USPSTF Breast Cancer Screening:
Science, Policy & Politics
The Good, The Bad and The Ugly
J. Sanford Schwartz, MD
Leon Hess Professor of Medicine and
Health Management & Economics
Perelman School of Medicine &
The Wharton School
University of Pennsylvania
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USPSTF Screening Mammography
Recommendation for Women Ages 40-50
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What is the USPSTF? (mandate, membership)
The USPSTF decision process
Why recommendation was made (and timing)
Data on which recommendation was generated
The recommendation and why it changed from
the previous USPSTF recommendation
• Why the recommendation generated controversy
– Importance
– How recommendation was communicated
– Political context
• My subjective assessment of how things went,
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why and why topic will remain controversial
“The USPSTF recommends against routine
screening mammography in women aged 40-49.
The decision to start…should be an individualized
one and take patient context into account,
including the patient’s values regarding specific
benefits and harms.”
(C recommendation)
Moderate certainty that the net benefit is small
Issued October 2009
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United States Preventive
Services Task Force
Government appointed, independent
advisory group established 1984 by
Congressional mandate
• Recommend preventive services that
should be incorporated routinely into
primary care medical care populations (age,
gender, risk factors)
• Identify research agenda for preventive care
• 16 PCPs (IM, FP, Peds, Ob–Gyn)
• Rotating 4–6 year terms
• Review scientific evidence clinical preventive
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services (Members with COI excluded)
United States Preventive
Services Task Force
Staffed by AHRQ (staff, fellows, medical officers)
Partner organizations:
Federal
• CDC
• NIH
• VA
Professional Societies
• ACP
• APA
• ACOG
• ACFM
Public Advocacy Groups
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• AARP
USPSTF Methodology:
A (Very) Short Primer
• Select topic (largely subjective process)
• Identify interventions and outcomes of interest
• Examines key questions via chain of evidence
within specified analytic framework
• Systematic review of evidence (AHRQ EPC)
• Assesses evidence, estimates magnitude and
certainty of benefits and harms, assigns
consensus recommendation grade
• Peer review evidence report & recommendation
• Draft recommendation posted on website*
• Final recommendation issued
• US government and Ann Intern Med review Penn
http://www.ahrq.gov/clinic/uspstf08/methods/procmanualap7.htm
USPSTF Recommendation Grade
Certainty
Net Benefit
Magnitude of Net Benefit
Substantial Moderate Small Zero/Neg
High
A
B
C
D
Moderate
B
B
C
D
Low
Evidence:
Insufficient
Convincing, Adequate, Inadequate
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USPSTF Recommendation
Provide
Routinely
A
B
C
D
I
Strongly recommends
Recommends
Recommends against routinely providing
Recommends against
Individual Risk/Benefit
insufficient evidence
•
Highlights Clinical/Other Considerations
•
Discussion & Recommendation of Others
Do Not Provide
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United States Preventive Services Task Force
Does not:
• Advise insurers
• Make health care coverage decisions
However, the Affordable Care Act of 2009
mandates that all preventive services that
receive an ‘A’ or ‘B’ recommendation by the
USPSTF must be covered by insurers at no cost
to the beneficiary
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Screening Mammography:
Primary MD and Patient Questions
• Should I get mammograms?
• If so, starting at what age, and how often?
• When, if ever, should I stop?
– What is the benefit?
– What are the harms?
– How do my personal risk factors for breast
cancer affect the decision?
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“I have yet to see any problem,
however complicated,
which … looked at it in the right way,
did not become still more complicated”
– Poul Anderson
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USPSTF Breast Ca Screening:
Methods of Analysis
• Meta-analysis of RCTs of screening effectiveness
• Trials rated “fair-quality” or better from 2002
review and any new trials or updates since then
• Rates and proportions calculated using primary
data from Breast Cancer Surveillance Consortium
• Outcomes Table constructed to estimate
magnitude of screening benefits & harms (by
age)
• Natural history modeling (CISNET)
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Diagnostic Test Performance
Test Result
Disease State
Disease Present Disease Absent
Test Positive
True Positive
(TP)
False Positive
(FP)
Test Negative
False Negative
(FN)
True Negative
(TN)
Sensitivity (Se) =
TP
TP+FN
Predictive Value (PV) + =
TP_
TP+FP
Specificity (Sp) =
TN
TN+FP
Predictive Value (PV) – =
TN_
TN+FN
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Lead Time
Time between disease detection by screening and
time of usual symptomatic diagnosis
• Rate biological progression disease
• Screening test sensitivity
Lead time bias
Artifactual survival prolongation resulting from
earlier disease detection in the absence of
increased effectiveness of earlier intervention
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Lead Time Bias
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Length/Time Bias
Artifactual increased measured survival from
selectively increased detection of less aggressive
disease with better prognosis
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Overestimation Screening Test Benefit:
Prevalence Bias
Unrepresentative impact of detection of prevalent
cases in early screening cycles
Impact incident cases increases with number
subsequent cycles
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Length Time Bias
Clinical symptoms
Disease
Begins
Death
Screen detection
Clinical symptoms
Disease
Begins
Death
Screen detection
Courtesy of Emily Conant, MD. University of Pennsylvania
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Screen Detection Capability Based on
Tumor Biology and Growth Rates
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Overdiagnosis Bias
Overdiagnosis of a condition (pseudodisease)
that would not become clinically significant in a
patient’s lifetime
The disease has no affect
on mortality and is the
major harm of screening
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New Evidence: Age 40-49 Yrs
Age Trial (UK 1991–1997)
Study Design
• RCT annual mammography to age 48 yrs
(n=53,884) vs. “usual care” (n=106,956)
• F/U through National Health Service register
– 81% attended at least 1 screen;
– 4.5 mean rounds
– 10.7 yrs follow-up.
Results
• Breast cancer mortality: RR 0.83 (0.66-1.04)
NNI 2,512 (1,149-13,544)
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• All-cause mortality: RR 0.97 (0.89-1.04)
New Evidence: Age 40-49 Yrs
Age Trial (UK 1991–1997)
Strengths
• Designed to determine effectiveness age 40-49
• Largest trial, community population
• Most recently conducted RCT
• Consistent with results of meta-analysis
previous RCTs
Limitations
• Applicability to US not clear (recall rate 3%–5%)
• Mortality lower than expected in control group
• Only 10 yrs follow–up
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• 30% attrition, contamination not reported
New Evidence: Age 40-49 Yrs:
Additional F/U Gothenburg Trial
RCT, ages 39–59 yrs, Gothenburg, Sweden 1982
• Mammography q18 mo (n = 20,724) vs. “usual
care” (n = 29,200)
• All offered screening at end of trial (year 5)
• 85% attended first screen; 5 mean rounds;
14 yrs follow-up.
• 25-40% attrition, 20% contamination
• Results: age 40-49 yrs:
Breast cancer mortality RR 0.69 (0.45-1.05)
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Meta-analysis Screening RCTs:
Women Ages 39 to 49 Year
Screening every 1-3 years, all “fair quality”
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10–Year Risk of Death from Breast Cancer:
Beginning Routine Screening Age 40 vs. Age 50
Ages 40-49
Ages 50-59
Without screening
0.33%
0.89%
With screening
0.28%
0.69%
Absolute RR
0.05%
0.20%
Source: Steve Woloshin, Veterans Affairs Outcomes Group
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Breast Cancer Surveillance Consortium Data
• Women in BCSC who had at least one prior
screening mammogram within 2 years (“routine
screening”)
• Screened between 2000-2005 at all 7 sites
• Data provided by age in decades beginning at
40 years (also collapsed for women 70+)
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Breast Cancer Surveillance Consortium:
Registry Advantages
• Represent current U.S. practice
• National multi-site sample of 8M mammograms.
• Reflects real world rather than study population
(especially useful when evaluating harms)
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Breast Cancer Surveillance Consortium Data
• Cancer rates increase and false positive
mammogram rates decrease with age
• Number women undergoing additional imaging
and biopsy per BCa diagnosed decrease with
age
• Biopsy rates are lower in younger than older
women
• Cancer detection rates similar in US, UK,
Europe
• Rates of false positives and recall rates in the
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US at least twice rates in Canada, UK, Europe
Outcomes Table
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Incremental Benefit of Extending Screening
Age 50–69 to Age 40–69:
CISNET Models
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Meta-analysis of Screening Trials and
CISNET Modeling: Limitations
• Subgroup analysis by age excludes data
• Trials use intention-to-treat analysis and report
“number needed to invite for screening,” not
those actually screened
• Trials are only “fair-quality” due to attrition
(>30%) and contamination (>20%)
• Applicability questionable: only one U.S. study,
>20 yrs ago, prior to current diagnostic and
treatment practices
• Harms and CISNET data are for those actually
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screened
Screening Mammography:
Benefits
• Eight RCTS enrolling more than 600,000 women:
– Screening mammography reduces breast
cancer mortality.
– Observed mortality reduction is ~ 15% (0 to
30%, with better designed trials – i.e., less
biased mortality ascertainment and
randomization)
• Effects on all-cause mortality are unknown.
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Screening Mammography:
Harms
• Overdiagnosis (screen detection and
subsequent treatment of breast cancer that
never would have surfaced clinically)
– Extent of overdiagnosis difficult to estimate,
requiring life-long f/u of screened and
unscreened cohorts
– Best estimate 2%-10%, with higher estimates
in more rigorous studies (i.e., up to 18% of
screen detected breast cancers would never
surface clinically)
• False positive mammograms resulting in
unnecessary biopsies, anxiety and expense
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USPSTF Screening Mammography:
Benefits vs. Harms Beginning Age 40 vs. Age 50
Benefit
Harms
Magnitude
Very Large
Very small – moderate
Frequency
Rare
< 1:1,000
Very common
10–50/1,000
Timing
Late
Early
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Summary of Evidence
• Primary evidence is not changing (and likely will
not change, given no active prospective trials)
• Interpretation of evidence is changing, but
slowly (as usual)
– Benefits are modest
– Consensus benefits of mammography
outweigh harms in women ages 50–69
– Disagreement RE: frequency (annual vs.
biennial)
– Disagreement RE: screening ages 40–49
– Disagreement RE: when to discontinue
screening (age 74; age 79; never)
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Evidence Limitations:
Why there is so much disagreement
Data limitations
• RCTs comparing start ages 40 vs. 50
inadequate power and f/u duration
• No RCTs RE: screening frequency (and
unlikely to be conducted)
Cultural limitations
• Harms difficult for many people to grasp
• Bias toward inherent belief in earlier detection,
regardless of impact on outcome
• Misinformation (incidence, prevalence,
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benefits, harms)
Evidence Limitations:
Why there is so much disagreement
Evidence based medicine is not value free:
• Harms and benefits involve comparison of
dissimilar outcomes
• Subjective expertise – just locus of control
shifted from physician to methodologist
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"What we've got here
is a failure to communicate”
Paul Newman
Cool Hand Luke, 1967
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The decision to start…should be an individualized
one and take patient context into account,
including the patient’s values regarding specific
benefits and harms.”
(C recommendation)
Moderate certainty that the net benefit is small
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“In the midst of every challenge
lies opportunity”
-Albert Einstein
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