Economics Department, Rovira i Virgili University, Reus, Catalonia, Spain

Evaluation and Clinical Epidemiology Department, Parc de Salut Mar and CIBER of Epidemiology and Public Health (CIBERESP), Barcelona, Catalonia, Spain

Terres de l'Ebre Region, Catalan Institute of Health, Catalonia, Spain

Basic Medical Sciences Department, Biomedical Research Institut of Lleida (IRBLLEIDA)-University of Lleida, Lleida, Catalonia, Spain

Abstract

Background

Breast cancer (BC) causes more deaths than any other cancer among women in Catalonia. Early detection has contributed to the observed decline in BC mortality. However, there is debate on the optimal screening strategy. We performed an economic evaluation of 20 screening strategies taking into account the cost over time of screening and subsequent medical costs, including diagnostic confirmation, initial treatment, follow-up and advanced care.

Methods

We used a probabilistic model to estimate the effect and costs over time of each scenario. The effect was measured as years of life (YL), quality-adjusted life years (QALY), and lives extended (LE). Costs of screening and treatment were obtained from the Early Detection Program and hospital databases of the IMAS-Hospital del Mar in Barcelona. The incremental cost-effectiveness ratio (ICER) was used to compare the relative costs and outcomes of different scenarios.

Results

Strategies that start at ages 40 or 45 and end at 69 predominate when the effect is measured as YL or QALYs. Biennial strategies 50-69, 45-69 or annual 45-69, 40-69 and 40-74 were selected as cost-effective for both effect measures (YL or QALYs). The ICER increases considerably when moving from biennial to annual scenarios. Moving from no screening to biennial 50-69 years represented an ICER of 4,469€ per QALY.

Conclusions

A reduced number of screening strategies have been selected for consideration by researchers, decision makers and policy planners. Mathematical models are useful to assess the impact and costs of BC screening in a specific geographical area.

Background

In Catalonia (Spain), as in the majority of Western countries, breast cancer (BC) is the cancer with the highest incidence among women (almost 1/3 of all malignant neoplasms). BC causes more deaths than any other cancer (18% of the cancer deaths in Catalan women in the 1998-2002 period)

The economic cost of BC is also important. In the USA, BC drugs are the second biggest category of all pharmaceutical sales, growing at double the overall market

Economic evaluation is based on a comprehensive assessment of effects and costs. Cost-effectiveness analysis makes it possible to determine the cost of obtaining an additional unit of health outcome, comparing the costs and outcomes of different health interventions or strategies within the same intervention. Costs are valued in monetary units and effectiveness is valued in years of life, mortality reduction or quality adjusted life years (QALYs). What society is willing to pay for an additional unit of outcome is an open discussion. Most of the economic evaluation guidelines refer to this issue and propose different solutions. In Spain, Sacristan

In Spain, population based screening programs were established during the 1990s. Some economic evaluations of these programs have been performed. For instance, in Catalonia, Plans

There are two major elements of the cost-effectiveness of mammography screenings that characterize a specific screening strategy: 1) the age interval and 2) the periodicity of the exams. In Spain, most of population-based early detection programs target women aged 50-69 and perform biennial mammograms. Extending the program to older or younger women has been a matter of interest for health policy-makers. In a previous work we analyzed the benefit, in terms of mortality reduction and years of life gained, of different screening strategies in Catalonia

Methods

Based on different BC screening recommendations, we generated 20 possible screening strategies by varying the periodicity of screening exams and the age intervals of women screened. We combined annual or biennial screening with age intervals that started at 40, 45 and 50 years and ended at 69, 70, 74 and 79 years. We also included the

The Lee and Zelen stochastic model

Lee and Zelen (LZ) developed a probabilistic model that predicts mortality as a function of the early detection modality. The characteristics and assumptions of the LZ model are described in detail elsewhere _{0}), preclinical disease state (S_{p}: capable of being diagnosed by a special exam), clinical state (S_{c}: diagnosis by symptomatic detection), and a death from BC state (S_{d}
^{bc}); (2) age-dependent transitions into the different states; (3) age-dependent examination sensitivity; (4) age-dependent sojourn times in each state; and (5) exam-diagnosed cases have a stage-shift in the direction of more favorable prognosis relative to the distribution of stages in symptomatic detection.

The basic LZ model calculates the cumulative probability of death for the cohort ν exposed to any screening program after

Estimation of BC incidence, prevalence and mortality under different screening scenarios

We estimated the probability of being an incident case at age

Incidence

Under a screening scenario, the probability of having a BC diagnosis at age _{p}. A detailed specification of the incidence estimation can be found in the Appendix in Additional file

**Appendix**. The file contains further details of the model for the estimation of BC incidence, prevalence, mortality, false positive tests, and additional tables.

Click here for file

Prevalence

Once a woman is diagnosed of BC, she can die from BC or from other causes. The proportion of women that remain alive at the end of the first year,

where ^{
-bc
}

The last summand of equation (1) represents the proportion of incident women that die from other causes in the age interval [

Similarly, for successive years

Breast cancer mortality

Detailed equations for estimating the probability of death from BC at different time periods for different screening scenarios can be found elsewhere

Measuring the effect of different screening scenarios

For each screening scenario and for the background, we measured the effect of screening with three outcomes: 1) the number of lives extended (LE); 2) the number of years of life gained (YL); 3) the number of quality-adjusted life years gained (QALY). QALYs were estimated applying the weights derived from EuroQol EQ-5D quality-of-life utility scores that Stout

All the calculations assumed an initial population of 100,000 women at birth. Incidence of BC and mortality from other causes refer to the cohorts born in the period 1948-1952. The time horizon for the study was 40-79 years of age.

Costs' considerations

Costs can be categorized as direct (either healthcare or non-healthcare costs), indirect or intangible and each one of these categories is considered or not depending on the study's perspective and on the availability of data. We have adopted the perspective of the national health system and considered only the direct healthcare costs.

We have partitioned the estimation of costs into four parts: screening and diagnosis confirmation, initial treatment, follow-up and advanced care costs. All costs were valued in 2005 euros and both costs and outcomes have been discounted at an annual rate of 3%, according to the economic evaluation guidelines of the Spanish Ministry of Heath

Costs of the breast cancer diagnosis under a screening scenario

The costs of screening mammograms, complementary tests and administrative expenses were obtained from the Early Detection Program of IMAS in the city of Barcelona. The program covers 42% of women living in Barcelona. We considered the following costs: screening mammogram plus administrative costs, 35 €; early recall mammogram, 23 €; non-invasive complementary tests, 298 €; and invasive tests, 473 €.

To obtain the costs of screening and diagnosis confirmation we made the following assumptions:

Part A): While women are screened

• All women at risk of BC in the target population participated in the screening exams and received a mammogram according to the periodicity and age interval of each screening scenario.

• There were 7% of women that received an additional mammogram for further assessment or early recall.

• We used the false positive (FP) rates for non-invasive and invasive tests obtained from a Spanish study that included eight Breast Cancer Early Detection Programs

• In the interval between exams there were no FP and all the women with BC diagnosed during the interval would undergo a non-invasive plus an invasive test.

• Sensitivity of mammogram was 0.55 for ages 40-45 years, 0.70 for 45-50 years, 0.75 for 50-70 years and 0.80 for > 70 years. These values, used previously by Lee and Zelen, were derived from the Breast Cancer Surveillance Consortium database which contains mammogram screening data and follow-up for approximately one million US women, dating back to 1994

The results obtained when applying the FP rates to the target population allowed us to estimate the ratio of negative results/positive results for invasive tests and the ratio of non-invasive/invasive tests (Table

Ratios Non-invasive/Invasive tests and Invasive test -/Invasive test + for a screening scenario with annual periodicity and exams in the age interval 50 to 69 and for the background scenario

**Age**

**Non-invasive/invasive tests**

**Invasive test -/Invasive test +**

**Screening**

**Background**

**Screening**

**Background**

**50**

6.92

2.54

2.03

0.57

**55**

6.19

2.32

0.65

0.5

**60**

4.21

2.1

0.42

0.42

**65**

3.79

1.89

0.35

0.35

**70**

1.67

0.28

**75**

1.46

0.2

Part B): After the last screening exam

For screening scenarios where the last screening exam was performed before the age of 79, we proceed as follows:

• Since we do not have FP rate estimates for a population without screening, we assumed that the ratio of benign to malignant biopsy was the same among screened and non-screened women

• We multiplied the projected ratios by the number of women diagnosed with BC (true positives) obtained from the LZ model and we obtained the number of FP for the invasive tests.

• We also linearly projected the ratio of non-invasive/invasive tests during the screening interval, up to age 79.

• We considered that the cumulative rate of invasive tests in screened women was double this rate in non-screened women

• We multiplied the estimated non-invasive/invasive ratio by the number of invasive tests to obtain the number of non-invasive tests.

Costs of the BC diagnosis under the background scenario

For the background scenario, the cost for non-invasive and invasive complementary tests are the same as for screening. The cost of mammogram for background was the same as for early recall screening mammography, 23 €.

To obtain the number of non-invasive and invasive tests and the FP rates under the background scenario, we proceeded as described in

Costs of the initial treatment, follow-up and advanced care

Data on costs was obtained from a database that included 592 women consecutively diagnosed and initially treated for BC at the IMAS-Hospital del Mar in Barcelona in the period January 1st, 2000 - December 31, 2003. Cost categories are shown in Table

Costs' categories

**Cost category**

**Items**

**In-hospital**

Length of stay by specialty ward

**Ambulatory visits**

Type of ambulatory visit

Emergency visits

**Chemotherapy**

Drugs

Treatment protocol

**Other hospital costs**

Other drugs

Lab tests

Radiological tests

**Radiotherapy**

Number and type of courses

**Hormone therapy**

Drugs

**Initial treatment costs **were higher when the BC was diagnosed in a more advanced stage. The initial treatment lasted approximately one year. The mean costs for non-metastatic disease were 9,529 € for Stage I, 14,184 € for Stage II and 16,898 € for Stage III. To the previous values we added 638 €, the cost of adjuvant tamoxifen for five years, which was prescribed to 67.6% of women diagnosed with a Stage I, II and III. For women diagnosed at stage IV, the initial treatment cost was assumed to be zero and we considered only diagnosis and advanced care costs.

**Follow-up costs **for women diagnosed with BC include ambulatory visits and diagnostic tests that women receive, starting the year after the BC diagnosis. Based on the data, we assumed that the cost during the first year of follow-up was 1,365 € and 530 € for the following four years.

**Advanced care cost **was obtained using data from the 32 patients diagnosed with metastatic cancer, followed during five years. Survival at the end of the period was 37.5%. We estimated the cost of advanced care as the mean of all the direct costs from diagnosis until death or last follow-up, 28,413 €. Costs for living women after five years of follow-up did not change the mean significantly.

Cost-effectiveness analysis

To compare the relative costs and outcomes of the different scenarios, we calculated the incremental cost-effectiveness ratio (ICER). The ICER is defined as the ratio of the change in costs to the change in effects of a specific intervention compared to an alternative. The ICER indicates the additional cost of obtaining one additional unit of outcome. We have compared each scenario with the next most effective alternative. Strategies were classified into three categories: non-dominated, dominated and extended dominated. A strategy was considered

Once dominated or extended dominated strategies are excluded, the remaining strategies form the

Sensitivity analysis

We performed a sensitivity analysis to study the impact on our conclusions when some of the inputs were modified. First, we investigated the effect of increasing all costs that could be due to, for instance, the introduction of new drugs. Second, we examined the impact of longer follow-up times. Third, we changed the ratio of screening/background non-invasive tests because there is limited information for estimating this ratio with high confidence. Fourth, we set the screening participation to 50% in the screening program. Fifth, we doubled the cost of invasive tests for screen-detected tumors to account for the difficulty of detecting non-palpable lesions. All the tested scenarios can be found in Table

Baseline assumptions and ranges tested in the sensitivity analysis

**Parameter**

**Baseline model**

**Sensitivity analysis**

**Initial treatment cost by stage**

I: 9,960 €, II: 14,616 €, III: 17,329 €

2, 3, 4, 5, 10-fold

**Follow-up cost**

1,365 € 1st yr., 530 € afterwards

2, 3, 4, 5, 10-fold

**Advanced care cost**

28,413 €

2, 3, 4, 5, 10-fold

**Years of follow-up**

5

11, 16, 21

**Screening/background noninvasive test**

2

1, 3

**Screening participation**

100%

50%

**Screening/background cost of invasive tests**

1

2

Results

Figures

Cost and effectiveness

**Cost and effectiveness**. Cost-effectiveness analysis of different screening strategies: **A - **Incremental cost (×10^{6 }€) per life extended (LE); **B - **per year of life (YL); and **C - **per quality-adjusted life year (QALY). Empty figures correspond to annual strategies and full figures to biennial. Screening start age: 40 (big), 45 (medium) and 50 (small). Screening end age: 69 (circle), 70 (diamond), 74 (triangle), and 79 (square). The line joins the dominant scenarios. **D - **Cost for all strategies by phase: detection (pink), initial treatment (green), follow-up (blue), and advanced care (yellow).

Table

Cost-effectiveness of mammography screening strategies in Catalonia (Spain)

**Scenario**

**Cost**

**ΔCost**

**Effect**

**ΔEffect**

**ΔC/ΔE (ICER)**

**(×10**
^{
6
}
**€)**

**(×10**
^{
6
}
**€)**

**LE**

**ΔLE**

**€/LE**

**Bg**

127.3

0

**B 50-69**

143.4

16.2

567

567

28,465

**B 50-70**

144.6

1.1

590

23

49,184

**B 50-74**

147.0

2.5

640

50

50,188

**A 50-74**

167.0

20.0

757

117

170,304

**A 45-74**

182.1

15.1

803

46

330,098

**A 40-74**

201.5

19.4

837

34

573,062

**A 40-79**

210.4

8.9

849

12

715,941

**YL**

**ΔYL**

**€/YL**

**Bg**

127.3

0

**B 50-69**

143.4

16.2

4,691

4,691

3,444

**B 45-69**

151.5

8.1

5,842

1,151

7,015

**A 45-69**

176.0

24.4

7,917

2,075

11,777

**A 40-69**

195.4

19.4

9,117

1,200

16,166

**A 40-74**

201.5

6.2

9,370

253

24,415

**A 40-79**

210.4

8.9

9,390

20

451,370

**QALY**

**ΔQALY**

**€/QALY**

**Bg**

127.3

0

**B 50-69**

143.4

16.2

3,614

3,614

4,469

**B 45-69**

151.5

8.1

4,447

833

9,694

**B 45-74**

153.9

2.4

4,633

186

12,633

**A 45-69**

176.0

22.1

5,979

1,346

16,411

**A 40-69**

195.4

19.4

6,756

777

24,975

**A 40-74**

201.5

6.2

6,987

231

26,720

Incremental cost per effect (LE, YL and QALY) assuming a cohort of 100,000 women at birth. Dominated or extended-dominated strategies are not included.

The detailed results for all the 21 scenarios can be found in Table A.1 in Additional file

Measuring effectiveness with number of lives extended (LE)

Figure

Moving from the background to B50-69, the current public screening program in Catalonia, represented an incremental cost of 28,465 € per LE. Moving from B50-69 to the next alternative, B50-70, represented an incremental cost of 49,184 € per LE. The ICER increased considerably when moving from biennial strategies to annual strategies. The last non-dominated strategy, A40-79 had an ICER of 715,941 €, which should be interpreted with caution because we did not account for the reduction in mortality after age 79.

Measuring effectiveness with years of life gained (YL)

Figure

All the selected alternatives, except A40-79, had an incremental cost lower than 30,000 € per additional YL and therefore, could be implemented if economic resources were available. As for the LE analysis, the results for alternative A40-79 should be interpreted with caution.

Measuring effectiveness with quality-adjusted life years (QALYs)

Figure

Sensitivity analysis

Tables A.2, A.3 and A4 in Additional file

The input that caused more changes in the selected screening scenarios was the advanced care cost. When advanced care cost was three times higher than the initial value, the background scenario was no longer the least expensive scenario. The following scenarios: B50-74, B50-70 and B50-69, in that order, had lower costs than the background. At the extreme value of advanced care cost (10-fold) only annual screening strategies were selected.

When we changed the follow-up time from four to ten or twenty years, the selected scenarios were the same as the baseline except for B50-70, which became dominated when the effect was measured in LE. The selected screening scenarios did not change after modifying the ratio of non-invasive tests among screened and background strategies. Assuming that only 50% of the invited population is screened produced only slight changes in the selected scenarios but increased the ICER considerably. When costs of invasive tests were doubled for the screened women there were no changes in the selected scenarios, there were only slight changes in the ICER values.

Discussion

Principal findings

This study performed an economic evaluation of different BC mammography screening strategies in Catalonia (Spain), using mathematical models. We assumed the perspective of the national health system and considered the direct healthcare costs over time of screening, diagnosis, initial treatment, follow-up and advanced care.

Our results show that, based on the incremental cost-effectiveness ratio (ICER), a reduced number of strategies can be selected for consideration from a set of 20 screening scenarios. Strategies that start at age 50 and end at age 74 predominate among those selected when the effectiveness of screening is measured in terms of the number of lives extended, and strategies that start at the ages 40 or 45 and end at age 69 predominate when the effect is measured as YL or QALYs. Independently of how the effect is measured, the ICER increases considerably when moving from biennial to annual scenarios.

An interesting result is that, assuming 100% participation in the studied screening strategies, the background is always the reference scenario because it has the lowest cost. But the effectiveness of the background is the lowest, for all the effect measures. In addition, once the non dominated scenarios are ordered according the ICER, always the next alternative is B50-69 that corresponds to the current public screening program in Catalonia. However, in some cases, other alternatives are more effective at a cost that could be considered implementable, given the generally accepted reference values. When the effect is measured as YL or QALY, these alternatives start the screening at ages younger than 50 years whereas only a few suggest to finish after the age 69.

Sensitivity analysis showed that the results were robust to moderate changes in costs of treatment or length of follow-up after initial treatment. Only dramatic increases of advanced cancer care modified the scenarios selected in the cost-effectiveness analysis in favor of annual screening scenarios. We did not perform a sensitivity analysis of changes in the survival functions as a result of improvements in mortality and prognosis after a BC diagnosis. The CISNET groups verified that there was a negative interaction between screening and adjuvant treatments. That means that the benefits of screening are smaller if treatments are more effective. As Cronin

Costs of diagnosing and treating breast cancer

Many authors have studied the costs of diagnosing and/or treating BC

A major challenge is to estimate the costs of advanced disease. Even though clinical practice guidelines provide standard treatment for advanced disease, very often treatments are customized according to the tumor or the patient's characteristics and the response to each treatment line. De Koning

Campbell

The distribution of costs by phase in our study is consistent with the results found in the literature. When averaging the cost by phase over the different scenarios, for a cohort of 100,000 women at birth, the highest cost corresponded to initial treatment (63,083,670 €), followed by detection cost (55,353,560 €), advanced cancer care (33,011,915 €) and the costs of follow-up (3,031,631 €).

Cost-effectiveness of mammography screening

Several authors have studied the cost-effectiveness of mammography screening using mathematical models

Wong

In Spain, Plans

Beemsterboer

Limitations

We have used a very detailed model that allowed us to thoroughly assess the cost and effectiveness of different screening scenarios. However, our study has several limitations, among them are the following. Our model relies on data and assumptions that may be not correct. When available, we have used Catalan or Spanish data from population based registries or BC screening programs. If the input data was not available at the region or country level, we used data that the CISNET had prepared for BC mortality modeling research groups in the USA

We have not obtained confidence intervals of the model outputs. Our model is probabilistic because it works with the probability density functions of the different inputs related to the natural history or detection of BC. It is also an analytic model that consists of a set of equations describing BC mortality over time. There is uncertainty associated with the model inputs and there is also uncertainty associated with the model structure. It is complex and computationally intensive to obtain the variance of the model estimates. Instead, we have carried out a sensitivity analysis to explore how changes in the input parameters affect the results.

With respect to the outcome measures, we have included LE as a measure of effect together with YLG and QALYs. We want to highlight that the standard and internationally recognized measure to compare different health interventions and measure their effectiveness is the QALY

With respect to costs, we have several considerations. a) Costs were obtained from a single public hospital in Barcelona, which may not be representative of the hospitals in the region. However, we believe that the costs of diagnosing and treating BC in the hospitals of the Catalan public health system are not remarkably different. b) Advanced care costs were obtained from a small sample of metastatic BC patients at diagnosis. About one third of them were still alive after five years. Including living patients in the calculations may have underestimated the average cost of treating advanced disease. Excluding living patients from the analysis would have produced a biased sample. More adequate methods, based on the Kaplan-Meier sample average estimate

The decision of implementing a specific alternative is influenced by the budget assigned to the screening program and also by the amount that society is willing to pay for each effectiveness unit. Since the number of LE or mortality reduction is not a standard effectiveness measure in economic assessments, is not easy to find reference values for comparison.

There is scarce information in the literature about rates of false positives for invasive and non-invasive tests to diagnose BC in a non-screened population. Again, the sensitivity analysis showed that the selected scenarios were robust to changes in the assumptions.

Our study did not take into account either overdiagnosis of BC as a consequence of screening or DCIS. The impact of overdiagnosis would be to increase costs and decrease quality of life. Strategies with a higher number of screening exams (annual) would have a higher incremental cost per additional effect unit and, therefore, would end up being dominated by less intensive screening strategies (biennial). We have not accounted for overdiagnosis because there is high variability in overdiagnosis estimates. When we studied overdiagnosis in our region, we obtained a high association between exposure to screening and an increase in incidence, beyond what was expected by the advance of diagnosis, but the precision of the overdiagnosis estimate was low

In our model, DCIS cases were not included. The researchers that developed the probabilistic model that we have used considered that available information on the natural history of

Conclusions

We have studied the cost-effectiveness of several BC screening scenarios and have selected a reduced number of them for consideration by researchers, decision makers and policy planners. Mathematical models have been useful to assess the impacts and costs of BC screening interventions, accounting for the population and epidemiological data of a specific geographical area.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

MC and MR codeveloped the project that includes this study, participated in the statistical analysis, wrote drafts and obtained author's feedback. MC, AG and FC developed costs estimations, performed the cost-effectiveness analysis and participated in writing and revising the manuscript. EV developed the computer programs that estimate incidence, prevalence, mortality, and costs, and participated in writing and revising the manuscript. RP codeveloped the project and participated in the interpretation of data. RR and FM provided data from the RAFP project and the hospital Cancer Registry, and participated in revising the manuscript. MS and XC coordinated the RAFP project and participated in revising the manuscript. All authors read and approved the final version of the manuscript.

Acknowledgements

This study was funded by grants PI06/1649, PI06/90355 and PS09/01340 from the Health Research Fund (Fondo de Investigación Sanitaria) of the Spanish Ministry of Health and by grant 068/27/06 from the Catalan Agency for Health Technology Assessment (Agència d'Avaluació de Tecnologia i Recerca Mèdiques).

We thank Sandra Lee, Marvin Zelen and Hui Huang for their support in the development of the probabilistic models used. We are also grateful to Montserrat Martínez-Alonso for her support in the statistical analysis, to Dr. José Expósito for his comments on preliminary versions of the manuscript, to JP Glutting for review and editing and to the Cumulative False Positive Research Group (RAFP) project researchers.

Pre-publication history

The pre-publication history for this paper can be accessed here: