SCREENING

SCREENING

Dr. Aliya HisamCommunity Medicine Dept. Army Medical College, RWP

Learning Objectives Screening Test

Aims & Objectives Types , uses and cut-off point Wilsons Criteria

To be able to construct a 2 * 2 table. To be able to evaluate screening test

and Interpret result in words.

Screening Definition:-

Screening Definition:-

“The presumptive identification of unrecognized diseases or defect by the application of tests, examinations or other procedures which can be applied rapidly to sort out apparently well persons who probably have a disease from those who probably do not.”

SCREENING

Application of a test to asymptomatic people to classifying them into diseased or non-disease ppl.

The screening test itself does not necessarily diagnose illness, those who test positive are evaluated by a subsequent diagnostic procedure to determine whether they in fact do or do not have the disease

Difference Between Screening &

Diagnostic Test

Screening test Diagnostic test1 Done on apparently

healthyDone on those with indications or sick

2 Applied to groups Applied to single patients, all diseases are considered

3 Test result are arbitrary and final

Diagnosis is not final but modified in light of new evidence, diagnosis is the sum of all evidence

4 Based on one criterion or cut-off point (e.g, diabetes)

Based on evaluation of a number of symptoms, signs and laboratory findings

5 Less accurate More accurate

6 Less expensive More expensive

7 Not a basis for treatment Used as a basis for treatment

8 The initiative comes from the investigator or agency providing care

The initiative comes from a patient with a complaint

Aims & Objectives of Screening ?

Aims & Objectives of Screening It is carried out in a hope that

earlier diagnosis and subsequent treatment favorably alters the natural history of the disease in a significant proportion of those who are identified as positives

Iceberg Phenomenon of Disease

Iceberg Phenomenon of Disease

Submerge portion:- Hidden mass of the disease (e.g.

subclinical cases, carriers, undiagnosed cases).

Floating tip:- What the physician sees in his practice.

Types Of Screening

Types Of Screening

1. Mass2. Multiple or multiphasic 3. Targeted4. Case finding or opportunistic

Uses of Screening

Uses of Screening

Case detection Control of disease Research purpose Educational opportunities

Disease onset

detection Outcome

Cured/Death

Lead Time

Model for early detection programmes

First Possible

Point

Final Critical

Diagnosis

Usual Time of

Diagnosis

Screening Time

Lag Time

Errors in evaluation of Screening Tests

a. An error in the evaluation of a screening test, known as lead time bias, can occur when persons with disease detected by screening appear to live longer simply because of the earlier recognition of their illnesses.

b. An error in the evolution of a screening test, known as length-biased sampling, can occur when persons with disease detected by screening appear to live longer simply because they have more slowly progressing illnesses.

Criteria of Screening

Criteria of Screening

Depends on two consideration:-

a. Diseaseb. Screening Test

Disease appropriate for screening(Wilson’s Criteria)

1. Disease should be serious

2. Screening may help in the prevention of transmission of disease

3. Prevalence of pre-clinical disease should be high.

4. Early asymptomatic stage.

5. The Disease natural history should be adequately understood

6. Facilities should be available for confirmation of the diagnosis.

7. There is an effective treatment.

8. There should be an agreed-on policy concerning whom to treat as patients

9. Treatment reduces morbidity and mortality.

10. The expected benefits of early detection exceed the risks and costs.

10. Done as a regular and on-going process.

Screening Test Criteria:-

Screening Test Criteria:- Acceptability Validity (accuracy)

Sensitivity Specificity

Yield + predictive value - predictive value

Simplicity Safety Rapidity Ease of admin. Cost Repeatability

Validity of a screening test is measured by its ability to do what it is supposed to do i.e., provides a good preliminary indication of which individuals actually have the disease and which do not.

Validity has two components:

1. Sensitivity 2. Specificity

Sensitivity of a test is the ability of the test to detect disease in all those who actually have the disease (i.e., correctly identify all those who have the disease)

Specificity of a test is the ability of the test to rule out disease in all those in whom the disease is actually absent (i.e., correctly identify all those who do not have the disease).

A DCB

BiologicOnset

Disease detectable By screening

Cured/DeathSymptoms develop

Detectable preclinical phase

Detectable preclinical phase in natural history of diseases

PATHOGENESIS

Sensitivity and Specificity at Different screening Test values

Distribution of cases and no cases by screening

test values

000

000000

000

xxx

xxx xx

xx

00 0

90 100 110 120 130 140

Test Result

Number ofpersons

Value chosen to Define a “positive”

Screening result 0 =non-cases=cases

115

Below Point A: Very low range of test results indicate absence of disease with very high probability,

Above Point B : Very high range that indicates the presence of disease with very high probability.

However, where the distributions overlap, there is a "gray zone" in which there is much less certainly about the results.

3030

130 Sick people incorrectly identified as healthy

Healthy people incorrectly identified as

sick

If we move the cut-off to the left, we can increase the sensitivity, but the specificity will be worse.

If we move the cut-off to the right, the specificity will improve, but the sensitivity will be worse.

Altering the cut-off point/criterion for a positive test will always influence both the sensitivity and specificity of the test.

Sick people incorrectly identified as healthy

Healthy people incorrectly identified as

sick

3535

Reference Reference Value Value (mg/dl)(mg/dl)

Blood Blood Glucose Glucose (mM)(mM)

SensitivitySensitivity11 (%)(%)

SpecificitySpecificity11 (%) (%)

8080 4.44.4 100.0 (63/63)100.0 (63/63) 0.6(2/340)0.6(2/340)

5.05.0 100.0 (63/63)100.0 (63/63) 19.7(67/340)19.7(67/340)

5.65.6 98.4 (62/63)98.4 (62/63) 63.2 63.2 (215/340)(215/340)

6.16.1 95.2 (60/63)95.2 (60/63) 93.5 93.5 (318/340)(318/340)

115115 6.46.4 95.2 (60/63)95.2 (60/63) 97.4 97.4 (331/340)(331/340)

6.76.7 84.1 (53/63)84.1 (53/63) 100 (335/340)100 (335/340)

7.27.2 74.6 (47/63)74.6 (47/63) 100 (340/340)100 (340/340)

140140 7.87.8 66.7 (42/63)66.7 (42/63) 100 (340/340)100 (340/340)

8.38.3 55.6 (35/63)55.6 (35/63) 100 (340/340)100 (340/340)

8.98.9 44.4 (28/63)44.4 (28/63) 100 (340/340)100 (340/340)

9.49.4 36.5 (23/63)36.5 (23/63) 100 (340/340)100 (340/340)

10.010.0 33.3 (21/63)33.3 (21/63) 100 (340/340)100 (340/340)

10.610.6 30.2 (19/63)30.2 (19/63) 100 (340/340)100 (340/340)

200200 11.111.1 23.8 (15/63)23.8 (15/63) 100 (340/340)100 (340/340)

Yield

Yield of a screening test is the number of persons detected by a screening program.

It is an important measure for determining the usefulness of a test under field conditions.

Positive Predictive Value Negative Predictive Value

Positive predictive value (PV+) is the proportion of positive tests in people with disease.

Negative predictive value (PV-) is the proportion of negative tests in people without disease.

Sensitivity

?

Specificity

+ Predictive Value

- Predictive Value

Sensitivity

Proportion of peoplewith disease having

+ test result.

Specificity

?

+ Predictive Value

- Predictive Value

Sensitivity


+ test result.

Specificity

Proportion of peoplewithout disease

having– test result

+ Predictive Value

?

- Predictive Value

Sensitivity


+ test result.

Specificity



+ Predictive Value

Proportion of + test

results in people with disease

- Predictive Value

?

Sensitivity


+ test result.

Specificity



+ Predictive Value

Proportion of + test

results in people with disease

- Predictive Value

Proportion of – test result in people without disease

DiseaseYes No

Test Result

Positive

Negative

a

a + b + c + d

b

d

b + d ?a + c ?

c

a + b ?

c + d ?

DiseaseYes No

Test Result

Positive

Negative

a

a + b + c + d

b

d

b + da + c

c

a + b ?

c + d ?

All those who actually have

the disease.

All those who actually do not

have the disease

DiseaseYes No

Test |Result

Positive

Negative

a

a + b + c + d

b

d

b + da + c

c

a + b

c + d

All those who test + on ST

All those who test - on ST

Validity

An ideal screening test is one that is 100% sensitive and 100% specific.

In practice this does not occur.

If disease is present an ideal, or truly accurate, test will always give a positive result.

If disease is not present, the test will always give a negative result.

But this is not the case….

In a 100 group of population,

60 have disease 40 do not have disease

= 60 + with disease,

So test is 100 %

Sensitiveas 60/60= 1 or

100%

= 40 -Without disease, So test is

100 % Specificas 40/40= 1 or

100 %

Test gives a positive result for 48 out

of 60 who have the disease.

So Sensitivity is 48/60 = 0.8 or

80 %

Test gives a negative result for 28 out

of 40 who have the disease.

So Specificity is 28/40 = 0.7 or

70 %

Screening test is 80 % Sensitive and 70% Specific

TruePositive

TrueNegative

What about the rest of the population

i.e. 12 in disease & 8 in non-disease

False Negative

False Negative

False Positive

TruePositive

False Positive

FalseNegative

TrueNegative

Cross tabulation of data

The simplest cross tabulation is a 2 x2 table

A 2 x 2 table is one which has only two rows and two columns.

Disease

Present Absent

Disease

Present AbsentTest Result

Positive

Negative

DiseaseYes No

Test Result

Positive

Negative

DiseaseYes No

Test Result

Positive

Negative

a b

dc

a ? d ?

a= The number of individuals for whom the screening test is positive and they actually have the disease (True positives)

d= The number of individuals for whom the screening test is negative and they actually do not have the disease (True negatives)

DiseaseYes No

Test Result

Positive

Negative

a TP b

d TNc

b ? c ?

b= The number of individuals for whom the screening test is positive but they do not have the disease (False positives)

c= The number of individuals for whom the screening test is negative but they actually have the disease (False negatives)

DiseaseYes No

Test Result

Positive

Negative

a TP b FP

d TNc FN

Remember

9 values in Table

a b a + b

c d c + d

a + c b + d a +b+c+d

DiseaseYes No

Test Result

Positive

Negative

a TP b FP

d TNc FN

Total

Total

Total

Total a+b+c+d

a= The number of individuals for whom the screening test is positive and they actually have the disease (True positives)

b= The number of individuals for whom the screening test is positive but they do not have the disease (False positives)

c= The number of individuals for whom the screening test is negative but they actually have the disease (False negatives)

d= The number of individuals for whom the screening test is negative and they actually do not have the disease (True negatives)

DiseaseYes No

Test Result

Positive

Negative

a

a + b + c + d

b

d

b + d ?a + c ?

c

a + b ?

c + d ?

DiseaseYes No

Test Result

Positive

Negative

a

a + b + c + d

b

d

b + da + c

c

a + b ?

c + d ?


the disease.


have the disease

DiseaseYes No

Test |Result

Positive

Negative

a

a + b + c + d

b

d

b + da + c

c

a + b

c + d



DiseaseYes No

Test |Result

Positive

Negative

a

a + b + c + d

b

d

b + da + c

c

a + b

c + d


the disease.


have the disease



So, interpreting the cells

a+c = All those who actually have the disease. b+d = All those who actually do not have the disease. a+b = All those who test positive on the screening test. c+d = All those who test negative on the screening test.

DiseaseYes No

Test Result

Positive

Negative

a b

dc

True Positive ? True Negative?

DiseaseYes No

Test Result

Positive

Negative

a b

dc

True Positive

True Negative

DiseaseYes No

Test Result

Positive

Negative

a b

dc

True Positive

True Negative

False Positive ?False Negative ?

DiseaseYes No

Test Result

Positive

Negative

a b

dc

True Positive

True NegativeFalse Negative

False Positive

Sensitivity ?

Sensitivity Proportion of people with

disease who have a positive test result.

Specificity ?

Specificity Proportion of people without

disease who have a negative test result.

DiseaseYes No

Test Result

Positive

Negative

a b

dc

Sensitivity ? Specificity ?

DiseaseYes No

Exposure

Yes

No

a b

d

SpecificitySensitivity

c

a/a +c d/ d +b

Positive Predictive Value ?

Positive Predictive Value Proportion of positive test in

people with disease

Negative Predictive Value ?

Negative Predictive Value ? Proportion of negative tests in

people without disease

DiseaseYes No

Test Result

Positive

Negative

a b

dc

DiseaseYes No

Test Result

Positive

Negative

a b

dc

+ Predictive value

- Predictive value

a/ a +b

d/ d +c

DiseaseYes No

Test Result

Positive

Negative

a

a + b + c + d

b

d

SpecificitySensitivity

c

+ Predictive value

- Predictive value

Sensitivity = a/(a+c) (%)

Specificity = d/(b+d) (%)

Positive predictive value= a/(a+b) (%)

Negative predictive value= d/(c+d) (%)

ExampleA fecal occult blood screening test is used in 203 people to look for bowel cancer:-

disease no disease

2 18 20

1 182 182

3 200 203

Test +

Test -

Patients with bowel cancer (as confirmed by endoscopy)


disease no disease

2 18 20

1 182 183

3 200 203

Test +

Test -


Find out

1. Sensitivity2. Specificity3. + predictive

value4. - predictive

value

Sensitivity = a/(a+c) (%)

Specificity = d/(b+d) (%)

Positive predictive value= a/(a+b) (%)

Negative predictive value= d/(c+d) (%)

Sensitivity = 2/(3) (%) = 66.67 % Specificity = 182/(200) (%)

= 91 % Positive predictive value = 2/(20) (%)

= 10% Negative predictive value = 182/(183) (%)

= 99.45%

The ability to detect true positives is 66.67 %

The ability to detect true negatives is 91%

The test is able to predict that 10% of persons with a positive test will have the disease and 99.45% of persons with a negative test will not have the disease.


disease no disease

2 18 20

1 182 183

3 200 203

Test +

Test -


Find out

1. Prevalence2. Accuracy

For Prevalence

For Prevalence

P= Total # of Diseases Individuals

Total PopulationX 100

For Prevalence

disease no

disease

2 18 20

1 182 183

3 200 203

Test +

Test -


a + c a+b+c+d

X 100

For Accuracy

For Accuracy

P= True Positive + True Negatives

Total PopulationX 100

For Accuracy

disease no

disease

2 18 20

1 182 183

3 200 203

Test +

Test -


a + d a+b+c+d

X 100

Remember

9a b a + b

c d c + d

a + c b + d a +b+c+d

Exercise- Case Scenario 1

A mammography screening test for breast cancer was performed on 500 females. Screening test was positive in 100 individuals out of which only 35 female were positive for disease by Fine needle aspiration cytology. 250 females were true negative. Construct 2 x 2 table by the above

information Label a, b, c & d. Calculate Validity of the screening test &

interpret your results in words.

Validity has 2 components Sensitivity Specificity

disease no disease

35 65 100

150 250 400

185 315 500

Test +

Test -

Patients with Breast cancer (as confirmed by FNAC)

Sensitivity = 35/185 x 100 = 18.91 % Specificity = 250/315 x 100

= 79.36 %

Accuracy = = TP + TN X

100 a+b+c+d

= 35 + 250 X 100 500

= 85 %

Interpretation of result


The ability to detect true negatives is 79.36%

Accuracy of the Screening test is 85 %.

Exercise- Case Scenario 2

A screening test was applied on to diagnose lung cancer in 1000 individuals. Out of 1000 individuals, 100 were smokers, out of whom 75 were diagnosed with lung cancer on Ling Biopsy. 900 were non-smokers out of whom 125 were diagnosed with lung cancer Construct a 2 * 2 table by the above

information Calculate Yield of the screening test

& interpret your results in words.

Yield has 2 components Positive Predictive value Negative Predictive value

disease no disease

75 25 100

125 775 900

200 800 1000

SmokersTest +

Non-SmokersTest -

Patients with Lung cancer (as confirmed by Lung Biopsy)Screening Test

Results

PPV = 75/100 x 100 = 75 %

NPV = 775/900 x 100 = 86.11 %


The test is able to predict that 75% of persons with a positive test will have the disease.

The test is able to predict that 86.11 % of persons with a negative test will not have the disease.

Exercise- Case Scenario 3:

Disease No disease

45 20

98 737

Test +

Test -

Patients with Disease (as confirmed by Gold Standard method)

Calculate 1. Prevalence2. Accuracy & Validity

Screening Test Results

Prevalence = Total diseased Individuals

Accuracy = Validity Two components

Sensitivity Specificity

Sensitivity = 45/143 x 100 = 31.46 % Specificity = 737/757 x 100

= 97.35 %

Accuracy = = TP + TN X

100 a+b+c+d

= 45+ 737 X 100 900

= 86.88 %



The ability to detect true negatives is 97.35%

Accuracy of the Screening test is 86.88%.

Exercise – Case Scenario 4 In Village XYZ of Rawalpindi whose

population is 1000, diabetes prevalence is 2 %. A screening test was applied on all population. Screening test was applied with sensitivity of 90 % and specificity of 95 %. Construct a 2 X 2 table with the

above information. Calculate positive predictive value

and negative predictive value & interpret your results in words.

125125

Population of 1000

Disease prevalence: 2%Sensitivity of Test: 90%Specificity of Test: 95%

Calculate Positive Predictive Value Negative Predictive Value Interpret your result in words

If prevalence is 2%/1000 popSensitivity is 90% & Specificity is 95%

disease disease no diseaseno disease

10001000

Test +

Test -



=2 / 100 x =2 / 100 x 10001000

=20=20

10001000

Test +

Test -



=90/100 x =90/100 x 20 =1820 =18

=2 / 100 x =2 / 100 x 10001000

=20=20

10001000

Test +

Test -



=90/100 x =90/100 x 20 =1820 =18

= 95 / 100 x = 95 / 100 x 980 =931980 =931

=2 / 100 x =2 / 100 x 10001000

=20=20

10001000

Test +

Test -



=90/100 x =90/100 x 20 =1820 =18

=20-18=20-18

=2=2= 95 / 100 x = 95 / 100 x 980 =931980 =931

=2 / 100 x =2 / 100 x 10001000

=20=20

10001000

Test +

Test -



=90/100 x =90/100 x 20 =1820 =18

= 980 – 931= 980 – 931

=49=49

=20-18=20-18

=2=2= 95 / 100 x = 95 / 100 x 980 =931980 =931

=2 / 100 x =2 / 100 x 10001000

=20=20

10001000

Test +

Test -

ExampleIf prevalence is 2%/1000 pop

Sensitivity is 90% & Specificity is 95%


=90/100 x =90/100 x 20 =1820 =18

= 980 – 931= 980 – 931

=49=49

=20-18=20-18

=2=2= 95 / 100 x = 95 / 100 x 980 =931980 =931

=2 / 100 x =2 / 100 x 10001000

=20=20

=931 + 49=931 + 49

=980=98067 + 67 + 933933

=1000=1000

Test +

Test -




=90/100 x =90/100 x 20 =1820 =18

= 980 – 931= 980 – 931

=49=49=49+1=49+188

=67=67

=20-18=20-18

=2=2= 95 / 100 x = 95 / 100 x 980 =931980 =931

=931 + =931 + 22

=933=933

=2 / 100 x =2 / 100 x 10001000

=20=20

=931 + 49=931 + 49

=980=98067 + 67 + 933933

=1000=1000PPV = 18 / 67 x 100 = 26%

Test +

Test -




=90/100 x =90/100 x 20 =1820 =18

= 980 – 931= 980 – 931

=49=49=49+1=49+188

=67=67

=20-18=20-18

=2=2= 95 / 100 x = 95 / 100 x 980 =931980 =931

=931 + =931 + 22

=933=933

=2 / 100 x =2 / 100 x 10001000

=20=20

=931 + 49=931 + 49

=980=98067 + 67 + 933933

=1000=1000NPV = 931 / 933 x 100 = 99.78%

Test +

Test -


The test is able to predict that 26% of persons with a positive test will have the disease.

The test is able to predict that 99.78% of persons with a negative test will not have the disease.

Any Questions?

Last Exercise A screening test was applied on population of

1105. Results showed that disease prevalence was 55%. Positive predictive value came out to be 54 % and negative predictive value came out to be 55.5%. 205 individuals are those who are positive on screening test and they actually have the disease a confirmed by gold standard method.

Set up a 2 X 2 table

Solution Disease confirmedYes No

ScreeningTest Positive 205

1105608

Negative

Total

1105-608 =

497



1105608

Negative

Total

1105-608=

497

608-205=

403



1105608

Negative

Total

1105-608 =

497

608-205 =

403



1105608

Negative

Total

1105-608 =

497

608-205=

403

a/a+b=54%205/54%=a+b205 *100/54=a+b20500/54=a+b380=a+b



1105608

Negative

Total

1105-608 =

497

608-205=

403

a/a+b=54%205/54%=a+b205 *100/54=a+b20500/54=a+b380=a+b

380-205 =

175



1105608

Negative

Total

1105-608=

497

608-205=

403

a/a+b=54%205/54%=a+b205 *100/54=a+b20500/54=a+b380=a+b

380-205 =

175

497-175 =

322



1105608

Negative

Total

1105-608 =

497

608-205=

403

a/a+b=54%205/54%=a+b205 *100/54=a+b20500/54=a+b380=a+b

725

380-205 =

175

497-175 =

322

Last Exercise A screening test was applied on population of

5000. Results showed that disease prevalence was 50%. Positive predictive value came out to be 54 % and negative predictive value came out to be 53%. 535 individuals are those who are positive on screening test and they actually have the disease a confirmed by gold standard method.

Set up a 2 X 2 table



50002500

Negative

Total

5000-2500 =

2500



50002500

Negative

Total

5000-2500 =

2500

2500-535=

1965



50002500

Negative

Total

5000-2500 =

2500

2500-535=

1965



50002500

a/a+b=54%535/54%=a+b535 *100/54=a+b53500/54=a+b990=a+b

Negative

Total

5000-2500 =

2500

2500-535=

1965



50002500

a/a+b=54%535/54%=a+b535 *100/54=a+b53500/54=a+b990=a+b

Negative

Total

5000-2500 =

2500

2500-535=

1965

990-535=

455



50002500

a/a+b=54%535/54%=a+b535 *100/54=a+b53500/54=a+b990=a+b

Negative

Total

5000-2500 =

2500

2500-535=

1965

990-535=

455

2500-455=

2045



50002500

a/a+b=54%535/54%=a+b535 *100/54=a+b53500/54=a+b990=a+b

Negative

Total

5000-2500 =

2500

2500-535=

1965

990-535=

455

2500-455=

2045 4010

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