Improving breast cancer screening in Australia: a public health perspective.

There are currently no single disruptors to breast cancer screening akin to the impact of human papillomavirus testing and vaccination on cervical cancer screening. However, there is a groundswell of interest to review the BreastScreen Australia program to consider more risk-based screening protocols and to establish whether to routinely inform women about their breast density. We propose a framework for a considered, evidence-based review. Population-level effectiveness of breast cancer screening is ultimately measured through its impact on breast cancer mortality, and this has been realised in Australia. Effectiveness can also be measured through treatment intensity, estimated overdiagnosis, false-positive screens and health economics measures. Key levers to improve such population-level outcomes include screening participation, screening test sensitivity and specificity, risk assessment and screening protocols. We propose that the review of the program should fall under an evidence-based, consensus-guided framework comprising four complementary elements: improved evidence on current program performance for population risk subgroups; regularly updated evidence on key levers for change; clinical trials and population simulation modelling working in tandem; and consensus-based decision making about the degree of improvement required to justify change. Informing women about their breast density is feasible and would be valued by some BreastScreen clients to help understand the accuracy of their screening test. However, without agreed protocols for screening women with dense breasts, increases in supplemental screening as observed in other settings would, in Australia, shift screening costs to clients and Medicare. This would reduce equity of access to population screening, and maintaining BreastScreen’s usual standard of monitoring and quality management (such as screen-detected and interval cancer diagnoses, and imaging and biopsy rates) would require data linkage between BreastScreen and other services. The proposed framework assesses screening effectiveness in the era of personalised medicine, allows review of multiple factors that may together warrant change, and gives full, evidence-based consideration of the benefits, harms and costs of various approaches to breast cancer screening. To be effective, the framework requires a coordinated approach to generating the evidence required for policy makers, with time to prepare appropriate health services

HPV-FRAME: A consensus statement and quality framework for modelled evaluations of HPV-related cancer control.

Intense research activity in HPV modelling over this decade has prompted the development of additional guidelines to those for general modelling. A specific framework is required to address different policy questions and unique complexities of HPV modelling. HPV-FRAME is an initiative to develop a consensus statement and quality-based framework for epidemiologic and economic HPV models. Its development involved an established process. Reporting standards have been structured according to seven domains reflecting distinct policy questions in HPV and cancer prevention and categorised by relevance to a population or evaluation. Population-relevant domains are: 1) HPV vaccination in pre-adolescent and young adolescent individuals; 2) HPV vaccination in older individuals; 3) targeted vaccination in men who have sex with men; 4) considerations for individuals living with HIV and 5) considerations for low- and middle-income countries. Additional considerations applicable to specific evaluations are: 6) cervical screening or integrated cervical screening and HPV vaccination approaches and 7) alternative vaccine types and alternative dosing schedules. HPV-FRAME aims to promote the development of models in accordance with an explicit framework, to better enable target audiences to understand a model's strength and weaknesses in relation to a specific policy question and ultimately improve the model's contribution to informed decision-making

A Large Linked Study to Evaluate the Future Burden of Cancer in Australia Attributable to Current Modifiable Behaviours

Introduction The cancer burden preventable through modifications to risk factors can be quantified by calculating their population attributable fractions (PAFs). PAF estimates require large, prospective data to inform risk estimates and contemporary population-based prevalence data to inform the current exposure distributions, including among population subgroups. Objectives and Approach We provide estimates of the preventable future cancer burden in Australia using large linked datasets. We pooled data from seven Australian cohort studies (N=367,058) and linked them to national registries to identify cancers and deaths. We estimated the strength of the associations between behaviours and cancer risk using a proportional hazards model, adjusting for age, sex, study and other behaviours. Exposure prevalence was estimated from contemporary National Health Surveys. We harmonised risk factor data across the data sources, and calculated PAFs and their 95% confidence intervals using a novel method accounting for competing risk of death and risk factor interdependence. Results During the first 10-years follow-up, there were 3,471 incident colorectal cancers, 640 premenopausal and 2,632 postmenopausal breast cancers, 2,025 lung cancers and 22,078 deaths. The leading preventable causes were current smoking (53.7% of lung cancers), body fatness or BMI ≥ 25kg/m2 (11.1% of colorectal cancers, 10.9% of postmenopausal breast cancers), and regular alcohol consumption (12.2% of premenopausal breast cancers). Three in five lung cancers, but only one in four colorectal cancers and one in five breast cancers, were attributable to modifiable factors, when we also considered physical inactivity, dietary and hormonal factors. The burden attributable to modifiable factors was markedly higher in certain population subgroups, including men (colorectal, lung), people with risk factor clustering (colorectal, breast, lung), and individuals with low educational attainment (breast, lung). Conclusion/Implications Estimating PAFs for modifiable risk factors across cancers using contemporary exposure prevalence data can inform timely public health action to improve health and health equity. Testing PAF effect modification may identify population subgroups with the most to gain from programs that support behaviour change and early detection

Menopausal hormone therapy: a systematic review of cost-effectiveness evaluations

Abstract Background Several evaluations of the cost-effectiveness (CE) of menopausal hormone therapy (MHT) have been reported. The aim of this study was to systematically and critically review economic evaluations of MHT since 2002, after the Women’s Health Initiative (WHI) trial results on MHT were published. Methods The inclusion criteria for the review were: CE analyses of MHT versus no treatment, published from 2002-2016, in healthy women, which included both symptom relief outcomes and a range of longer term health outcomes (breast cancer, coronary heart disease, stroke, fractures and colorectal cancer). Included economic models had outcomes expressed in cost per quality-adjusted life year or cost per life year saved. MEDLINE, EMBASE, Evidence-Based Medicine Reviews databases and the Cost-Effectiveness Analysis Registry were searched. CE evaluations were assessed in regard to (i) reporting standards using the CHEERS checklist and Drummond checklist; (ii) data sources for the utility of MHT with respect to menopausal symptom relief; (iii) cost derivation; (iv) outcomes considered in the models; and (v) the comprehensiveness of the models with respect to factors related to MHT use that impact long term outcomes, using breast cancer as an example outcome. Results Five studies satisfying the inclusion criteria were identified which modelled cohorts of women aged 50 and older who used combination or estrogen-only MHT for 5-15 years. For women 50-60 years of age, all evaluations found MHT to be cost-effective and below the willingness-to-pay threshold of the country for which the analysis was conducted. However, 3 analyses based the quality of life (QOL) benefit for symptom relief on one small primary study. Examination of costing methods identified a need for further clarity in the methodology used to aggregate costs from sources. Using breast cancer as an example outcome, risks as measured in the WHI were used in the majority of evaluations. Apart from the type and duration of MHT use, other effect modifiers for breast cancer outcomes (for example body mass index) were not considered. Conclusions This systematic review identified issues which could impact the outcome of MHT CE analyses and the generalisability of their results. The estimated CE of MHT is driven largely by estimates of QOL improvements associated with symptom relief but data sources on these utility weights are limited. Future analyses should carefully consider data sources and the evidence on the long term risks of MHT use in terms of chronic disease. This review highlights the considerable difficulties in conducting cost-effectiveness analyses in situations where short term benefits of an intervention must be evaluated in the context of long term health outcomes

Abstract P2-10-05: The estimated impact of COVID-19 on population breast cancer screening outcomes, and options for risk-based recovery

Abstract OBJECTIVES AND RATIONALE Estimating the impact of COVID-19 on cancer screening programs and related outcomes can help health services prepare for potential delays in diagnoses and different demands on treatment services and plan for best approaches to recovery. Simulation modelling enables estimation of outcomes for a range of scenarios. In this study, we estimate the impact of various disruptions and recovery strategies for the Australian biennial mammographic breast screening program (BreastScreen). METHOD Policy1-Breast is a continuous-time, multiple-cohort micro-simulation model that simulates the whole Australian female population, incorporating breast cancer risk and natural history, breast density, menopause, hormone therapy use and breast cancer screening. Firstly, in the early stages of the COVID pandemic we used Policy1-Breast to evaluate how 3, 6, 9 and 12-month pauses to BreastScreen would impact on population-level breast cancer diagnoses, tumour staging, and breast cancer survival, compared to business-as-usual (BAU) outcomes. Secondly, to explore options for recovery after an actual one-month screening pause in April 2020, we evaluated a range of assumed throughput levels following screening resumption (50% or 80% up to December, then 100% to 120% from Jan 2021), comparing various protocols where specific sub-groups of clients were prioritised for screening during the recovery period. Outcomes are reported for the target age range for the BreastScreen program (50-74 years). RESULTS For 3- to 12-month pauses, we estimated a slight reduction in 5-year survival following diagnosis for women directly affected by a pause, but no discernible changes to population-level breast cancer mortality rates up to 2023. We estimated marked fluctuations in population rates of invasive breast cancer diagnoses with a 10% increase in cancer diagnoses between 2020-2021 and 2022-2023. For a 12-month pause to screening we estimate that population-level breast cancers would increase in size (with an additional 4% >15mm at diagnosis) and be more likely to involve the nodes (increasing from 26% to 30% of all cancers). We estimate that median screening intervals during 2020-2021 would increase from 104 weeks under BAU up to 130 weeks with a 12-month pause, and BreastScreen recall rates and false positive recall rates would fluctuate markedly over time. For the second evaluation of a one-month pause followed by various throughput and prioritisation scenarios, we estimated that screen-detected cancer rates would vary markedly with throughput but interval cancer rates would not, leading to fluctuations in program sensitivity of up to 6%. Reflecting the periodic nature of screening participation, we estimated the extent to which longer-term future screening participation rates are expected echo the peaks and troughs in participation due to the impacts of the COVID pandemic in 2020. We estimate that for a given throughput assumption, client prioritisation could lead to different rescreening rates, screening intervals, and time required for prioritisation protocols, with little change to cancer outcomes. CONCLUSION These modelled evaluations estimate short and longer-term effects of COVID-19 on the impact of population breast cancer screening in Australia. The estimated changes in breast cancer rates and characteristics would be expected to have a flow-on effect on the demand for treatment services in terms of throughput and case-mix. Preparing for such outcomes is critical given that treatment services are also directly impacted by the pandemic. The modelled outcomes are likely to be relevant to other high-income settings with established population breast cancer screening programs. Citation Format: Pietro Procopio, Sabine Deij, Louiza S Velentzis, Amanda Tattam, Lara Petelin, Carolyn Nickson. The estimated impact of COVID-19 on population breast cancer screening outcomes, and options for risk-based recovery [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-10-05

The impact of HPV vaccination beyond cancer prevention : effect on pregnancy outcomes

While the benefits of human papillomavirus (HPV) vaccination relating to cervical cancer prevention have been widely documented, recent published evidence is suggestive of an impact on adverse pregnancy outcomes (APOs) in vaccinated mothers and their infants, including a reduction in rates of preterm births and small for gestational age infants. In this review, we examine this evidence and the possible mechanisms by which HPV vaccination may prevent these APOs. Large-scale studies linking HPV vaccination status with birth registries are needed to confirm these results. Potential confounding factors to consider in future analyses include other risk factors for APOs, and historical changes in both the management of cervical precancerous lesions and prevention of APOs. If confirmed, these additional benefits of HPV vaccination in reducing APO rates will be of global significance, due to the substantial health, social and economic costs associated with APOs, strengthening the case for worldwide HPV immunization

Breast Cancer Risk Assessment Tools for Stratifying Women into Risk Groups: A Systematic Review

Background: The benefits and harms of breast screening may be better balanced through a risk-stratified approach. We conducted a systematic review assessing the accuracy of questionnaire-based risk assessment tools for this purpose. Methods: Population: asymptomatic women aged ≥40 years; Intervention: questionnaire-based risk assessment tool (incorporating breast density and polygenic risk where available); Comparison: different tool applied to the same population; Primary outcome: breast cancer incidence; Scope: external validation studies identified from databases including Medline and Embase (period 1 January 2008–20 July 2021). We assessed calibration (goodness-of-fit) between expected and observed cancers and compared observed cancer rates by risk group. Risk of bias was assessed with PROBAST. Results: Of 5124 records, 13 were included examining 11 tools across 15 cohorts. The Gail tool was most represented (n = 11), followed by Tyrer-Cuzick (n = 5), BRCAPRO and iCARE-Lit (n = 3). No tool was consistently well-calibrated across multiple studies and breast density or polygenic risk scores did not improve calibration. Most tools identified a risk group with higher rates of observed cancers, but few tools identified lower-risk groups across different settings. All tools demonstrated a high risk of bias. Conclusion: Some risk tools can identify groups of women at higher or lower breast cancer risk, but this is highly dependent on the setting and population

Types of MHT used by current users, in hysterectomised women and in women with an intact uterus.

<p>* Oest: oestrogen; P: progestagen; sys: systemic. * Missing values have been excluded from the proportions but are 4% or less. ‡ MHT for local use and combination of different MHT types.</p

Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women

Abstract Background There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population. Methods We used data from 40,158 women aged 50–69 years (via the lifepool cohort) participating in Australia’s BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance. Results Over a median of 4.3 years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O) = 1.09, 95% confidence interval (CI) 1.00–1.18). There was good agreement across decile groups of Gail scores (χ2 = 7.1, p = 0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O = 1.65, 95% CI 1.33–2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73–3.02, p < 0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06–1.70, p = 0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44–0.79, p < 0.0001). Similar patterns were observed separately for women aged 50–59 and 60–69 years. The model’s overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56–0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups. Conclusions This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50–69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening