Screening

1
Screening

• Principles of Epidemiology
• Lecture 12
• Dona Schneider, PhD, MPH, FACE

2
Principles Underlying Screening Programs

• Validity the ability to predict who has the
  disease and who does not
• Sensitivity the ability of a test to correctly
  identify those who have the disease
• A test with high sensitivity will have few false
  negatives
• Specificity the ability of a test to correctly
  identify those who do not have the disease
• A test that has high specificity will have few
  false positives

3
Principles Underlying Screening Programs (cont.)

• An ideal screening test would be 100 sensitive
  and 100 specific that is there would be no
  false positives and no false negatives
• In practice these are usually inversely related
• It is possible to vary the sensitivity and
  specificity by varying the level at which the
  test is considered positive

4
Calculating Measures of Validity
True Diagnosis
Disease
Total
No Disease
Test Result
ab
b
a
Positive
cd
c
d
Negative
abcd
bd
ac
Total
5
Note the Following Screening Relationships

• Specificity false positive rate 1
• d/(bd) b/(bd) 1
• If the specificity is increased, the false
  positive rate is decreased
• If the specificity is decreased, the false
  positive rate is increased
• Sensitivity false negative rate 1
• a/(ac) c/(ac) 1
• If the sensitivity is increased, the false
  negative rate is decreased
• If the sensitivity is decreased, the false
  negative rate is increased

6
Probability of Disease

• Pre-test probability of disease disease
  prevalence
• Post-test probability of disease
• If normal, c/(cd)
• If negative, a/(ab)

7
Interrelationship Between Sensitivity and
Specificity
8
Sensitivity and Specificity of a Blood Glucose
Level
Sensitivity and Specificity of a Blood Glucose
Level of 110 mg/100 ml for Presumptive
Determination of Diabetes Status
Nondiabetics (Percent)
Diabetics (Percent)
Blood Glucose Level (mg/100 ml)
51.6 (false positives)
92.9 (true positives)
All those with level over 110 mg/100 ml are
classified as diabetics
All those with level under 110 mg/100 ml are
classified as nondiabetics
48.4 (true negatives)
7.1 (false negatives)
100.0
100.0
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Adjusting Sensitivity and Specificity by
Adjusting Cut Points
10
Which is Preferred High Sensitivity orHigh
Specificity?

• If you have a fatal disease with no treatment
  (such as for early cases of AIDS), optimize
  specificity
• If you are screening to prevent transmission of a
  preventable disease (such as screening for HIV in
  blood donors), optimize sensitivity

11
Remember.

• Sensitivity and specificity are functions of the
  screening test
• If you use a given screening test on a low
  prevalence population, you will have a low
  positive predictive value and potentially many
  false positives

12
Translated into Real Life..
Elisa is about 90 sensitive and 99 specific
PV-
PV
Population
Prevalence of HIV
99.8
58
1.5
NJ (7 million)
Total
Disease No
Disease Yes
163,450
68,950
94,500
Test
Test -
6,836,550
6,826,050
10,500
7 million
6,895,000
105,000
Total
Efficiency of test (TP TN)/Total tested
98.9
But, 10,500 people who are HIV think they are
disease free
Another 68,950 are frightened into believing they
have the disease and require more testing
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If You Change To a High Risk Population, You Get
Better Results.
PV-
PV
Population
Prevalence of HIV
90.8
98.9
50
IV Drug User
Total
Disease No
Disease Yes
3,185
3,150
Test
35
3,465
350
Test -
3,185
7,000
3,500
3,500
Total
Efficiency of test (TP TN)/Total tested
94.5
Now 350 people who are HIV think they are
disease free
But only 35 are frightened into believing they
have the disease and require more testing
14
Suppose You Have a Very High Prevalence?

• HIV seropositivity is 90 among IV drug users in
  Newark
• PV 99.9
• PV- 52
• But, why bother to screen?

15
Example Breast Cancer Screening
Breast Cancer
Mammogram Results
Total
No Disease
Disease
132
Positive
1,115
983
Negative
63,695
63,650
45
64,810
64,633
177
Total
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Example Disease X (prevalence 2)
True Diagnosis of Disease X
Total
No Disease
Disease
Test Results
67
18
Positive
49
2
933
Negative
931
1000
Total
980
20
17
Example Disease X (prevalence 1)
True Diagnosis of Disease X
Total
No Disease
Disease
Test Results
58.5
49.5
9
Positive
941.5
940.5
Negative
1
1000
980
10
Total
To increase positive predictive value increase
prevalence by screening high risk populations
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Importance of Prevalence in Screening
Assume we have a test for AIDS which has a
sensitivity of 100 and a specificity of 99.995.
We wish to apply it to female blood donors who
have an HIV prevalence of 0.01 and we wish to
apply it to male homosexuals in San Francisco, in
whom the prevalence is 50. For every 100,000
screened we find
True Diagnosis of HIV
Female Donors
Total
No Disease
Disease
Test Results
15
5
10
Positive
99,985
99,985
0
Negative
100,000
99.990
10
Total
PV 0.66667
True Diagnosis of HIV
Male Homosexuals
Total
No Disease
Disease
50,003
3
50,000
Positive
49,997
49,997
0
Negative
100,000
50,000
50,000
Total
PV 0.99994
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Relationship of Specificity to Predictive Value
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Suppose You Are Faced With the Following Brain
Teaser

• In a given population of 1,000 persons, the
  prevalence of Disease X is 10. You have a
  screening test that is 95 sensitive and 90
  specific.
• What is the positive predictive value?
• What is the efficiency of the test?

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Suppose You Are Faced With the Following Brain
Teaser (cont.)

• Set up a 2x2 table

True Diagnosis of Disease X
No Disease
Disease
Test Results
Total
Positive
False Positive
True Positive
Negative
True Negative
False Negative
900
100
Total
1000
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Suppose You Are Faced With the Following Brain
Teaser (cont.)
True Diagnosis of Disease X
Test Results
Total
No Disease
Disease
185
90
Positive
95
Negative
815
810
5
Total
900
100
1000
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Principles Underlying Screening Programs

• Reliability the ability of a test to give
  consistent results when performed more than once
  on the same individual under the same conditions
• Variation in the method due to variability of
  test chemicals or fluctuation in the item
  measured (e.g., diurnal variation in body
  temperature or in relation to meals)
• Standardize fluctuating variables
• Use standards in laboratory tests, run multiple
  samples whenever possible
• Observer variation
• Train observers
• Use more than one observer and have them check
  each other

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Principles Underlying Screening Programs

• Yield the amount of previously unrecognized
  disease that is diagnosed and brought to
  treatment as a result of the screening program
• Sensitivity
• You must detect a sufficient population of
  disease to be useful
• Prevalence of unrecognized disease
• Screen high risk populations
• Frequency of screening
• Screening on a one time basis does not allow for
  the natural history of the disease, differences
  in individual risk, or differences in onset
• Diseases have lead time
• Participation and follow-up
• Tests unacceptable to those targeted for
  screening will not be utilized

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Conditions for Establishing Screening Programs

• The condition should be an important health
  problem
• There should be an accepted treatment for
  patients with recognized disease
• If there is no treatment, it is premature to
  institute screening
• Facilities for diagnosis and treatment should be
  available
• It is unethical to screen without providing
  possibilities for follow-up
• There should be a recognizable latent or early
  symptomatic stage
• If early detection does not improve survival,
  there is no benefit from screening

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Conditions for Establishing Screening Programs
(cont.)

• There should be a suitable test for examination,
  with sufficient sensitivity and specificity to be
  of use in identifying new cases
• The test should be acceptable to the population
• The natural history of the condition, including
  development from latent to declared disease,
  should be adequately understood
• There should be an agreed-upon policy concerning
  whom to treat as patients

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Conditions for Establishing Screening Programs
(cont.)

• The cost of case-finding should be economically
  balanced in relation to possible expenditure on
  medical care as a whole
• Case-finding should be in a continuing process
  and not a one-time project

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Biases in Screening

• Referral Bias (volunteer bias)
• Length Bias
• Screening selectively identifies those with a
  long preclinical and clinical phase (i.e., those
  who would have a better prognosis regardless of
  the screening program)

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Biases in Screening (cont.)

• Lead Time Bias
• The apparently better survival that is observed
  for those screened is not because these patients
  are actually living longer, but instead because
  diagnosis is being made at an earlier point in
  the natural history of the disease

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Biases in Screening (cont.)

• Overdiagnosis Bias (a misclassification bias)
• Enthusiasm for a new screening program may result
  in a higher rate of false positives and give
  false impression of increased rates of diagnosis
  and detection
• Also, false positives would result in
  unrealistically favorable outcomes in persons
  thought to have the disease