thomas b. newman, md, mph andi marmor, md, msed october 18, 2007
TRANSCRIPT
Outline
Overview and definitions Observational studies of screening Randomized trials of screening Conclusion – ecologic view
What is screening?
Common definition: “Testing to detect asymptomatic disease”
Better definition*: “Application of a test to detect a potential disease
or condition in people with no known signs or symptoms of that disease or condition”
*Common screening tests. David M. Eddy, editor. Philadelphia, PA: American College of Physicians, 1991
What is screening?
Common definition: “Testing to detect asymptomatic disease”
Better definition*: “Application of a test to detect a potential disease
or condition in people with no known signs or symptoms of that disease or condition”
*Common screening tests. David M. Eddy, editor. Philadelphia, PA: American College of Physicians, 1991
What is screening?
Common definition: “Testing to detect asymptomatic disease”
Better definition*: “Application of a test to detect a potential disease
or condition in people with no known signs or symptoms of that disease or condition”
“ Condition” includes a risk factor for a disease…
*Common screening tests. David M. Eddy, editor. Philadelphia, PA: American College of Physicians, 1991
Screening Spectrum
Risk factor
Recognized symptomatic disease
Presymptomatic disease
Unrecognized symptomatic disease
Fewer people recognized and treated Easier to demonstrate benefit Less potential for harm
Examples of Screening Along the Spectrum Risk factor for disease:
Hypercholesterolemia, hypertension Presymptomatic disease:
Neonatal hypothyroidism, syphilis, HIV Unrecognized symptomatic disease:
Vision and hearing problems in young children; iron deficiency anemia, depression
Somewhere in between?: Prostate cancer, breast carcinoma in situ, more
severe hypertension
Screening for risk factors Relationship between risk factor, disease and
treatment difficult to establishDoes test predict disease?Does treatment of risk factor reduce disease?Does treatment reduce risk factor? (eg: CAST)
Measures of test accuracy apply to disease that is prevalent at the time the test is done
With risk factors, trying to measure incidence of disease over time
Potential for harm greatest when screening for risk factors!
Goals of Screening for Presymptomatic Disease Detect disease in earlier stage than would
be detected by symptomsOnly possible if an early detectable phase is
presentOnly beneficial if earlier treatment is more
effective than later treatment Do this without incurring harm to the
patientNet benefit must exceed net harmLong follow up and randomized trial may be
needed to prove this
Screening for Cancer Natural history heterogeneous
Screening test may pick up slower growing or less aggressive cancers
Not all patients diagnosed with cancer will become symptomatic
Diagnosis is subjectiveThere is no gold standard
Possible harms from screening To all To those with negative results To those with positive results To those not tested
Public Health Threats from Excessive Screening “When your only tool is a hammer, you
tend to see every problem as a nail.”Abraham
Maslow
Interventions aimed at individuals are overemphasized
Biggest threats are public health threats Biggest gains in longevity have been
PUBLIC HEALTH interventions
Top Ten Countries’ Per Capita Healthcare Spending, 1997 ($)
0 1000 2000 3000 4000 5000
Norway
Netherlands
Denmark
Iceland
France
Canada
Germany
Luxembourg
Switzerland
United States
Anderson GF and Poullier JP Health Affairs 18;178-88 May/June 1999
Potential Years of Life Lost*/100,000 population, top 10 spending Countries, 1995
0 2000 4000 6000 8000 10000
Norway
Netherlands
Denmark
Iceland
France
Canada
Germany
Luxembourg
Switzerland
United States
Male
Female
Before age 70. From Anderson GF and Poullier JP Health Affairs 18;178-88 May/June 1999
Economic and Political Forces behind excessive screening Companies selling machines to do the
test Companies selling the test itself Companies selling products to treat the
condition Managed care organizations Politicians who are (or want to appear)
sympathetic
Copyright restrictions may apply.Schwartz, L. M. et al. JAMA 2004;291:71-78.
Screening as an Obligation
Cultural characteristics "We live in a wasteful, technology
driven, individualistic and death-denying culture.“ George Annas, New Engl J Med, 1995
E-mail Excerpt
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…Insist on the CA-125 BLOOD TEST; DO
NOT take "NO" for an answer!
Evaluating Studies of Screening Ideal Study:
Randomized to screen/control Compares outcomes in ENTIRE screened group to
ENTIRE unscreened group Observational studies
Compare outcomes in screened patients vs unscreened (not randomized)
Among patients with disease, compare outcomes among those dx by screening vs those dx by symptoms
Screened
Not screened
Survival from Randomization
R
Diagnosed by symptoms
Diagnosed by screening
Not screened
Screened
Survival after Diagnosis
D+
D-
D-
D-
D-D+
Patients with Disease
D+
D+
R
Survival after Diagnosis
Survival from Randomization
Survival from Enrollment
Survival from Enrollment
Patients with Disease Not screened
ScreenedSurvival after
Diagnosis
Survival after Diagnosis
Biases in Observational Studies of Screening Tests Volunteer bias Lead time bias Length bias Stage migration bias Pseudodisease
Volunteer Bias People who volunteer for studies differ from
those who do not Examples
HIP Mammography study: ○ Women who volunteered for mammography had lower
heart disease death ratesCoronary drug project:
○ RCT of medications for secondary prevention of CAD○ Men who took their medicine (drug or placebo!) had
half the mortality of men who didn't Can occur in any non-randomized trial of
screening
Avoiding Volunteer Bias
Randomize patients to screened and unscreened groups
Control for factors which might be associated with both receiving screening AND the outcome eg: family history, level of health concern,
other health behaviors
Lead Time Bias (zero-time bias) Screening identifies disease during a
latent period before it becomes symptomatic
If survival is measured from time of diagnosis, screening will always improve survival even if treatment is ineffective
Latent Phase
Onset of symptoms DeathDetectable by screening
Detected by screening
Biological Onset
Survival After Diagnosis
Survival After Diagnosis
Lead Time
Lead Time Bias
Contribution of lead time to survival measured from diagnosis
Avoiding Lead Time Bias
Only present when survival from diagnosis is compared between diseased personsScreened vs not screened Diagnosed by screening vs by symptoms
Avoiding lead time biasMeasure survival from time of randomization
How Much Lead Time is Present? Depends on relative lengths of latent phase
(LP) and screening interval (S) Screening interval shorter than LP:
Maximum false increase in survival = LPMinimum = LP – S
Screening interval longer than LP: Max = LPProportion of disease dx by screening = LP/S
Figure 2: Maximum lead time bias possible when screening interval is longer than latent phase
Max = LPProportion of disease diagnosed by screening: P = LP/S
SLP
Max
Screen ScreenScreen
Length Bias (Different Natural History Bias) If disease is heterogeneous:
Slowly progressive : more time in presymptomatic phase
Cases picked up by screening disproportionately those that are slowly developing
Higher proportion of less aggressive disease in group detected by screening creates appearance of reduced mortality even if treatment is ineffective
Avoiding Length Bias Only present when survival from diagnosis is
compared between diseased persons AND disease is heterogeneous Lead time bias usually present as well Avoiding length bias:
Compare mortality in the ENTIRE screened group to the ENTIRE unscreened group
Stage Migration Bias Also called the "Will Rogers Phenomenon"
"When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states."
Described by Feinstein and colleagues (1985) as an explanation for lower stage-specific survival in a 1954 cohort of patients with lung cancer in comparison to a 1977 cohort
New technologies resulted in the 1977 group diagnosed with more advanced lung cancer
Stage Migration Bias
Stage 1
Stage 2
Stage 3
Stage 4
Stage 0Stage 0
Stage 2
Stage 3
Stage 4
Stage 1
Old test New test
A Non-Cancer Example “Infants in each of 3 birthweight strata
(VLBW, LBW and NBW) who are exposed to Factor X have decreased mortality compared with unexposed weight-matched infants”
Is factor X beneficial? Maybe not! Factor X could be cigarette
smoking! Smoking moves otherwise healthy babies to
lower birthweight group, improving mortality in each group
Other Examples Abound… The more you look for disease, and the
more advanced the technologythe higher the prevalence, the higher the
stage, and the better the (apparent) outcome for the stage
Beware of stage migration in any stratified analysisCheck OVERALL survival in screened vs
unscreened group
Pseudodisease
A condition that looks just like the disease, but never would have bothered the patientType I: Indolent forms of disease which would
never cause symptomsType II: Preclinical disease in people who will
die from another cause before disease presents
The Problem:Treating pseudodisease can only cause harm
Analogy to Double Gold Standard Bias Screening (test) result negative
Clinical FU (first gold standard)
Screening (test) result positive Biopsy (2nd gold standard)
If pseudodisease existsSensitivity (true positive rate) of screening
falsely increasedScreening will also prolong survival among
diseased individuals
Example: Mayo Lung Project RCT of lung cancer screening 9,211 male smokers randomized to two
study armsIntervention: CXR and sputum cytology every
4 months for 6 years (75% compliance)Usual care: recommendation to receive
same tests annually
*Marcus et al., JNCI 2000;92:1308-16
MLP Extended Follow-up Results Among those with lung cancer, intervention
group had more cancers diagnosed at early stage and better survival
Marcus et al., JNCI 2000;92:1308-16
MLP Extended Follow-up Results Intervention group: slight increase in lung-cancer
mortality (P=0.09 by 1996)
Marcus et al., JNCI 2000;92:1308-16
What happened?
After 20 years of follow up, there was a significant increase (29%) in the total number of lung cancers in the screened groupExcess of tumors in early stageNo decrease in late stage tumors
Overdiagnosis (pseudodisease)
Black, cause of confusion and harm in cancer screening. JNCI 2000;92:1280-1
Looking for Pseudodisease Impossible to distinguish from successfully
treated asymptomatic disease in individual patientVery few compelling stories describe patients or
physician’s victories over pseudodisease… Appreciate the varying natural history of
disease, and limits of diagnosis Clues to pseudodisease:
Higher cumulative incidence of disease in screened group
No difference in overall mortality between screened and unscreened groups
Diagnosed by symptoms
Diagnosed by screening
Not screened
Screened
Survival after Diagnosis
D-
D-
Patients with Disease
D+
D+
R
Survival after Diagnosis
Survival from Enrollment
Survival from Enrollment
Screened
Not screened
Survival from Randomization
R
D+D-
D-D+ Survival from
Randomization
Issues with RCTs of Cancer Screening
Quality of randomization
Cause-specific vs total mortality
Poor Quality Randomization Edinburgh mammography trial Randomization by healthcare practice 7 practices changed allocation status Highest SES
26% of women in control group53% of women in screening group
26% reduction in cardiovascular mortality in mammography group
Cause-Specific Mortality
Problems:Assignment of cause of death is subjective Screening or treatment may have important
effects on other causes of death
Bias introduced can make screening appear better or worse!
Example Meta-analysis of 40 RCT’s of radiation
therapy for early breast cancer (N = 20,000)*Breast cancer mortality reduced (20-yr ARR
4.8%; P = .0001)BUT mortality from “other causes” increased
(20-yr ARR -4.3%; P = 0.003)
Were these additional deaths actually due to screening
*Early Breast Cancer Trialists Collaborative Group. Lancet 2000;355:1757
Biases in Cause-Specific Mortality “Sticky diagnosis” bias:
If cancer diagnosis made, deaths of unclear cause more often attributed to cancer
Effect: overestimates cancer mortality in screened group
“Slippery linkage” bias: Linkage lost between death and cancer
diagnosis (eg: due to screening or treatment) Death less likely counted in cause specific
mortalityEffect: underestimates cancer mortality in
screened group
The truth about total mortality
Mortality from other causes generally exceeds screening or cancer-related mortality
Effect on condition of interest more difficult to detect
Total mortality more important for some screening tests than others…
Conclusions -1
Promotion of screening by entities with a vested interest and public enthusiasm for screening are challenges to EBM
High-quality RCT’s are needed Attention to study design, size of effect
and unmeasured costs
Conclusions - 2
Dysfunctional metaphors for health care * Military metaphor – battle disease, no cost too
high for victory, no room for uncertaintyMarket metaphor -- medicine as a business;
health care as a product; success measured economically
Reframing of priorities is needed
*Annas G. Reframing the debate on health care reform by replacing our metaphors. NEJM 1995;332:744-7
Reframing Priorities:Ecology Metaphor Sustainability Limited resources Interconnectedness More critical of technology Move away from domination, buying,
selling, exploiting Focus on the big picture
Populations rather than individualsCauses rather than symptoms