Thomas B. Newman, MD, MPH Andi Marmor, MD, MSEd. Outline Overview and definitions Observational studies of screening Randomized trials of screening

Thomas B. Newman, MD, MPH

Andi Marmor, MD, MSEd

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?





What is screening?




“ Condition” includes a risk factor for a disease…


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

“It’s just a simple blood test.”How can screening

be bad???

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

Ad by company that

makes the machines

Ad for:

Frosted flakes!

( no cholesterol)

Ad sponsored by the company

that makes interferon.

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

PLEASE, PLEASE, PLEASE TELL ALL YOUR FEMALE FRIENDS AND RELATIVES TO INSIST ON A CA-125 BLOOD TEST EVERY YEAR AS PART OF THEIR ANNUAL PHYSICAL EXAMS. Be forewarned that their doctors might try to talk them out of it, saying, "IT ISN'T NECESSARY."

…Insist on the CA-125 BLOOD TEST; DO

NOT take "NO" for an answer!

Source: Funny Times. (1-888-Funnytimes x 476)

Evaluating Studies of Screening

Screening test

Detect disease early

Treat disease

Patient outcome

Screening test


Treat disease

Patient outcome


Screening test


Treat disease

Patient outcome


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 from Enrollment


Patients with Disease Not screened

ScreenedSurvival 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

ScreenScreen Screen Screen

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

Screen 1 Screen 2TIME

Mortality when cancer detected by screening

Mortality when cancer detected by symptoms

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

Screened Group Prolonged survival

Better health behaviors

Volunteer Bias

Early detection Prolonged survival

Earlier “zero time”

Lead Time Bias

Early detection Higher cure rate

Slower growing tumor with better prognosis

Length Bias


Lower stage assignment

Stage Migration Bias


Pseudodisease

Overdiagnosis

Diagnosed by symptoms

Diagnosed by screening

Not screened

Screened


D-

D-

Patients with Disease

D+

D+

R




Screened

Not screened


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

Documents

Thomas B. Newman, MD, MPH Andi Marmor, MD, MSEd. Outline Overview and definitions Observational studies of screening Randomized trials of screening