Cost-Effectiveness and Harm-Benefit Analyses of RiskBased Screening Strategies for Breast Cancer
Ester Vilaprinyo1,2., Carles Forné1,2., Misericordia Carles3, Maria Sala4,5, Roger Pla6,7, Xavier Castells4,5,
Laia Domingo4, Montserrat Rue1,2,5*, the Interval Cancer (INCA) Study Group"
1 Basic Medical Sciences Department, Biomedical Research Institut of Lleida (IRBLLEIDA), Lleida, Catalonia, Spain, 2 Basic Medical Sciences Department, University of
Lleida, Lleida, Catalonia, Spain, 3 Economics Department and Research Centre on Industrial and Public Economics (CREIP), Rovira i Virgili University, Reus, Catalonia, Spain,
4 Department of Epidemiology and Evaluation, Institut Municipal d’Investigació Mèdica-Parc de Salut Mar, Mar Teaching Hospital, Barcelona, Catalonia, Spain, 5 Health
Services Research Network in Chronic Diseases (REDISSEC), Spain, 6 Surgery Department, Rovira i Virgili University, Reus, Catalonia, Spain, 7 General and Digestive Surgery
Department, Joan XXIII Teaching Hospital, Tarragona, Catalonia, Spain
Abstract
The one-size-fits-all paradigm in organized screening of breast cancer is shifting towards a personalized approach. The
present study has two objectives: 1) To perform an economic evaluation and to assess the harm-benefit ratios of screening
strategies that vary in their intensity and interval ages based on breast cancer risk; and 2) To estimate the gain in terms of
cost and harm reductions using risk-based screening with respect to the usual practice. We used a probabilistic model and
input data from Spanish population registries and screening programs, as well as from clinical studies, to estimate the
benefit, harm, and costs over time of 2,624 screening strategies, uniform or risk-based. We defined four risk groups, low,
moderate-low, moderate-high and high, based on breast density, family history of breast cancer and personal history of
breast biopsy. The risk-based strategies were obtained combining the exam periodicity (annual, biennial, triennial and
quinquennial), the starting ages (40, 45 and 50 years) and the ending ages (69 and 74 years) in the four risk groups.
Incremental cost-effectiveness and harm-benefit ratios were used to select the optimal strategies. Compared to risk-based
strategies, the uniform ones result in a much lower benefit for a specific cost. Reductions close to 10% in costs and higher
than 20% in false-positive results and overdiagnosed cases were obtained for risk-based strategies. Optimal screening is
characterized by quinquennial or triennial periodicities for the low or moderate risk-groups and annual periodicity for the
high-risk group. Risk-based strategies can reduce harm and costs. It is necessary to develop accurate measures of individual
risk and to work on how to implement risk-based screening strategies.
Citation: Vilaprinyo E, Forné C, Carles M, Sala M, Pla R, et al. (2014) Cost-Effectiveness and Harm-Benefit Analyses of Risk-Based Screening Strategies for Breast
Cancer. PLoS ONE 9(2): e86858. doi:10.1371/journal.pone.0086858
Editor: Anna Sapino, University of Torino, Italy
Received April 8, 2013; Accepted December 16, 2013; Published February 3, 2014
Copyright: ß 2014 Vilaprinyo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was funded by grants PS09/01340 and PS09/01153 from the Health Research Fund (Fondo de Investigación Sanitaria) of the Spanish Ministry
of Health. The authors thank the Breast Cancer Surveillance Consortium and the funding that the BCSC received from the National Cancer Institute (U01CA63740,
U01CA86076, U01CA86082, U01CA63736, U01CA70013, U01CA69976, U01CA63731, U01CA70040, and HHSN261201100031C). The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: montse.rue@cmb.udl.cat
. These authors contributed equally to this work.
" Membership of the Interval Cancer (INCA) Study Group is provided in the Acknowledgments.
cancer risk. In 2005 the Institute of Medicine (IOM) identified that
personalized screening was crucial to improving the early
detection of breast cancer [7]. More recently, Schousboe et al.
[8], using a Markov microsimulation model, found that the costeffectiveness of screening mammography depended on a woman’s
age, breast density, family history, and history of breast biopsy.
Based on their results, mammography every two years was costeffective for women aged 40 to 49 years with relatively high breast
density or additional risk factors for breast cancer. And
mammography every three to four years was cost-effective for
women aged 50 to 79 years with low breast density and no other
risk factors. van Ravesteyn et al. [9], using different microsimulation models, determined that women aged 40 to 49 years with a
twofold increase in risk have similar harm-benefit ratios for
biennial screening mammography as average-risk women aged 50
to 74 years.
Introduction
Early detection of breast cancer (BC) reduces mortality and may
improve quality of life for most of the women diagnosed early by
mammographic exams [1]. Nevertheless, screening healthy
women is expensive and may cause harms (e.g. false positive
results, overdiagnosis) in many of them [2–5]. In order for
organized screening programs to be justified in this time of
economic constraints, overall benefits should outweigh harms at a
reasonable cost. Moreover, an economic evaluation is especially
necessary when screening is funded by community resources.
Organized screening programs for early detection of BC
provide screening services where all eligible women are treated
as equal risk. For instance, the European guidelines recommend
offering mammography screening to women aged 50–69 every
two years [6]. This one-size-fits-all or uniform paradigm is starting
to shift toward personalizing screening strategies based on breast
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Category 2 breast density with no risk factors; 2) Medium-Low
(ML) risk which included Category 1 breast density with two risk
factors, Category 2 breast density with one risk factor, and
Categories 3 or 4 breast density with no risk factors; 3) MediumHigh (MH) risk which included Category 2 breast density with two
risk factors, Categories 3 or 4 breast density with one risk factor;
and 4) High (H) risk which included Categories 3 or 4 breast
density with two risk factors. The frequency distributions of the
risk groups was 39.6%, 42.8%, 15.6% and 2.0% for L, ML, MH
and H, respectively.
The incidence rates of the four aggregated risk groups were
estimated as weighted sums of detailed incidence curves (see
Section B.2, Tables S2, S3, and Figure S1 in Appendix S1). The
weights were based on the prevalences of each combination of risk
factors obtained from the Risk Estimation Dataset of the Breast
Cancer Surveillance Consortium (BCSC) [22].
In a previous study, we performed an economic evaluation of
uniform screening strategies that had different periodicities and
varied in the ages at starting or ending the screening exams [10].
The present study has two objectives that extend our previous
work: 1) To perform an economic evaluation and to assess the
harm-benefit ratios of screening strategies that vary in their
intensity and interval ages based on BC risk; and 2) To estimate
the gain in terms of cost and harm reductions using risk-based
screening with respect to the usual practice.
Methods
The model and model inputs
We used the probabilistic model developed by Lee and Zelen
(LZ), which has been described elsewhere [11–13]. Further details
of the model can be found in Appendix S1, section A. The model
assumes a four-state progressive disease with S0 : disease-free state,
Sp : preclinical state (asymptomatic disease that can be diagnosed
by a special exam), Sc : clinical state (diagnosis by symptomatic
detection), and Sd : death from BC. The LZ model consists of a set
of equations that allow to estimate the cumulative probability of
death for a particular cohort exposed to a specific screening
scenario or to no screening, after T years of follow-up. Since the
model is analytical, for each specific set of inputs, the model run
produces the same results. The model also provides incidence and
prevalence of BC over time, both measures necessary for the
estimation of treatment and follow-up costs.
The model requires input data that was obtained from different
sources. BC incidence and survival, and mortality from other
causes refer to cohorts born in Catalonia (Spain) in the period
1948–1952 [10,14–17]. The sojourn time in the pre-clinical state,
the distribution of stages at diagnosis (Table S1 in Appendix S1)
and the sensitivity of mammography were obtained by Lee and
Zelen from published randomized clinical trials and observational
studies [12]. Based on previous work of Zelen and Feinleib [18]
and Day and Walter [19] on the randomized clinical trial of the
Health Insurance Plan (HIP), it was assumed that the preclinical
sojourn time follows an exponential distribution with an age
dependent mean equal to 2 years for age,40 and 4 years for age.
50. In the (40–50] age interval the mean sojourn time increases
linearly from 2 to 4 years. The additional inputs are described
below in the next subsections. All the calculations assumed an
initial population of 100,000 women at birth. The time horizon for
the study was 40–79 years of age.
The research protocol was approved by the institutional review
board and ethics committee of the Hospital Universitari Arnau de
Vilanova de Lleida (Spain) which waived the need for informed
consent.
The screening strategies
We analyzed 2,625 screening strategies, 24 of them uniform and
2,601 risk-based. The risk-based strategies were obtained combining the exam periodicity (annual (A), biennial (B), triennial (T),
and quinquennial (Q, [every five years])), the starting ages (40, 45
and 50 years) and the ending ages (69 and 74 years) in the four risk
groups, L, ML, MH and H. In the following sections, uniform
strategies are abbreviated as B5069 or B4574, for biennial exams
in the 50–69 or in the 45–74 age groups, respectively. Risk-based
strategies are abbreviated with four strings, e.g. Q5069-Q4574T4574-A4074, that correspond to the L, ML, MH and H risk
groups, respectively. A sample of the studied screening strategies is
presented in Table S4 in Appendix S1.
The benefits
For each screening strategy and for the background, we
measured the benefit of screening with two outcomes: the number
of lives extended, LE, and the number of quality-adjusted life years
gained, QALY. Because of the lack of Spanish data, the QALYs
were estimated using the work of Lidgren et al. [23] in a sample of
361 Swedish women with localized, recurrent, or metastatic breast
cancer (See Table S5 in Appendix S1). We considered the
Lidgren’s study more robust and suitable than other studies that
used expert opinion or healthy population to obtain quality of life
estimates associated with breast cancer. We used the values
obtained from the EuroQol EQ-5D in the Lidgren’s study. For
women that did not die of BC we considered a loss of QALYs in
the first five years following the diagnosis. For women that died of
breast cancer, we considered that the last four years of their lives
or the time from diagnosis to death, if they lived less than four
years, were spent in a metastatic stage, independently of the stage
at diagnosis. See section C in Appendix S1 for further details.
Risk of invasive breast cancer
The harms
We started estimating the age-specific risk of invasive BC for our
study cohort, using the model published elsewhere by MartinezAlonso et al. [17]. Details of the model can be found in Appendix
S1, Section B.1. Then, following Tice et al. [20] and Schousboe et
al. [8], age-specific BC risk groups were defined according to the
following variables: breast density (measured using the Breast
Imaging Report and Database System (BI-RADS) categories 1 to 4
[21]), family history of BC in first degree relatives (yes/no) and
personal history of breast biopsy (yes/no). Details can be found in
Appendix S1, section B.2.
We obtained four aggregated risk groups that combined the
profiles of women that had similar levels of BC incidence over
time: 1) Low (L) risk which included Category 1 breast density with
at most one risk factor - family history or breast biopsy - and
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False positive (FP) results. We used the FP rates for noninvasive and invasive tests obtained from the Cumulative Risk of
False Positive Study (RAFP) study which included 74 distinct
radiology units in eight regions of Spain, from March 1990 to
December 2006 [2]. The RAFP study included 1,565,364 women
that underwent 4,739,498 mammographic exams. The FP rates
were age and exam specific. We multiplied the FP rates by the
number of women at risk for BC, at each specific exam, to estimate
the number of women that would receive additional non-invasive
(e.g. ultrasound) or invasive tests (e.g. biopsy). See further details in
Appendix S1, section D and Tables S6 and S7.
Interval cancers and false-negative (FN) results. In our
model, true interval tumors correspond to those that appear
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All costs were valued in 2012 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 Health
[32].
The costs of screening mammograms, complementary tests and
administrative expenses were obtained from the Early Detection
Program of Parc de Salut Mar (PSMAR) in the city of Barcelona.
Data on treatment costs were obtained from a database that
included 592 women consecutively diagnosed and initially treated
for BC at the PSMAR in Barcelona in the period January 1st,
2000–December 31, 2003 [10].
between exams and were not in the pre-clinical state when the
previous exam was performed. FN cases are tumors that were not
detected in the previous exams due to lack of sensitivity of the
screening test. We considered that all tumors in pre-clinical state in
the previous exam were FN.
Overdiagnosis. Screening may cause overdiagnosis when it
detects tumors which would never have been diagnosed during a
lifetime without screening because of the lack of progressive
potential or death from other causes. To estimate overdiagnosis we
made some additional assumptions. We differentiated between
overdiagnosis of invasive BC and ductal carcinoma in situ (DCIS).
For both types of tumors we assumed that: 1) overdiagnosis only
happens when a mammographic exam is performed, 2) a woman
with an overdiagnosed tumor would not die of breast cancer, and
3) QALYs and costs of treatment (initial and follow-up) for women
with overdiagnosed tumors are the same as for Stage I BC.
Overdiagnosis of invasive breast cancer. Estimates of
overdiagnosis show high variation depending on the study design
and the method used [3,4,17,24–28]. Based on the reported data,
an overdiagnosis rate of 15% can be considered a sensible value.
Using the incidence model described in Appendix S1, section
B.1 [17], and taking into account the distribution of the sojourn
times in the preclinical state and the sensitivity of mammography
(as in the LZ model), we estimated the number of BC cases that
would be detected in the screening exams. Then, for each
screening strategy, we assumed an overdiagnosis rate of 15% in
the mammography exams. This assumption makes it possible to
associate overdiagnosis with mammography exams, in the sense
that more intensive screening strategies are considered to produce
a higher overdiagnosis burden. For any screening strategy, an
overdiagnosis estimate of 15% of the screen-detected cases gives an
overall estimate lower than 15%, depending on the distribution of
exam-detected and interval cases. For further details about how
the overdiagnosis rate has been applied see section E and Table S8
in Appendix S1.
DCIS attributable to screening. To estimate the impact of
screening on detection of DCIS we obtained the incidence and
Census data from the Girona and Tarragona Cancer Registries in
the period 1983–2008. Data on mammography use was obtained,
for the Girona and Tarragona provinces, from three health
surveys performed in the years 1994, 2002 and 2006 [29,30].
Section F in Appendix S1 explains in detail the analysis conducted
to estimate the excess of DCIS attributable to sceening. From this
analysis we estimated an excess of 31.13 DCIS cases per 100,000
mammograms, with respect to a strategy of no screening (Table S9
and Figures S2 and S3 in Appendix S1).
Because DCIS is treated when detected, it is not possible to
accurately estimate the fraction of detected DCIS that would
progress to invasive disease. A review of the literature showed that
between 14% and 53% of DCIS may progress to invasive cancer
over a period of 10 or more years [31]. In our study we have
assumed that 1/3 of the DCIS detected by mammography would
progress to invasive cancer. With this assumption, the estimated
excess number of DCIS attributable to screening was approximately 21^2=3 31:13~20:75 per 100,000 mammograms, or
0.21 per 1,000 mammograms. In the sensitivity analysis we have
estimated the proportion of DCIS that progress to be equal to 2/3
or to 1/6 of 31.13 per 100,000 mammograms.
Cost-effectiveness and harm-benefit analyses
To compare the relative costs and outcomes of the different
strategies, 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 obtained the cost-effectiveness
frontier, also called the Pareto frontier, which contains the efficient
alternatives for which no alternative policy exists that results in
better effects for lower costs.
To perform a harm-benefit analyses, we ordered the studied
strategies from less to more adverse effects and obtained the
incremental harm-benefit ratio of each strategy in relation to the
previous one. We also obtained the harm-benefit frontier.
Selection of optimal strategies
To search for optimal strategies taking into account benefit,
costs and harms, we selected the most recommended uniform
strategy in Europe, biennial exams in the 50–69 age interval
(B5069), or the alternative towards which some countries are
moving, biennial exams in the 45–74 age interval (B4574), as
reference strategies. Then, for each reference strategy we obtained
the intersection of the subsets that contained strategies with similar
benefit (between 1 and 1.05 times) than the reference strategy and
lower cost and harms in terms of FP results and overdiagnosed
cases (invasive and DCIS). The resulting strategies were located at
or near the cost-effectiveness and harm-benefit frontiers with
values in the x-axis near the B5069 or B4574 benefit values. We
did not include the FN results in the intersection but we assessed
them in the resulting optimal subset.
Validation of the model
We have compared our results with the results of three
published reviews, the Cochrane systematic review [33], the
Independent UK Panel on Breast Cancer Screening review [34],
and the Euroscreen comprehensive review of European screening
programs [35]. In addition, we have checked the results of the
INterval CAncer (INCA) study in Spain, which included 645,764
women aged 45/50 to 69 years that participated biennially in
seven population-based screening programs, from January 2000 to
December 2006 (not yet published). A total of 1,508,584
mammograms were included in the study. The cohort was
followed until June 2009 for breast cancer identification, resulting
in 5,311 cases screen-diagnosed and 1,682 interval cancers.
We have compared the following summary indicators in the
INCA study and the uniform B4569 strategy of our model: 1)
frequencies of screen-detected and interval cancer, by age-group,
2) sensitivity of the program defined as the ratio of the number of
tumors detected in the screening exams between all the detected
tumors, 3) distribution of true interval cases and FN, by time since
last mammogram, and 4) distribution of stages at diagnosis, by
type of detection (screening or symptomatic).
Costs
We have adopted the perspective of the national health system
and considered only 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.
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changes in FN results is affected differently by the periodicity of
the exams and the age-interval of screening. For instance, moving
from uniform B5069 to uniform A5069 reduces the amount of FN
by 29%, but moving from uniform B5069 to uniform B4574
increases the amount of FN by 33%. Figures 1 and 2 show that
there were no strategies in the lower left part of the incremental
FN per incremental benefit analyses and the harm-benefit frontier
for FN per LE or per QALY only included annual screening
strategies.
The last column of Table 1 shows the percentages of changes in
FN results for the selected risk-based strategies with respect to the
uniform B5069 and B4574 strategies. Compared to the uniform
B5069 strategy, the selected risk-based strategies, which have a
similar benefit, have more FN results (20% or more when the
measure of benefit is LE, 25% or more when the benefit is
measured in QALY). Nevertheless, when considering the uniform
B4574 strategy, the selected risk-based strategies not only have less
FP results and overdiagnosed cases but also have less FN results.
The finding that there are more FN results from risk-based
screening compared to uniform B5069 than compared to uniform
B4574 is mostly due to the fact that, in general, the selected riskbased strategies screen women until age 74.
Summary of optimal strategies. When all the risk-based
strategies that are at or near the Pareto frontier are considered and
benefit is measured as LE, the risk-based strategies that provide a
similar benefit than the B5069 strategy are caracterized by
quinquennial for the L and ML, triennial for the MH and tri-, bior annual periodicities for the H risk groups. When benefit is
measured as QALYs, the risk-based strategies are characterized by
quinquennial periodicities for the L, ML and MH and annual for
the H risk groups. When the standard of comparison is the
uniform strategy B4574, the risk-based strategies that provide
similar benefits, either LE or QALY, are characterized by
quinquennial for the L, triennial for the ML, and annual
periodicities for the MH and the H risk groups.
Figures S4 and S5 in section G of Appendix S1 show how the
uniform screening strategies, other than B5069 and B4574,
performed in the cost-efectiveness and harm-benefit analyses.
Sensitivity analysis
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 performed univariate sensitivity
analyses to study the impact on our conclusions when some of the
inputs were modified. First, we changed the four risk group
distributions assuming that 20% of women in the L, ML, and MH
groups migrated to the next higher risk group. The new risk group
distributions was 31.7%, 42.1%, 21.1% and 5.1%, for L, ML, MH
and H, respectively. Second, we changed the amount of
overdiagnosis of invasive tumors to 0%, 5% and 25%. Third,
we changed the excess of DCIS to 0.1 and 0.26 per 1,000
mammograms. Fourth, we tested the effect of changing the costs of
cancer treatment to two-fold and five-fold the costs of the main
analysis. Fifth, we assessed the effect of changes in the disutility by
false-positive result on QALY. We used zero and two times the
disutility of the main analysis.
Data availability
All the input data will be available to researchers upon request.
Results
Cost-effectiveness and harm-benefit analyses
Benefits, harms, and costs of each screening strategy were
obtained as a function of the risk-groups’ incidence and the
screening characteristics (periodicity and age-interval of exams by
risk group). Figures 1 and 2 contain an overview of benefits,
harms, and costs of all 2,625 strategies evaluated. The strategies
that gave the best value for money can be found in Tables S10 and
S11 in Appendix S1.
Measuring effectiveness with LE. Figure 1 and Table S10
in Appendix S1 present the results of the cost-effectiveness and
harm-benefit analyses. Table 1 (A) shows two selected strategies
that improve on the B5069 uniform strategy and two that improve
on the B4574 uniform strategy. As an example, compared to
B5069, the optimal strategy Q5074-Q5074-T5074-A5074 for the
L, ML, MH and H risk groups, respectively, has 3.8% higher
benefit in terms of LE and achieves reductions of 8.9% in costs,
25.1% in FP and 20.6% in overdiagnosed cases. In absolute
numbers, with an annual discount rate of 3% for every 2,000
women screened, the risk-based strategy Q5074-Q5074-T5074A5074 would extend about the same number of lives (4) as the
uniform B5069 strategy but would avoid 1.5 overdiagnosed cases,
97 FP mammograms (six of them ending with a biopsy) and would
save 250,000 euros. The only drawback would be one additional
FN. If we consider the uniform strategy B4574 as a reference, the
risk-based strategy T5074-T5074-A4574-A4574 results in a 5%
higher benefit and reductions of 6.8% in costs, 21.9% in FP and
10.1% in overdiagnosed cases.
Measuring effectiveness with QALYs. Figure 2 and Table
S11 in Appendix S1 present the results of the cost-effectiveness and
harm-benefit analyses. Table 1 (B) shows that, compared to the
B5069 uniform strategy, the risk-based Q5069-Q4574-Q4574A4074 strategy results in reductions of 8% in costs, 17.2% in FP
and 25% in overdiagnosed cases. Similarly, compared to the
uniform strategy B4574, the risk-based Q4574-Q4574-A4574A4074 strategy achieves an increase of 4% in QALYs and
reductions of 9.2% in costs, 20.4% in FP and 23% in
overdiagnosed cases.
False negative results. We have analyzed the incremental
ratios of FN results per unit of benefit separately from the other
cost-effectiveness or harm-benefit ratios because the pattern of
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Validation of the model inputs
When we assumed a scenario without screening, for the age
interval 0 to 74 years, we obtained a cumulative incidence of BC
equal to 5.8% and a mortality rate from BC equal to 1.5%. These
values were consistent with the literature [36,37]. Section G.1, and
Tables S12 and S13 in Appendix S1 compare our results for the
biennial strategy B4569 with the results obtained in the INCA
study. The detection rates obtained with our model are slightly
higher than the INCA rates for both types of detection (screening
or interval), except for the 44–49 age group. The overall program
sensitivity was very similar (68.1% in the INCA study versus
68.4% in our model). The stage distributions of the models, either
screen-detected or interval, were more favorable than the cases in
the INCA study. Table 2 shows the distribution of the interval
cases in true interval and FN, by time since last mammogram. The
timing of overall interval cases and true interval cases was similar
in the INCA study and our model. We observed differences in the
distribution of FN results at first and second year after the exams.
While in the INCA study there was a higher proportion of FN in
the second year, our model had a higher proportion of FN in the
first year of the interval. Table 3 compares the overall benefit and
harm results for the uniform strategies B5069 and B4574 with
published reviews [33–35]. We observe similarities between the
Lancet review for mortality reduction and with the Cochrane and
Euroscreen reviews for overdiagnosis. The ratios of overdiagnosed
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Figure 1. Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies. Effect measured in lives extended. Dots
represent specific screening strategies. Results obtained with an annual discount of 3%. : uniform B5069; &: uniform B4574. m: risk-based Q5074Q5074-Q4574-A4574; .: risk-based Q5074-Q5074-T5074-A5074. b: risk-based T5069-B5074-A5074-A5074; c: risk-based T5074-T5074-A4574-A4574.
Exams periodicities: A = annual, B = biennial, T = triennial, Q = quinquennial. The first two numbers refer to the age at starting the exams and the last
two numbers refer to the age at the last exam. In the risk-based strategies, the four strings correspond to the Low, Medium-Low, Medium-High and
High risk groups, respectively.
doi:10.1371/journal.pone.0086858.g001
N
per LE for the B5069 and B4574 strategies in our study were 1.3
and 1.4, respectively, in the lower range of the reviews.
FP per incremental QALY also increased. Section G.2 in
Appendix S1 includes further details of the sensitivity analyses.
Sensitivity analysis
Discussion
Figures S6 and S7 in Appendix S1 show that if there was a
migration of women to higher risk groups, the selected risk-based
strategies would achieve even higher benefit than the uniform
B5069 and B4574 strategies at similar cost and harm values.
Tables S14 and S15 in Appendix S1 present the results of the
sensitivity analysis, when the assumptions on the overdiagnosis
rates for invasive BC and DCIS, on the costs of cancer treatment,
and on the disutility by FP were changed. Tables S14 and S15 also
show the relative changes with respect to the uniform B5069
strategy. In general, the cost-benefit and the harm-benefit analyses
were robust to changes in the inputs, but we observed changes in
the incremental cost-benefit or harm-benefit ratios. When the
overdiagnosis rate of invasive or DCIS tumors increased, the
incremental cost- or harm-benefit ratios also increased which
means that the cost or the harm for each additional unit of benefit
was higher. When treatment costs increased, a reduced number of
the strategies located in the left part of the frontier were not
optimal anymore. This phenomenon was common to both benefit
measures (LE and QALY) and was more marked for a 5-fold than
for a 2-fold increase. Finally, when the disutility of FP results
increased, the optimal strategies were similar, but the incremental
Our analysis aimed to be a global assessment of the impact that
a new paradigm of screening would have on benefit, costs and
harms rather than a detailed guideline of how personalized
screening should be done.
Using probabilistic models, we have found that risk-based
screening strategies are more efficient and have lower harmbenefit ratios than uniform strategies. If, instead of screening
biennially all women 50 to 69 years old, we combined
quinquennial, triennial and annual exam periodicities for women
at L or ML, MH, and H risk, respectively, in the age interval 50 to
74, we would avert the same number of deaths. Similarly,
strategies that combine quinquennial exams for women at L or
ML risk with annual exams for women at MH or H risk,
respectively, in the age interval 45 to 74, result in similar gain in
QALYs than the uniform biennial strategy in the age interval 45 to
74. But, the important result is that in both cases the risk-based
strategies would result in remarkable reductions of costs, FP results
and overdiagnosis.
It is important to notice that a risk-based screening strategy
Q5074-Q5074-Q4574-A4574 has similar benefits and less costs
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Risk-Based Assessment of Breast Cancer Screening
Figure 2. Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies. Effect measured in quality-adjusted life years.
Dots represent specific screening strategies. Results obtained with an annual discount of 3%. : uniform B5069; &: uniform B4574. m: risk-based
Q5069-Q4574-Q4574-A4574; .: risk-based Q5069-Q4574-Q4574-A4074. b: risk-based Q5074-Q5074-A4074-A4074; c: risk-based Q4574-Q4574A4574-A4074. Exams periodicities: A = annual, B = biennial, T = triennial, Q = quinquennial. The first two numbers refer to the age at starting the exams
and the last two numbers refer to the age at the last exam. In the risk-based strategies, the four strings correspond to the Low, Medium-Low,
Medium-High and High risk groups, respectively.
doi:10.1371/journal.pone.0086858.g002
N
other two risk factors. In our study, women with breast density 3 or
4 belong to ML, MH, and H risk groups, depending on having 0,
1 or 2 additional risk factors, respectively, and therefore the
optimal strategy would have recommended different periodicities
and age intervals for these three risk groups. In addition,
Schousboe et al. concluded that annual mammography was not
cost-effective for any group, regardless of age or breast density.
These recommendations do not agree with our results, probably
due to differences in the studies’ objectives and the methodological
approaches used.
van Ravesteyn et al. used different models - one of them was the
LZ model that we used in the present study - to assess the falsepositive mammography findings per death averted and per years
of life gained in women aged 40 to 49 years [9]. In all models,
screening women with increased risk for breast cancer lead to
more breast cancer deaths averted with approximately the same
number of false-positive results.
Ayer et al. [38], using a Markov decision process that considers
personal risk characteristics and the personal history of screening,
showed that personalized screening strategies outperform the
existing guidelines with respect to the total expected qualityadjusted life years, while significantly decreasing the number of
mammograms and false-positives. They concluded that screening
is less beneficial for most women over age 74 and, as we found,
and harms than the uniform B5069. This does not mean that
Q5074-Q5074-Q4574-A4574 should be recommended, only that
the same benefits as B5069 can be achieved more efficiently and
safely. In fact, in terms of LE, Q5074-Q5074-T5074-A5074
improves the uniform B5069 and has similar costs and harms to
Q5074-Q5074-Q4574-A4574. The cost-effectiveness and harmbenefit analyses show the trade-offs when moving along the Pareto
frontier. Drawing horizontal lines at the level of uniform strategies,
one can estimate the improvement in benefit for a specific cost or
harm. Drawing vertical lines allows estimation of the reduction in
costs or harms for a specific benefit.
Some recent works have proposed personalized recommendations for BC screening based on cost-effectiveness or cost-utility
analyses [8,9] or in decision models that compare harm and
benefits [38]. Schousboe et al. [8] established cost-effectivenes
thresholds of $100,000 or $50,000 per QALY gained and
compared different periodicities for the screening exams in 10year age groups, BI-RADS breast density categories and the
presence/absence of personal history of biopsy and family history
of breast cancer. They recommended that women aged 50 to 79
years who have low breast density and no other breast cancer risk
factors may consider having mammography less frequently than
every 2 years, which is consistent with our results. But, they
recommended biennial screening for women aged 50 to 79 with
breast densities of 3 or 4, independently of the presence of the
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Table 1. Uniform B5069 and B4574 strategies compared with alternative risk-based strategies.
A) Effect measured in lives extended (LE)1
Schedule
LE
Cost (6106J) False positive2
Overdiagnosis3
False negative
Uniform B5069
201.9
139.6
347.6
223.9
Risk-based strategies4
Percentage of change, compared to fixed B5069
19,256.3
Q5074-Q5074-Q4574-A4574
0.6
29.3
225.1
225.9
22.7
Q5074-Q5074-T5074-A5074
3.8
28.9
225.1
220.6
20.8
Uniform B4574
264.7
154.5
26,578.5
493.1
298.2
Risk-based strategies4
Percentage of change, compared to fixed B4574
T5069-B5074-A5074-A5074
0.5
27.7
223.0
212.4
221.6
T5074-T5074-A4574-A4574
5.0
26.8
221.9
210.1
29.7
B) Effect measured in quality-adjusted life years (QALY)1
Schedule
QALY
Cost (6106J) False positive2
Overdiagnosis3
False negative
Uniform B5069
2,333.3
139.6
347.6
223.9
Risk-based strategies4
Percentage of change, compared to fixed B5069
19,256.3
Q5069-Q4574-Q4574-A4574
0.3
28.3
218.3
225.9
24.9
Q5069-Q4574-Q4574-A4074
1.5
28.0
217.2
225.0
26.2
Uniform B4574
2,848.8
154.5
26,578.5
493.1
298.2
Risk-based strategies4
Percentage of change, compared to fixed B4574
Q5074-Q5074-A4074-A4074
0.4
29.2
225.3
223.4
210.5
Q4574-Q4574-A4574-A4074
4.0
29.2
220.4
223.0
27.2
1
Data correspond to a cohort of 100,000 women at birth assessed in the age-interval 40–79 years.
All the absolute values have been discounted at an annual rate of 3%.
2
False positive includes both non-invasive and invasive procedures.
3
Overdiagnosis of invasive and DCIS cases.
4
Periodicity and age-interval for Low, Medium-Low, Medium-High and High risk groups, respectively.
Exams periodicities: A = annual, B = biennial, T = triennial, Q = quinquennial. The first two numbers refer to the age at starting the exams and the last two numbers refer
to the age at the last exam.
doi:10.1371/journal.pone.0086858.t001
provides significant QALY gains, for the high-risk women in the
age group 40–49.
First, 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 Cancer Intervention and Surveillance Modeling
Network (CISNET) had prepared for BC mortality modeling
research groups in the USA, like the distribution of disease stages
at diagnosis [12], or from the Breast Cancer Surveillance
Consortium, like the distribution of risk factors in the population
Limitations and other considerations
We have used a very detailed model that allowed us to
thoroughly assess the cost-effectiveness and harm-benefit of 2,625
different screening scenarios, either risk-based or not. However,
our study has several limitations.
Table 2. Distribution of the interval cases by time since last mammogram.
Time since last mammogram
(months)
Interval cancer
True interval and minimal signs
False negative and occult
tumors
N
%
N
%
N
%
0–11
420
32.4
142
26.2
117
38.7
12–23
876
67.6
399
73.8
185
61.3
The INCA study
1
Probabilistic model, biennial screening
0–11
529
35.3
287
26.8
242
56.5
12–23
971
64.7
785
73.2
186
43.5
1
The total number of interval cases in the INCA study is higher than the sum of true interval and FN, occult and minimal signs, because 60.3% of all the interval cases
were reviewed.
doi:10.1371/journal.pone.0086858.t002
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Risk-Based Assessment of Breast Cancer Screening
Table 3. Comparison with published reviews.
Our study
1
Independent UK
Panel on Breast
Cancer Screening
review [34]
Cochrane
systematic review2 Euroscreen review3
[33]
[35]
B5069
B4574
Mortality reduction (%)
14.4
19.6
20.0
15.0
23.0–30.0
Deaths averted
4.3
5.8
4.3
0.5
7–9
Overdiagnosis
5.5
8.1
12.9
5.0
4
Non invasive FP
265.5
347.8
-
.100
200
Invasive FP
24.9
28.7
-
-
30
Number needed to screen to extend 1 live
233
172
235
2000
111–143
Benefits and harms per 1,000 women screened.
1
time horizon 40–79 years.
2
10 years of follow-up.
3
time horizon 50–79 years.
doi:10.1371/journal.pone.0086858.t003
that the proportion of women in the risk groups remained constant
over time and it was the overall sample estimate for the BCSC
data. This assumption may not be correct, because as women get
older breast density tends to decrease and personal history of
biopsy and family history of breast cancer have more chances to be
present. We think that our results are robust to changes in the risk
group weights over time, as the sensitivity analysis has shown to be
the case for changes in the risk group distributions. However,
when considering personalized screening, BC risk should be
updated when new information on risk factors or their trends is
available.
Forth, our model used age-specific sensitivities of the screening
exam that correspond to a more prevalent use of film mammography than digital mammography. We did not assess the impact of
changing the mammography performance in this study. van
Ravesteyn et al. [9] found that there was greater harm relative to
benefit from digital than from film mammography in women aged
40–49 years, an age group were it seems that digital mammography has higher sensitivity, detects more cases of DCIS and
results in more FP results [44,45].
Fifth, our probabilistic model assumes that screening results in a
stage-shift at BC diagnosis, but does not consider DCIS as one of
the BC stages. Therefore, the fraction of DCIS tumors that would
have progressed and been diagnosed as invasive in the absence of
screening, are re-distributed under screening in more favorable
stages at diagnosis, but not as DCIS. This may have produced an
underestimation of the benefit of the screening strategies, both
uniform or risk-based. If bias had affected uniform and risk-based
strategies similarly, the cost-effectiveness and harm-benefit analyses would remain valid.
We agree with Mandelblatt [46] and Ayer [38] on that riskbased approaches show promise, but there are important issues
that need further research. One issue is the need to know more
about the underlying relationships between risk factors and the
biology of breast cancer and, the other issue, is to overcome the
practical issues of implementing appropriate screening strategies
based on personalized risk. The PROCAS study in the UK [47],
the KARMA project in Sweden [48–50], and the PROSPR
network in the USA [51] are examples of advancing towards a
tailored screening through improving BC risk prediction. Creating
new strategies for communicating individual estimates of benefit
and risk of alternative screening methods, to better inform patients
and health care providers, is a challenge for researchers.
or the relative risks of the considered risk factors [8,20,22]. Finally,
there were some inputs that had been obtained from published
randomized clinical trials and observational studies [12,23]. This
variety of data sources and modeling assumptions makes it
necessary to carefully analyze the model outputs. To validate our
model, on one hand, we have performed sensitivity analyses either
in this study or in previous publications that show that the model
and results are robust to the model assumptions [10,16]. On the
other hand, we have reviewed the literature to check whether our
results were consistent, at least for the screening strategies that
have been included in reviews - mostly biennial strategies in the
50–69 year age interval. The three examined reviews, the
Cochrane systematic review [33], the Independent UK Panel on
Breast Cancer Screening review [34], and the Euroscreen
comprehensive review of European screening programs [35]
provide a wide range of values for the benefits and harms of
screening. Our results have similarities and differences with the
three reviews. We obtained a value close to the Lancet review for
number of deaths averted per 1,000 women. Our ratios of
overdiagnosed cases per death averted were in the low range of
values obtained in the mentioned reviews, 0.5, 3 and 10
overdiagnosed cases per death averted in the Euroscreen, the
UK Panel and the Cochrane reviews, respectively. Our estimates
of false-positive mammography results were higher than in the
reviews, nevertheless for invasive false-positives we were close to
the Euroscreen result. Finally, when we compared our results for
the uniform screening strategies B4569 or B5069 with the INCA
study or other studies of interval cancer [39–41], we found a high
consistency in most of the results relative to the number of cancer
cases detected per mammography, sensitivity of the program,
distribution of screen-detected and interval cases, and distribution
of true interval and false-negative cases.
Second, we have assumed that BC risk influenced only the
incidence of the disease and not the distribution of stages at
diagnosis, the sensitivity and specificity of mammography, the
sojourn time in the preclinical state or the mortality from other
causes. It could happen that tumors for women at MH or H risk
groups had a less favorable stage distribution at diagnosis and the
benefit of screening for these groups was lower than estimated.
Also, it is known that mammography performance is associated
with the considered risk factors [42,43].
Third, we have assumed that there are no changes in the risk
factors after the age at which screening exams start. We considered
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Risk-Based Assessment of Breast Cancer Screening
the risk groups distribution. Cost-effectiveness and harm-benefit
analyses for 2,625 early detection strategies. Effect measured in
lives extended. Figure S7, Sensitivity analysis of a change in the
risk groups distribution. Cost-effectiveness and harm-benefit
analyses for 2,625 early detection strategies. Effect measured in
QALY.
(PDF)
In conclusion, risk-based screening strategies seem to be more
efficient and have better harm-benefit ratios than the standard
uniform strategies. We have proposed a reduced number of riskbased screening strategies that combine quinquennial or triennial
exams for women in low or moderate-low risk groups and annual
exams for women in the moderate-high or high risk groups, for the
consideration of researchers, decision makers and policy planners.
Now, it is necessary to develop accurate measures of individual risk
of BC and to work on how to organise risk-based screening
programs.
Acknowledgments
We thank Sandra Lee, Marvin Zelen and Hui Huang for their support and
for providing the initial code of the probabilistic models used, Jordi Blanch
for data analysis of the INCA study and revising the manuscript and
Montserrat Martinez-Alonso for developing the breast cancer incidence
and mortality models and revising the manuscript. We are also grateful to
JP Glutting for review and editing, to the Cumulative False Positive
Research Group (RAFP) project researchers, and to Rebecca Hubbard and
two anonymous reviewers for their valuable comments on a preliminary
version of the manuscript.
Interval Cancer (INCA) Study Group (alphabetical order):
IMIM (Hospital del Mar Medical Research Institute), Barcelona: Jordi
Blanch, Xavier Castells, Mercè Comas, Laia Domingo, Francesc Macià,
Juan Martı́nez, Ana Rodrı́guez-Arana, Marta Román, Anabel Romero,
Maria Sala. General Directorate Public Health and Centre for Public
Health Research (CSISP), FISABIO, Valencia: Carmen Alberich, Marı́a
Casals, Josefa Ibáñez, Amparo Lluch, Inmaculada Martı́nez, Josefa
Miranda, Javier Morales, Dolores Salas, Ana Torrella. Galician Breast
Cancer Screening Program, Xunta de Galicia: Raquel Almazán, Miguel
Conde, Montserrat Corujo, Ana Belén Fernández, Joaquı́n Mosquera,
Alicia Sarandeses, Manuel Vázquez, Raquel Zubizarreta. General
Directorate of Health Care Programmes. Canary Islands Health Service:
Teresa Barata, Isabel Dı́ez de la Lastra, Juana Marı́a Reyes. Basque
Country Breast Cancer Screening Program. Osakidetza: Arantza Otegi,
Garbiñe Sarriugarte. Corporació Sanitària Parc Taulı́, Sabadell: Marisa
Baré, Núria Torà. Hospital Santa Caterina, Girona: Joana Ferrer,
Francesc Castanyer, Gemma Renart. Epidemiology Unit and Girona
Cancer Registry; and University of Girona: Rafael Marcos-Gragera,
Montserrat Puig-Vives. Universitat de Lleida-IRBLleida: Carles Forné,
Montserrat Martı́nez-Alonso, Albert Roso, Montserrat Rué, Ester
Vilaprinyó. Universitat Rovira i Virgili, Tarragona: Misericordia Carles,
Aleix Gregori, Marı́a José Pérez, Roger Pla.
Supporting Information
Appendix S1 Supporting information on methods used
and results obtained, containing Tables S1 to S15 and
Figures S1 to S7. Table S1, Distribution of stages at diagnosis of
BC. Table S2, Relative risk of breast cancer based on age and
breast density. Table S3, Prevalences of risk factors by age group
for each category of breast density. Table S4, Characteristics of the
2,625 screening strategies analized. Table S5, The utilities for the
general population and for women diagnosed with BC, either
DCIS or invasive. Table S6, Model for false positives of noninvasive tests. Table S7, Model for false positives of invasive tests.
Table S8, Distribution of stages at diagnosis of BC for screendetected cases. Different overdiagnosis rates. Table S9, Linear
regression model with dependent variable being the DCIS rate per
105 women-year. Table S10, Cost-effectiveness and harm-benefit
analysis. Lives extended. Table S11, Cost-effectiveness and harmbenefit analysis. Quality-adjusted life years (QALY). Table S12,
Number of mammograms and detection rates for screen-detected
and interval cases and program sensitivity by age groups. Invasive
cancer (DCIS not included). Table S13, Distribution of stages at
diagnosis of BC. Table S14, Sensitivity analysis. Changes in lives
extended. Table S15, Sensitivity analysis. Changes in QALY.
Figure S1, Incidence curves for twelve risk profiles grouped by risk
level: (A) Low Risk, (B) Medium-Low Risk, (C) Medium-High
Risk, and (D) High Risk. Graphic (E) shows the smoothed
incidence rates for each risk group. Figure S2, Observed and
smoothed DCIS rates over time in Catalonia (1983–2008). Figure
S3, Index of mammography use (IMU) and smoothed DCIS rates
over time in Catalonia (1983–2008). Figure S4, Cost-effectiveness
and harm-benefit analyses for 2,625 early detection strategies, with
uniform strategies marked. Effect measured in lives extended.
Figure S5, Cost-effectiveness and harm-benefit analyses for 2,625
early detection strategies, with uniform strategies marked. Effect
measured in QALY. Figure S6, Sensitivity analysis of a change in
Author Contributions
Analyzed the data: EV CF MR. Wrote the paper: MR EV CF. Codeveloped the project that includes this study: MR EV MC RP. Developed
costs estimations, contributed to the cost-effectiveness analysis: MC.
Developed the code for the data analysis: EV CF. Coordinated the RAFP
and the INCA projects: MS XC. Participated in the design and analysis of
the INCA study: LD. Revised and approved the manuscript: EV CF MC
MS RP XC LD MR.
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February 2014 | Volume 9 | Issue 2 | e86858