Cost-effectiveness of mammography from a publicly funded health care system perspective
=======================================================================================

* Nicole Mittmann
* Natasha K. Stout
* Anna N.A. Tosteson
* Amy Trentham-Dietz
* Oguzhan Alagoz
* Martin J. Yaffe

## Abstract

**Background:** The implementation of population-wide breast cancer screening programs has important budget implications. We evaluated the cost-effectiveness of various breast cancer screening scenarios in Canada from a publicly funded health care system perspective using an established breast cancer simulation model.

**Methods:** Breast cancer incidence, outcomes and total health care system costs (screening, investigation, diagnosis and treatment) for the Canadian health care environment were modelled. The model predicted costs (in 2012 dollars), life-years gained and quality-adjusted life-years (QALYs) gained for 11 active screening scenarios that varied by age range for starting and stopping screening (40-74 yr) and frequency of screening (annual, biennial or triennial) relative to no screening. All outcomes were discounted. Marginal and incremental cost-effectiveness analyses were conducted. One-way sensitivity analyses of key parameters assessed robustness.

**Results:** The lifetime overall costs (undiscounted) to the health care system for annual screening per 1000 women ranged from $7.4 million (for women aged 50-69 yr) to $10.7 million (40-74 yr). For biennial and triennial screening per 1000 women (aged 50-74 yr), costs were less, at about $6.1 million and $5.3 million, respectively. The incremental cost-utility ratio varied from $36 981/QALY for triennial screening in women aged 50-69 versus no screening to $38 142/QALY for biennial screening in those aged 50-69 and $83 845/QALY for annual screening in those aged 40-74.

**Interpretation:** Our economic analysis showed that both benefits of mortality reduction and costs rose together linearly with the number of lifetime screens per women. The decision on how to screen is related mainly to willingness to pay and additional considerations such as the number of women recalled after a positive screening result.

The implementation of a population-wide breast cancer screening program has important budget implications for publicly funded health care systems because of the use of substantial resources. Mammography screening recommendations are periodically updated by different countries.1-4 The age range and frequency for population mammography screening programs as well as their effectiveness and cost-effectiveness have been topics of debate over many years.5-10 Given the fact that screening parameters (e.g., age range, frequency) vary among the various organized publicly funded screening programs across Canada, it is important to understand the trade-offs between improved health outcomes, potential harm and monetary costs of the decisions regarding whether to screen, whom to screen, and the age range and interval for screening. We recently published an economic analysis of the impact of various screening scenarios on costs and outcomes from an overall societal perspective.11 In that analysis, we found that screening every 3 years and screening every 2 years in women aged 50-69 years were the most cost-effective strategies, at $94 762 and $97 006 per quality-adjusted life-year (QALY), respectively, compared with no screening. Screening annually had a much higher ratio ($226 278 per QALY). However, we did not assess the value of screening programs from a publicly funded health care system perspective.

Given the fact that policy decision-makers are interested in understanding the specific impact of new interventions/strategies on their health care systems, the objective of this work was to evaluate the costs, outcomes and cost-effectiveness of mammography for various screening policies in the general population of Canadian women from the perspective of a single-payer publicly funded health care system. To do so, we used a previously validated computer model for the natural history, detection and treatment of breast cancer and conducted lifetime analyses for several relevant screening strategies.

## Methods

### Model design

We modified the University of Wisconsin Breast Cancer Epidemiology Simulation Model12 to reflect the Canadian context and conduct our analysis. The model was developed under the Cancer Intervention and Surveillance Modeling Network program, funded by the US National Cancer Institute.13,14 It is a discrete-event, stochastic simulation modelling approach to replicate incidence and mortality in breast cancer based on the US population. Complex interacting processes, including natural history, detection of breast cancer, treatment for breast cancer and competing mortality, are modelled over time, simulating the lives of women at 6-month intervals. Model outputs include age-specific incidence rates by stage and age-specific mortality rates. This microsimulation model is applied to a birth cohort of 2 000 000 women. Based on empirical probabilities for events, breast cancers are stochastically initiated at various time points in a fraction of these women. All tumours, including ductal carcinoma in situ, grow following a Gompertz function,15 with a distribution of randomly assigned growth rates. In the model, tumours are followed over time as the cohort ages. Tumours are assumed to initially be in situ, and all tumours grow until they reach a maximum size.16,17 Size is used as a surrogate for stage, and cancers are classified into 4 groups: in situ, localized, regional metastasis or distant metastasis. Thresholds for detection are defined for clinical discovery of the cancers or for detection by screening. The sensitivity of detection by screening is calibrated by adjusting model parameters so that the model outputs match empirical cancer detection data. The model also contains a specificity function to create false-positive detections. Screening sensitivity and specificity parameters are specific for the age and breast density of the women as well as for the examination's being an initial one or a recurring annual or biennial screen.18 Women are also randomly assigned hormonal and HER2 status of breast cancers via a weighted distribution reflecting population data. Treatments are given according to current guidelines based on in situ or invasive disease, age, hormonal status and HER2 status. Published clinical response data are used to predict outcome. The model tracks outcome (alive, breast cancer death, other cause of death) at 6-month intervals. The use of a discrete-event system simulation modelling approach allowed us to not make Markovian assumption for tumour growth, as tumour growth is most likely not memoryless. State-transition modelling would have forced us to represent tumour growth as a first-order or second-order Markovian process. The model conducts individual and separate simulation modelling based on all women born in 1960 (1960 birth cohort). It has been validated against US data12,16 and, in its modified form, against Canadian data.19

### Screening scenarios

Our screening scenarios involved various frequencies (annual, biennial, triennial and hybrids of these) across various age bands. We modelled the costs and outcomes for 11 screening scenarios as well as no screening from the perspective of the publicly funded health care system (in this case, Ontario). The scenarios included screening regimens that are currently being used in Canada19 and the United States as well as those that have been recommended by bodies such as the US Preventive Services Task Force,3 the Canadian Task Force on Preventive Health Care,1 Choosing Wisely Canada20 and the American Cancer Society.4 Health care system resources (mammography, diagnostics, medical personnel, cancer management) were included. Treatment for breast cancer included surgery (mastectomy, lumpectomy), hormonal therapy, chemotherapy as appropriate (by stage of disease) and radiation (number of fractions), depending on stage at diagnosis. We modelled that women with newly diagnosed invasive breast cancer would receive some form of adjuvant systemic treatment (chemotherapy and/or hormonal therapy). Finally, we assumed that all women with invasive HER2+ cancer would receive trastuzumab, whereas women with ductal carcinoma in situ would not.

### Data sources

Evidence for resource use and costs included guidelines, reports, peer-reviewed literature and expert opinion as found in formal and informal searches of the peer-reviewed and grey literature (Supplementary Table 1, Appendix 1, available at [www.cmajopen.ca/content/6/1/E77/suppl/DC1](http://www.cmajopen.ca/content/6/1/E77/suppl/DC1)). We assumed that 100% of eligible women would be screened, that all positive screening results and all clinical diagnoses incurred a noninvasive investigation cost, and that a subset of positive screening results and clinical diagnoses incurred costs of further invasive investigation. Model assumptions and input data related to benefit have been fully described elsewhere.11,21,22

### Outcomes

We used life-years and QALYs as benefit measures. The model provided survival information, and we applied health preference values to determine QALYs. We used age-specific population health preference values derived from studies based on US populations (2001 Medical Expenditures Panel Survey, based on 22 523 subjects, and 2001 National Health Interview Survey, based on 32 472 subjects) and applied the EuroQol EQ-5D instrument using US scoring.23,24

For women with newly diagnosed ductal carcinoma in situ or nonmetastatic invasive breast cancer, we applied decrements for stage lasting for 2 years after diagnosis based on values assigned to disease and treatment phases by experts in breast cancer and public health (Appendix 1), after which each woman would return to her appropriate age-specific health preference value. For those with regional disease, we applied decrements for 2 years after diagnosis of regional disease. For women with a diagnosis of metastatic breast cancer, the decrement was applied to their remaining lifetime. We also assumed small, short-term decrements in quality of life for screening and diagnostic investigation in cases with positive screening results (0.006 for 1 wk and 0.105 for 5 wk, respectively).23

### Resources and costs

The model estimated costs of screening and treatment for each scenario for the lifetime of the cohort from the health care system perspective. We applied Canadian unit costs (in 2012 Canadian dollars) to each of the resources used and modelled (Can$1 = US$1.01 based on Dec. 31, 201225). Costs from years other than 2012 were converted to 2012 values with the use of the Consumer Price Index ([www.bankofcanada.ca](http://www.bankofcanada.ca)). Cost sources included formularies, statistics and the published literature. Capital or institutional costs of equipment were not included in this analysis. For medications, we identified the average costs of first-line, second-line and third-line medications through expert opinion and guidelines. We determined costs for eligible therapies used and an average value for all medications.

### Statistical analysis

We calculated both marginal (relative to no screening) and incremental cost-effectiveness ratios (ICERs) to evaluate the screening scenarios, as both types provide information that is useful for different purposes. A public health screening program is generally launched in an effort to make the maximum impact in reducing mortality and/or morbidity for a target population. When implementing such a program, one needs to examine the number of deaths averted or QALYs gained and the average cost per death averted compared to no screening; hence, the marginal cost-effectiveness ratio or cost-utility ratio would be of interest. In cases of competing priorities for limited resources, where there is consideration of trading some degree of benefit for a reduction of cost, it is the cost of the last death averted or QALY gained that is of interest and where incremental analysis is more useful. We determined ICERs (cost/life-year gained) and incremental cost-utility ratios (ICURs) (cost/QALY) comparing screening scenarios. All costs and health outcomes were discounted at a rate of 1.5%, recently proposed by the Canadian Agency for Drugs and Technologies in Health.26 To understand the impact of resource costs on overall costs and the value of each screening scenario, we varied the input cost for key resources in one-way sensitivity analyses (Supplementary Table 2, Appendix 1). We also briefly explored the effect of the reduction of the annual discount rate on incremental values by comparing ICURs estimated at 5%, 3% and 1.5%.27

## Results

The overall cost (undiscounted) to the health care system associated with the no-screening scenario was $3.0 million per 1000 women over a lifetime time horizon. The overall health care system cost for annual screening per 1000 women ranged from $7.4 million (for those aged 50-69 yr) to $10.7 million (for those aged 40-74 yr). For biennial and triennial screening per 1000 women (50-74 yr), costs were less, at about $6.1 million and $5.3 million, respectively (Table 1).

View this table:
[Table 1:](http://www.cmajopen.ca/content/6/1/E77/T1)

Table 1:   Disaggregated undiscounted and total costs per 1000 women for various breast cancer screening scenarios from the perspective of a single-payer publicly funded health care system

In the marginal analysis, all screening scenarios improved life-years and QALYs but did so at an added cost compared to no screening (Table 2, Figure 1) (full data given in Supplementary Table 3, Appendix 1). The marginal cost-effectiveness ratios for each screening scenario compared to no screening were generally under $50 000/life-years gained and $60 000/QALY. The lowest ratio modelled was for the least frequent screening with the smallest age band (triennial screening for women aged 50-69), at $30 536/life-year gained and $36 981/QALY. The most aggressive scenario compared to no screening, namely, annual screening for women aged 40-74 years, was associated with the highest marginal ratio, at $48 718/life-year gained and $57 938/QALY.

![Figure 1](http://www.cmajopen.ca/https://www.cmajopen.ca/content/cmajo/6/1/E77/F1.medium.gif)

[Figure 1](http://www.cmajopen.ca/content/6/1/E77/F1)

Figure 1 
Marginal cost-utility plane for various screening scenarios compared to no screening from health care system perspective. Note: Ann = annual, Bi = biennial, QALY = quality-adjusted life-year, Tri = triennial.

View this table:
[Table 2](http://www.cmajopen.ca/content/6/1/E77/T2)

Table 2  Marginal cost-effectiveness and cost-utility ratios of various screening scenarios (discount = 1.5%) per 1000 women compared to no screening

In the univariate sensitivity analysis, in most cases, the marginal ratios did not change dramatically. The notable exception was omission of systemic treatments, which increased the marginal cost-effectiveness ratio to more than $150 000/life-year gained (Table 3) (full data given in Supplementary Table 4, Appendix 1), but this is not a viable clinical option. The model was also sensitive to modifications in health preference values, with more favourable marginal cost-utility ratios when the health preference values were increased by 25%, thereby showing greater benefits between the screening and no-screening scenarios. Participation, mammography sensitivity and use of trastuzumab did not affect model results markedly. The insensitivity of marginal cost-effectiveness ratios and cost-utility ratios to the decreased screening compliance rate is understandable because screening costs account for one-third to one-half of the total health cost in the scenarios. A decline in screening results in decreased screening costs, but this is paralleled by a corresponding decrease in the number of invasive cancers detected, affecting life-years gained and QALYs. This results in the ratios' being fairly stable. The sensitivity of mammography is already fairly high for most women; therefore, the impact of an increase is limited. Finally, a relatively small fraction of the cohort would receive and benefit from trastuzumab treatment.

View this table:
[Table 3](http://www.cmajopen.ca/content/6/1/E77/T3)

Table 3  Univariate sensitivity analysis, marginal: active screening scenarios compared to no screening where the outcome is cost per quality-adjusted life-year (discount = 1.5%)

Results of the incremental analysis are presented in Table 4 and Figure 2 (complete data given in Supplementary Table 5, Appendix 1). Several of the scenarios are weakly dominated. For those that are not dominated, ICURs range from $36 981/QALY for triennial screening for women aged 50-69 to $110 994/QALY for annual screening for women aged 40-74, with a difference of 67 QALYs per 1000 women between these extremes.

![Figure 2](http://www.cmajopen.ca/https://www.cmajopen.ca/content/cmajo/6/1/E77/F2.medium.gif)

[Figure 2](http://www.cmajopen.ca/content/6/1/E77/F2)

Figure 2 
Incremental cost-utility plane for various screening scenarios compared to no screening from health care system perspective. Only nondominated scenarios are shown. Note: Ann = annual, Bi = biennial, QALY = quality-adjusted life-year, Tri = triennial.

View this table:
[Table 4](http://www.cmajopen.ca/content/6/1/E77/T4)

Table 4 Incremental cost-utility ratios of various screening scenarios (discount = 1.5%) per 1000 women compared to no screening*

With triennial screening in women aged 50-74 as the reference, the ICURs for decreasing the screening interval to biennial or annual were $42 900 and $71 481, respectively (Table 5) (full data presented in Supplementary Table 6, Appendix 1). The ICUR for extending the age range to 40-74 years for annual screening was $80 986.

View this table:
[Table 5:](http://www.cmajopen.ca/content/6/1/E77/T5)

Table 5:   Incremental cost-utility ratio for changes in screening frequency and age at which to start screening

We also used biennial screening in women aged 50-74 years, which is the standard of several programs in Canada, as a reference for an incremental analysis (Table 6) (full data given in Supplementary Table 7, Appendix 1). Screening annually in women aged 50-74 years was weakly dominated, but the ICUR for annual screening in those aged 50-69 was $62 549/QALY, for those aged 40-69 years, $79 266/QALY, and for those aged 40-74 years, $110 994/QALY. Less-intensive screening reduced both QALYs and costs. For example, eliminating biennial screening for women aged 70-74 years resulted in a decrease of 5 QALYs, with a cost reduction of $77 308 per QALY lost.

View this table:
[Table 6:](http://www.cmajopen.ca/content/6/1/E77/T6)

Table 6:   Effect of changing the screening scenario from the baseline of biennial screening in women aged 50-74 years

Decreasing the discounting rate from 5% to 3% to 1.5% (with no screening as the reference) resulted in a reduction in the ICUR from $65 743/QALY to $52 672/QALY to $38 142/QALY, respectively, for biennial screening in women aged 50-69 years and from $156 743/QALY to $121 160/QALY to $83 845/QALY, respectively, for annual screening in those aged 40-74 (data not shown).

## Interpretation

We compared the cost-effectiveness of various policy-driven mammography screening programs conducted from the perspective of a Canadian publicly funded health care system using a validated breast cancer risk model. From a pure cost perspective, and not considering clinical outcomes, all active screening scenarios modelled (undiscounted) were cost drivers and represented one-third to two-thirds of the total cost of breast cancer management (screening, investigation and treatment) to the health care system. The ratio of the cost of screening to overall cost was directly proportional to the aggressiveness of the screening strategy. Treatment costs were slightly higher for screening than for no screening. Lowering the age at which screening starts to 40 years from 50 years added roughly $1.3 million-$2.4 million per 1000 women ($1300-$2400 per woman over her lifetime) to the overall cost. Increasing the upper limit of 69 years by 5 years added about $0.5 million-$0.9 million per 1000 women ($500-$900 per woman) to the overall cost. Interestingly, the scenario commonly used in Canada, biennial screening for women aged 50-74, was weakly dominated by annual screening for those aged 50-69. Presumably, the impact of reducing interval cancers in younger women outweighs that of detecting cancer in women aged 70-74.

Several models of breast cancer natural history have been developed to project the impact of different mammography screening scenarios in women.13,28-32 We selected the modified Wisconsin Cancer Intervention and Surveillance Modelling Network model for our analysis because it allowed simulation of the growth of a distribution of breast cancers within a cohort of women and separate consideration of the individual effects of various detection strategies and treatment regimens on mortality or other outcomes. In addition, the Canadianized version used empirical data on the sensitivity and specificity of modern screening mammography specific to the Canadian perspective. The model performed quite well in predicting breast cancer incidence in the absence of screening in the Canadian context.21 Despite the fact that annual-screening scenarios had higher incremental ratios than less-frequent scenarios, they were associated with greater life-years gained and QALY benefits. The more aggressive the screening strategy, the more cancers are detected and the more breast cancer deaths are averted and life-years gained.

A comparison of the ratios for all the active screening scenarios compared to the no-screening scenario showed a relatively tight range of marginal ratios, within roughly $20 000 of one another. Extending the upper age limit for screening from 69 to 74 years marginally increased the ratios owing to additional screening costs, but this was balanced by improved outcomes. Lowering the age at which screening started to 40 years resulted in increased ratios, mostly due to the increased screening costs, but also yielded more life-years gained and QALYs. Since both life-years gained and QALYs and costs rise together almost linearly with the number of lifetime screens per woman, the decision on how to screen is related mainly to willingness to pay by the system and avoiding recalling too many women for further examinations after positive screening results. Certainly, if examined by age band (50-69 yr and 50-74 yr), the modelled ratios for annual, biennial and triennial screening scenarios compared to no screening were very similar. Interestingly, the current standard in some provinces and the regimen recommended by the US Preventive Services Task Force and the Canadian Task Force on Preventive Health Care, biennial screening for women aged 50-74, was a weakly dominated scenario, as were the 2 hybrids of annual screening for those aged 40-49 followed by biennial examinations. When choosing a screening scenario based on the value assessments, one should also consider the improvement in life-years gained and QALYs associated with more frequent screening.

It is useful to consider the effect of the discounting rate on cost-effectiveness and cost-utility estimates. Discounting assigns progressively reducing values to costs and improvements in health outcome that occur in the future. It is traditional to use the same annual discounting rate for both.33 With higher discount rates, this essentially has the effect of making both costs and benefits that occur many years after the beginning of a program virtually negligible. When, as in a screening program, many of the costs are borne toward the beginning of the program, whereas the benefits (absence of breast cancer death or years or QALYs gained) occur many years later, the effect is to reduce estimated benefits much more than costs, increasing ICURs. It has been argued that this puts a disproportionate emphasis on the "here and now."34 In 2012, it was common to use a discounting rate of 5%. Recommended reductions since that time to 3% and now to 1.5% have reduced ICURS by about 20% and 45%, respectively, making them more attractive to payers.26

Our discounted model predicted that all screening scenarios were more effective than no screening. All ratios comparing active scenarios to no screening fell below commonly accepted and proposed thresholds.35-38 Several studies have evaluated the cost-effectiveness of screening strategies, but most have been conducted from the perspective of the US health care system and/or have considered different risk factors such as early and late age and genetic profile.4,13,39-41 Gocgun and colleagues42 constructed a model to estimate the Canadian cost per life saved using data from the Canadian National Breast Cancer Screening Study.43,44 That model was not validated and was based on study results from the 1980s, when film mammography (now obsolete) was used. Unlike several other studies,45 the Canadian National Breast Cancer Screening Study did not show a mortality benefit of mammographic screening, which would make any screening strategy not effective or cost-effective. The study showed that there was a decrease in mortality when the frequency of screening was increased. However, when considering cost-effectiveness, the strategy that Gocqun and colleagues42 found to be the most cost-effective included avoiding screening in women aged 40-49 and screening those aged 50-69 every 5 years, at a cost of $537 000 per life saved. Screening women aged 50-69 every 3 years or every other year yielded results close to the optimal strategy ($626 973 and $654 940, respectively) while continuing to avoid screening in women aged 40-49. Other differences between our model and that of Gocgun and colleagues42 include variability in costs, variability in discount factor (3.1% v. 1.5%), differential sources of the treatment distribution data, older survival data and no examination of QALYs, only of life-years.

Pataky and colleagues46 also used a Markov model to predict the cost-effectiveness of screening mammography in the British Columbia Screening Program. They focused exclusively on the question of using annual versus biennial screening for women with high breast density and did not consider the various scenarios. Furthermore, those authors did not describe the independent validation of the model, whereas the Cancer Intervention and Surveillance Modelling Network model has been extensively validated.

### Limitations

Limitations of the model that we used were outlined in previous work.11,21,22 Essentially, our baseline assumption was that 100% of eligible women would be screened, whereas, in reality, compliance is lower with an organized screening program.47 We did not include the cost of premature death in the economic evaluation to avoid the possibility of double counting. However, given the significance of premature death to society, we expect that more frequent screening would substantially decrease the costs associated with premature death due to breast cancer. Work examining the cost of premature death in this model is planned.

### Conclusion

The current work will be helpful in informing the question regarding the most appropriate mammography screening scenario for a population. We have shown that the greatest single cost contributor in a screening program is screening itself. The more screens that a women receives in her life, the greater the financial cost to the health care system, but the greater the gain in life-years and QALYs. The decision on how to screen is related mainly to willingness to pay and a determination as to what is an acceptable rate for recalling women for further examinations after positive screening results.

### Supplemental information

For reviewer comments and the original submission of this manuscript, please see [www.cmajopen.ca/content/6/1/E77/suppl/DC1.](http://www.cmajopen.ca/content/6/1/E77/suppl/DC1.)

## Acknowledgements

Acknowledgement: The authors thank the participating women, mammography facilities and radiologists for the data they provided for this study.

## Footnotes

*   **Competing interests:** Oguzhan Alagoz received personal fees from Ally Clinical Diagnostics outside the submitted work. Martin Yaffe received grants from GE Healthcare, Mammographic Physics and Volpara Health Technologies outside the submitted work. No other competing interests were declared.

*   **Contributors:** Nicole Mittmann and Martin Yaffe conceived and designed the study. All of the authors acquired the data, contributed to data analysis and interpretation and the drafting of the manuscript, gave final approval of the version to be published and agreed to be accountable for all aspects of the work.

*   **Funding:** This work was supported by a contract from the Canadian Breast Cancer Foundation to Nicole Mittmann. The University of Wisconsin Breast Cancer Epidemiology Simulation Model used in this analysis was supported in part by grants U01 CA152958 and U01 CA199218 from the National Cancer Institute through the Cancer Intervention and Surveillance Modeling Network. Model input data on the performance of screening mammography was supported by grant UC2CA148577 and contract HHSN261201100031C from the Breast Cancer Surveillance Consortium (BCSC), funded by the National Cancer Institute. The collection of BCSC cancer data used in this study to develop input parameters was supported in part by several state public health departments and cancer registries throughout the United States. For a full description of these sources, please see [www.breastscreening.cancer.gov/work/acknowledgement.html.](http://www.breastscreening.cancer.gov/work/acknowledgement.html.) A list of the BCSC investigators and procedures for requesting BCSC data for research purposes is provided at [http://breastscreening.cancer.gov/.](http://breastscreening.cancer.gov/.)

*   **Disclaimer:** The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

## References

1.  Canadian Task Force on Preventive Health Care, Tonelli M, Connor Gorber S, Joffres M, et al. (2011) Recommendations on screening for breast cancer in average-risk women aged 40-74 years. CMAJ 183:1991–2001.
    
    [FREE Full Text](http://www.cmajopen.ca/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiRlVMTCI7czoxMToiam91cm5hbENvZGUiO3M6NDoiY21haiI7czo1OiJyZXNpZCI7czoxMToiMTgzLzE3LzE5OTEiO3M6NDoiYXRvbSI7czoxOToiL2NtYWpvLzYvMS9FNzcuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

2.  Perry N, Broeders M, de Wolf C, et al. (2008) European guidelines for quality assurance in breast cancer screening and diagnosis. Fourth edition - summary document. Ann Oncol 19:614–22.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1093/annonc/mdm481&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=18024988&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000254470400003&link_type=ISI) 

3.  US Preventive Service Task Force (2009) Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 151:716–26, W-236.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.7326/0003-4819-151-10-200911170-00008&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=19920272&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000272145100005&link_type=ISI) 

4.  Oeffinger KC, Fontham ETH, Etzioni R, et al., American Cancer Society (2015) Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA 314:1599–614.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1001/jama.2015.12783&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=26501536&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

5.  Green BB, Taplin SH (2003) Breast cancer screening controversies. J Am Board Fam Pract 16:233–41.
    
    [Abstract/FREE Full Text](http://www.cmajopen.ca/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NToiamFiZnAiO3M6NToicmVzaWQiO3M6ODoiMTYvMy8yMzMiO3M6NDoiYXRvbSI7czoxOToiL2NtYWpvLzYvMS9FNzcuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

6.  Duffy SW, Tabár L, Smith RA (2002) The mammographic screening trials: commentary on the recent work by Olsen and Gøtzsche. CA Cancer J Clin 52:68–71.
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=11929006&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000174534700002&link_type=ISI) 

7.  Monticciolo DL, Newell MS, Hendrick RE, et al. (2017) Breast cancer screening for average-risk women: recommendations from the ACR Commission on Breast Imaging. J Am Coll Radiol 14:1137–43.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1016/j.jacr.2017.06.001&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=28648873&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

8.  Biller-Andorno N, Jüni P (2014) Abolishing mammography screening programs? A view from the Swiss Medical Board. N Engl J Med 370:1965–7.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1056/NEJMp1401875&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=24738641&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000336125500002&link_type=ISI) 

9.  Keating NL, Pace LE (2015) New guidelines for breast cancer screening in US women. JAMA 314:1569–71.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1001/jama.2015.13086&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=26501532&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

10. Moy L (2016) Screening mammography and age recommendations. JAMA 315:1404–5.
    
    

11. Mittmann N, Stout NK, Lee P, et al. (2015) Total cost-effectiveness of mammography screening strategies. Health Rep 26:16–25.
    
    

12. Fryback DG, Stout NK, Rosenberg MA, et al. (2006) The Wisconsin Breast Cancer Epidemiology Simulation Model. J Natl Cancer Inst Monogr 36:37–47.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1093/jncimonographs/lgj007&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=17032893&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

13. Feuer EJ, Etzioni R, Cronin KA, et al. (2004) The use of modeling to understand the impact of screening on U.S. mortality: examples from mammography and PSA testing. Stat Methods Med Res 13:421–42.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1191/0962280204sm376ra&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=15587432&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000225102100002&link_type=ISI) 

14. Breast cancer modeling. Bethesda (MD): Cancer Intervention and Surveillance Modeling Network, National Cancer Institute, Available[www.cisnet.cancer.gov/breast/](http://www.cisnet.cancer.gov/breast/). accessed 2014 July 9.
    
    

15. Gompertz B. (1825) On the nature of the function expressive of the law of human mortality and on a new model of determining life contingencies. Phil Trans R Soc 115:513–85.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1098/rstl.1825.0026&link_type=DOI) 

16. Alagoz O, Ergun MA, Cevik M, et al. (2018) The University of Wisconsin Breast Cancer Epidemiology Simulation Model: an update. Med Decis Making 38(1S).
    
    

17. van Ravesteyn NT, van den Broek JJ, Li X, et al. Modeling ductal carcinoma in situ (DCIS) - an overview of CISNET model approaches. Med Decis Making.
    
    

18. Mandelblatt JS, Cronin KA, Bailey S, et al., Breast Cancer Working Group of the Cancer Intervention and Surveillance Modeling Network (2009) Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms. Ann Intern Med 151:738–47.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.7326/0003-4819-151-10-200911170-00010&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=19920274&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000272145100007&link_type=ISI) 

19. Breast cancer screening in Canada: environmental scan. Toronto: Canadian Partnership Against Cancer; 2017, Available[https://content.cancerview.ca/download/cv/prevention\_and\_screening/screening\_and\_early\_diagnosis/documents/breastcancerenviroscanpptx?attachment=0](https://content.cancerview.ca/download/cv/prevention\_and\_screening/screening_and_early_diagnosis/documents/breastcancerenviroscanpptx?attachment=0). accessed 2017 Nov. 2.
    
    

20. College of Family Physicians of Canada Eleven things physicians and patients should question [updated June 2017], Available[https://choosingwiselycanada.org/family-medicine/](https://choosingwiselycanada.org/family-medicine/). accessed 2017 Nov.2.
    
    

21. Yaffe MJ, Mittmann N, Lee P, et al. (2015) Modelling mammography screening for breast cancer in the Canadian context: modification and testing of a microsimulation model. Health Rep 26:3–8.
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

22. Yaffe MJ, Mittmann N, Lee P, et al. (2015) Clinical outcomes of modelling mammography screening strategies. Health Rep 26:9–15.
    
    

23. Hanmer J, Vanness D, Gangnon R, et al. (2010) Three methods tested to model SF-6D health utilities for health states involving comorbidity/co-occurring conditions. J Clin Epidemiol 63:331–41.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1016/j.jclinepi.2009.06.013&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=19896802&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

24. Fleishman JA Methodology report #15: demographic and clinical variations in health status. Rockville (MD): Agency for Healthcare Research and Quality; 2005, Available[http://meps.ahrq.gov/mepsweb/data_files/publications/mr15/mr15.shtml](http://meps.ahrq.gov/mepsweb/data_files/publications/mr15/mr15.shtml). accessed 2014 July 7.
    
    

25. 10-year converter. Ottawa: Bank of Canada, Available[www.collectionscanada.gc.ca/eppp-archive/100/201/301/bank\_can\_review/2006/spring/cover/en/rates/exchform.html](http://www.collectionscanada.gc.ca/eppp-archive/100/201/301/bank_can_review/2006/spring/cover/en/rates/exchform.html). accessed 2014 July 7.
    
    

26. Guidelines for the economic evaluation of health technologies: Canada. 4th ed. Ottawa: Canadian Agency for Drugs and Technologies in Health; 2017, Available[https://www.cadth.ca/dv/guidelines-economic-evaluation-health-technologies-canada-4th-edition](https://www.cadth.ca/dv/guidelines-economic-evaluation-health-technologies-canada-4th-edition). accessed 2017 Apr. 1.
    
    

27. Stout NK, Lee SJ, Schechter CB, et al. (2014) Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography. J Natl Cancer Inst 106:dju092.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1093/jnci/dju092&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=24872543&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

28. Salzmann P, Kerlikowske K, Phillips K (1997) Cost-effectiveness of extending screening mammography guidelines to include women 40 to 49 years of age. Ann Intern Med 127:955–65.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.7326/0003-4819-127-11-199712010-00001&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=9412300&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=A1997YJ08000001&link_type=ISI) 

29. Mandelblatt J, Saha S, Teutsch S, et al., Cost Work Group of the U.S. Preventive Services Task Force (2003) The cost-effectiveness of screening mammography beyond age 65: a systematic review for the U.S. Preventive Services Task Force. Ann Intern Med 139:835–42.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.7326/0003-4819-139-10-200311180-00011&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=14623621&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000186663500007&link_type=ISI) 

30. Tosteson ANA, Stout NK, Fryback DG, et al., DMIST Investigators (2008) Cost-effectiveness of digital mammography breast cancer screening. Ann Intern Med 148:1–10.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.7326/0003-4819-148-1-200801010-00002&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=18166758&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000252149300001&link_type=ISI) 

31. Stout NK, Rosenberg MA, Trentham-Dietz A, et al. (2006) Retrospective cost-effectiveness analysis of screening mammography. J Natl Cancer Inst 98:774–82.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1093/jnci/djj210&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=16757702&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000238390300013&link_type=ISI) 

32. Schousboe JT, Kerlikowske K, Loh A, et al. (2011) Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness. Ann Intern Med 155:10–20.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.7326/0003-4819-155-1-201107050-00003&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=21727289&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000292447800002&link_type=ISI) 

33. Katz DA, Welch HG (1993) Discounting in cost-effectiveness analysis of healthcare programmes. Pharmacoeconomics 3:276–85.
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=10146991&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

34. West RR (1996) Discounting the future: influence of the economic model. J Epidemiol Community Health 50:239–44.
    
    [Abstract/FREE Full Text](http://www.cmajopen.ca/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoiamVjaCI7czo1OiJyZXNpZCI7czo4OiI1MC8zLzIzOSI7czo0OiJhdG9tIjtzOjE5OiIvY21ham8vNi8xL0U3Ny5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=) 

35. Economic guidance reports. Toronto: Pan-Canadian Oncology Drug Review; 2014. Available[https://www.cadth.ca/sites/default/files/pcodr/The%20pCODR%20Expert%20Review%20Committee%20%28pERC%29/pcodr\_expertreviewcom\_tor.pdf](https://www.cadth.ca/sites/default/files/pcodr/The%20pCODR%20Expert%20Review%20Committee%20%28pERC%29/pcodr_expertreviewcom_tor.pdf). accessed 2017 Jan. 30.
    
    

36. Berry SR, Bell CM, Ubel PA, et al. (2010) Continental divide? The attitudes of US and Canadian oncologists on the costs, cost-effectiveness, and health policies associated with new cancer drugs. J Clin Oncol 28:4149–53.
    
    [Abstract/FREE Full Text](http://www.cmajopen.ca/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiamNvIjtzOjU6InJlc2lkIjtzOjEwOiIyOC8yNy80MTQ5IjtzOjQ6ImF0b20iO3M6MTk6Ii9jbWFqby82LzEvRTc3LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

37. Paulden M, O'Mahony J, McCabe C (2017) Determinants of change in the cost-effectiveness threshold. Med Decis Making 37:264–76.
    
    

38. Claxton K, Martin S, Soares M, et al. (2015) Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold. Health Technol Assess 19:1–503, v–vi.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.3310/hta19880&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=26507206&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

39. Kerlikowske K, Salzmann P, Phillips KA, et al. (1999) Continuing screening mammography in women aged 70 to 79 years: impact on life expectancy and cost-effectiveness. JAMA 282:2156–63.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1001/jama.282.22.2156&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=10591338&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000083971400035&link_type=ISI) 

40. Lindfors KK, Rosenquist CJ (1995) The cost-effectiveness of mammographic screening strategies. JAMA 274:881–4.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1001/jama.1995.03530110043033&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=7674501&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=A1995RU60300026&link_type=ISI) 

41. Pataky R, Phillips N, Peacock S, et al. (2014) Cost-effectiveness of population-based mammography screening strategies by age range and frequency. J Cancer Policy 2:97–102.
    
    

42. Gocgun Y, Banjevic D, Taghipour S, et al. (2015) Cost-effectiveness of breast cancer screening policies using simulation. Breast 24:440–8.
    
    

43. Miller AB, To T, Baines CJ, et al. (2002) The Canadian National Breast Screening Study-1: breast cancer mortality after 11 to 16 years of follow-up. A randomized screening trial of mammography in women age 40 to 49 years. Ann Intern Med 137:305–12.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access\_num=10.7326/0003-4819-137-5_Part_1-200209030-00005&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=12204013&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000177865600001&link_type=ISI) 

44. Miller AB, To T, Baines CJ, et al. (2000) Canadian National Breast Screening Study-2: 13-year results of a randomized trial in women aged 50-59 years. J Natl Cancer Inst 92:1490–9.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1093/jnci/92.18.1490&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=10995804&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000089300400007&link_type=ISI) 

45. Independent UK Panel on Breast Cancer Screening (2012) The benefits and harms of breast cancer screening: an independent review. Lancet 380:1778–86.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1016/S0140-6736(12)61611-0&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=23117178&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 
    
    [Web of Science](http://www.cmajopen.ca/lookup/external-ref?access_num=000311153700035&link_type=ISI) 

46. Pataky R, Ismail Z, Coldman AJ, et al. (2014) Cost-effectiveness of annual versus biennial screening mammography for women with high mammographic breast density. J Med Screen 21:180–8.
    
    [CrossRef](http://www.cmajopen.ca/lookup/external-ref?access_num=10.1177/0969141314549758&link_type=DOI) 
    
    [PubMed](http://www.cmajopen.ca/lookup/external-ref?access_num=25186116&link_type=MED&atom=%2Fcmajo%2F6%2F1%2FE77.atom) 

47. Ontario cancer screening performance report 2016. Toronto: Cancer Care Ontario; 2016, Available[https://www.cancercareontario.ca/en/screening-performance-report-2016](https://www.cancercareontario.ca/en/screening-performance-report-2016). accessed 2017 Nov. 2.
    
    

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