SCREENING Dr. Aliya Hisam Community 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:-

“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 testDiagnostic test 1Done on apparently healthyDone on those with indications or sick 2Applied to groupsApplied to single patients, all diseases are considered 3Test result are arbitrary and final Diagnosis is not final but modified in light of new evidence, diagnosis is the sum of all evidence 4Based on one criterion or cut-off point (e.g, diabetes) Based on evaluation of a number of symptoms, signs and laboratory findings 5Less accurateMore accurate 6Less expensiveMore expensive 7Not a basis for treatmentUsed as a basis for treatment 8The 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

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

1. Mass 2. Multiple or multiphasic 3. Targeted 4. Case finding or opportunistic

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

Depends on two consideration:- a. Disease b. Screening Test

Disease appropriate for screening (Wilson’s Criteria) 1. Disease should be s erious 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:-

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).

AD C B Biologic Onset 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 xxxxxx xxxxxx x x xxxx Test Result Number of persons 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.

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

35 Reference Value (mg/dl) Blood Glucose (mM) Sensitivity 1 (%) Specificity 1 (%) (63/63) 0.6(2/340) (67/340) (62/63) 63.2 (215/340) (60/63) 93.5 (318/340) (60/63) 97.4 (331/340) (53/63) 100 (335/340) (47/63) 100 (340/340) (42/63) 100 (340/340) (35/63) 100 (340/340) (28/63) 100 (340/340) (23/63) 100 (340/340) (21/63) 100 (340/340) (19/63) 100 (340/340) (15/63) 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 people with disease having + test result. Specificity ? + Predictive Value - Predictive Value

Sensitivity Proportion of people with disease having + test result. Specificity Proportion of people without disease having – test result + Predictive Value ? - Predictive Value

Sensitivity Proportion of people with disease having + test result. Specificity Proportion of people without disease having – test result + Predictive Value Proportion of + test results in people with disease - Predictive Value ?

Sensitivity Proportion of people with disease having + test result. Specificity Proportion of people without disease having – test result + Predictive Value Proportion of + test results in people with disease - Predictive Value Proportion of – test result in people without disease

Disease Yes No Test Result Positive Negative a a + b + c + d b d b + d ?a + c ? c a + b ? c + d ?

Disease Yes 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

Disease Yes 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 % Sensitive as 60/60= 1 or 100%

= 40 - Without disease, So test is 100 % Specific as 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

True Positive True Negative

What about the rest of the population i.e. 12 in disease & 8 in non-disease

False Negative

False Negative False Positive

True Positive False Positive False Negative True Negative

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 Absent Test Result Positive Negative

Disease Yes No Test Result Positive Negative

Disease Yes No Test Result Positive Negative ab 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)

Disease Yes No Test Result Positive Negative a TPb 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)

Disease Yes No Test Result Positive Negative a TPb FP d TNc FN

Remember 9 values in Table aba + b cdc + d a + cb + da +b+c+d

Disease Yes No Test Result Positive Negative a TPb FP d TNc FN 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)

Disease Yes No Test Result Positive Negative a a + b + c + d b d b + d ?a + c ? c a + b ? c + d ?

Disease Yes 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

Disease Yes 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

Disease Yes 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.

Disease Yes No Test Result Positive Negative ab dc True Positive ?True Negative?

Disease Yes No Test Result Positive Negative ab dc True Positive True Negative

Disease Yes No Test Result Positive Negative ab dc True Positive True Negative False Positive ?False Negative ?

Disease Yes No Test Result Positive Negative ab 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.

Disease Yes No Test Result Positive Negative ab dc Sensitivity ? Specificity ?

Disease Yes No Exposure Yes No ab 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 ?

Proportion of negative tests in people without disease

Disease Yes No Test Result Positive Negative ab dc

Disease Yes No Test Result Positive Negative ab dc + Predictive value - Predictive value a/ a +b d/ d +c

Disease Yes 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) (%)

Example A fecal occult blood screening test is used in 203 people to look for bowel cancer:- diseaseno disease Test + Test - Patients with bowel cancer (as confirmed by endoscopy)

Example A fecal occult blood screening test is used in 203 people to look for bowel cancer:- diseaseno disease Test + Test - Patients with bowel cancer (as confirmed by endoscopy) Find out 1. Sensitivity 2. Specificity 3. + predictive value 4. - 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) (%) = % Specificity=182/(200) (%) =91 % Positive predictive value =2/(20) (%) =10% Negative predictive value =182/(183) (%) =99.45%

The ability to detect true positives is % 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.

Example A fecal occult blood screening test is used in 203 people to look for bowel cancer:- diseaseno disease Test + Test - Patients with bowel cancer (as confirmed by endoscopy) Find out 1. Prevalence 2. Accuracy

For Prevalence

P= Total # of Diseases Individuals Total Population X 100

For Prevalence disease no disease Test + Test - Patients with bowel cancer (as confirmed by endoscopy) a + c a+b+c+d X 100

For Accuracy

P= True Positive + True Negatives Total Population X 100

For Accuracy disease no disease Test + Test - Patients with bowel cancer (as confirmed by endoscopy) a + d a+b+c+d X 100

Remember 9 aba + b cdc + d a + cb + da +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

diseaseno disease Test + Test - Patients with Breast cancer (as confirmed by FNAC)

Sensitivity=35/185 x 100 = % Specificity=250/315 x 100 =79.36 % Accuracy = = TP + TN X 100 a+b+c+d = X = 85 %

Interpretation of result The ability to detect true positives is % 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

diseaseno disease Smokers Test + Non-Smokers Test - 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 % of persons with a negative test will not have the disease.

Exercise- Case Scenario 3: DiseaseNo disease Test + Test - Patients with Disease (as confirmed by Gold Standard method) Calculate 1. Prevalence 2. Accuracy & Validity Screening Test Results

Prevalence = Total diseased Individuals Accuracy = Validity Two components Sensitivity Specificity

Sensitivity=45/143 x 100 = % Specificity=737/757 x 100 =97.35 % Accuracy = = TP + TN X 100 a+b+c+d = X = %

Interpretation of result The ability to detect true positives is % 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.

125 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 pop Sensitivity is 90% & Specificity is 95% disease no disease Test + Test -

If prevalence is 2%/1000 pop Sensitivity is 90% & Specificity is 95% disease no disease =2 / 100 x 1000 = Test + Test -

If prevalence is 2%/1000 pop Sensitivity is 90% & Specificity is 95% disease no disease =90/100 x 20 =18 =2 / 100 x 1000 = Test + Test -

If prevalence is 2%/1000 pop Sensitivity is 90% & Specificity is 95% disease no disease =90/100 x 20 =18 = 95 / 100 x 980 =931 =2 / 100 x 1000 = Test + Test -

If prevalence is 2%/1000 pop Sensitivity is 90% & Specificity is 95% disease no disease =90/100 x 20 =18 =20-18=2 = 95 / 100 x 980 =931 =2 / 100 x 1000 = Test + Test -

If prevalence is 2%/1000 pop Sensitivity is 90% & Specificity is 95% disease no disease =90/100 x 20 =18 = 980 – 931 =49 =20-18=2 = 95 / 100 x 980 =931 =2 / 100 x 1000 = Test + Test -

Example If prevalence is 2%/1000 pop Sensitivity is 90% & Specificity is 95% disease no disease =90/100 x 20 =18 = 980 – 931 =49 =20-18=2 = 95 / 100 x 980 =931 =2 / 100 x 1000 =20 = = =1000 Test + Test -

Example If prevalence is 2%/1000 pop Sensitivity is 90% & Specificity is 95% disease no disease =90/100 x 20 =18 = 980 – 931 =49=49+18=67 =20-18=2 = 95 / 100 x 980 =931 = =933 =2 / 100 x 1000 =20 = = =1000 PPV = 18 / 67 x 100 = 26% Test + Test -

Example If prevalence is 2%/1000 pop Sensitivity is 90% & Specificity is 95% disease no disease =90/100 x 20 =18 = 980 – 931 =49=49+18=67 =20-18=2 = 95 / 100 x 980 =931 = =933 =2 / 100 x 1000 =20 = = =1000 NPV = 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 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 confirmed YesNo Screening Test Positive Negative Total = 497

Solution Disease confirmed YesNo Screening Test Positive Negative Total = = 403

Solution Disease confirmed YesNo Screening Test Positive Negative Total = = 403

Solution Disease confirmed YesNo Screening Test Positive Negative Total = = 403 a/a+b=54% 205/54%=a+b 205 *100/54=a+b 20500/54=a+b 380=a+b

Solution Disease confirmed YesNo Screening Test Positive Negative Total = = 403 a/a+b=54% 205/54%=a+b 205 *100/54=a+b 20500/54=a+b 380=a+b = 175

Solution Disease confirmed YesNo Screening Test Positive Negative Total = = 403 a/a+b=54% 205/54%=a+b 205 *100/54=a+b 20500/54=a+b 380=a+b = = 322

Solution Disease confirmed YesNo Screening Test Positive Negative Total = = 403 a/a+b=54% 205/54%=a+b 205 *100/54=a+b 20500/54=a+b 380=a+b = = 322

Last Exercise A screening test was applied on population of 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 confirmed YesNo Screening Test Positive Negative Total = 2500

Solution Disease confirmed YesNo Screening Test Positive Negative Total = = 1965

Solution Disease confirmed YesNo Screening Test Positive Negative Total = = 1965

Solution Disease confirmed YesNo Screening Test Positive a/a+b=54% 535/54%=a+b 535 *100/54=a+b 53500/54=a+b 990=a+b Negative Total = = 1965

Solution Disease confirmed YesNo Screening Test Positive a/a+b=54% 535/54%=a+b 535 *100/54=a+b 53500/54=a+b 990=a+b Negative Total = = = 455

Solution Disease confirmed YesNo Screening Test Positive a/a+b=54% 535/54%=a+b 535 *100/54=a+b 53500/54=a+b 990=a+b Negative Total = = = = 2045

Solution Disease confirmed YesNo Screening Test Positive a/a+b=54% 535/54%=a+b 535 *100/54=a+b 53500/54=a+b 990=a+b Negative Total = = = =