Screening for Disease
Sponsored Links
This presentation is the property of its rightful owner.
1 / 42

An Ounce of Prevention is Worth a Pound of Cure. PowerPoint PPT Presentation


  • 149 Views
  • Uploaded on
  • Presentation posted in: General

Screening for Disease. An Ounce of Prevention is Worth a Pound of Cure. An Ounce of Prevention is Better Than a Pound of Cure. Actually,…. If prevention hasn ’ t been effective, perhaps early identification of disease by a screening test would be beneficial. Disease detectable

Download Presentation

An Ounce of Prevention is Worth a Pound of Cure.

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Screening for Disease

An Ounce of Prevention is Worth a Pound of Cure.

An Ounce of Prevention is Better Than a Pound of Cure.

Actually,…

If prevention hasn’t been effective,

perhaps early identification of disease

by a screening test would be beneficial.


Disease

detectable

by screen

DPCP

(Detectable

pre-clinical

phase)

Death with

screening &

early treatment

A Hypothetical Time Line for Disease

Death

without

screening

& early Rx

Biologic

onset of

disease

Symptoms

develop

Birth

With screening, early diagnosis and treatment may result in longer survival, less disability, decreased recurrence, etc.


You can think about these as screening tests that hopefully enable you to identify disease at an early stage:

Self breast exam

Clinical Breast exam

Mammography

Digital rectal exam

PSA

Dip stick test for sugar

“Routine” blood tests

Routine EKG or “stress test”

Skin test for TB

Characteristics of a good screening test?


Characteristics of a Good Screening Test

  • Inexpensive (dip stick for diabetes vs. MRI for brain tumor)

  • Easy to administer

  • Minimal discomfort

  • Reliable: The test gives the same result each time.

  • High Test Accuracy (Test Validity) – Test results

  • accurately identify diseased & non-diseased people?


Sources of Variability (Error)

BP = 165/95

Observation (Test) Variability:

  • Within-an-observer: Consistency when a single observer performs repeated measurements.

  • Between-observers: Similarity of values when different observers perform measurements on a set of subjects.

  • Within-an-instrument: Consistent measurements from a single instrument give.

  • Between-instruments: Do different instruments give consistent measurements?


Other Sources of Variability

Biological (Subject) Variability:

  • Within-subject: Is the measurement the same over time?

    • Did he just walk up the stairs?

    • Did he just smoke a cigarette?

    • Is he stressed out?

    • Does he have “white coat” syndrome?

    • Is he over weight?

  • Between-subjects: How much variability is there from subject to subject?


    • Even if the PSA test is:

    • Precise (consistent measurements with repeated tests) &

    • Accurate (close to his true PSA level)…

      … how good is the PSA test with respect to determining whether he has prostate cancer or not? (Test validity)


    You have prostate cancer.

    PSA=10

    Is it possible he has been misclassified?


    What do we do if PSA=5.6?

    Measurement

    NCI Risk Classification


    Does the test accurately distinguish healthy & diseased people?

    The ideal is to have a test that is exquisitely sensitive & specific….

    30

    Test -

    Test +

    20

    Men without cancer

    # with a given test result

    10

    Men with

    cancer

    0

    1 5 10 15

    PSA (Prostate-specific Antigen) Values


    The reality is that test values from diseased and non-diseased people often overlap….

    No cancer

    30

    # of men

    with a given

    test result

    20

    Men with

    prostate cancer

    10

    0

    1 5 10 15

    PSA (Prostate-specific Antigen) Values

    What if >5 is deemed abnormal? Is the test valid?


    … but maybe not as much overlap as in the previous example.

    No cancer

    30

    # of men

    with a given

    test result

    20

    Men with

    prostate cancer

    10

    0

    1 5 10 15

    PSA (prostate specific antigen)

    How can we think about test validity in a structured way?


    Men with a Variety of PSA Test Results

    1

    1

    1

    1

    1

    1

    1

    1

    1

    These have low probability of having cancer.

    1

    1

    1

    1

    1

    1

    1

    1

    1

    1

    2

    2

    2

    2

    2

    2

    2

    2

    4

    4

    4

    4

    4

    4

    4

    4

    4

    10

    10

    These have a higher probability of having cancer.

    10

    10

    10

    10

    10

    10


    9

    9

    10

    10

    8

    8

    6

    10

    10

    10

    10

    10

    10

    10

    10

    9

    9

    1

    1

    2

    2

    1

    1

    1

    1

    1

    1

    1

    1

    1

    1

    1

    1

    2

    1

    1

    2

    1

    1

    2

    4

    2

    4

    6

    1

    4

    4

    1

    2

    2

    4

    1

    1

    4

    4

    1

    1

    2

    2

    4

    4

    4

    1

    1

    2

    1

    2

    2

    1

    1

    4

    4

    2

    1

    4

    1

    1

    2

    4

    2

    .

    Of those who were diseased, what was the probability (%) of correctly being identified by a + screening test?

    Of those who were NOT diseased, what was the probability (%) of correctly having a (-) screening test?

    If you screened (+), what was the probability that you had cancer?

    Test +

    If you screened (-), what was the probability that you did NOT have cancer?

    Test -


    Measures of Test Validity

    Does the test accurately distinguish between healthy and diseased people?

    Two Perspectives

    {

    Sensitivity: probability that diseased people test +

    Specificity: probability that non-diseased people test -

    Probability

    of correct test

    (screening)

    {

    Predictive value (+): if someone has a + test, what is the probability that they actuallyhavethe disease?

    Predictive value (-): if someone has a - test, what is the probability that they don’t have the disease?

    Probability

    of disease


    A Different Type of 2x2 Table

    True disease status

    Diseased

    Not Diseased

    Positive

    Negative

    a

    b

    Test Results

    c

    d


    Sensitivity

    Among those who really had disease, the probability that the test correctly identified .

    Not Diseased

    Diseased

    Test Positive

    Test Negative

    True + ?

    a

    b

    c

    d

    Total diseased

    = a+c

    a

    a+c

    Sensitivity=


    Sensitivity

    Among those who really had disease, the probability that the test correctly identified them as positive.

    Not Diseased

    Diseased

    Test Positive

    Test Negative

    132

    983

    True Positive

    a

    b

    45

    63,650

    c

    d

    177 64,633

    132

    177

    Sensitivity =

    =74.6%


    Specificity

    d

    b+d

    Among those who really did NOT have disease, the probability that the test correctly identified them as (-).

    Not Diseased

    Diseased

    Test Positive

    Test Negative

    132

    983

    True Positive

    a

    b

    True Negative

    45

    63,650

    c

    d

    177 64,633

    63,650

    64,633

    =

    Specificity =

    =98.5%


    Very specific

    Very sensitive

    The Trade-Off: Sensitivity Versus Specificity

    The ideal is to have a test that is exquisitely sensitive & highly specific, but this frequently isn’t the case.

    No cancer

    30

    20

    # with a given test result

    Men with

    cancer

    10

    0

    1 5 10 15

    If PSA values for men with prostate cancer overlap those of men without cancer, what do you use as the criterion for “abnormal”?


    We’ve been looking at the performance of the test (its validity) by asking:

    Among people who truly have the disease, what is the probability that the test will identify it them as diseased?

    and

    Among people who don’t have the disease, what is the probability that the test will categorize them as non-diseased?


    Another Perspective

    If I have a test done and it is positive (abnormal), what is the probability that I really have the disease?

    If I have a test done and it is negative (normal), what is the probability that I really don’t have the disease?


    2) If the test was negative,

    how likely is it that he really does NOT have it?

    [How reassured should he be?]

    -

    When thinking about

    Predictive Value of a Test, ...

    … imagine you are a physician discussing

    the results of a screening test with a patient.

    • If the test was positive,

    • how likely is it that he really has the disease?

    • [How worried should he be?]

    +


    Positive

    Predictive Value

    Among men with positive test results, what is the probability that they have disease?

    Diseased

    Not Diseased

    Test Positive

    132 983 1,115

    45 63,650 63,695

    177 64,633 64,810

    a

    b

    Test Negative

    c

    d

    Predictive value (+) = a = 132 = 11.8%

    a+b 1,115


    Negative

    Predictive Value

    Among men with negative test results, what is the probability that they DON’T have disease?

    Diseased

    Not Diseased

    132 983 1,115

    45 63,650 63,695

    177 64,633 64,810

    Test Positive

    a

    b

    Test Negative

    c

    d

    Predictive value (-) = d = 63,650 = 99.9%

    c+d 63,695


    What is the prevalence of HIV in this population?

    True Disease Status

    HIV+ HIV -

    10 510 520

    0 99,480 99,480

    10 99,990 100,000

    Positive

    Negative

    Test

    Prevalence = 10/100,000 = 0.0001= 0.01%


    Sensitivity=100%

    Specificity=99.5%

    True Disease Status

    HIV+ HIV -

    10 510 520

    0 99,480 99,480

    10 99,990 100,000

    Positive

    Negative

    Test

    Prevalence in female

    blood donors=0.01%

    Predictive value (+) = a = 10 = 1.9%

    a+b 520


    Sensitivity=100%

    Specificity=99.5%

    True Disease Status

    HIV+ HIV -

    4,000 480 4,480

    0 95,520 95,520

    4,000 96,000 100,000

    Positive

    Negative

    Test

    Prevalence in males

    at an STD clinic=4%

    Predictive value (+) = a = 4,000 = 83%

    a+b 4,480


    NOTE: Predictive value is GREATLY influenced by the prevalence of disease in the population being screened.

    Sensitivity=100%

    Specificity=99.5%

    True Disease Status

    HIV+ HIV -

    20,000 400 20,400

    0 79,600 79,600

    20,000 80,000 100,000

    Positive

    Negative

    Test

    Prevalence in IV drug users=20%

    Predictive value (+) = a = 20,000 = 98%

    a+b 20,400


    True Positives

    False Positives

    False Negatives

    True Negatives

    What About Cells “b” and “c” ???

    True disease status

    Diseased Not Diseased

    132 983 1,115

    45 63,650 63,695

    177 64,633 64,810

    Positive

    Negative

    b

    a

    Test

    d

    c


    Hazards of Screening

    True disease status

    Diseased Not Diseased

    132 983 1,115

    45 63,650 63,695

    177 64,633 64,810

    Positive

    Negative

    False Positives: worry, cost, complications, risk, of diagnostic tests

    Test

    False Negatives: false reassurance


    What Should We Screen For?

    • Periodic Pap smears for cancer of cervix?

    • Annual chest x-ray for lung cancer?

    • Annual ultrasound for gallstones?

    • Should everyone be tested for HIV?

    • Fecal occult blood testing to screen for colorectal cancer?

      • Annual colonoscopy for all adults?


    What Diseases are Appropriate for Screening?

    • 1) Serious disease. (Cervical cancer vs. gallstones)

    • 2) When treatment before symptoms is better than treatment after symptoms appear. (e.g. cervical cancer.)

      • Need a detectable pre-clinical phase (DPCP)

      • Presumes existence of an effective test

  • 3) High prevalence of undiagnosed disease in the DPCP. (Prevalence of HIV in couples applying for a marriage certificate is extremely low.)

  • High blood pressure leads to kidney damage, atherosclerosis, & stroke. It has a long DPCP and can be effectively treated with diet and medication. Undiagnosed HBP is very common.


    How Do We Assess Screening Programs?(Feasibility and Effectiveness)

    • Follow-up of those who test positive (positive predictive value)

    • Assessment of cost per case detected

    • Compare outcome measures to assess effectiveness in screened vs. unscreened groups (RCT is best):

      • Overall mortality rates

      • Disease-specific mortality rates


    Evaluating a Screening Program

    • If one compares survival time in cancer patients identified by screening to those identified clinically, biases can occur.

    • Those identified by screening may appear to have longer survival times because of:

      • Self-selection bias

      • Lead time bias

      • Length time bias


    Self-Selection Bias (Volunteer Bias)

    • People who choose to participate in screening programs:

      • tend to be healthier and have lower mortality rates

      • tend to adhere to therapy better

      • but, may also represent the “worried well”,

      • people who are asymptomatic, but at higher risk

      • (e.g. breast cancer)


    Lead Time Bias

    Lead time is a good thing!

    But, it exaggerates the benefit of screening.

    Survival time with screening

    DPCP

    Death with

    screening

    Biologic

    onset of

    disease

    Disease

    detectable

    by screen

    Survival time without screening

    • How do the survival times compare (from diagnosis to death)?

    • By how much was life extended?

    DPCP

    Death without

    screening


    Lead Time Bias

    Survival time with screening

    DPCP

    Death with

    screening

    Biologic

    onset of

    disease

    Actual increase

    in survival

    Disease

    detectable

    by screen

    Survival time without screening

    DPCP

    Death without

    screening


    Length Time Bias

    Note that the length of the DPCP varies from person to person.

    Some prostate cancers

    are biologically aggressive and have short DPCPs.

    What does a short DPCP mean?

    … others are slower growing

    and have longer DPCPs.


    Length Time Bias

    Screening has a better chance of detecting those with a long detectable pre-clinical phase, e.g. less aggressive tumors with more favorable prognosis.

    Screening

    No Screening

    Mean DPCP of unscreened: 6 yrs

    Mean survival: 4 yrs

    Mean DPCP of screen +: 8 yrs

    Mean survival of screen +: 6 yrs


    Evaluating a Screening Program

    • Correlational studies

    • Case-control studies

    • Cohort studies

    • Randomized clinical trials*


  • Login