Screening and Diagnostic Testing

Sue Lindsay, Ph.D., MSW, MPH

Division of Epidemiology and Biostatistics

Institute for Public Health

San Diego State University

Early Diagnosis of Disease

• Prompt attention to the earliest symptoms

• Detection of disease in asymptomatic individuals

Early Diagnosis of Disease

• Screening and diagnostic tests improve the ability to estimate the probability of the presence or absence of a disease

Screening vs. Diagnostic Tests

Screening Tests

• Tests performed on asymptomatic individuals with the goal of detecting pre-clinical cases of disease

Diagnostic Tests

• Tests performed to increase probability of disease identification and confirmation in cases of suspected disease

How good is your test?

The Progress of Disease

Exposure Death

Disease

begins

Disease or precursor detectable by screening

Screening

Test +

Symptoms

begin

Disease confirmed by diagnostic testing

“Gold standard”

pre-clinical

lead time

Considerations for Screening Programs

1. The disease should be a significant public health problem

2. There should be a recognizable latent or early symptomatic

stage

3. There should be a suitable screening test acceptable to the

population

4. There should be well-established and available diagnostic tests

5. There should be an accepted treatment for the disease

6. Facilities for diagnosis and treatment should be available

7. The cost of case-finding, diagnosis, and treatment should be

anticipated

8. The process should be regular and on-going

Participation in Screening Programs

1. The disease must be known to the individual.

2. It must be regarded as a serious threat to health

3. Each individual must feel vulnerable to the disease

4. There must be a firm belief that action will have meaningful results

The Screening 2X2 Table

a

true-positives

Disease No Disease

Test Positive

Test Negative

b

false-positives

c

false-negatives

d

true-negatives

a + c

a+b+c+dPrevalence of disease =

Sensitivity and Specificity

a

true-positives

Disease No Disease

Test Positive

Test Negative

b

false-positives

c

false-negatives

d

true-negatives

a

a+cSensitivity =

True positives

All with disease=

Specificity =d

b+d

=True negatives

All without disease

Important!

• Determination of the sensitivity and specificity of a test requires that a diagnosis of disease be established or ruled out for every person tested by the screening procedure, regardless of whether he screens negative or positive

• The diagnosis must be established by techniques independent of the screening test

Sensitivity • The greater the sensitivity, the more likely the tests will detect persons with the

disease.

• A negative result on a test with excellent sensitivity can virtually rule out disease

Specificity• The greater the specificity, the more likely it is that persons without the disease

will be excluded

• A positive result on a test with excellent specificity will strongly suggest the presence of disease.

Sensitivity and Specificity are descriptors of the accuracy of a test

Sensitivity and Specificity

a

true-positives

Disease No Disease

Test Positive

Test Negative

b

false-positives

c

false-negatives

d

true-negatives

a

a+cSensitivity =

True positives

All with disease=

Specificity =d

b+d

=True negatives

All without disease

Sensitivity and Specificity

34

Diabetes No Diabetes

Glucose Tolerance Positive

Glucose Tolerence

Negative

20

116 9,830

34

34 +116Sensitivity =

22.6%=

Specificity =9,830

20 + 9,830

= 99.7%

Predictive Value

a

true-positives

Disease No Disease

Test Positive

Test Negative

b

false-positives

c

false-negatives

d

true-negatives

aa+b

Positive Predictive Value =

PV+

True positives

All who test positive=

Negative Predictive Value =

PV-

d

c+d

=True negatives

All who test negative

Positive Predictive Value • The percentage of persons with positive test results who actually have the

disease

• How likely is it that the disease of interest is present if the test is positive?

Negative Predictive Value• The percentage of persons with negative test results who do not have the

disease of interest

• How likely is it that the disease of interest is not present if the test is negative?

Predictive Values are estimates of the probability of the presence or absence

of disease based on the test result

Predictive Value

a

true-positives

Disease No Disease

Test Positive

Test Negative

b

false-positives

c

false-negatives

d

true-negatives

aa+b

Positive Predictive Value =

PV+

True positives

All who test positive=

Negative Predictive Value =

PV-

d

c+d

=True negatives

All who test negative

expensive !

Predictive Value

140

Glaucoma No glaucoma

Intraocular pressure +

Intraocular pressure -

80

10 910

140

140 + 80Positive Predictive Value =

PV+

Negative Predictive Value =

PV-

910

10+910

= 64% = 99%

Screening and Diagnostic Tests

Breast Cancer

• Clinical Breast Exam

• Screening Mammogram

• Diagnostic Mammogram

• Fine Needle Aspiration Biopsy

• Core Biopsy

• Excisional Biopsy (gold standard)

Predictive Values are Influenced by Prevalence of Disease

36

Disease No disease

Test positive

Test negative

Sensitivity = 36/40 = 90%

4 912

48 Test positive

Test negative 940

50

1

9

Disease No disease

Specificity = 912/960 = 95%

PV+ = 36/84 = 43%

PV- = 912/916 = 99.5%

Prevalence = 40/1,000 = 4% Prevalence = 10/1,000 = 1% Sensitivity = 9/10 = 90% Specificity = 940/990 = 95%

PV+ = 9/59 = 15.3%

PV- = 940/941 = 99.8%

1,000 1,000

Yield

The yield of a screening test is the amount of previously unrecognized disease that is diagnosed with screening

1. Yield is influenced by:

1. The sensitivity of the test

2. The prevalence of unrecognized disease in the population

2. In screening tests, a high positive predictive value is desirable.

3. However, if the prevalence of a disease is low, even a highly sensitive test will yield a low positive predictive value

4. For the most yield, screening should be aimed at populations with a high prevalence of disease

An Example

A manufacturer would like to sell you a new rapid screening test developed to screen for strep throat. You know the prevalence of strep throat in your pediatric population in the high peak season is 27%. The manufacturer of the new test describes the sensitivity as 70% and the specificity as 73%. Assuming that you will use this test with 1,000 children, what are the positive and negative predictive values of this test in your population? Would you buy this product?

Strep Throat Example

189

Strep Throat No Strep Throat

Test positive

Test negative

197

81 533

1,000

Prevalence is 27%

270

Sensitivity is 70%

Specificity is 73%

730

Positive predictive value = 189/386 = 49%

Negative predictive value = 533/614 = 87%

386

614

The probability of a particular test result for a person with the disease

Likelihood Ratios

The probability of a particular test result for a person without the disease

Likelihood ratios do not vary with prevalence

Likelihood Ratio for a Positive Test• The probability of a positive test result for a person with the disease The probability of

a positive test result for a person without the disease

• The larger the size of the LR+, the better the diagnostic value of the test

• An LR+ value of 10 or greater is considered a good test

Likelihood Ratio for a Negative Test• The probability of a negative test result for a person with the disease The probability of

a negative test result for a person without the disease

• The smaller the size of the LR-, the better diagnostic value of the test

• An LR- value of 0.10 or less is considered a good test

Likelihood Ratios

Likelihood Ratio

a

true-positives

Disease No Disease

Test Positive

Test Negative

b

false-positives

c

false-negatives

d

true-negatives

a/a+cb/b+d

Likelihood ratio for positive test =

Sensitivity

(1-Specificity)

Likelihood ratio for neg test = c/a+cd/b+d

=(1-Sensitivity)

=LR+ LR-Specificity

Likelihood Ratio is Not Influenced by Prevalence

36

Disease No disease

Test positive

Test negative

Sensitivity = 36/40 = 90%

4 912

48 Test positive

Test negative 940

50

1

9

Disease No disease

Specificity = 912/960 = 95%

LR+ = 36/40 = 0.90 = 18

48/960 0.05

LR- = 4/40 = 0.10 = 0.10

912/960 0.95

Prevalence = 40/1,000 = 4% Prevalence = 10/1,000 = 1% Sensitivity = 9/10 = 90% Specificity = 940/990 = 95%

LR+ = 9/10 = 0.90 = 18

50/990 0.05

LR- = 1/10 = 0.10 = 0.10

940/990 0.95

1,000 1,000

Screening Tests with Categorical Results:• Mammography:

• BIRADS 1: negative• BIRADS 2: benign• BIRADS 3: probably benign• BIRADS 4: suspicious for cancer• BIRADS 5: highly suggestive for malignancy

• What is Abnormal?• The decision about what results to call “abnormal” will effect sensitivity, specificity, and

predictive values of your screening tests.

Cut-Points for Screening Tests

Screening Tests with Continuous Results:• Blood Pressure

• Cholesterol Levels

• Blood sugar

• What is Abnormal?• There are many options concerning where to set the cut-off point• Along a continuous scale, different cut-off points will result in differing levels of sensitivity and specificity• As sensitivity increases, specificity decreases• Low cut-points are very sensitive, but not specific

• Those with disease are correctly classified, but those without disease are not

• High cut-points are very specific, but not sensitive• Those without disease are correctly classified, but those with disease are not

• How to you decide the cut-off point?

Cut-Points for Screening Tests

Blood Glucose and Diabetes Sensitivity and Specificity at Different Cut-Off Points

Blood Glucose Level Sensitivity Specificity

200

180

160

140

120

100

80

37

50

56

74

89

97

100

100

99

98

91

68

25

2

Percent diabetics

correctly identified

Percent non-diabetics

correctly identified

ROC Curves(Receiver Operating Characteristics)

Sensitivity

(signal)

(1-Specificity)

(noise)

The Evaluation of Screening Programs

Does early detection of disease:

1. Reduce morbidity?

2. Reduce mortality?

3. Improve quality of life?

4. Reduce cost of disease?

Lead-Time Bias• Survival time is increased in those screened because of earlier detection

• May be no actual improvement in disease progression or mortality

Length-Biased Sampling• Disease detected by screening is less aggressive than disease detected without screening. Cases

detected with a screening program tend to have longer pre-clinical stages than those missed by screening

Patient Self-Selection Bias• Individuals who participate in screening programs may differ from those who do not on

characteristics that may be related to survival

Bias in the Evaluation of Screening Programs