Possible net harms of breast cancer screening: updated modelling of Forrest report

James Raftery, professor of health technology assessment,
Maria Chorozoglou, research fellow

Objective To assess the claim in a Cochrane review that mammographic
breast cancer screening could be doing more harm than good by updating
the analysis in the Forrest report, which led to screening in the
United Kingdom.

Design Development of a life table model, which replicated Forrest’s
results before updating and extending them with data from relevant
systematic reviews, trials, and other models based on purposive
literature searches.

Participants Women aged 50 and over invited for breast cancer screening.

Main outcome measures Quality adjusted life years (QALYs), combining
life years gained from screening with losses of quality of life from
false positive diagnoses and surgery.

Results Inclusion of the effects of harms reduced the updated estimate
of net cumulative QALYs gained after 20 years from 3301 to 1536 or by
more than half. The best estimates from the Cochrane review generated
negative QALYs for the first seven years of screening, 70 QALYs after
10 years, and 834 QALYs after 20 years. Sensitivity analysis showed
these results were robust to a range of assumptions, particularly up
to 10 years. It also indicated the importance of the level and
duration of harms from surgery.

Conclusions This analysis supports the claim that the introduction of
breast cancer screening might have caused net harm for up to 10 years
after the start of screening.

The Forrest report in 1986,1 which led to the introduction of
mammographic breast screening in the United Kingdom, analysed the
costs and benefits in terms of quality adjusted life years (QALYs).
One of the earliest uses of QALYs to guide policy, it suggested that
screening would reduce the death rate from breast cancer by almost one
third with few harms and at low cost (for details see appendix on

The key data used in the Forrest report were drawn from two randomised
trials, the Swedish two counties trial2 and the Health Insurance Plan
(HIP) New York trial.3 The Forrest report claimed that overdiagnosis
was not a problem, based on the New York trial, but noted that the
Swedish trial found possible overdiagnosis of 20%. It stated that
“further follow up is required to find out whether this excess
persisted.” We have updated the Forrest report’s estimates for
mortality and extended them to include the effects of false positives
and overdiagnosis.

Since the Forrest report, the harms of mammographic breast cancer
screening have been acknowledged. A WHO report defined false positives
and overdiagnosis:

“The term false positive refers to an abnormal mammogram (one
requiring further assessment) in a woman ultimately found to have no
evidence of cancer. Overdiagnosis refers to the diagnosis and
treatment of cancer that would never have caused symptoms. Thus a
false positive result can be found only in a woman without cancer,
while overdiagnosis can only be made for women with cancer.”4

It went on to note that “overdiagnosis is a foreign concept to most
prospective screenees (and many clinicians).”

The WHO report noted that a considerable part of overdiagnosis
involved ductal carcinoma in situ, which accounts for around a fifth
of mammographically detected cancers. While this is a risk factor for
breast cancer, only a minority of these develop into breast cancer.
Indeed the inclusion of the term “carcinoma” in ductal carcinoma in
situ has been questioned.5

The WHO report claimed that the success of breast cancer screening
programmes should be assessed only in terms of mortality: “Screening
programmes should ultimately be monitored in terms of deaths, the
measure directly related to the purpose of screening.” A focus solely
on deaths, however, implies ignoring harms to the living.

Gøtzsche and Nielsen’s Cochrane review6 raised the disturbing
possibility that mammographic breast cancer screening could be doing
more harm than good. This was because of their lower estimate of the
reduction in mortality from breast cancer and their inclusion of the
harms from overtreatment. They said that “this means that for every
2000 women invited for screening throughout 10 years, one will have
her life prolonged, and 10 healthy women, who would not have been
diagnosed if there had not been screening, will be diagnosed as breast
cancer patients and will be treated unnecessarily. Furthermore, more
than 200 women will experience important psychological distress for
many months because of false positive findings. It is thus not clear
whether screening does more good than harm.”6

Their meta-analysis included eight randomised trials, three of which
they considered adequately randomised and five suboptimally
randomised. Only the suboptimally randomised trials found a
significant effect of screening on deaths ascribed to breast cancer.
For all the eight trials taken together the relative risk reduction
for mortality from breast cancer was 19% (95% confidence interval 26%
to 13%) after 13 years. Given the quality of the evidence, Gøtzsche
and Nielsen’s best estimate of the effect of screening was a 15%
decline in mortality.

The increased risk of surgery was the basis of Gøtzsche and Nielsen’s
estimate of unnecessary treatment. Four trials provided data on breast
operations (mastectomies and lumpectomies), with more performed in the
screened groups than in the control groups: the relative risk increase
was 31% (22% to 42%) for the two adequately randomised trials and 35%
(26% to 44%) for all four trials. For false positive results, Gøtzsche
and Nielsen stated “it seems that screening inflicts important
psychological distress for many months on more than a 10th of the
healthy population of women who attend a screening program.”

A systematic review and meta-analysis by Nelson and colleagues for the
US Preventive Services Task Force independently analysed the same
eight clinical trials in the Cochrane review but by age group.7 8 This
put the reduction in mortality from breast cancer at 15% for those
aged 39-49, 14% for those aged 50-59, and 32% for those aged 60-69. It
used US registry data to suggest that about 10% of those screened
would have a false positive result requiring further investigation.7
It differed from the Cochrane review in relation to overdiagnosis.
“Rates of overdiagnosis vary from less than 1% to 30% with most from
1% to 10%. Estimates differ by outcome (invasive vs in situ breast
cancer), by whether cases are incident or prevalent, and by age. The
studies are too heterogeneous to combine statistically.”7 These
studies, it should be noted, included both randomised trials and
observational studies.

Thus the two systematic reviews agreed that screening reduced
mortality from breast cancer but differed in how much. Nelson and
colleagues estimated a false positive rate around 10% per round of
screening, while Gøtzsche and Nielsen put it at around 10% over 10
years. Only Gøtzsche and Nielsen provided data on the increased
relative risk of surgery with screening, with two estimates: 31% based
on the better quality trials and 35% based on all trials reporting
this outcome.

We assessed the claim of Gotzsche and Nielsen by updating the Forrest
report framework, extended to include harms. The Forrest report used
life tables to estimate the number of women surviving by year up to 15
years in two cohorts aged 50, only one of which was screened. Deaths
could be from breast cancer or all other causes. Baseline mortality
and the reduction from triennial breast cancer screening were based on
the two randomised controlled trials then available. The difference in
life years between the two cohorts after 15 years was expressed in
QALYs by reducing their quality of life by 8% to reflect the effects
of treatment.

The Southampton model used the same life table approach as Forrest to
estimate life years. To ensure that the Southampton model was fully
compatible, we confirmed that use of Forrest inputs generated the same
number of deaths in our model.

Forrest took baseline mortality from breast cancer from the two trials
then available but acknowledged that as this was below the English
mortality rate from breast cancer, his results were underestimates. In
updating Forrest, we corrected this by using the mortality rate from
breast cancer for England.9 We also took the baseline risk of surgery
for breast cancer from the English NHS.10 11 Data for both these
baselines were for 1985, the latest year before screening for which we
could locate data. These changes meant more favourable results for
screening than if we used the control arms of trials as baselines.

We drew parameter inputs for the Southampton model (table 1⇓) from the
published literature, giving priority to systematic reviews, followed
by randomised clinical trials and other published models, and then
observational data supplemented by clearly stated assumptions when
necessary. Sensitivity analysis varied mean estimates to their 95%
confidence intervals and other inputs by ±33%. The results of
individual sensitivity analyses are reported in the appendix on
bmj.com. Probabilistic sensitivity analysis varied key inputs
simultaneously by sampling from their probability distributions for 10
000 iterations.
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Table 1

Data used to estimate QALYs for mammographic breast cancer screening
of women aged 50, by scenario

All the input values are listed in table 1 with sources and discussed
more fully in the appendix on bmj.com. In brief, the changed relative
risks for breast cancer mortality and surgery from screening were
based on the meta-analyses of the relevant trials.6 7 8 The losses of
quality of life from false positive results and surgery were based on
a systematic review, supplemented by relevant randomised trials and
values used in previous models. The extent and duration of the loss of
quality of life from surgery have been least researched. We assumed a
6% permanent loss from surgery, less than in the Forrest report but
informed by recent randomised trials.12 13 Sensitivity analyses
explored changing the extent and duration of these and other values.
Figure 1 illustrates the modelling approach⇓.
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Fig 1 Outline of Southampton breast screening model: this applies to
two cohorts of women aged 50, one screened the other not
Setting, participants, and outcome measures

The setting was England. The outcomes of 100 000 women aged 50 were
modelled in two cohorts, one screened the other not. The outcome
measures were deaths from breast cancer, deaths from all other causes,
and the number of women having false positive diagnoses and surgery,
which we combined into the main outcome—quality adjusted life years

Figure 2 graphically presents the five scenarios⇓, and table 2
summarises the results⇓. Scenario 1 shows the QALY gains that Forrest
would have got if he had used English breast cancer mortality rates as
baseline with his risk reductions. Scenario 2 updates this with the
reduction in mortality from breast cancer for all ages from all eight
trials. The losses of quality of life from surgery and false positive
diagnoses were added in scenario 3. Scenario 4 used the reduction in
mortality from breast cancer suggested by Gøtzsche and Nielsen.6 In
scenario 5, we used the reductions in mortality from breast cancer by
age group from Nelson et al.7 8 The results are based on 100 000 women
being invited for mammographic screening, with 73% attending, and are
presented for each year up to 20 years after the entry to the
screening programme.
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Fig 2 Breast cancer screening over 20 years: net QALYs by year after
start of screening according to different scenarios
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Table 2

Net QALYs gained over time in women undergoing breast screening by scenario

Scenario 1 accumulated just over 3300 net QALYs after 20 years. This
is what Forrest would have got had he used as baseline the breast
cancer mortality rate for England and the mortality reduction from the
two trials. When we updated the estimate for reduction in breast
cancer mortality for all ages, with the meta-analysis of the eight
trials (scenario 2), the net cumulative QALY gain at 20 years fell to
around 3100 QALYs or by about 6%.When we added harms in scenario 3,
this was reduced to just over 1500 QALYs or by half. When we changed
the reduction in mortality from breast cancer to that suggested by
Gøtzsche and Nielsen.6 the net QALYs at year 20 fell to 834 (scenario
4). Scenario 5, based on the reductions in mortality from breast
cancer by age group suggested by Nelson et al,7 8 generated 1685 QALYs
by year 20.

Scenarios 3, 4, and 5 had negative cumulative QALY values for the
first four, seven, and eight years, respectively, but had positive
values after 10 years. The harms from surgery and false positive
diagnoses impacted from the start because they were linked to each
round of screening. Mortality from breast cancer, however, was reduced
only after several years but accumulated over time so that positive
net QALY applied by 10 and especially by 20 years.
Sensitivity analyses

Sensitivity analyses explored the effects of varying input values
independently (see appendix on bmj.com). When we combined four key
parameters (reduced mortality from breast cancer, increased surgery
for breast cancer, and losses of quality of life from false positive
results and from surgery) in a probabilistic sensitivity analysis for
scenario 3 (Forrest updated including harms), the net cumulative gain
in QALYs after 20 years was between 771 and 2136 (mean 1532), with
lower values in the earlier years (fig 3⇓). The mean number of years
with negative QALYs was four, with a range of two to nine.
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Fig 3 Probabilistic sensitivity analysis, including reduced mortality
from breast cancer, increased surgery for breast cancer, and losses of
quality of life from false positive results and from surgery, showing
cumulative QALYs for 100 000 iterations, scenario 3 (Forrest updated
with harms)

We did not include the duration of harms from surgery in the
probabilistic sensitivity analysis because of uncertainty about the
appropriate distribution. Instead we used deterministic sensitivity
analyses to explore reducing the duration of harms from surgery,
assumed as permanent in the base case, to five and 10 years (see
appendix on bmj.com). This led to unchanged net QALYs up to five and
10 years but with more QALYs over longer periods.

Assessment of the effects of mammographic breast screening in terms of
mortality or life years inevitably shows positive benefits because of
the omission of harms. Despite its espousal of a QALY framework, the
Forrest report focused mainly on life years gained, which it adjusted
for quality of life only from necessary surgery and ignored all other
harms. Our analysis shows that inclusion of the harms from false
positive results and unnecessary surgery reduced the benefits of
screening by about half with negative net QALYs in the early years
after the introduction of screening.

We assumed that the loss of quality of life in women who had
unnecessary surgery was the same as for those who had had “necessary”
surgery. A key feature of overtreatment is that individuals affected
cannot be identified. Of Gøtzsche and Nielsen’s 10 women who had
unnecessary surgery, all believed that it was necessary.6 This has
been dubbed the paradox of overtreatment—“overdiagnosis and
overtreatment create a paradoxical popularity because each individual
justifies their experience by believing they have had a dramatic
benefit.”20 The more people are (over)treated, the more people think
screening saved their lives.

Would knowing whether or not treatment was necessary affect quality of
life? None of the surveys of quality of life included overtreatment,
implicitly assuming all surgery was necessary. To answer this question
surveys would have to ask each woman whether her quality of life would
be affected if it could be shown that her surgery had been
unnecessary. While the methodological problems of measuring quality of
life in cancer screening are considerable,21 ignoring overtreatment is

Ways of reducing the harms from screening might include less frequent
screens, particularly for younger women. While further modelling might
explore the clinical and cost effectiveness of various options,
conclusions will inevitably be limited without better estimates of the
level and impact of overtreatment.
Strengths and limitations

Our analysis does have limitations. Following the Forrest report, it
relies heavily on clinical trials, most of which were completed in
other countries several decades ago. As mortality from breast cancer
in 1985 was higher in England than in those trials, we took as
baseline the rate for England for 1985, before screening was
introduced. We have assumed that the risk reductions shown in the
trials apply to this higher baseline rate. An assessment of the value
added by screening today might require disentangling the effects of
screening from the effects of improved treatments, which is
difficult.22 23 24 It would also require consideration of how
screening methods have changed. Double view mammography and improved
imaging might reduce the false positive rate but could have increased
overtreatment by creating more (harmful) true positive diagnoses.25

As with breast cancer mortality, our baseline risk of breast cancer
surgery was that for England in 1985. We assumed that the risk
increase shown in the trials applied to this risk. Observational
studies, including those summarised in the US systematic review,
provided a wide range of estimates of overtreatment from 1%7 to 52%6 7
26 27 28 but have also been criticised for poor quality.29 We assigned
a single loss of quality of life to all forms of surgery but
acknowledge that lesser harms are likely with lumpectomy than with
mastectomy. Against this, we have not included the harms from
radiotherapy and chemotherapy. Future studies on quality of life might
usefully distinguish between the effects of different treatments.

How plausible are the losses in QALYs from surgery? The Forrest report
estimate of 8% seems to have been based on a single small study from
which it took the lowest estimate30 (see appendix on bmj.com). A 2010
systematic review of health state utilities in breast cancer15 found
only two relevant studies. One put the loss in utility at 8% in year
one, 4% in intervening years, and 11% in the last year of life. The
other put the loss at 38% in year one after diagnosis, 31% in years
one to five, and 29% after five years. The 2010 UK COMICE trial put
the loss in quality of life from surgery in 1625 women with a low risk
breast cancer at 5% after 12 months.12 The five year follow-up to the
PRIME trial showed that the quality of life losses after surgery were
unchanged after five years.13 Overall, our assumption of a permanent
6% loss in quality of life from surgery does not seem unreasonable,
but more robust estimates are needed.

We assumed the base case to be a loss in quality of life from false
positive results of 5% of full health for 0.2 years. This is lower
than that of a relevant US model16 but similar to the Dutch model.17
The 2010 systematic review of the utility losses from breast cancer
included estimates of this loss of between 11% and 34%15 but warned
that the studies could not be synthesised.

The time frame of up to 20 years is long relative to the duration of
the trials, results of which have been synthesised up to 13 years.
Extrapolation required an assumption of constant benefits and harms
from additional rounds of screening. Longer time frames generate
greater net QALYs but rely on increasingly strong assumptions, both to
do with the rate of survival from breast cancer and the pattern of
losses in quality of life over time.

We assumed no recurrence of cancer, despite the 10 year survival rate
for breast cancer being 72% in the UK.30 We also assumed no
re-operations, even though 17% of women with tumours detected at
screening in the UK had more than one therapeutic operation in
2006-7.31 While it is possible that some cancers that were detected
early by screening might have progressed in longer time frames, a
recent analysis has shown no decline in the incidence of advanced
breast cancer.32 Modelling the longer term effects of breast cancer
screening should include these factors.

Finally, our list of benefits and harms excluded the potential
reassurance from a negative result on mammography. As a negative
mammogram has little predictive value, any reassurance is limited to
relief at not having cancer at that time.33 The 2010 systematic review
of utility states in breast cancer15 found no evidence of improved
quality of life from negative results.
Comparison with other studies

Our results can be compared with attempts to model the effectiveness
of mammographic screening in terms of cost per QALY. Although Stout et
al16 included only losses of quality of life in a sensitivity
analysis, their inclusion roughly doubled the cost per QALY. The Dutch
MISCAN study concluded that including the effects on quality of life
of both treatment and false positive results had little consequence,17
but this seems to be because of the relatively low level of surgery
assumed in that model. In a review of the cost effectiveness of
extending the age range for the UK breast screening programme, Madan
et al18 showed that inclusion of losses of quality of life from false
positive results considerably increased the cost per QALY.
Conclusions and policy implications

Overall, our study supports the suggestion by Gøtzsche and Nielsen
that mammographic breast cancer screening could be causing more harm
than good after 10 years.6 Scenario 4, based on Gotzsche and Nielsen’s
best estimate, had negative QALYs for the first seven years after
screening and minimal gains of 70 QALYs after 10 years. Thereafter,
net QALYs accumulate but much less than would be expected by our
updating of the Forrest report. The uncertainty around this result,
explored in scenarios 3 and 5 and in greater detail in other
scenarios, applies more to the longer than the shorter term. Harms
largely offset the gains up to 10 years, after which the gains
accumulate at an increasing rate.

More research is required on the extent of unnecessary treatment and
its impact on quality of life. Most of the observational studies of
overtreatment have focused on the relation between the incidence of
breast cancer and mortality rather than on the levels of treatment,
especially surgery. The effects of treatment on quality of life could
be established observationally or in longer follow-up studies of
trials.13 Improved ways of identifying those most likely to benefit
from surgery and for measuring the levels and duration of the harms
from surgery should be research priorities.

As randomised trials might be the only way to resolve the extent of
overtreatment, researchers in countries that have not yet implemented
breast cancer screening should consider trials that include the harms
of screening. There have been suggestions for more sophisticated
approaches to the prevention and treatment of breast cancer.33 34 From
a public perspective, the meaning and implications of overdiagnosis
and overtreatment need to be much better explained and communicated to
any woman considering screening.
What is already known on this topic

Mammographic screening for breast cancer saves lives but also
imposes losses in quality of life from false positive results and
unnecessary treatment

It has been suggested that the harms outweigh the benefits, but
this has not been quantified

What this study adds

By combining the life years saved with the quality of life losses
in quality adjusted life years (QALYs), this study combined the
benefits and harms into a single measure

The net QALYs from screening were negative for the early years
after the introduction of screening, after which net positive QALYs
accumulated but by much less than predicted by the Forrest report


Cite this as: BMJ 2011;343:d7627-

We thank Ruairidh Milne for noting that the Forrest report used
QALYs, the authors of both the Cochrane review (Peter Gøtzsche) and US
Preventive Task Force review (Heidi Nelson) for help with queries,
David Turner, Jason Madan and Lily Yao for advice on modelling, and
Maryrose Tarpey for ongoing insights. Responsibility for errors
remains with the authors.

Contributors: JR was responsible for the idea, early modelling,
writing up of successive drafts, and is guarantor. MC was responsible
for the detailed modelling. Both authors were involved in the final
drafts of both the paper and the appendix.

Funding: This research received no specific grant from any funding
agency in the public, commercial, or not-for-profit sectors.

Competing interests: All authors have completed the ICMJE uniform
disclosure form at www.icmje.org/coi_disclosure.pdf (available on
request from the corresponding author) and declare: no support from
any organisation for the submitted work; no financial relationships
with any organisations that might have an interest in the submitted
work in the previous three years; no other relationships or activities
that could appear to have influenced the submitted work.

Ethical approval: Not required.

Data sharing: No additional data available.

This is an open-access article distributed under the terms of the
Creative Commons Attribution Non-commercial License, which permits
use, distribution, and reproduction in any medium, provided the
original work is properly cited, the use is non commercial and is
otherwise in compliance with the license. See:
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BMJ 2011; 343 doi: 10.1136/bmj.d7627 (Published 8 December 2011)
Cite this as: BMJ 2011;343:d7627

Author Affiliations

Correspondence to: J P Raftery j.p.raftery@soton.ac.uk

Accepted 18 November 2011