the next 50–60 years), cervical screening will make an important contribution to outcomes, and over the longer term the full benefits of vaccination will be realised. The realisation of the major benefits of screening and vaccination over the intermediate and longer term will, however, require immediate action to implement these initiatives.

cervical screening will make an important contribution to outcomes, and over the longer term the full benefits of vaccination will be realised. The realisation of the major benefits of screening and vaccination over the intermediate and longer term will, however, require immediate action to implement these initiatives. Scaling up to national vaccination, screening, and cancer treatment services in LMICs will be greatly facilitated by the successful realisation of universal health coverage in countries (SDG target 3.8). The 2019 Political Declaration of the UN high-level meeting on universal health coverage reaffirmed that health is a precondition for, and an outcome and indicator of, all dimensions of sustainable development, and countries strongly recommitted to achieving universal health coverage by 2030.38 Building resilient and sustainable health systems could also be facilitated by the cervical cancer elimination initiative.39 For example, cervical screening initiatives might be able to support or build on HIV services, since women receiving antiretroviral therapy return for refills regularly. Opportunities exist to link screening with sexual and reproductive health services, potentially increasing both uptake of screening and of contraception services. The elimination initiative could assist with building cancer literacy and addressing stigma in communities, and scaling up treatment as well as supportive and palliative care services for cervical cancer should have positive implications for various other tumour types. Access to universal health coverage will be a key underlying factor for the achievement of SDG goal 3.4, to reduce premature mortality from non-communicable diseases by a third by 2030. We have shown that, when considered at a level across all 78 LMICs, the cervical cancer elimination initiative will specifically support efforts to achieve this target. More broadly, the elimination agenda will support a reduction in poverty (SGD1), an increase in gender equality (SDG5), and reduction in inequalities (SDG10). Thus, successful implementation of the elimination initiative will have both nearer-term and enduring positive consequences, not only for women but also for their families and broader society.

genda will support a reduction in poverty (SGD1), an increase in gender equality (SDG5), and reduction in inequalities (SDG10). Thus, successful implementation of the elimination initiative will have both nearer-term and enduring positive consequences, not only for women but also for their families and broader society. In conclusion, these findings emphasise the importance of acting now on three fronts to scale up HPV vaccination, screening, and treatment for cervical cancer. In the next 10 years, achieving substantial reductions in cervical cancer mortality will depend on successful scale-up of cancer treatment services in LMICs, and supportive and palliative care will need to be scaled up alongside such services. Implementing the WHO strategy towards cervical cancer elimination will result in large-scale mortality reductions and more than 62 million women's lives saved over the next century in LMICs. These findings have informed the draft WHO global strategy for cervical cancer elimination, which will be presented to the WHO Executive Board in February, 2020, and thereafter considered at the World Health Assembly in May, 2020. Supplementary Material Supplementary appendix

In conclusion, these findings emphasise the importance of acting now on three fronts to scale up HPV vaccination, screening, and treatment for cervical cancer. In the next 10 years, achieving substantial reductions in cervical cancer mortality will depend on successful scale-up of cancer treatment services in LMICs, and supportive and palliative care will need to be scaled up alongside such services. Implementing the WHO strategy towards cervical cancer elimination will result in large-scale mortality reductions and more than 62 million women's lives saved over the next century in LMICs. These findings have informed the draft WHO global strategy for cervical cancer elimination, which will be presented to the WHO Executive Board in February, 2020, and thereafter considered at the World Health Assembly in May, 2020. Supplementary Material Supplementary appendix Acknowledgments We acknowledge May Abdel-Wahab and Jose Alfredo Polo Rubio from the Division of Human Health of the International Atomic Energy Agency for providing updated DIRAC data, which have been used to estimate cervical cancer treatment access rates in this study. Where authors are identified as personnel of WHO or the International Agency for Research on Cancer, the authors alone are responsible for the views expressed in this Article and they do not necessarily represent the decisions, policy, or views of WHO or the International Agency for Research on Cancer.

s in this study. Where authors are identified as personnel of WHO or the International Agency for Research on Cancer, the authors alone are responsible for the views expressed in this Article and they do not necessarily represent the decisions, policy, or views of WHO or the International Agency for Research on Cancer. Contributors KC, JJK, and MB co-designed the study and co-led overall data interpretation. KC led the Policy1-Cervix analysis, JJK led the Harvard analysis, and MB led the HPV-ADVISE analysis. AK, KTS, MC, EAB, JT, FB, NB, and RH also participated in study design. AI, DT, EF, NB, and RH led the systematic review and analysis of cancer treatment access and survival in LMICs. KC, JJK, MB, MC, AK, DTNN, KTS, EAB, CR, SS, MD, GG, DM, EB, J-FL, AI, DT, EF, and FB participated in data collection. KC, JJK, MB, AK, KTS, MC, EAB, DM, DTNN, EB, SS, CR, MD, GG, J-FL, MAS, EF, DT, AI, and FB participated in data analysis. MC, AK, DTNN, KTS, and KC produced the tables and figures. KC, JJK, and MB drafted the Article and RH coordinated the CCEMC. All authors interpreted the results and critically revised the manuscript for scientific content. All authors approved the final version of the Article.

AI, and FB participated in data analysis. MC, AK, DTNN, KTS, and KC produced the tables and figures. KC, JJK, and MB drafted the Article and RH coordinated the CCEMC. All authors interpreted the results and critically revised the manuscript for scientific content. All authors approved the final version of the Article. Declaration of interests KC, AK, KTS, MC, DTNN, and MAS report grants from the National Health and Medical Research Council Australia during the conduct of the study. KC and MC are investigators of an investigator-initiated trial of cervical screening in Australia (Compass; ACTRN12613001207707 and NCT02328872), which is conducted and funded by the VCS Foundation, a government-funded health promotion charity; the VCS Foundation received equipment and a funding contribution from Roche Molecular Systems and Ventana USA but KC and MC (or their institution on their behalf) do not receive direct funding from industry for this trial or any other project. MAS also reports grants from Cancer Institute NSW during the conduct of the study. JJK, MB, EAB, MD, GG, DM, EB, J-FL, SS, and CR report grants from WHO during the conduct of the study. JT, EF, DT, FB, AI, NB, and RH declare no competing interests.

Introduction Cervical cancer is the second most frequent cancer among women in low-income and lower-middle-income countries (LMICs).1 In 2018, 290 000 (51%) of the 570 000 new cervical cancer cases worldwide occurred in women living in LMICs (500 000 [88%] when including upper-middle-income countries).1 Without further intervention, these inequalities in the burden of cervical cancer are expected to grow, because recent increases in the uptake of human papillomavirus (HPV) vaccination and cervical cancer screening have mainly occurred in high-income countries. Less than 30% of LMICs have introduced HPV vaccination compared with more than 85% of high-income countries.2, 3 Additionally, only about 20% of women in LMICs have ever been screened for cervical cancer compared with more than 60% in high-income countries.4, 5 Research in context Evidence before this study

Introduction Cervical cancer is the second most frequent cancer among women in low-income and lower-middle-income countries (LMICs).1 In 2018, 290 000 (51%) of the 570 000 new cervical cancer cases worldwide occurred in women living in LMICs (500 000 [88%] when including upper-middle-income countries).1 Without further intervention, these inequalities in the burden of cervical cancer are expected to grow, because recent increases in the uptake of human papillomavirus (HPV) vaccination and cervical cancer screening have mainly occurred in high-income countries. Less than 30% of LMICs have introduced HPV vaccination compared with more than 85% of high-income countries.2, 3 Additionally, only about 20% of women in LMICs have ever been screened for cervical cancer compared with more than 60% in high-income countries.4, 5 Research in context Evidence before this study In May, 2018, WHO issued a global call to eliminate cervical cancer as a public health problem. To inform its global strategy to accelerate cervical cancer elimination, WHO created the Cervical Cancer Elimination Modelling Consortium (CCEMC) to examine the following key questions: what elimination threshold should be used; what prevention strategies can lead to elimination; when could elimination be reached for different countries; and how many cancers could be averted. The current working definition of elimination is an age-standardised cervical cancer incidence of four or fewer cases per 100 000 women-years. Alternative definitions, such as an incidence of ten or fewer cases per 100 000 women-years and an 80–90% reduction in incidence, have also been suggested. The only previous multicountry modelling study of cervical cancer elimination suggests that global elimination is possible through girls-only human papillomavirus (HPV) vaccination at 80–100% coverage with a perfectly effective 9-valent vaccine and twice-lifetime HPV-based screening. Given that models necessarily include simplifying assumptions, the goal of the consortium is to use multiple models, taking into account their respective strengths and limitations, to illustrate the robustness of predictions. A systematic comparative modelling approach was used. To form the CCEMC, WHO selected three models that met the predefined eligibility criteria: HPV-ADVISE, Harvard, and Policy1-Cervix. The models projected reductions in cervical cancer incidence over time based on standardised HPV vaccination and cervical screening scenarios determined after consultations at various WHO technical expert, advisory group, and global stakeholder meetings. Three elimination thresholds were examined (cervical cancer incidence of four or fewer cases per 100 000 women-years, ten or fewer cases per 100 000 women-years, and ≥85% reduction in incidence).

g scenarios determined after consultations at various WHO technical expert, advisory group, and global stakeholder meetings. Three elimination thresholds were examined (cervical cancer incidence of four or fewer cases per 100 000 women-years, ten or fewer cases per 100 000 women-years, and ≥85% reduction in incidence). Added value of this study This comparative modelling analysis, which includes projections from three independent transmission-dynamic models, provides consistent results suggesting that 90% HPV vaccination coverage of girls can lead to cervical cancer elimination in most low-income and lower-middle-income countries (LMICs) within the next century. However, countries with the highest cervical cancer incidence (>25 cases per 100 000 women-years) might not reach elimination at the threshold of four or fewer cases per 100 000 women-years by vaccination alone, although these countries are predicted to have the greatest absolute reductions. More than 90% of these LMICs are in sub-Saharan Africa. Screening would accelerate elimination by 11–31 years and will be necessary to eliminate cervical cancer in countries with the highest incidence. Profound health benefits are predicted on the path to elimination. Intensive scale-up of girls-only vaccination with twice-lifetime screening is predicted to halve the age-standardised cervical cancer incidence by 2048 (and by 2061 with vaccination only), and to avert more than 74 million cervical cancer cases (61 million with vaccination only) in LMICs over the next century. Implications of all the available evidence

This comparative modelling analysis, which includes projections from three independent transmission-dynamic models, provides consistent results suggesting that 90% HPV vaccination coverage of girls can lead to cervical cancer elimination in most low-income and lower-middle-income countries (LMICs) within the next century. However, countries with the highest cervical cancer incidence (>25 cases per 100 000 women-years) might not reach elimination at the threshold of four or fewer cases per 100 000 women-years by vaccination alone, although these countries are predicted to have the greatest absolute reductions. More than 90% of these LMICs are in sub-Saharan Africa. Screening would accelerate elimination by 11–31 years and will be necessary to eliminate cervical cancer in countries with the highest incidence. Profound health benefits are predicted on the path to elimination. Intensive scale-up of girls-only vaccination with twice-lifetime screening is predicted to halve the age-standardised cervical cancer incidence by 2048 (and by 2061 with vaccination only), and to avert more than 74 million cervical cancer cases (61 million with vaccination only) in LMICs over the next century. Implications of all the available evidence The results of the CCEMC suggest that cervical cancer elimination as a public health problem is possible by the end of the century. However, to achieve elimination across all LMICs under the most ambitious threshold (four or fewer cases per 100 000 women-years), both high HPV vaccination coverage and screening uptake will be necessary, which will require considerable international commitment. These results have directly informed WHO's target of 90% HPV vaccination coverage, 70% screening coverage, and 90% of cervical lesions treated by 2030, as well as the WHO global strategy to accelerate cervical cancer elimination, which will be presented at the World Health Assembly in May, 2020.

ternational commitment. These results have directly informed WHO's target of 90% HPV vaccination coverage, 70% screening coverage, and 90% of cervical lesions treated by 2030, as well as the WHO global strategy to accelerate cervical cancer elimination, which will be presented at the World Health Assembly in May, 2020. Inequalities in HPV vaccination and screening uptake persist, despite the large body of evidence demonstrating that these interventions are highly effective and cost-effective. Large international randomised control clinical trials have shown that HPV vaccines are safe and highly effective against vaccine-type persistent infection and cervical precancerous lesions in women (with vaccine efficacy ≥93%).6, 7, 8 These vaccines target high-risk HPV types that cause about 70% (bivalent and quadrivalent vaccines: HPV types 16 and 18) and 90% (9-valent vaccine: HPV types 16, 18, 31, 33, 45, 52, and 58) of cervical cancers.9, 10 Countries that have achieved high vaccination coverage have observed declines of 73–85% in vaccine-type HPV prevalence, and declines of 41–57% in high grade lesions (cervical intraepithelial neoplasia, grade 2 or worse) among young women, less than 10 years after implementation of HPV vaccination.11 The effectiveness of population-based cervical cancer screening has also been shown, through the sharp declines in age-standardised cervical cancer incidence in high-income countries following the implementation of cytology-based screening.12, 13 Randomised controlled trials have shown that HPV-based tests are highly effective at detecting precancerous lesions and are likely to be more effective at preventing cervical cancer than visual inspection with acetic acid or cytology.14, 15, 16 Finally, mathematical modelling studies have consistently shown that girls-only HPV vaccination and cervical cancer screen-and-treat programmes are cost-effective in LMICs.17, 18, 19, 20, 21, 22

are likely to be more effective at preventing cervical cancer than visual inspection with acetic acid or cytology.14, 15, 16 Finally, mathematical modelling studies have consistently shown that girls-only HPV vaccination and cervical cancer screen-and-treat programmes are cost-effective in LMICs.17, 18, 19, 20, 21, 22 Given the substantial global burden of cervical cancer, the increasing inequalities, and opportunities for effective and cost-effective primary and secondary prevention, the WHO Director-General made a global call in May, 2018, for action towards the elimination of cervical cancer as a public health problem.23 To achieve this goal, WHO is developing, with its partners, a global strategy towards the elimination of cervical cancer.24 Fundamental questions that must be addressed in the global strategy include: what elimination definition and threshold should be used, what prevention strategies can lead to elimination, when could elimination be reached, how many cervical cancers and deaths can be averted on the path to elimination, and what are the most efficient and cost-effective strategies to reach elimination? These important questions can only be addressed through mathematical modelling, which integrates our understanding of HPV transmission, cervical carcinogenesis, vaccine efficacy, and cervical screening and treatment performance to project the long-term health consequences of alternative cancer control policies. Hence, to inform its global strategy to accelerate cervical cancer elimination, WHO assembled the Cervical Cancer Elimination Modelling Consortium (CCEMC).25, 26

nesis, vaccine efficacy, and cervical screening and treatment performance to project the long-term health consequences of alternative cancer control policies. Hence, to inform its global strategy to accelerate cervical cancer elimination, WHO assembled the Cervical Cancer Elimination Modelling Consortium (CCEMC).25, 26 In this Article, we describe the comparative modelling approach used by the CCEMC to inform WHO's global strategy towards the elimination of cervical cancer,24 and present the CCEMC's predictions of the impact of various HPV vaccination and screening elimination strategies on cervical cancer incidence in 78 LMICs. The specific objectives of this analysis were to identify prevention strategies that lead to elimination, estimate the timing of elimination, and predict the number of cervical cancer cases averted on the path to elimination, for different elimination thresholds and country characteristics. In an accompanying Article,27 we present the CCEMC's predictions of the impact of HPV vaccination, screening, and treatment scale-up on cervical cancer mortality. Methods Comparative modelling approach This comparative modelling analysis adhered to recently published guidelines for multi-model comparisons28 and for reporting model-based analyses of HPV vaccination and cervical screening29 (appendix pp 26–28). A three-step systematic comparative modelling approach was used.

In this Article, we describe the comparative modelling approach used by the CCEMC to inform WHO's global strategy towards the elimination of cervical cancer,24 and present the CCEMC's predictions of the impact of various HPV vaccination and screening elimination strategies on cervical cancer incidence in 78 LMICs. The specific objectives of this analysis were to identify prevention strategies that lead to elimination, estimate the timing of elimination, and predict the number of cervical cancer cases averted on the path to elimination, for different elimination thresholds and country characteristics. In an accompanying Article,27 we present the CCEMC's predictions of the impact of HPV vaccination, screening, and treatment scale-up on cervical cancer mortality. Methods Comparative modelling approach This comparative modelling analysis adhered to recently published guidelines for multi-model comparisons28 and for reporting model-based analyses of HPV vaccination and cervical screening29 (appendix pp 26–28). A three-step systematic comparative modelling approach was used. The aim of the first step was to identify and select the mathematical models. To minimise selection bias, WHO selected models that met the following predefined eligibility criteria: the models explicitly included the dynamic transmission of HPV infection, were capable of projecting the impact of HPV vaccination and cervical screening for all 78 LMICs, were independently developed and had been previously peer reviewed and published, and were able to provide predictions in a short timeframe to inform the WHO global strategy.24 Four independent models were identified: HPV-ADVISE,30, 31 Harvard,32, 33 Policy1-Cervix,34, 35, 36 and Spectrum.37, 38

ening for all 78 LMICs, were independently developed and had been previously peer reviewed and published, and were able to provide predictions in a short timeframe to inform the WHO global strategy.24 Four independent models were identified: HPV-ADVISE,30, 31 Harvard,32, 33 Policy1-Cervix,34, 35, 36 and Spectrum.37, 38 The aim of the second step was to identify HPV vaccination and screening strategies that can lead to cervical cancer elimination and examine between-model variability. The four models were used to predict the change in cervical cancer incidence over time for 40 standardised HPV vaccination and screening scenarios, with a subset of ten LMICs (appendix pp 14–15). Impact predictions were done without harmonising the basic structure of the models or parameters governing the setting and disease. The results were presented at various WHO technical expert, advisory group, and global stakeholder meetings, and ultimately three HPV vaccination and screening scenarios were identified to proceed in a larger number of countries (78 LMICs).39 The three final scenarios that were selected for the global analysis (see scenario descriptions below and in the appendix p 16) were chosen as they showed potential for cervical cancer elimination in LMICs and follow WHO recommendations for HPV vaccination and cervical screening.40, 41

number of countries (78 LMICs).39 The three final scenarios that were selected for the global analysis (see scenario descriptions below and in the appendix p 16) were chosen as they showed potential for cervical cancer elimination in LMICs and follow WHO recommendations for HPV vaccination and cervical screening.40, 41 Finally, the aim of the third step was to produce predictions of the population-level impact of the three HPV vaccination and cervical screening scenarios for all 78 LMICs. Three of the four models (HPV-ADVISE, Harvard, and Policy1-Cervix) were able to provide predictions for all 78 LMICs within the required timelines, and thus form the core models of the CCEMC. The structure of the models and the comparative modelling approach were presented and reviewed by the WHO Advisory Committee on Immunization and Vaccines related Research (IVIR).39

olicy1-Cervix) were able to provide predictions for all 78 LMICs within the required timelines, and thus form the core models of the CCEMC. The structure of the models and the comparative modelling approach were presented and reviewed by the WHO Advisory Committee on Immunization and Vaccines related Research (IVIR).39 Model description The three CCEMC models (HPV-ADVISE, Harvard, and Policy1-Cervix) have been used extensively to inform recommendations on cervical screening and HPV vaccination in Australia, Canada, the UK, the USA, and at a global level.30, 31, 32, 33, 34, 35, 36 Although developed independently, the models have common features. First, they are transmission-dynamic models of HPV infection and the natural history of cervical cancer. Second, they include the following components: sexual behaviour and HPV transmission, natural history of cervical cancer, vaccination, and screening, diagnosis, management, and treatment of cervical lesions and cancer. HPV transmission and cervical carcinogenesis are modelled for the HPV types in the 9-valent vaccine (HPV types 16, 18, 31, 33, 45, 52, 58) and other high-risk types. The models simulate type-specific HPV transmission through sexual activity, based on different risk groups and sexual mixing. The models reproduce the type-specific natural history of cervical cancer, from persistent HPV infection to cervical cancer via precancerous cervical lesions (cervical intraepithelial neoplasia grade 1 to 3). All models assume that HPV vaccines are prophylactic and capture post-vaccination herd effects. They can also simulate complex cervical screening and treatment algorithms at the individual level, by tracking and simulating each woman's screening history. Finally, all models were calibrated to highly stratified sexual behaviour and epidemiological data, validated to clinical trials or post-vaccination data, or both, and reproduce the age-specific cervical cancer incidence estimates from the Global Cancer Observatory (GLOBOCAN) 2018 for all 78 LMICs42 (see the appendix pp 18–23 for further details of the CCEMC models).

atified sexual behaviour and epidemiological data, validated to clinical trials or post-vaccination data, or both, and reproduce the age-specific cervical cancer incidence estimates from the Global Cancer Observatory (GLOBOCAN) 2018 for all 78 LMICs42 (see the appendix pp 18–23 for further details of the CCEMC models). Vaccination and screening scenarios Three standardised base-case HPV vaccination and cervical screening scenarios were examined. The first was vaccination only: routine vaccination of girls aged 9 years (with a 1-year multi-age cohort catch-up to age 14 years) reaching 90% coverage in the first year (2020). The second was vaccination and once-lifetime screening: scenario 1 plus one lifetime screen at age 35 years, assuming screening uptake ramp-up over time (45% in 2023, 70% in 2030, and 90% in 2045). The third was vaccination and twice-lifetime screening: scenario 1 plus two lifetime screens at ages 35 years and 45 years, assuming screening uptake ramp-up over time (45% in 2023, 70% in 2030, and 90% in 2045).

n at age 35 years, assuming screening uptake ramp-up over time (45% in 2023, 70% in 2030, and 90% in 2045). The third was vaccination and twice-lifetime screening: scenario 1 plus two lifetime screens at ages 35 years and 45 years, assuming screening uptake ramp-up over time (45% in 2023, 70% in 2030, and 90% in 2045). For the base-case scenarios, HPV vaccination was assumed to provide 100% efficacy against HPV types 16, 18, 31, 33, 45, 52, and 58, and lifelong duration of protection. Cervical screening was assumed to involve primary HPV screen-and-treat testing, with 100% precancer treatment efficacy and 10% of individuals lost to follow-up (due to treatment non-compliance). To estimate the population-level impact of the base-case scenarios, we also modelled a status quo scenario, which assumes no further scale-up of preventive interventions (see appendix p 16 for more details). The 40 HPV vaccination and cervical screening scenarios from step 2 of the comparative modelling approach were used to understand the impact of model assumptions on predictions. The sensitivity analysis included varying HPV vaccination coverage, the targeted population (girls only vs girls and boys), ages at vaccination, screening frequency, the HPV types targeted by the vaccine, and the duration of vaccine protection. Results of the sensitivity analysis are shown for two example countries, representing one low-income country in sub-Saharan Africa (Uganda) and one lower-middle-income country in east Asia (Vietnam).

ages at vaccination, screening frequency, the HPV types targeted by the vaccine, and the duration of vaccine protection. Results of the sensitivity analysis are shown for two example countries, representing one low-income country in sub-Saharan Africa (Uganda) and one lower-middle-income country in east Asia (Vietnam). Outcomes Population-level impact was measured with three main outcomes: age-standardised cervical cancer incidence, relative reductions in age-standardised cervical cancer incidence (vs status quo), and number of cases averted (vs status quo). The time horizon of the analysis was from 2020 to 2120. The age-standardised cervical cancer incidence and relative reductions in incidence over time were used to assess the feasibility and timing of cervical cancer elimination at different thresholds. We used the CCEMC models to independently estimate the outcomes for each of the 78 countries. Results were also aggregated by World Bank income level and region (see appendix p 17 for a description of country characteristics). Outcomes are presented with the median (range) of the predictions of the three models to represent between-model uncertainty.28

ndently estimate the outcomes for each of the 78 countries. Results were also aggregated by World Bank income level and region (see appendix p 17 for a description of country characteristics). Outcomes are presented with the median (range) of the predictions of the three models to represent between-model uncertainty.28 The age-standardised cervical cancer incidence over time was estimated for each CCEMC model, vaccination, and screening scenario, and for each country using the predictions of age-specific cervical cancer incidence over time and applying the age structure of the 2015 global female population aged 0–99 years.43 Reductions (absolute and relative) in age-standardised cervical cancer incidence over time were estimated compared to the status quo. Finally, the cumulative number of cases averted over time was estimated with a three-step process. First, for each CCEMC model, vaccination, and screening scenario, and country, we estimated the number of cervical cancers by year and age group by multiplying the predicted age-specific cervical cancer incidence and the age-specific UN population growth projections.43 Second, we estimated the number of cervical cancers in each year by summing the cases predicted in each age group. Third, the number of cases averted in each year was estimated by subtracting the number of cases predicted under each vaccination and screening scenario from those predicted under the status quo. The number of cancer cases averted in each World Bank income level or region was estimated by aggregating the country-specific results. The model predictions were done independently by each group and collated by the study's coordinating centre (Laval University, Québec, QC, Canada). See the appendix (pp 18–25) for more methodological details.

es averted in each World Bank income level or region was estimated by aggregating the country-specific results. The model predictions were done independently by each group and collated by the study's coordinating centre (Laval University, Québec, QC, Canada). See the appendix (pp 18–25) for more methodological details. Elimination thresholds Our base-case definition of elimination is an age-standardised (2015 world standard) cervical cancer incidence of four or fewer cases per 100 000 women-years, which is the current working definition used by WHO and the proposed WHO global strategy towards elimination of cervical cancer.24 The threshold was determined following multiple WHO technical expert meetings and global stakeholder consultations held between March and September, 2018.24 Alternative definitions, such as a higher incidence threshold (ten cases per 100 000 women-years) and a percentage reduction in incidence (85–90%), were also discussed.39 Thus, as a sensitivity analysis, two alternative definitions were explored: age-standardised cervical cancer incidence of ten or fewer cases per 100 000 women-years and a reduction in age-standardised cervical cancer incidence of ≥85% (vs status quo). Elimination was predicted to occur the first year in which a country reached the threshold definition. Elimination within a region or income level was predicted to occur the year in which all countries within the region or income level reached elimination.

standardised cervical cancer incidence of ≥85% (vs status quo). Elimination was predicted to occur the first year in which a country reached the threshold definition. Elimination within a region or income level was predicted to occur the year in which all countries within the region or income level reached elimination. Role of the funding source This study was partly funded by WHO. WHO contributed to study design, data analysis, data interpretation, and writing of the report. The other funding sources had no role in this work. MB, JJK, and KC had full access to all the data in the study and had final responsibility for the decision to submit for publication.

e This study was partly funded by WHO. WHO contributed to study design, data analysis, data interpretation, and writing of the report. The other funding sources had no role in this work. MB, JJK, and KC had full access to all the data in the study and had final responsibility for the decision to submit for publication. Results The CCEMC models predicted that girls-only HPV vaccination with 90% coverage will reduce the median age-standardised cervical cancer incidence in LMICs from 19·8 (range 19·4–19·8) to 2·1 (2·0–2·6) cases per 100 000 women-years over the next century, which represents an 89·4% (86·2–90·1) reduction in cervical cancer (vs the status quo; figure 1, table). The addition of screening was predicted to substantially accelerate declines in cervical cancer and to lead to lower cervical cancer incidence at equilibrium. HPV vaccination and once-lifetime screening was predicted to reduce the average age-standardised cervical cancer incidence in LMICs to 1·0 (0·9–2·0) cases per 100 000 women-years over the next century (95·0% [89·0–95·3] reduction), whereas HPV vaccination and twice-lifetime screening was predicted to reduce the average age-standardised cervical cancer incidence to 0·7 (0·6–1·6) cases per 100 000 women-years at equilibrium (96·7% [91·3–96·7] reduction). Additionally, the models predicted that cervical cancer incidence will be halved in LMICs by 2061 (2060–63) with HPV vaccination alone, by 2055 (2055–56) when adding once-lifetime screening, and by 2048 (2047–49) when adding twice-lifetime screening. Notably, the models predicted that HPV vaccination with or without screening will reduce age-standardised cervical cancer incidence in women of childbearing age (<45 years) by more than 85% before 2050 (appendix p 5).Figure 1 Dynamics of cervical cancer incidence after HPV vaccination and cervical screening

screening. Notably, the models predicted that HPV vaccination with or without screening will reduce age-standardised cervical cancer incidence in women of childbearing age (<45 years) by more than 85% before 2050 (appendix p 5).Figure 1 Dynamics of cervical cancer incidence after HPV vaccination and cervical screening Average age-standardised cervical cancer incidence per 100 000 women-years (A) and relative reduction in incidence (B) after HPV vaccination and screening ramp-up in low-income and lower-middle-income countries. Median prediction from the three models. Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. Equilibrium occurs 90–100 years after the introduction of HPV vaccination only (and earlier for the screening scenarios). HPV=human papillomavirus. Table Change in age-standardised cervical cancer incidence over time, percentage of countries reaching elimination for different thresholds, and year of elimination, by World Bank income level and region

Average age-standardised cervical cancer incidence per 100 000 women-years (A) and relative reduction in incidence (B) after HPV vaccination and screening ramp-up in low-income and lower-middle-income countries. Median prediction from the three models. Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. Equilibrium occurs 90–100 years after the introduction of HPV vaccination only (and earlier for the screening scenarios). HPV=human papillomavirus. Table Change in age-standardised cervical cancer incidence over time, percentage of countries reaching elimination for different thresholds, and year of elimination, by World Bank income level and region Incidence per 100 000 women-years Reduction in incidence (%) Base-case elimination threshold: ≤4 cases per 100 000 Alternative elimination threshold: ≤10 cases per 100 000 Alternative elimination threshold: ≥85% reduction 2020 2045 Equilibrium 2045 Equilibrium Countries (%) Year of elimination Countries (%) Year of elimination Countries (%) Year of elimination All low-income and lower-middle-income countries (n=78) Vaccination only 19·8 (19·4–19·8) 16·9 (16·2–17·5) 2·1 (2·0–2·6) 12·9 (11·6–14·4) 89·4 (86·2–90·1) 60·3 (57·7–65·4) X (X–X) 98·7 (88·5–100·0) X (2096–X) 87·2 (37·2–98·7) X (X–X) Vaccination and one lifetime screen 19·8 (19·4–19·8) 13·7 (13·3–13·8) 1·0 (0·9–2·0) 30·3 (28·6–30·8) 95·0 (89·0–95·3) 96·2 (60·3–97·4) X (X–X) 100·0 (94·9–100·0) 2090 (2082–X) 100·0 (100·0–100·0) 2085 (2080–2100) Vaccination and two lifetime screens 19·8 (19·3–19·9) 11·0 (10·7–11·6) 0·7 (0·6–1·6) 41·5 (40·6–46·1) 96·7 (91·3–96·7) 100·0 (70·5–100·0) 2098 (2097–X) 100·0 (98·7–100·0) 2085 (2078–X) 100·0 (100·0–100·0) 2081 (2077–2094) World Bank income levels Low-income countries (n=34) Vaccination only 32·7 (32·7–33·6) 28·6 (28·0–31·1) 3·9 (3·4–5·7) 13·4 (12·7–14·3) 88·1 (84·1–89·5) 44·1 (41·2–50·0) X (X–X) 100·0 (82·4–100·0) 2093 (2090–X) 82·4 (14·7–100·0) X (2091–X) Vaccination and one lifetime screen 32·7 (32·7–33·6) 22·6 (22·6–25·7) 1·8 (1·7–4·6) 31·1 (28·4–31·1) 94·7 (87·1–94·9) 97·1 (44·1–97·1) X (X–X) 100·0 (94·1–100·0) 2082 (2079–X) 100·0 (100·0–100·0) 2083 (2079–2098) Vaccination and two lifetime screens 32·8 (32·7–33·7) 19·0 (17·5–21·8) 1·2 (1·2–3·8) 41·9 (39·3–46·6) 96·4 (89·5–96·5) 100·0 (52·9–100·0) 2089 (2088–X) 100·0 (100·0–100·0) 2076 (2074–2099) 100·0 (100·0–100·0) 2079 (2073–2092) Low-income and lower-middle-income countries (n=44) Vaccination only 17·8 (17·2–17·8) 15·3 (14·0–15·9) 1·8 (1·8–2·1) 12·3 (11·0–14·1) 89·7 (87·0–90·2) 72·7 (70·5–77·3) X (X–X) 97·7 (93·2–100·0) X (2096–X) 90·9 (54·5–97·7) X (X–X) Vaccination and one lifetime screen 17·8 (17·2–17·9) 12·4 (11·5–12·5) 0·9 (0·8–1·6) 29·8 (28·3–30·4) 95·1 (89·8–95·4) 95·5 (72·7–97·7) X (X–X) 100·0 (95·5–100·0) 2090 (2082–X) 100·0 (100·0–100·0) 2085 (2080–2100) Vaccination and two lifetime screens 17·8 (17·2

(X–X) 97·7 (93·2–100·0) X (2096–X) 90·9 (54·5–97·7) X (X–X) Vaccination and one lifetime screen 17·8 (17·2–17·9) 12·4 (11·5–12·5) 0·9 (0·8–1·6) 29·8 (28·3–30·4) 95·1 (89·8–95·4) 95·5 (72·7–97·7) X (X–X) 100·0 (95·5–100·0) 2090 (2082–X) 100·0 (100·0–100·0) 2085 (2080–2100) Vaccination and two lifetime screens 17·8 (17·2 –17·9) 9·7 (9·5–10·5) 0·6 (0·6–1·3) 41·1 (40·8–45·7) 96·8 (92·0–96·8) 100·0 (84·1–100·0) 2098 (2097–X) 100·0 (97·7–100·0) 2085 (2078–X) 100·0 (100·0–100·0) 2081 (2077–2094) World Bank regions East Asia and Pacific (n=12) Vaccination only 19·9 (19·3–19·9) 17·0 (15·0–17·5) 2·2 (2·2–2·5) 13·7 (12·0–14·5) 87·3 (87·2–89·2) 100·0 (91·7–100·0) 2102 (2087–X) 100·0 (100·0–100·0) 2067 (2066–2069) 100·0 (91·7–100·0) 2091 (2087–X) Vaccination and one lifetime screen 19·9 (19·2–19·9) 13·4 (12·0–13·7) 1·2 (0·9–1·7) 31·4 (30·8–32·5) 93·8 (90·3–95·3) 100·0 (100·0–100·0) 2079 (2075–2091) 100·0 (100·0–100·0) 2061 (2060–2061) 100·0 (100·0–100·0) 2078 (2078–2087) Vaccination and two lifetime screens 19·9 (19·1–20·1) 10·3 (9·6–11·4) 0·8 (0·7–1·3) 44·7 (42·8–48·5) 96·0 (92·4–96·7) 100·0 (100·0–100·0) 2071 (2069–2085) 100·0 (100·0–100·0) 2052 (2050–2054) 100·0 (100·0–100·0) 2073 (2073–2081) Europe and central Asia (n=6) Vaccination only 15·7 (15·6–15·7) 11·8 (11·6–12·3) 1·4 (1·2–1·7) 22·9 (21·7–25·0) 90·9 (88·5–92·7) 100·0 (100·0–100·0) 2080 (2078–2080) 100·0 (100·0–100·0) 2059 (2059–2060) 100·0 (100·0–100·0) 2085 (2081–2088) Vaccination and one lifetime screen 15·7 (15·6–15·8) 8·9 (8·8–9·0) 0·7 (0·7–1·3) 43·2 (40·1–44·2) 95·6 (91·1–95·8) 100·0 (100·0–100·0) 2070 (2069–2075) 100·0 (100·0–100·0) 2052 (2052–2053) 100·0 (100·0–100·0) 2073 (2078–2079) Vaccination and two lifetime screens 15·7 (15·5–15·7) 7·6 (6·8–7·7) 0·5 (0·5–1·1) 51·0 (49·6–56·7) 96·7 (92·5–96·8) 100·0 (100·0–100·0) 2065 (2063–2069) 100·0 (100·0–100·0) 2048 (2046–2049) 100·0 (100·0–100·0) 2068 (2066–2074) Latin America and Caribbean (n=5) Vaccination only 26·8 (25·6–27·0) 21·4 (18·9–21·9) 3·0 (2·7–3·7) 18·3 (18·2–20·3) 88·8 (84·1–90·1) 80·0 (80·0–80·0) X (X–X) 100·0 (100·0–100·0) 2070 (2070–2071) 100·0 (X-100·0) 2091 (2086- X) Vaccination and one lifetime screen 26·8 (25·8–27·0) 16·5 (15·3–16·7) 1·5 (1·3–3·0) 37·8 (33·7–38·5) 94·5 (87·0–95·2) 100·0 (80·0–100·0) 2079 (2074–X) 100·0 (100·0–100·0) 2065 (2061–2066) 100·0 (100·0–100·0) 2079 (2077–2099) Vaccination and two lifetime screens 26·8 (25·7–26·8) 13·3 (13·2–14·3) 1·1 (1·0–2·6) 46·6 (43·1–50·3) 96·0 (88·9–96·4) 100·0 (100·0–100·0) 2073 (2069–2089) 100·0 (100·0–100·0)

·0) 37·8 (33·7–38·5) 94·5 (87·0–95·2) 100·0 (80·0–100·0) 2079 (2074–X) 100·0 (100·0–100·0) 2065 (2061–2066) 100·0 (100·0–100·0) 2079 (2077–2099) Vaccination and two lifetime screens 26·8 (25·7–26·8) 13·3 (13·2–14·3) 1·1 (1·0–2·6) 46·6 (43·1–50·3) 96·0 (88·9–96·4) 100·0 (100·0–100·0) 2073 (2069–2089) 100·0 (100·0–100·0) 2057 (2056–2058) 100·0 (100·0–100·0) 2076 (2072–2094) North Africa and Middle East (n=7) Vaccination only 6·8 (6·5–6·8) 6·1 (5·2–6·4) 0·8 (0·5–0·9) 8·2 (6·9–10·5) 88·5 (84·9–92·9) 100·0 (100·0–100·0) 2081 (2076–2081) 100·0 (100·0–100·0) 2062 (2061–2066) 100·0 (71·4–100·0) 2090 (2085- X) Vaccination and one lifetime screen 6·8 (6·5–6·9) 5·2 (4·5–5·2) 0·3 (0·3–0·7) 23·8 (21·3–23·9) 95·0 (87·5–95·9) 100·0 (100·0–100·0) 2073 (2073–2078) 100·0 (100·0–100·0) 2058 (2057–2058) 100·0 (100·0–100·0) 2081 (2080–2097) Vaccination and two lifetime screens 6·8 (6·5–6·9) 4·1 (3·8–4·4) 0·2 (0·2–0·6) 35·7 (34·1–39·6) 96·6 (90·1–97·2) 100·0 (100·0–100·0) 2068 (2068–2074) 100·0 (100·0–100·0) 2050 (2048–2051) 100·0 (100·0–100·0) 2079 (2077–2094) South Asia (n=7) Vaccination only 15·5 (14·6–15·5) 13·3 (11·3–13·8) 1·4 (1·1–1·5) 12·3 (10·9–14·6) 91·3 (88·3–92·8) 100·0 (100·0–100·0) 2074 (2072–2077) 100·0 (100·0–100·0) 2060 (2058–2061) 100·0 (100·0–100·0) 2087 (2082–2092) Vaccination and one lifetime screen 15·5 (14·6–15·6) 10·8 (9·2–11·0) 0·7 (0·6–1·2) 28·9 (28·3–30·7) 95·8 (90·8–96·2) 100·0 (100·0–100·0) 2070 (2069–2071) 100·0 (100·0–100·0) 2053 (2053–2054) 100·0 (100·0–100·0) 2079 (2079–2087) Vaccination and two lifetime screens 15·5 (14·6–15·6) 8·6 (7·6–9·3) 0·4 (0·4–0·9) 40·6 (40·4–44·6) 97·1 (92·9–97·3) 100·0 (100·0–100·0) 2063 (2063–2065) 100·0 (100·0–100·0) 2046 (2046–2049) 100·0 (100·0–100·0) 2074 (2074–2082) Sub-Saharan Africa (n=41) Vaccination only 37·4 (37·4–38·7) 33·6 (33·0–37·5) 4·9 (4·5–6·7) 10·7 (10·0–11·7) 87·0 (84·1–87·9) 26·8 (24·4–36·6) X (X–X) 97·6 (78·0–100·0) X (2096–X) 75·6 (X-97·6) X (X–X) Vaccination and one lifetime screen 37·4 (37·4–38·6) 27·2 (27·1–31·5) 2·2 (2·0–5·5) 27·4 (25·0–27·5) 94·1 (87·0–94·7) 92·7 (26·8–95·1) X (X–X) 100·0 (90·2–100·0) 2090 (2082–X) 100·0 (100·0–100·0) 2085 (2079–2100) Vaccination and two lifetime screens 37·5 (37·4–38·8) 22·7 (21·0–26·7) 1·4 (1·4–4·4) 39·4 (36·5–43·9) 96·3 (89·6–96·4) 100·0 (43·9–100·0) 2098 (2097–X) 100·0 (97·6–100·0) 2085 (2078–X) 100·0 (100·0–100·0) 2081 (2073–2094) Data are median (range) predictions from three dynamic models.

00·0 (100·0–100·0) 2085 (2079–2100) Vaccination and two lifetime screens 37·5 (37·4–38·8) 22·7 (21·0–26·7) 1·4 (1·4–4·4) 39·4 (36·5–43·9) 96·3 (89·6–96·4) 100·0 (43·9–100·0) 2098 (2097–X) 100·0 (97·6–100·0) 2085 (2078–X) 100·0 (100·0–100·0) 2081 (2073–2094) Data are median (range) predictions from three dynamic models. Equilibrium occurs 90–100 years after the introduction of human papillomavirus (HPV) vaccination only (and earlier for the screening scenarios). X=elimination not reached in all countries in the income or regional group. Girls-only vaccination refers to vaccination coverage of 90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime cervical screening. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%.

age of 90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime cervical screening. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. The predicted dynamics of cervical cancer incidence following HPV vaccination only, and for HPV vaccination with once-lifetime or twice-lifetime screening, were very similar for the three models (figure 2). Additionally, although the age-standardised cervical cancer incidence in 2020 varied widely by country income level and region (figure 2; appendix p 5), the models predicted that the post-intervention dynamics and percentage reduction in cervical cancer incidence will be similar (figure 2, table). For example, the predicted percentage reduction in cervical cancer following HPV vaccination only varied from 87% (range 84–88) in sub-Saharan Africa to 91% (88–93) in South Asia, and percentage reductions following HPV vaccination with twice-lifetime screening varied from 96% (90–96) in sub-Saharan Africa to 97% (93–97) in South Asia. However, the models predicted that age-standardised cervical cancer incidence following HPV vaccination with or without screening will vary greatly between regions and countries because of the large heterogeneity in the starting incidence (figure 2, table), which contributed to variability between countries in the potential for and timing of elimination.Figure 2 Variability in model predictions of the impact of HPV vaccination and screening strategies

vary greatly between regions and countries because of the large heterogeneity in the starting incidence (figure 2, table), which contributed to variability between countries in the potential for and timing of elimination.Figure 2 Variability in model predictions of the impact of HPV vaccination and screening strategies The average age-standardised cervical cancer incidence per 100 000 women-years over time in low-income countries and lower-middle-income countries, by World Bank income level (A) and region (B). The solid line represents the median prediction and shaded area represents the minimum and maximum predictions of the three models. Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV types 16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. Equilibrium occurs 90–100 years after the introduction of HPV vaccination only (and earlier for the screening scenarios). HPV=human papillomavirus.

cine duration=lifetime. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. Equilibrium occurs 90–100 years after the introduction of HPV vaccination only (and earlier for the screening scenarios). HPV=human papillomavirus. With the base-case elimination threshold (four or fewer cases per 100 000 women-years), the CCEMC models predicted that girls-only HPV vaccination could lead to cervical cancer elimination in 60% (range 58–65) of LMICs, HPV vaccination with once-lifetime screening could lead to elimination in 96% (60–97) of LMICs, and HPV vaccination with twice-lifetime screening could lead to elimination in 100% (71–100) of LMICs (figure 3, table). HPV vaccination alone was predicted to result in elimination in all regions in the world, except for sub-Saharan Africa, where 27% (range 24–37) of countries would reach elimination, and Latin America and Caribbean, where 80% (80–80) of countries would reach elimination. The countries that were not predicted to reach elimination through HPV vaccination alone were those with an age-standardised cervical cancer incidence of more than 25 cases per 100 000 women-years in 2020 (figure 4, appendix p 7). These same countries were predicted to have the greatest absolute reductions in cervical cancer incidence following HPV vaccination (figure 4). Importantly, for these countries, mainly in sub-Saharan Africa, once-lifetime or twice-lifetime screening was required to achieve elimination. Country-specific and model-specific predictions of elimination and the age-specific cervical cancer incidence at equilibrium are shown in the appendix (p 7).Figure 3 Global map of cervical cancer elimination in 78 low-income and lower-middle-income countries

twice-lifetime screening was required to achieve elimination. Country-specific and model-specific predictions of elimination and the age-specific cervical cancer incidence at equilibrium are shown in the appendix (p 7).Figure 3 Global map of cervical cancer elimination in 78 low-income and lower-middle-income countries Age-standardised incidence of cervical cancer at equilibrium (2100–20), assuming status quo (A), girls-only vaccination (B), and girls-only vaccination and two lifetime screens (C). Median prediction from the three models. Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58.Vaccine duration=lifetime. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. See videos 1–3 for the global maps of cervical cancer elimination over time and the appendix (p 6) for the change in the distribution of the country-specific age-standardised cervical cancer incidence over time. HPV=human papillomavirus. Figure 4 Impact of current cervical cancer incidence on elimination predictions

Age-standardised incidence of cervical cancer at equilibrium (2100–20), assuming status quo (A), girls-only vaccination (B), and girls-only vaccination and two lifetime screens (C). Median prediction from the three models. Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58.Vaccine duration=lifetime. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. See videos 1–3 for the global maps of cervical cancer elimination over time and the appendix (p 6) for the change in the distribution of the country-specific age-standardised cervical cancer incidence over time. HPV=human papillomavirus. Figure 4 Impact of current cervical cancer incidence on elimination predictions The age-standardised incidence of cervical cancer (A) and relative (B) and absolute (C) reduction in incidence at equilibrium (2100–20) following vaccination and screening, as a function of initial age-standardised incidence of cervical cancer for each low-income and lower-middle-income country. Median prediction from the three models. Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. HPV=human papillomavirus.

90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. HPV=human papillomavirus. The models predicted that among the regions that can achieve elimination (four or fewer cases per 100 000 women-years) with girls-only HPV vaccination alone, elimination will occur between 2074 and 2102 (table). Adding twice-lifetime screening was predicted to accelerate elimination by 11–31 years. In sub-Saharan Africa, where both HPV vaccination and twice-lifetime screening are required to achieve elimination, elimination is predicted to occur slightly before 2100 (table). Country-specific and model-specific predictions of the year of elimination are provided in the appendix (p 8). The CCEMC models predicted that girls-only HPV vaccination could lead to cervical cancer elimination in 99% (range 89–100) of LMICs based on a threshold of ten or fewer cases per 100 000 women-years, and in 87% (37–99) of LMICs based on a threshold of an 85% or greater reduction (table; figure 3; figure 4). Adding once or twice-lifetime screening was predicted to result in cervical cancer elimination for 100% of LMICs under both thresholds. Elimination was also predicted to occur faster with these thresholds (table).

d in 87% (37–99) of LMICs based on a threshold of an 85% or greater reduction (table; figure 3; figure 4). Adding once or twice-lifetime screening was predicted to result in cervical cancer elimination for 100% of LMICs under both thresholds. Elimination was also predicted to occur faster with these thresholds (table). The CCEMC models predicted that 21·3 million (range 20·7–21·3) cervical cancer cases will occur in LMICs between 2020 and 2060 without further interventions (status quo). During the same period, including girls-only HPV vaccination with 90% coverage was predicted to avert 3·2 million (3·0–3·6) cervical cancer cases; adding once-lifetime screening to vaccination was predicted to avert an extra 2·2 million (1·8–2·7) cases, and adding twice-lifetime screening was predicted to avert an extra 4·6 million (3·9–4·8) cancer cases (figure 5; appendix pp 2–4). Hence, in the short to medium term (<40 years), adding screening could more than double the number of cervical cancer cases averted in LMICs (vs HPV vaccination alone). In the longer term, the models predicted that 93·5 million (93·5–95·3) cervical cancer cases will occur in LMICs between 2020 and 2120 without further scale-up of HPV vaccination or cervical screening (ie, the status quo scenario). During this period, including girls-only HPV vaccination with 90% coverage was predicted to avert 61·0 million (60·5–63·0) cervical cancer cases; adding once-lifetime screening to vaccination was predicted to avert an extra 6·8 million (4·3–9·4) cases and adding twice-lifetime screening was predicted to avert an extra 12·1 million (9·5–13·7) cervical cancer cases (figure 5; appendix pp 2–4). Overall, an estimated 74·1 million (70·4–75·1) cases would be averted by 2120 through intensive scale-up of girls-only HPV vaccination with twice-lifetime screening. Predictions of the number of cervical cancer cases averted over time were similar for the three models, at the global and regional levels (appendix p 9).Figure 5 Cervical cancer cases averted

0·4–75·1) cases would be averted by 2120 through intensive scale-up of girls-only HPV vaccination with twice-lifetime screening. Predictions of the number of cervical cancer cases averted over time were similar for the three models, at the global and regional levels (appendix p 9).Figure 5 Cervical cancer cases averted Cumulative cases averted by girls-only vaccination or girls-only vaccination plus screening, and incremental cases averted by screening in addition to vaccination over time, for lower-middle-income countries (A), low-income countries (B), and by region (C). Median prediction from the three models. Error bars represent the minimum and maximum estimates from the three models. Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV types 16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. HPV=human papillomavirus. Most cervical cancer cases averted through HPV vaccination and screening in LMICs were predicted to be among women living in sub-Saharan Africa (figure 5; appendix pp 2–4). For example, our models predicted that HPV vaccination and twice-lifetime screening will avert 49·9 million (range 49·5–50·9) cases in sub-Saharan Africa over the next century, which represents about 70% of all cases averted in LMICs.

predicted to be among women living in sub-Saharan Africa (figure 5; appendix pp 2–4). For example, our models predicted that HPV vaccination and twice-lifetime screening will avert 49·9 million (range 49·5–50·9) cases in sub-Saharan Africa over the next century, which represents about 70% of all cases averted in LMICs. The sensitivity analysis suggests that a small reduction in HPV vaccination coverage from 90% to 80% would have little impact on the decline in cervical cancer incidence in the first 30 years following girls-only HPV vaccination (without screening), but would lead to slightly higher long-term incidence (appendix pp 10–11). Hence, some LMICs that can eliminate cervical cancer with 90% vaccination coverage (using the threshold of four or fewer cases per 100 000 women-years) might not with 80% coverage (eg, countries with current age-standardised cervical cancer incidence of 20–25 cases per 100 000 women-years). In general, if HPV vaccination coverage was high among girls, vaccinating boys was predicted to produce very small incremental gains in cervical cancer prevention (appendix pp 10–11). For example, the CCEMC models predicted that girls-only HPV vaccination with 90% coverage would produce the same reduction in cervical cancer incidence as vaccinating both girls and boys at 80% coverage. Hence, vaccinating boys in addition to girls would not be sufficient to help countries with the highest age-standardised cervical cancer incidence (eg, Uganda) reach the elimination threshold of four or fewer cases per 100 000 women-years. Finally, the models predicted that multi-age cohort vaccination up to age 25 years would substantially accelerate the declines in cervical cancer incidence, but would not change cervical cancer incidence at equilibrium (appendix pp 10–11). Thus, vaccinating older cohorts of girls or women would not ultimately change the potential for elimination.

d that multi-age cohort vaccination up to age 25 years would substantially accelerate the declines in cervical cancer incidence, but would not change cervical cancer incidence at equilibrium (appendix pp 10–11). Thus, vaccinating older cohorts of girls or women would not ultimately change the potential for elimination. A sensitivity analysis examining the impact of screening suggests that although twice-lifetime screening without HPV vaccination would substantially reduce cervical cancer incidence, the age-standardised cervical cancer incidence would remain higher than four cases per 100 000 women-years in the countries examined (appendix pp 10–11). Hence, HPV vaccination is required for most LMICs to reach cervical cancer elimination. In the context of high-coverage girls-only vaccination, adding a third lifetime screen (to HPV vaccination and twice-lifetime screening) was predicted to provide very small additional gains in cervical cancer prevention, and only slightly accelerated time to elimination.

most LMICs to reach cervical cancer elimination. In the context of high-coverage girls-only vaccination, adding a third lifetime screen (to HPV vaccination and twice-lifetime screening) was predicted to provide very small additional gains in cervical cancer prevention, and only slightly accelerated time to elimination. Finally, our sensitivity analysis showed that the duration of protection and the number of types included in the HPV vaccine can affect whether girls-only HPV vaccination with twice-lifetime screening leads to cervical cancer elimination (appendix pp 10–11). When assuming 20 years of vaccine protection (instead of lifelong), the models predicted that the age-standardised cervical cancer incidence would be higher than four cases per 100 000 women-years in the countries examined. Thus, a long-term duration of vaccine protection (>20 years) is required to reach elimination in LMICs. The models predicted that cervical cancer elimination might be possible in LMICs with an age-standardised incidence of fewer than 25 cases per 100 000 women-years (eg, Vietnam) by use of a vaccine that includes only HPV types 16 and 18. However, for LMICs with the highest cervical cancer incidence (eg, Uganda), broad-spectrum protection against HPV types 16, 18, 31, 33, 45, 52, and 58 was predicted to be required for these countries to reach elimination.

per 100 000 women-years (eg, Vietnam) by use of a vaccine that includes only HPV types 16 and 18. However, for LMICs with the highest cervical cancer incidence (eg, Uganda), broad-spectrum protection against HPV types 16, 18, 31, 33, 45, 52, and 58 was predicted to be required for these countries to reach elimination. Elimination was generally easier to achieve under the different scenarios examined in the sensitivity analysis with the thresholds of fewer than ten cases per 100 000 women-years and 85% or greater reduction. The models predicted that all vaccination strategies will achieve elimination, except for girls-only vaccination with 80% coverage. Twice-lifetime screening (without vaccination) could also potentially lead to elimination with these thresholds in LMICs that have an age-standardised cervical cancer incidence of less than 25 cases per 100 000 women-years (eg, Vietnam).

ies will achieve elimination, except for girls-only vaccination with 80% coverage. Twice-lifetime screening (without vaccination) could also potentially lead to elimination with these thresholds in LMICs that have an age-standardised cervical cancer incidence of less than 25 cases per 100 000 women-years (eg, Vietnam). Discussion Our comparative modelling analysis, which includes projections from three independent transmission-dynamic models, provides consistent results predicting that cervical cancer can be eliminated as a public health problem by the end of the century, based on WHO's proposed elimination threshold (ie, cervical cancer incidence of four or fewer cases per 100 000 women-years). Our modelling study shows that girls-only HPV vaccination would lead to cervical cancer elimination in most LMICs, if high coverage is reached (>90% coverage) and the vaccine provides long-term protection. However, countries with the highest cervical cancer incidence at present (>25 cases per 100 000 women-years), more than 90% of which are in sub-Saharan Africa, would not reach elimination by vaccination alone. To achieve cervical cancer elimination in all 78 LMICs, our models predict that scale-up of both girls-only HPV vaccination and twice-lifetime screening is necessary, with 90% HPV vaccination coverage, 90% screening uptake, and long-term protection against HPV types 16, 18, 31, 33, 45, 52, and 58. If this global elimination strategy of combined intensive scale-up of HPV vaccination and cervical screening can be achieved, our results suggest that cervical cancer elimination could be achieved in all countries by 2100. In doing so, cervical cancer incidence would be reduced by 97% and more than 74 million cases would be averted over the next century.

of combined intensive scale-up of HPV vaccination and cervical screening can be achieved, our results suggest that cervical cancer elimination could be achieved in all countries by 2100. In doing so, cervical cancer incidence would be reduced by 97% and more than 74 million cases would be averted over the next century. In January, 2019, the Executive Board of WHO requested the Director-General to lead the development of a draft global strategy to accelerate cervical cancer elimination, with clear targets for 2030.24 The draft global strategy will be presented for consideration at the World Health Assembly in May, 2020. The results presented in this study were used to help inform the following key elements of the global strategy: the cervical cancer elimination threshold, the intervention strategies needed to achieve global elimination, and the 2030 targets towards global elimination.

eration at the World Health Assembly in May, 2020. The results presented in this study were used to help inform the following key elements of the global strategy: the cervical cancer elimination threshold, the intervention strategies needed to achieve global elimination, and the 2030 targets towards global elimination. Elimination of cervical cancer requires a clear and commonly agreed upon threshold, under which cervical cancer would no longer be considered a public health problem.24 Establishment of this threshold thus requires a careful and informed process, as it is more complex than the definition of elimination (or eradication) of an infectious disease, which is simply reduction to zero incidence. The proposed threshold of four or fewer cases per 100 000 women-years was established on the basis of the definition of a rare cancer,44 on the global distribution of cervical cancer incidence showing that this threshold is currently reached in only a few countries (compared with many countries reaching ten or fewer cases per 100 000),42 as well as on our modelling results (and those of Simms and colleagues34) showing that cervical cancer elimination can be achieved in every country with this threshold. In this study, we examined the consequences of using alternative thresholds (ten or fewer cases per 100 000 women-years and ≥85% reduction), which were proposed during various WHO meetings and consultations,24 on the achievability and timing of elimination in LMICs for different prevention strategies and country characteristics. Our results show that intensive scale-up of both HPV vaccination and twice-lifetime screening would eliminate cervical cancer in all LMICs for all thresholds investigated.

etings and consultations,24 on the achievability and timing of elimination in LMICs for different prevention strategies and country characteristics. Our results show that intensive scale-up of both HPV vaccination and twice-lifetime screening would eliminate cervical cancer in all LMICs for all thresholds investigated. However, the choice of threshold can produce disparities in the effort required by countries to achieve elimination. For example, based on the threshold or ten or fewer cases per 100 000 women-years, only 1% of LMICs were unable to achieve elimination through HPV vaccination alone. By contrast, based on the proposed threshold of four or fewer cases per 100 000 women-years, 40% of LMICs were unable to achieve elimination through vaccination alone. These countries have the highest burden of cervical cancer (incidence >25 per 100 000 women-years) and are mostly in sub-Saharan Africa. For these countries, up to 90% uptake of twice-lifetime screening is required, in addition to vaccination, to reach the proposed elimination threshold. More generally, our results indicate that elimination will be hardest to achieve in countries with the highest burden of cervical cancer and lowest income level. Considerable financial and political international commitment is needed so that HPV vaccination and cervical screening resources can be prioritised for these countries, not only to achieve global elimination but also to reduce the enormous disparities in the worldwide cervical cancer burden. This is particularly important since current HPV vaccination and cervical screening uptake is very low in most low-income and sub-Saharan African countries.2, 3, 4, 5

oritised for these countries, not only to achieve global elimination but also to reduce the enormous disparities in the worldwide cervical cancer burden. This is particularly important since current HPV vaccination and cervical screening uptake is very low in most low-income and sub-Saharan African countries.2, 3, 4, 5 Partly based on the CCEMC projections presented here and the considerations described above, WHO has proposed the following triple-intervention global cervical cancer elimination strategy: intensive scale-up of girls-only HPV vaccination, twice-lifetime screening, and treatment of cancer and precancers.24 The 2030 targets for this strategy are for 90% of girls to be fully vaccinated, for 70% of women to be screened at 35 years and 45 years of age, and for 90% of women diagnosed with cervical precancer or cancer to receive treatment or care. Our findings suggest that to achieve global elimination by the end of the century, these targets need to be met in the countries with the highest burden of cervical cancer, and these countries also need to be supported to scale up twice-lifetime screening from 70% to 90% by 2045. Although we show that many LMICs could achieve elimination with HPV vaccination alone, the triple-intervention strategy was chosen as the global elimination strategy as it would accelerate elimination by 11–31 years and prevent an additional 12 million cervical cancer cases over the next century (compared with vaccination alone). Furthermore, combining cervical screening with HPV vaccination has been predicted to be cost-effective across several LMICs.20, 21, 22 The CCEMC is currently examining the incremental cost-effectiveness of the triple-intervention cervical cancer elimination strategy at the global level. Importantly, the proposed global cervical cancer elimination strategy provides general direction about the country-specific strategies that should be used, which should be customised to country-specific epidemiological, economic, and social contexts. For example, countries might want to scale up vaccination and screening at different ages than those modelled, because of logistical issues or to maximise uptake.

ion about the country-specific strategies that should be used, which should be customised to country-specific epidemiological, economic, and social contexts. For example, countries might want to scale up vaccination and screening at different ages than those modelled, because of logistical issues or to maximise uptake. The base-case vaccination-only strategy examined in the comparative-model analysis was routine girls-only HPV vaccination at age 9 years with a 1-year multi-age cohort catch-up for girls aged 10–14 years. This strategy was chosen as it is the recommended strategy by the WHO Strategic Advisory Group of Experts on Immunization (SAGE)41 and a large body of evidence shows that it is highly cost-effective in LMICs and high-income countries.17, 19, 31, 32 However, given the recent worldwide shortage of vaccine supply, SAGE recommended in October, 2019, that multi-age cohort catch-up vaccination for girls aged 10–14 years should be postponed to alleviate the demand for vaccine doses in the coming years. The recommended WHO alternative strategies are variants of our base-case vaccination-only strategy: routine vaccination of girls aged 14 years, with a later switch to routine vaccination at an earlier age (eg, 9 years); and routine vaccination at age 9 years, with an extended interval of 3–5 years between doses.45 The recommendations were partly based on results from HPV-ADVISE showing that these strategies would produce similar benefits to girls-only vaccination at age 9 years with a 1-year catch-up for girls aged 10–14 years.46 Implementation of these alternative strategies would alleviate vaccine supply to allow sufficient doses for all LMICs to reach 90% coverage within the next few years.45 Hence, assuming countries follow SAGE recommendations, the HPV vaccine shortage should have little long-term impact on our projections of time to elimination provided supply constraints are relieved over the next decade. In our sensitivity analysis, we examined the impact of gender-neutral and multi-age cohort vaccination up to 25 years of age on cervical cancer incidence over time. Because our models predict that 90% girls-only vaccination can almost eliminate HPV vaccine types, the incremental benefits of vaccinating boys on cervical cancer incidence were predicted to be small.

impact of gender-neutral and multi-age cohort vaccination up to 25 years of age on cervical cancer incidence over time. Because our models predict that 90% girls-only vaccination can almost eliminate HPV vaccine types, the incremental benefits of vaccinating boys on cervical cancer incidence were predicted to be small. Multi-age cohort vaccination up to 25 years of age was predicted to substantially accelerate elimination and avert additional cervical cancer cases but would have no effect on whether a country reaches elimination, which is only determined by long-term routine vaccination coverage. Given their low incremental impact in relation to the number of doses required, WHO recommended that countries should temporarily postpone the implementation of gender-neutral and multi-age cohort HPV vaccination strategies, to maximise the number of countries that can introduce vaccination.45

ine vaccination coverage. Given their low incremental impact in relation to the number of doses required, WHO recommended that countries should temporarily postpone the implementation of gender-neutral and multi-age cohort HPV vaccination strategies, to maximise the number of countries that can introduce vaccination.45 The two base-case screening strategies examined, primary HPV screen-and-treat testing with once-lifetime and twice-lifetime screening, were chosen as they are the recommended strategies by WHO.40 These screening scenarios were meant to represent a wide range of validated HPV tests and future screening tests, given their high sensitivity and specificity (see Canfell, Kim, Brisson and colleagues27 for an in-depth discussion of the screening strategies). Our results suggest that including screening in addition to HPV vaccination would substantially increase the number of cervical cancer cases averted and would accelerate elimination, mainly by preventing cases in older, unvaccinated women. Additionally, cervical cancer elimination can be achieved in all but three LMICs (in sub-Saharan Africa) with once-lifetime screening and in all LMICs with twice-lifetime screening. This is because even if HPV vaccination were to eradicate HPV types 16, 18, 31, 33, 45, 52, and 58, a proportion of LMICs (mainly in sub-Saharan Africa) would still have cervical cancer incidence greater than the threshold of four cases per 100 000 women-years; about 10% of cervical cancers are due to HPV types that are not in the currently available HPV vaccines10 and many countries have cervical cancer incidence higher than 40 cases per 100 000 women-years.1 For these countries, high cervical screening uptake will have to be sustained for elimination to be maintained (or additional types would have to be included in future HPV vaccines). Finally, in the sensitivity analysis, we predicted relatively small additional gains in cervical cancer prevention by including a third lifetime screen.

ries, high cervical screening uptake will have to be sustained for elimination to be maintained (or additional types would have to be included in future HPV vaccines). Finally, in the sensitivity analysis, we predicted relatively small additional gains in cervical cancer prevention by including a third lifetime screen. Our study has two major strengths. First, we used a comparative modelling approach including three models that have been extensively peer reviewed and validated with post-vaccination surveillance data.30, 31, 32, 33, 34, 35, 36 Without harmonising the model structure or parameters, the three models produced very similar results in terms of absolute and relative reductions in cervical cancer incidence and cancer cases averted over time following HPV vaccination and cervical screening by country, income level, and region. Our results are consistent in part because the key drivers of our predictions (eg, achievability and timing of elimination) are country-specific baseline cervical cancer incidence and percentage of cancers due to the HPV vaccine types, which were based on the same data sources.1, 10 However, the results were not sensitive to the main differences between our models, which were the sexual behaviour components. At high HPV vaccination coverage and vaccine efficacy, our models predicted similar dynamics and herd effects across the different LMICs, even though sexual behaviour varies substantially. Although we could not directly compare our results to other HPV transmission-dynamic models in LMICs because of the scarcity of such models and their incompatibility in intervention scenarios, a previous systematic comparison of 16 HPV models in high-income countries (including the three CCEMC models) showed consistent predictions of the population-level impact of HPV vaccination when coverage is high.47 Second, key knowledge users from WHO were involved in all aspects of the study, from its design to interpretations of findings. Additionally, the modelling results were presented and discussed at multiple WHO advisory group and global stakeholder meetings.24, 39, 48 This process has ensured that the study was responsive to the needs of global policy decisions and, importantly, that those using the findings are aware of both its strengths and limitations.

ly, the modelling results were presented and discussed at multiple WHO advisory group and global stakeholder meetings.24, 39, 48 This process has ensured that the study was responsive to the needs of global policy decisions and, importantly, that those using the findings are aware of both its strengths and limitations. Our study has four main limitations. First, our projections are for more than 100 years, a period over which substantial demographic and behavioural changes and technological development are anticipated that can have an impact on cervical cancer incidence.43, 49 Population growth and changes in life expectancy can have an important impact on our predictions of cervical cancer cases averted. When producing projections with low population predictions from the UN,43 we estimated that 62 million cervical cancer cases would be averted with the triple-intervention global elimination strategy, and that 88 million cases would be averted with the UN's high population predictions, versus 74 million cases with base-case projections (appendix p 12). However, given that the definition of elimination is based on age-standardised cervical cancer incidence, demographic changes are expected to have minimal impact on our predictions of the achievability and timing of elimination. Sexual behaviour has been changing in many LMICs, from a more traditional pattern of sexual behaviour, with a lower reported number of lifetime partners and wider age gaps between partners, to a more sex-similar pattern of behaviour, where both sexes have a similar and higher number of partners and narrow age gaps. In these countries (mainly in Asia), age-adjusted HPV infection and cervical cancer rates might be increasing,49 and thus time to elimination might be slightly longer than predicted. Technological developments should not have major implications for our predictions, as we assumed 100% vaccine efficacy, high screening test sensitivity and specificity, and 100% treatment efficacy. Second, we assumed intensive scale-up and 90% uptake of HPV vaccination and cervical screening.

tly longer than predicted. Technological developments should not have major implications for our predictions, as we assumed 100% vaccine efficacy, high screening test sensitivity and specificity, and 100% treatment efficacy. Second, we assumed intensive scale-up and 90% uptake of HPV vaccination and cervical screening. These assumptions are based on data suggesting that worldwide coverage of measles, poliomyelitis, hepatitis B, and diphtheria-tetanus-pertussis vaccines have reached 84–90% (≥90% in many LMICs)50 and that more than 90% of women in high-income countries are screened for cervical cancer at least once in their lifetime.4 If scale-up is slower than modelled, this would delay the predicted timing of elimination and reduce the number of cancer cases averted, but it would not affect whether or not elimination can be achieved. Thirdly, our models do not include plausible biological interactions between HIV and HPV (eg, HPV acquisition and disease progression might be increased among people living with HIV).51 By not capturing such interactions, our models might overestimate the impact of HPV vaccination in high HIV prevalence settings (five of 78 LMICs have HIV prevalence ≥10%52). Specific prevention strategies might be required for people living with HIV to accelerate cervical cancer elimination in high HIV prevalence settings. Modelling work is ongoing as part of the CCEMC to examine these issues. Finally, our country-specific cervical cancer incidence data are based on GLOBOCAN estimates,42, 53 which, where possible, are derived from extrapolation of recent trends in incidence obtained from national or subnational population-based cancer registries. If cervical cancer incidence is underestimated because of underreporting in these countries, elimination might take longer than predicted. There is an overwhelming need to strengthen population-based cancer surveillance in many LMICs to improve the accuracy of GLOBOCAN estimates, to inform local cancer control strategies, and to monitor whether elimination targets are being met.

erreporting in these countries, elimination might take longer than predicted. There is an overwhelming need to strengthen population-based cancer surveillance in many LMICs to improve the accuracy of GLOBOCAN estimates, to inform local cancer control strategies, and to monitor whether elimination targets are being met. In conclusion, our comparative modelling analysis suggests that cervical cancer elimination as a public health problem is possible by the end of the century, resulting in a 97% reduction in cervical cancer incidence in LMICs. To achieve elimination across all LMICs under the proposed threshold (four or fewer cases per 100 000 women-years), both high HPV vaccination coverage and screening uptake will be necessary, particularly in countries with the highest burden. Considerable international commitment will be required to achieve WHO's triple-intervention targets, particularly in countries with the highest burden of cervical cancer, where scale-up of vaccination and screening resources are most urgently needed. Our results are being used by WHO to inform its global strategy to accelerate cervical cancer elimination, which will be presented at the World Health Assembly in May, 2020. Supplementary Materials Supplementary appendix Supplementary Video 1 Global map of cervical cancer elimination over time - Girls-only HPV vaccination

In conclusion, our comparative modelling analysis suggests that cervical cancer elimination as a public health problem is possible by the end of the century, resulting in a 97% reduction in cervical cancer incidence in LMICs. To achieve elimination across all LMICs under the proposed threshold (four or fewer cases per 100 000 women-years), both high HPV vaccination coverage and screening uptake will be necessary, particularly in countries with the highest burden. Considerable international commitment will be required to achieve WHO's triple-intervention targets, particularly in countries with the highest burden of cervical cancer, where scale-up of vaccination and screening resources are most urgently needed. Our results are being used by WHO to inform its global strategy to accelerate cervical cancer elimination, which will be presented at the World Health Assembly in May, 2020. Supplementary Materials Supplementary appendix Supplementary Video 1 Global map of cervical cancer elimination over time - Girls-only HPV vaccination Video shows the predicted change in the age-standardised incidence of cervical cancer over time in 78 low-income and lower-middle-income countries following the introduction of girls-only HPV vaccination. On the left hand side (left legend), we present the change, over time, in the distribution of cervical cancer incidence by World Bank income level and region, percentage of countries reaching elimination, and year of elimination using the ≤4 per 100 000 women-years (dark blue) and ≤10 per 100 000 women-years (light blue) elimination thresholds. Results are the median prediction from the three Cervical Cancer Elimination Modelling Consortium (CCEMC) models (HPV-ADVISE, Harvard, and Policy1-Cervix). Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. HPV=human papillomavirus.

ction from the three Cervical Cancer Elimination Modelling Consortium (CCEMC) models (HPV-ADVISE, Harvard, and Policy1-Cervix). Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. HPV=human papillomavirus. Youtube url: https://youtu.be/-KnLYrvNJAY Supplementary Video 2 Global map of cervical cancer elimination over time - Girls-only HPV vaccination & one lifetime screen Video shows the predicted change in the age-standardised incidence of cervical cancer over time in 78 low-income and lower-middle-income countries following the introduction of girls-only HPV vaccination and scale-up of once lifetime screening. On the left hand side (left legend), we present the change, over time, in the distribution of cervical cancer incidence by World Bank income level and region, percentage of countries reaching elimination, and year of elimination using the ≤4 per 100 000 women-years (dark blue) and ≤10 per 100 000 women-years (light blue) elimination thresholds. Results are the median prediction from the three Cervical Cancer Elimination Modelling Consortium (CCEMC) models (HPV-ADVISE, Harvard, and Policy1-Cervix). Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening at age 35 years. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. HPV=human papillomavirus.

ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening at age 35 years. Screening=HPV testing. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. HPV=human papillomavirus. Youtube url: https://youtu.be/rfHFEfGCDFk Supplementary Video 3 Global map of cervical cancer elimination over time - Girls-only HPV vaccination & two lifetime screens Video shows the predicted change in the age-standardised incidence of cervical cancer over time in 78 low-income and lower-middle-income countries following the introduction of girls-only HPV vaccination and scale-up of twice lifetime screening. On the left hand side (left legend), we present the change, over time, in the distribution of cervical cancer incidence by World Bank income level and region, percentage of countries reaching elimination, and year of elimination using the ≤4 per 100 000 women-years (dark blue) and ≤10 per 100 000 women-years (light blue) elimination thresholds. Results are the median prediction from the three Cervical Cancer Elimination Modelling Consortium (CCEMC) models (HPV-ADVISE, Harvard, and Policy1-Cervix). Vaccination coverage=90% at age 9 years (and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening at ages 35 years and 45 years. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. HPV=human papillomavirus.

(and at ages 10–14 years in 2020). Vaccine efficacy=100% against HPV16, 18, 31, 33, 45, 52, and 58. Vaccine duration=lifetime. Screening at ages 35 years and 45 years. Screening uptake=45% (2023–29), 70% (2030–44), and 90% (2045 onwards). Screen and treat efficacy=100%. Loss to follow-up=10%. HPV=human papillomavirus. Youtube url: https://youtu.be/FpiOn9Tp8Ss Acknowledgments Where authors are identified as personnel of the International Agency for Research on Cancer 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 International Agency for Research on Cancer or WHO. M-CB acknowledges Centre funding from the MRC Centre for Global Infectious Disease Analysis (MRC-GIDA). This award is jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and is also part of the European and Developing Countries Clinical Trials Partnership (EDCTP2) programme supported by the European Union (MR/R015600/1).

This award is jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and is also part of the European and Developing Countries Clinical Trials Partnership (EDCTP2) programme supported by the European Union (MR/R015600/1). Contributors MB, JJK, and KC co-designed the study and co-led overall data interpretation. MB led the HPV-ADVISE analysis, JJK led the Harvard analysis, and KC led the Policy1-Cervix analysis. NB and RH also participated in study design. MD, EAB, EB, CR, KTS, and AK did the literature searches. MB, MD, GG, KTS, EB, CR, AK, M-CB, MA, FB, EF, PJNB, NB, RH, and DTNN participated in data collection and MB, JJK, KC, GG, KTS, AK, EAB, EB, DM, SS, CR, J-FL, MC, RH, FB, and NB participated in data analysis. MB, GG, MD, and EB produced the tables and figures. MB, JJK, and KC drafted the Article and RH coordinated the CCEMC. All authors interpreted the results and critically revised the manuscript for scientific content. All authors approved the final version of the Article.

MC, RH, FB, and NB participated in data analysis. MB, GG, MD, and EB produced the tables and figures. MB, JJK, and KC drafted the Article and RH coordinated the CCEMC. All authors interpreted the results and critically revised the manuscript for scientific content. All authors approved the final version of the Article. Declaration of interests MB, MD, GG, DM, EB, J-FL, JJK, EAB, SS, CR, and DTNN report grants from WHO during the conduct of the study. KC, AK, KTS, MC, and MAS report grants from the National Health and Medical Research Council Australia during the conduct of the study. KC and MC are investigators of an investigator-initiated trial of cervical screening in Australia (Compass; ACTRN12613001207707 and NCT02328872), which is conducted and funded by the VCS Foundation, a government-funded health promotion charity; the VCS Foundation received equipment and a funding contribution from Roche Molecular Systems and Ventana USA but KC and MC (or their institution on their behalf) do not receive direct funding from industry for this trial or any other project. MAS also reports grants from Cancer Institute NSW during the conduct of the study. M-CB, MJ, MA, FB, EF, FE, PJNB, NB, and RH declare no competing interests.