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EPI-820 Evidence-Based Medicine

LECTURE 5: SCREENING

Mat Reeves BVSc, PhD

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Objectives

• Two components of screening (Dx & Tx).• Determinants of pre-clinical phase• Concept of lead time • Characteristics of screening tests (Se, Sp,

yield) • Biases - lead time, selection, length-biased• The only valid measures of screening efficacy• Evaluations and criteria for screening

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I. Introduction

• Objective: to reduce mortality and/or morbidity by early detection and treatment.

• Secondary prevention.

• Asymptomatic individuals are classified as either unlikely or possibly having disease.

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Introduction

• Screening involves both diagnostic and treatment components

• Screening differs from diagnostic testing:

Screening Testing Healthy non-patients Sick patients

No diagnostic intent Diagnostic intent

Very low to low disease prevalence

Low to high disease prevalence

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Screening - Introduction

• There are two forms of screening which involve fundamentally different formats, organization and intent:

• Mass or population-based screening

• Case finding

• This lecture covers mass screening for non-infectious (chronic) diseases only.

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II. Characteristics of Disease

The Pre-Clinical Phase (PCP)• the period between when early detection by

screening is possible and when the clinical diagnosis would usually be made.

Pathology begins

Disease detectable Normal Clinical Presentation

Pre-Clinical Phase

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Prevalence of Pre-clinical disease

• A critical determinant of the potential utility of screening.

• Prevalence is affected by:• disease incidence• average duration of the PCP • previous screening • sensitivity of the test

• Example • long PCP= Colorectal cancer• short PCP = childhood diabetes

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Lead Time

Lead time = amount of time by which diagnosis is advanced or made earlier

Pathology begins

Disease detectable Normal Clinical Presentation

Lead Time

Screen

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a) No screening - no lead time

Clinical phase

Preclinical phase

5 cases developing over 5 years

0 1 2 3 4 5

Years

Relationship between screening, pre-clinical phase, clinical phase and lead time

b) Screening with resultant lead time

Lead time

Preclinical phase

Clinical phase

Screen

5 cases, 3 identified by screening

0 1 2 3 4 5

Years

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Lead Time

• Equals the amount of time by which treatment is advanced or made “early”

• Not a theory or statistical artifact but what is expected and must occur with early detection

• Does not imply improved outcome

• Necessary but not sufficient condition for effective screening.

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Lead Time

• Impossible to determine lead time for any individual, can only compare distribution between screened and non-screened populations (RCT)

• Knowledge of expected lead time useful to:• Indicates amount of time diagnosis and treatment must be

advanced• Determine frequency of screening

• Example lead times:• Mam screening women 40-49 = 1-2 years• Mam screening women 50-69 = 3-4 years• Invasive Colo-rect cancer = 7-10 years

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Example of Estimation of Lead Time Distributions

within an Screening RCT

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0 1 2 3 4 5

Screened Group

Non-screened Group

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Cas

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Years after Screening

Total lead time = 5 + 4 + 5 + 2.5 + 2 = 18.5 years Average lead time = 18.5/ 9 = 2.05 years

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IV. Characteristics of screening tests

a) Sensitivity (Se)

• Definition: the proportion of cases with a positive screening test among all individuals with pre-clinical disease

• Influences:– the prevalence of pre-clinical disease – the distribution of lead times

• Concept of the Sensitivity Function– Average probability of detection for cases a certain time away

from clinical diagnosis

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Issues Related to Determining Sensitivity

• Determining the denominator• who are the false negatives? (FNs)• Cannot justify full work-up of negative test results• Verification bias• FN’s estimated by counting number of interval cases

• Spectrum bias • Se varies with spectrum of disease

• Screening intensity• No previous screening = more advanced PCP = higher Se• Se decreases with repeat screening

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IV. Characteristics of screening tests

• b) Specificity (Sp)• Definition: the proportion of individuals with a negative screening

test result among all individuals with no pre-clinical disease

• Imperfect Sp affects many (the healthy), imperfect Se affects few (the sick)

• Screening PVP usually low because prevalence of PCP is low

• Influences:– the number of false-positive test results – the PVP and thereby the feasibility and efficiency of the screening

program

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IV. Characteristics of screening tests

c) Yield• Definition: the amount of previously unrecognized

disease that is diagnosed and treated as a result of screening.

• Another measure of screening efficiency .

• Influenced by:– Se – Prevalence of PCP

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V. Effects of screening on disease incidence

Inc

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ate

(c

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Time

No screening

Time

No screening

Cu

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ses p

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Detected Prevalence

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V. Effects of screening on disease mortality

Mo

rta

lity

ra

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(d

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TimeScreen

Postponement

Mortality rate if no screening

Mo

rtality r

ate (

dea

th

s p

er p

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n-y

ear)

TimeScreen

Cure

Mortality rate if no screening

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VI. Evaluation of Screening Outcomes

a) Study Designs• RCT

• Compare disease-specific cumulative mortality rate between those randomized (or not) to screening

• Eliminates confounding and lead time bias• But, problems of:

– Expense, time consuming, logistically difficult, ethical concerns, changing technology.

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VI. Evaluation of Screening Outcomes

a) Study Designs• Observational Studies

• Cohort:– Compare disease-specific cumulative mortality rate

between those who choose (or not) to be screened

• Case-control:– Compare screening history between those with

advanced disease (or death) and healthy.

• Ecological:– Compare screening patterns and disease experience

(both incidence and mortality) between populations

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Problems with Observational Studies

• Confounding due to health awareness - screenees are more healthy (selection bias)

• More susceptible to effects of lead-time bias and length-biased sampling

• Poor quality, often retrospective data

• CCS: Difficult to determine appropriate control group

• Difficult to distinguish screening from diagnostic examinations

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b) Measures of Effect of Screening

1) Disease-specific Mortality Rate (MR)

the number of deaths due to disease Total person-years experience

• The only gold-standard outcome measure for screening

• NOT affected by lead time, • when calculated from a RCT - not affected by

selection bias or length-biased sampling.

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Effects of screening on disease survival - I

a) No screening

1,000 population, followed for 5 years, 5 cases

0 1 2 3 4 5

Years

Av. duration of survival= 6.5/5 = 1.3 yrsMR= 5/[(995X5)+5+4+3+2+2] x 100,000 = 5/ 4991 x 100,000 = 100/100,000 py's

Clinical phase

Preclinical phase

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Effects of screening on disease survival - II

b) Screening with no reduction in MR1,000 population, followed for 5 years, 5 cases

0 1 2 3 4 5

Years

Av. duration of survival= 12.5/5 = 2.5 yrsMR= 5/[(995X5)+5+4+3+2+2] x 100,000 = 100/100,000 py's

Lead time

Preclinical phase

Clinical phase

Screen

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Effects of screening on disease survival - III

c) Screening with reduction in MR

1,000 population, followed for 5 years, 5 cases

0 1 2 3 4 5

Years

Av. duration of survival= 12.5/5 = 2.5 yrsMR= 4/[(995X5)+5+4+3+2+2] x 100,000 = 4/4991 x 100,000 = 80/100,000 py's

Lead time

Preclinical phase

Clinical phase

Screen

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Biases that effect survival duration

• The efficacy of screening cannot be assessed by comparing survival rates (or CFR) because:

• Selection bias:– Volunteers are more healthy

• Lead-time bias– Introduced into survival experience of screen detected cases

• Length-biased sampling– Screening preferentially identified slower growing or less

progressive cases with a better prognosis

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Length-biased Sampling

SCREENING

DX

DX

DX

DX

X

X

X

X

Better Prognosis Cases

Worse Prognosis Cases

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VII. Pseudo-disease and Over-diagnosis

• Over-diagnosis• Limited malignant potential• Extreme form of length-biased sampling• Examp: Ductal-carcinoma in-situ

• Competing risks• Cases detected that would have been interrupted by an unrelated

death• Examp: Prostate CA and CVD death

• Serendipity• Chance detection due to diagnostic testing for another reason • Examp: PSA and prostate CA, FOBT and CR CA

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Over-diagnosis – Effect of Mass Pap Screening in Connecticut (Laskey 1976)

Age-adj. Incidence Rate (per 100,000)

Year In-situ Invasive Total % In-situ

1950-54 3.8 18.1 21.9 17

1955-59 9.7 17.1 26.8 36

1960-64 18.8 13.6 32.4 58

1965-69 28.6 11.6 40.2 71

1970-73 32.8 10.9 43.7 75

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VIII. Assessing the feasibility of screening

• Acceptability • convenience, comfort, efficiency, economical

• Efficiency• Low PVP• Potential to reduce mortality• Effectiveness of treatment without screening• Effect of competing mortality

• Cost-effectiveness• Mam screening = $30 – 50K /YLS

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IX. Summary

• Efficacy

• Effectiveness

• Cost-effectiveness

• Should we screen? (scientific)

• Can we screen? (practical)

• Is it worth it? (scientific, practical, policy, political)

• Three questions to ask before advocating screening:

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Need and Feasibility of Screening?

• All cases of disease can be classified into three groups relevant to screening:

1. Cure is necessary but not possible (Group I)

2. Cure is possible but not necessary (Group II)

3. Cure is necessary and maybe possible (Group III = only group that could benefit!)

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Group IGroup IIGroup III

Lung Cancer?

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Group IGroup IIGroup III

Colorectal Cancer?

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Group IGroup IIGroup III

Prostate Cancer?