Policy1-Cervix (CCNSW)

Cervical Cancer Model, Cancer Council NSW

Basic Model Attributes
Cancer site	Cervical
Host institution	Cancer Council, NSW
Purpose	The model platform known as ‘Policy1-Cervix’ was developed to address several questions related to cervical cancer, including the impact of cervical screening and vaccination on incidence and mortality, the predicted impact on resource utilisation (such as impact on colposcopy referrals, treatment procedures) and the cost-effectiveness of these interventions across a range of settings. The model has been used for a number of HPV vaccine evaluations, including effectiveness and cost-effectiveness of HPV vaccination in both girls and boys, taking into account catch-up vaccination, as well as the effectiveness and cost-effectiveness of the next generation nonavalent vaccine. The model has also been used to perform screening technology, screening interval and screening management evaluations performed on behalf of national cervical screening programs in Australia, New Zealand and England. These evaluations included assessing the impact of test-of-cure management in women after treatment for CIN2/3, comparing the use of different triage management pathways, and primary HPV screening evaluations. It has also been used to evaluate the effectiveness and cost-effectiveness of screening and vaccination in both rural and urban China, as well as the potential impact of screening and HPV vaccination on a global scale.
Contact	Karen Canfell (karen.canfell@nswcc.org.au)

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

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

Cervical carcinogenesis. This component takes cohort- and type-specific HPV incidence from the dynamic model as input and involves a complex multi-cohort semi-Markov implementation of the natural history of cervical pre-cancer. Progression and regression between states representing HPV infection, cervical intraepithelial neoplasia (CIN)1, 2 and 3 (due to particular HPV types or groups) are modeled, as is progression from CIN3 to invasive cervical cancer. The model accounts for age-specific hysterectomy rates (for any cause) in the population.

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

Screening, diagnosis, and treatment of pre-cancer. The sensitivity and specificity of cytology are setting-specific and fitted to data on the distribution of cytology test results (e.g., cytology-histology correlations) in a particular setting. (3-6) Fitted test characteristics are constrained to be consistent with findings from international meta-analyses (7-8) that report the absolute and relative sensitivity and specificity of cytology and HPV testing. Detailed analysis of registry data on the age at which young women initiate screening is performed, and for all ages rates of return to screening or follow-up management over a 10-year period is simulated, according to last screening test result, the follow-up recommendation, and 10-year age group. Following treatment for CIN, post-treatment natural history and surveillance for recurrent disease are based on meta-analysis of the literature on outcomes after pre-cancer treatment. (9) A separate model has been developed for estimating adverse reproductive outcomes in the population given alternative screening strategies and associated CIN excisional treatment rates by age.

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

Calibration and validation. Unobservable parameters, such as the duration of immunity following natural infection, were calibrated using a multi-parameter Bayesian approach to the age-specific HPV prevalence prior to vaccination (2) and the proportion of women positive for HPV-16, -18 and other high-risk types. Natural history progression and regression rates for cervical carcinogenesis have been calibrated across a large number of targets from national screening programs in Australia, England, and New Zealand, after incorporating setting-specific screening behavior and clinical management algorithms. Calibration targets from each country include age-and type-specific HPV prevalence, screen-detected high-grade abnormalities, CIN 2/3 treatment ratio, numbers of screening, diagnostic number of tests, and age-and type-specific rates of cancer incidence and mortality. The model produces accurate predictions of the relative contribution of specific HPV types to HPV infections in the population, screen-detected histologically-confirmed high-grade abnormalities, and invasive cervical cancer rates and case numbers. (3, 10) A close correspondence between prior model predictions of HPV vaccine impact and the observed post-vaccination data on HPV prevalence (11) in Australia has been demonstrated post-hoc. Ongoing validation will use findings from the Compass trial, a major Australian trial assessing primary HPV testing.

References

Smith MA, Canfell K, Brotherton JM, Lew JB, Barnabas RV. The predicted impact of vaccination on human papillomavirus infections in Australia. Int J Cancer. 2008;123(8):1854-63. [Abstract]
Smith MA, Lew JB, Walker RJ, Brotherton JM, Nickson C, Canfell K. The predicted impact of HPV vaccination on male infections and male HPV-related cancers in Australia. Vaccine. 2011;29(48):911222. [Abstract]
Lew JB, Simms K, Smith M, Kang Y-J, Xu X, Caruana M, et al. National Cervical Screening Program Renewal: Effectiveness modelling and economic evaluation in the Australian setting (Assessment Report). MSAC Application No. 1276. 2013.
Medical Services Advisory Committee. Automation Assisted and Liquid Based Cytology for Cervical Cancer Screening (Assessment Report). MSAC Application No. 1122. 2009.
Creighton P, Lew J, Clements M, Smith M, Howard K, Dyer S, et al. Cervical cancer screening in Australia: modelled evaluation of the impact of changing the recommended interval from two to three years. BMC Public Health. 2010;10:734. [Abstract]
Lew JB, Howard K, Gertig D, Smith M, Clements M, Nickson C, et al. Expenditure and resource utilisation for cervical screening in Australia. BMC Health Services Research. 2012:12;446. [Abstract]
Arbyn M, Bergeron C, Klinkhamer P, Martin-Hirsch P, Siebers AG, Bulten J. Liquid compared with conventional cervical cytology: a systematic review and meta-analysis. Obstet Gynecol. 2008;111(1):167-77. [Abstract]
Arbyn M, Ronco G, Anttila A, Meijer CJ, Poljak M, Ogilvie G, et al. Evidence regarding human papillomavirus testing in secondary prevention of cervical cancer. Vaccine. 2012;30 Suppl 5:F88-99. [Abstract]
Legood R, Smith M, Lew JB, Walker R, Moss S, Kitchener H, et al. Cost effectiveness of human papillomavirus test of cure after treatment for cervical intraepithelial neoplasia in England: economic analysis from NHS Sentinel Sites Study. BMJ. 2012;345:e7086.
Kitchener H, Canfell K, Gilham C, Sargent A, Roberts C, Desai M, et al. The clinical effectiveness and cost-effectiveness of primary human papillomavirus cervical screening in England: extended follow-up of the ARTISTIC randomised trial cohort through three screening rounds. Health Technol Assess. 2014;18(23):1-196. [Abstract]
Smith MA, Canfell K. Testing previous model predictions against new data on human papillomavirus vaccination program outcomes. BMC Res Notes. 2014;7(1):109. [Abstract]

Tip: Hover your cursor over the dashed attribute links below for more information. View the details of this model in a grid with other cervical models.

Detailed Package Attributes
Attribute Category	Attribute
Approach
Primary Purpose	Screening evaluation, Epidemiological analysis, Policy evaluation, Population trends (The model can shed light on trends due to e.g. vaccination, screening, changes in sexual behaviour or other demographic changes that may be observed through surveillance data.),
Features
Intervention	Prevention (We mainly simulate prevention through cervical screening and vaccination), Screening, Treatment, Vaccination,
Natural History	Metastases (We model cancer progresion to more advanced stages for cancers that have not yet been detected (and therefore not yet treated), including metastases, which is modelled by incorporating poorer survival for later stages of disease detection.), Precancer, Dysplasia (We explicitly model the precancer states HPV infected (without cell changes), CIN1, CIN2 and CIN3. We also model different underlying HPV types that cause these precancer states.), Virus (We explicitly model infection, precancer and cancer states separately for four HPV type groups: 1) HPV 16, 2) HPV 18, 3) other high-risk types included in the nonavalent vaccine (HPV 31/33/45/52/58); and 4) other high-risk types not included in the nonavalent vaccine (all other oncogenic HPV types).), Other (Our model assumes that "precancer" (defined as CIN3) is exclusively caused by HPV although CIN3 misclassification by particular tests is accounted for.),
Construction
Approach	Micro Simulation, Macro Simulation, Dynamic Transmission (Our model of HPV transmission and vaccination is a dynamic transmission model. This allows us to take into account herd immunity.),
Methods	Longitudinal (The chance of events occurring depends on the woman's history), Stochastic process, State Transition, Time to Event (We use time to event to simulate transitions between states.),
Unit of Analysis	Person, Population (We can extrapolate our person-level to the population level),
Data Source
Census
Cancer Registry	NSW-CCR (National registry data is used for cancer incidence and mortality. The stage-specific and interval specific cancer survival parameters used in the Australian model are based on analysis of data obtained from NSW Central Cancer Registry), ACD (Age-specific incidence data is a model target, and is based on data from the Australian Cancer Database), NMD (Age-specific mortality rates for cervical cancer are a model target, and are based on data from the National Mortality Database), VCCR (Age-specific screening behaviour is based on data from the Victorian Cervical Cytology Register),
Linked
Clinical Trial	Other (We are performing ongoing validation against Compass data, a large scale trial of screening in a vaccinated population - www.compasstrial.org.au),
Survey	ASHR (Our model was originally developed for use in Australia, and therefore incorporates Australian survey data on sexual behaviour (ASHR data).),
Meta Analysis	Other (Test characteristics for HPV test and liquid-based cytology, and adverse pregnancy-related outcomes after precancer treatment are based on meta-analyses. Local data are also used for calibration and validation of test characteristics (e.g. rates for abnormal cytology tests).), Observational Studies (Observational studies are used in the absence of other study types (e.g. meta-analyses). For example, HPV prevalence data in some settings is based on observational studies.),
Assumptions
Benefit Factors
Screening	Stage Shift (For Australia we simulate three stages - localised, regional or distant cancer stages. Once cancer is detected, survival depends upon the stage at which the cancer was detected, and also (within stage) on whether cancer was screen-detected or symptomatically detected.), Cure Rate (We assume that screen detected cancers have a marginally higher survival rates than cancers detected via symptomatic presentation.), Modality (Benefit shifts for HPV vs cytology based screening because we can capture HPV type-specific test characteristics in the model), Prevention (We capture both prevention and downstaging effects of screening),
Treatment	Temporal Trends (We have the capacty to incorporate differential survival by period), Modality (Survival of cancer after a cancer diagnosis is based on the stage at diagnosis and can also be setting-specific. We assume different survival in different regions, taking into account resources available in each setting.), Precancer (Women who are treated for a precancerous lesion will have the offending lesion removed. We capture treatmetn success rates and fully model downstream surveillance and re-treatments in this group.),
Vaccination	Prevention (We have a dynamic transmission model which simulates vaccination and sexual behaviour that is setting-specific and can be modified to incorporate different sexual behaviour data based on the setting. This model can capture the effects of herd immunity.),
Inputs	Disease (The time spent in the different stages of precancer and cancer are age-specific inputs.), Grade Distribution (The time spent in the different stages of precancer are age-specific inputs.), Stage Distribution (The time spent in the different stages cancer are age-specific inputs.), Other Conditions (Hysterectomies),
Screening	Attendance (We use detailed data on screening and follow-up attendance by age which is fully captured.), Dissemination, Test Performance, Effect, Risk Adaptive Factors, Incidental Finding Surveillance,
Diagnosis
Precancer	Attendance, Test Performance,
Treatment	Dissemination (The model has the capability to have varying success on treatment procedures based on geographic region, which is modelled by applying different survival by stage based on the region being simulated.), Efficacy (The model has the capability to incorporating varying degrees of success in treatment procedures due to emerging technologies or other situations where treatment success/failure may vary), Effect,
Precancer	Attendance (We model compliance with referral to colposcopy, unstaisfacoy colpsocopy, the non-taking of biopsy in a large proportion of satisfactory colposcopies, and the potential for women to "delay" their treatment for fertility reasons), Efficacy (We have an input parameter that captures the chance that a treatment was unsuccessful. Surveillance and re-treatment is explicitly modelled.),
Survival	Observed (Depending on the data available and the region we are simulating, cancer survival may be input as an 'observed' or 'relative' input parameter set.), Relative,
Mortality	Other cause, Disease-specific,
Risk Factor	Age, Personal History (The individual's screening history and sexual behaviours will influence their risk of developing cervical cancer.), Sexual behavior, Demography (Demographic inputs that will affect the risk of cervical cancer in a woman's lifetime include hysterectomy due to reasons other than cervical cancer.), Natural History, Cost,
Vaccination	Quadrivalent, Bivalent, Nonavalent, Efficacy,
Outputs	Cost, Incidence,
Disease	Stage Distribution, Grade Distribution, HPV Infection, Other Conditions (Other outputs include costs associated with screening, precancer treatment and cancer cases. We also output resource utilisation (such as number of colposcopies and precancer treatments). We also output adverse pregnancy outcomes due to precancer treatments.),
Prevalence	Disease, Other Conditions (type-specific HPV infection and well as the prevalence of CIN1, CIN2 or CIN3),
Treatment	Effect (Using our add-on model of adverse pregnancy outcomes in women treated for cervical precancer),
Precancer	Effect (We output the number of precancer treatments by age of woman. We also have an additional model that captures adverse pregnancy-related events due to precancer treatments (e.g increased risk of low birth weight)),
Screening	Effect, Test Performance (Our model outputs call-rates such as the number of abnormal test results by age),
Risk Factor	Demography, Natural History (An individual's history of disease can be an output. For instance, we can output each instance an individual had an HPV infection, or each time that infection cleared.),
Outcomes	Survival, Life years, QALY, Cause-specific Mortality, All-cause Mortality, Temporal trends (Cohort specific outputs can be obtained, which would capture temporal trends e.g. due to vaccination, changes in screening, changes in sexual behaviour or changes in hysterectomy rates over time.), Vaccination effect (We model detailed vaccination parameters, e.g. age-and cohorts-specific coverage rates, vaccine type, efficacy against vaccine-included and non-vaccine included types and duration of protection.),
Screening	False Positives (Not routinely extracted, but the model has the capacity to extract these outputs.), True Positives (Not routinely extracted, but the model has the capacity to extract these outputs.), False Negatives (Not routinely extracted, but the model has the capacity to extract these outputs.), True Negatives (Not routinely extracted, but the model has the capacity to extract these outputs.), Biopsies performed (Not routinely extracted, but the model has the capacity to extract these outputs.), Unnecessary biopsies (Not routinely extracted, but the model has the capacity to extract these outputs.), Overdiagnoses (Not routinely extracted, but the model has the capacity to extract these outputs.), History (Not routinely extracted, but the model has the capacity to extract these outputs.),
Treatment	History (Our model outputs a woman's history of Cone or LEEP procedures separately.), Overtreatment (Our model outputs treatments that may occur in women with CIN1.), Pregnancy (We have an add-on model which simulated fertility outcomes based on a woman's history of treatment for CIN2/3),
Implementation
Development
Tested Platforms	Windows,
Language	C or C++, C#, Python, Visual Basic,

2022

Burger EA, de Kok IMCM, O'Mahony JF, Rebolj M, Jansen EEL, de Bondt DD, Killen J, Hanley SJ, Castanon A, Regan MC, et al., A model-based analysis of the health impacts of COVID-19 disruptions to primary cervical screening by time since last screen for current and future disruptions., Elife, Oct. 12, 2022 [Abstract]
Burger EA, de Kok IMCM, O'Mahony JF, Rebolj M, Jansen EEL, de Bondt DD, Killen J, Hanley SJ, Castanon A, Kim JJ, et al., Health impacts of COVID-19 disruptions to primary cervical screening by time since last screen: A model-based analysis for current and future disruptions., medRxiv, July 25, 2022 [Abstract]

2021

Burger EA, Jansen EE, Killen J, Kok IM, Smith MA, Sy S, Dunnewind N, G Campos N, Haas JS, Kobrin S, et al., Impact of COVID-19-related care disruptions on cervical cancer screening in the United States., J Med Screen, June 1, 2021 [Abstract]
Kim JJ, Simms KT, Killen J, Smith MA, Burger EA, Sy S, Regan C, Canfell K, Human papillomavirus vaccination for adults aged 30 to 45 years in the United States: A cost-effectiveness analysis., PLoS Med, March 11, 2021 [Abstract]

2020

Simms KT, Yuill S, Killen J, Smith MA, Kulasingam S, de Kok IMCM, van Ballegooijen M, Burger EA, Regan C, Kim JJ, et al., Historical and projected hysterectomy rates in the USA: Implications for future observed cervical cancer rates and evaluating prevention interventions., Gynecol Oncol, Sept. 1, 2020 [Abstract]
de Kok IMCM, Burger EA, Naber SK, Canfell K, Killen J, Simms K, Kulasingam S, Groene E, Sy S, Kim JJ, et al., The Impact of Different Screening Model Structures on Cervical Cancer Incidence and Mortality Predictions: The Maximum Clinical Incidence Reduction (MCLIR) Methodology., Med Decis Making, May 1, 2020 [Abstract]
Burger EA, Smith MA, Killen J, Sy S, Simms KT, Canfell K, Kim JJ, Projected time to elimination of cervical cancer in the USA: a comparative modelling study., Lancet Public Health, April 1, 2020 [Abstract]

2019

Burger EA, de Kok IMCM, Groene E, Killen J, Canfell K, Kulasingam S, Kuntz KM, Matthijsse S, Regan C, Simms K, Smith M, et al., Estimating the Natural History of Cervical Carcinogenesis Using Simulation Models: A CISNET Comparative Analysis, J Natl Cancer Inst, Dec. 10, 2019 [Abstract]

Additional publications by this modeling group

You may also be interested in these publications by this modeling group, which were not supported by the CISNET grant.

2017

Velentzis LS, Caruana M, Simms KT, Lew JB, Shi JF, Saville M, Smith MA, Lord SJ, Tan J, Bateson D, Quinn M, et al., How will transitioning from cytology to HPV testing change the balance between the benefits and harms of cervical cancer screening? Estimates of the impact on cervical cancer, treatment rates and adverse obstetric outcomes in Australia, a high vaccination coverage country, Int J Cancer, Dec. 15, 2017 [Abstract]
Lew JB, Simms KT, Smith MA, Hall M, Kang YJ, Xu XM, Caruana M, Velentzis LS, Bessell T, Saville M, Hammond I, et al., Primary HPV testing versus cytology-based cervical screening in women in Australia vaccinated for HPV and unvaccinated: effectiveness and economic assessment for the National Cervical Screening Program, Lancet Public Health, Feb. 1, 2017 [Abstract]
Simms KT, Hall M, Smith MA, Lew JB, Hughes S, Yuill S, Hammond I, Saville M, Canfell K, Optimal Management Strategies for Primary HPV Testing for Cervical Screening: Cost-Effectiveness Evaluation for the National Cervical Screening Program in Australia, PLoS One, Jan. 1, 2017 [Abstract]

2016

Simms KT, Smith MA, Lew JB, Kitchener HC, Castle PE, Canfell K, Will cervical screening remain cost-effective in women offered the next generation nonavalent HPV vaccine? Results for four developed countries, Int J Cancer, Dec. 15, 2016 [Abstract]
Simms KT, Laprise JF, Smith MA, Lew JB, Caruana M, Brisson M, Canfell K, Cost-effectiveness of the next generation nonavalent human papillomavirus vaccine in the context of primary human papillomavirus screening in Australia: a comparative modelling analysis, Lancet Public Health, Dec. 1, 2016 [Abstract]
Brisson M, Bénard É, Drolet M, Bogaards JA, Baussano I, Vänskä S, Jit M, Boily MC, Smith MA, Berkhof J, Canfell K, et al., Population-level impact, herd immunity, and elimination after human papillomavirus vaccination: a systematic review and meta-analysis of predictions from transmission-dynamic models, Lancet Public Health, Nov. 1, 2016 [Abstract]
Smith MA, Gertig D, Hall M, Simms K, Lew JB, Malloy M, Saville M, Canfell K, Transitioning from cytology-based screening to HPV-based screening at longer intervals: implications for resource use, BMC Health Serv Res, April 26, 2016 [Abstract]
Smith M, Lew JB, Simms K, Canfell K, Impact of HPV sample self-collection for underscreened women in the renewed Cervical Screening Program, Med J Aust, March 21, 2016 [Abstract]
Lew JB, Simms K, Smith M, Lewis H, Neal H, Canfell K, Effectiveness Modelling and Economic Evaluation of Primary HPV Screening for Cervical Cancer Prevention in New Zealand, PLoS One, Jan. 1, 2016 [Abstract]

2014

C Kitchener H, Canfell K, Gilham C, Sargent A, Roberts C, Desai M, Peto J, The clinical effectiveness and cost-effectiveness of primary human papillomavirus cervical screening in England: extended follow-up of the ARTISTIC randomised trial cohort through three screening rounds, Health Technol Assess, April 1, 2014 [Abstract]
Smith MA, Canfell K, Testing previous model predictions against new data on human papillomavirus vaccination program outcomes, BMC Res Notes, Feb. 25, 2014 [Abstract]
Smith MA, Canfell K, Incremental benefits of male HPV vaccination: accounting for inequality in population uptake, PLoS One, Jan. 1, 2014 [Abstract]

2012

Lew JB, Howard K, Gertig D, Smith M, Clements M, Nickson C, Shi JF, Dyer S, Lord S, Creighton P, Kang YJ, et al., Expenditure and resource utilisation for cervical screening in Australia, BMC Health Serv Res, Dec. 5, 2012 [Abstract]
Arbyn M, Ronco G, Anttila A, Meijer CJ, Poljak M, Ogilvie G, Koliopoulos G, Naucler P, Sankaranarayanan R, Peto J, Evidence regarding human papillomavirus testing in secondary prevention of cervical cancer, Vaccine, Nov. 20, 2012 [Abstract]
Legood R, Smith M, Lew JB, Walker R, Moss S, Kitchener H, Patnick J, Canfell K, Cost effectiveness of human papillomavirus test of cure after treatment for cervical intraepithelial neoplasia in England: economic analysis from NHS Sentinel Sites Study, BMJ, Oct. 31, 2012 [Abstract]

2011

Smith MA, Lew JB, Walker RJ, Brotherton JM, Nickson C, Canfell K, The predicted impact of HPV vaccination on male infections and male HPV-related cancers in Australia, Vaccine, Nov. 8, 2011 [Abstract]
Shi JF, Canfell K, Lew JB, Zhao FH, Legood R, Ning Y, Simonella L, Ma L, Kang YJ, Zhang YZ, Smith MA, et al., Evaluation of primary HPV-DNA testing in relation to visual inspection methods for cervical cancer screening in rural China: an epidemiologic and cost-effectiveness modelling study, BMC Cancer, June 13, 2011 [Abstract]
Canfell K, Shi JF, Lew JB, Walker R, Zhao FH, Simonella L, Chen JF, Legood R, Smith MA, Nickson C, Qiao YL, et al., Prevention of cervical cancer in rural China: evaluation of HPV vaccination and primary HPV screening strategies, Vaccine, March 16, 2011 [Abstract]

2010

Creighton P, Lew JB, Clements M, Smith M, Howard K, Dyer S, Lord S, Canfell K, Cervical cancer screening in Australia: modelled evaluation of the impact of changing the recommended interval from two to three years, BMC Public Health, Nov. 26, 2010 [Abstract]

2008

Smith MA, Canfell K, Brotherton JM, Lew JB, Barnabas RV, The predicted impact of vaccination on human papillomavirus infections in Australia, Int J Cancer, Oct. 15, 2008 [Abstract]
Arbyn M, Bergeron C, Klinkhamer P, Martin-Hirsch P, Siebers AG, Bulten J, Liquid compared with conventional cervical cytology: a systematic review and meta-analysis, Obstet Gynecol, Jan. 1, 2008 [Abstract]

2006

Lehtinen M, Herrero R, Mayaud P, Barnabas R, Dillner J, Paavonen J, Smith PG, Chapter 28: Studies to assess the long-term efficacy and effectiveness of HPV vaccination in developed and developing countries, Vaccine, Aug. 31, 2006 [Abstract]

2004

Canfell K, Barnabas R, Patnick J, Beral V, The predicted effect of changes in cervical screening practice in the UK: results from a modelling study, Br J Cancer, Aug. 2, 2004 [Abstract]