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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
Proportion of peoplewith disease having
+ test result.
Specificity
Proportion of peoplewithout disease
having– test result
+ Predictive Value
?
- Predictive Value
Sensitivity
Proportion of peoplewith disease having
+ test result.
Specificity
Proportion of peoplewithout disease
having– test result
+ Predictive Value
Proportion of + test
results in people with disease
- Predictive Value
?
Sensitivity
Proportion of peoplewith disease having
+ test result.
Specificity
Proportion of peoplewithout disease
having– test result
+ 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 ?
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
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
All those who test + on ST
All those who test - on ST
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)
ExampleA fecal occult blood screening test is used in 203 people to look for bowel cancer:-
disease no disease
2 18 20
1 182 183
3 200 203
Test +
Test -
Patients with bowel cancer (as confirmed by endoscopy)
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.
ExampleA fecal occult blood screening test is used in 203 people to look for bowel cancer:-
disease no disease
2 18 20
1 182 183
3 200 203
Test +
Test -
Patients with bowel cancer (as confirmed by endoscopy)
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 -
Patients with bowel cancer (as confirmed by endoscopy)
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 -
Patients with bowel cancer (as confirmed by endoscopy)
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 positives is 18.91 %
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 %
Interpretation of result
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 %
Interpretation of result
The ability to detect true positives is 31.46 %
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 -
If prevalence is 2%/1000 popSensitivity is 90% & Specificity is 95%
disease disease no diseaseno disease
=2 / 100 x =2 / 100 x 10001000
=20=20
10001000
Test +
Test -
If prevalence is 2%/1000 popSensitivity is 90% & Specificity is 95%
disease disease no diseaseno disease
=90/100 x =90/100 x 20 =1820 =18
=2 / 100 x =2 / 100 x 10001000
=20=20
10001000
Test +
Test -
If prevalence is 2%/1000 popSensitivity is 90% & Specificity is 95%
disease disease no diseaseno disease
=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 -
If prevalence is 2%/1000 popSensitivity is 90% & Specificity is 95%
disease disease no diseaseno disease
=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 -
If prevalence is 2%/1000 popSensitivity is 90% & Specificity is 95%
disease disease no diseaseno disease
=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%
disease disease no diseaseno disease
=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 -
ExampleIf prevalence is 2%/1000 pop
Sensitivity is 90% & Specificity is 95%
disease disease no diseaseno disease
=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 -
ExampleIf prevalence is 2%/1000 pop
Sensitivity is 90% & Specificity is 95%
disease disease no diseaseno disease
=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 -
Interpretation of result
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
Solution Disease confirmedYes No
ScreeningTest Positive 205
1105608
Negative
Total
1105-608=
497
608-205=
403
Solution Disease confirmedYes No
ScreeningTest Positive 205
1105608
Negative
Total
1105-608 =
497
608-205 =
403
Solution Disease confirmedYes No
ScreeningTest Positive 205
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
Solution Disease confirmedYes No
ScreeningTest Positive 205
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
Solution Disease confirmedYes No
ScreeningTest Positive 205
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
Solution Disease confirmedYes No
ScreeningTest Positive 205
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
Solution Disease confirmedYes No
ScreeningTest Positive 535
50002500
Negative
Total
5000-2500 =
2500
Solution Disease confirmedYes No
ScreeningTest Positive 535
50002500
Negative
Total
5000-2500 =
2500
2500-535=
1965
Solution Disease confirmedYes No
ScreeningTest Positive 535
50002500
Negative
Total
5000-2500 =
2500
2500-535=
1965
Solution Disease confirmedYes No
ScreeningTest Positive 535
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
Solution Disease confirmedYes No
ScreeningTest Positive 535
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
Solution Disease confirmedYes No
ScreeningTest Positive 535
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
Solution Disease confirmedYes No
ScreeningTest Positive 535
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