screening for diseases by dr. san

Screening for Diseases

Dr San San Oo

Learning outcomes1. To describe the concept of screening2. To differentiate between screening test and

diagnostic test3. To explain the concept of “lead time”4. To understand aims and objectives of

screening5. To list the uses of screening

6. To enumerate the types of screening7. To describe the basic requirements of a

screening test8. To calculate the validity (sensitivity and

specificity) of a screening test and interpret them9. To calculate the predicative accuracy of a

screening test and interpret them10.To set the cutoff levels of a screening test for

different diseases

Introduction

• Necessary to distinguish – Who have the disease– Who do not

• Important challenge– Clinical arena (for patient care)– Public health arena (for early disease detection

and intervention)• Quality of screening and diagnostic tests

– a critical issue

Concept of Screening

• The search for unrecognized disease or defect by means of rapidly applied tests, examinations or other procedures in apparently healthy individuals

• A fundamental aspect of prevention• ACTIVE SEARCH FOR DISEASE

Screening test and diagnostic test

Screening test• Apparently healthy• Groups• Test results are arbitrary and

final• One criterion or cut-off

• Less accurate• Less expensive• Not a basis for treatment• Initiatives from investigators or

agency

Diagnostic test• With indications or sick• Single patients• Diagnosis not final, the sum of

all evidence• Numbers of symptoms, signs

and lab investigations• More accurate• More expensive• Basis for treatment• Initiatives from a patient with a

complaint

Concept of “lead time”

• “Lead time” – the advantage gained by screening i.e. the period between diagnosis by early detection and diagnosis by other means

• A = usual outcome of the disease• B= outcome to be expected when disease is

detected at the earliest possible moment• B-A = benefits of the programmes

Aims and objectives

Apparently healthy(Screening tests)

Apparently normal(Periodic re screening) Apparently abnormal

Normal

(Periodic re-

screening)

Intermediate (Surveillance)

Abnormal

(Treatment)

Uses of screening

1. Case detection– Prescriptive screening– Presumptive identification of unrecognized disease– E.g. Breast cancer, cervical cancer, diabetes

2. Control of disease– Prospective screening– For benefits of others– E.g. screening of immigrants from infectious

diseases

3. Research purposes– More basic knowledge about natural history of

diseases– E.g. chronic diseases (cancer, hypertension)

4. Educational opportunities– Creating public awareness and educating heath

professionals– E.g. screening for diabetes

Types of screening

1. Mass screening– Whole population– Sub groups

2. High risk or selective screening– High risk groups– Screening of diabetes, hypertension, breast

cancer in other members of family

3. Multiphasic screening– Two or more screening tests at one time

Criteria for screening

• Two considerations1. The disease2. The test

IATROGENIC

1. Condition should be important (I)2. An acceptable treatment should be available

for disease (A)3. Diagnostic and treatment facilities should be

available (T)4. A recognizable early symptomatic stage is

required (R)5. Opinions on who treat must be agreed (O)

6. The safety of the test is guaranteed (G)7. The test examination must be acceptable to

the patient (E)8. The untreated natural history of the disease

must be known (N)9. The test should be inexpensive (I)10. Screening must be continuous (C)

Some screening tests

Pregnancy• Anaemia• Hypertension toxaemia• Rh status• Syphilis (VDRL)• Diabetes• HIV• Neural tube defects• Down’s syndrome

Infancy• Hearing defects• Visual defects• Haemoglobinopathies• Spina bifida

Middle aged men and women• Hypertension• Cancer• Diabetes mellitus• Serum cholesterol• obesity

Elderly• Cancer• Glaucoma• Cataract• Chronic bronchitis• Nutritional disorders

Validity

• The extent the test accurately measures what it purports to measure

• The ability of a test to separate or distinguish those who have the disease from those who do not

• Two components (expressed as %)1. Sensitivity2. Specificity

Test with dichotomous results (positive or negative)

Two by two tableScreening test Diagnosis (Gold standard test) Total

Diseased Not diseased

Positive a (True positives) b (False negatives) a + b

Negative c (False negatives) d (True negatives) c + d

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

Evaluation of a screening test

1. Sensitivity2. Specificity3. Predictive value of a positive test4. Predictive value of a negative test5. Percentage of false negatives6. Percentage of false positives

Sensitivity

• The ability of a test to identify correctly those who have the disease

• Proportion of individuals with the disease who are correctly identified by the test

• True positives• a / a + c

Screening test

Diagnosis Total


Positive a (True

positives)

b (False

positives)

a + b

Negative c (False

negatives)

d (True

negatives)

c + d

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

• A measure of the probability of correctly diagnosing a case

• The probability that any given case will be identified by the test

• A 80% sensitivity means• 80% of the diseased people screened by the test will give a

“true positive” result• The proportion of diseased people who are correctly

identified as “positive” by the test is 80%

Specificity

• The ability of a test to identify correctly those who do not have the disease

• Proportion of individuals without the disease who are correctly identified by the test

• True negatives• d / b + d

Screening test

Diagnosis Total


Positive a (True

positives)

b (False

positives)

a + b

Negative c (False

negatives)

d (True

negatives)

c + d


• A measure of the probability of correctly identifying a non-diseased person with a screening test

• A 90% specificity means• 90% of the non-diseased people screened by the test will

give “ true negative” result• The proportion of non-diseased people who are correctly

identified as negative by the test is 90%

Example (1)Screening test Diagnosis (cervical biopsy) Total

Pap smear Diseased Not diseased

Positive 160 240 400

Negative 40 560 600

Total 200 800 1,000

Sensitivity = 160/200 * 100 = 80% •80% of women having Ca cervix screened by Pap smear will give “ true positive” result.•The proportion of women having Ca cervix who are correctly identified as positive by Pap smear is 80%.

Specificity = 560/800 * 100 = 70%•70% of women not having Ca cervix screened by Pap smear will give “true negative” result.•The proportion of women not having Ca cervix who are correctly identified as negative by Pap smear is 70%.

False negatives• Patients who actually have

the disease are told that they do not have the disease

• c/a + c• False reassurance• Ignore the development of

symptoms and signs• Critical

– if effective intervention is available (e.g. cancer)

• Very sensitive test has fewer FN

Screening test

Diagnosis Total


Positive a (True

positives)

b (False

positives)

a + b

Negative c (False

negatives)

d (True

negatives)

c + d


False positives• Patients who do not have

the disease are told that they have

• b/b+d• Further tests• Expenses• Anxiety and worry• Limitation in employment• A high specificity screening

test has fewer FP

Screening test

Diagnosis Total


Positive a (True

positives)

b (False

positives)

a + b

Negative c (False

negatives)

d (True

negatives)

c + d


Sensitivity or Specificity ?

• 100% as much as possible (Ideal)• Gain sensitivity at the expense of specificity and vice

versa (Practice)• High sensitivity with fewer false negatives

– Effective intervention especially at the early stage of the natural history of disease

• High specificity with fewer false positives– Serious and untreatable

• No screening test is perfect i.e. 100% sensitivity and 100% specificity

Tests of continuous variables

• Blood pressure No “positive” or • Blood glucose level “negative” result• The use of cut-off values

Trade-off between sensitivity and specificity

• Cut off level at 80 mg/dl– All diabetes are identified (100% sensitivity)– Many FP– Very low specificity

• Cut off level at 200 mg/dl– All non diabetes are correctly identified (100%

specificity)– Many FN– Very low sensitivity

Dilemma

• High cutoff or low cutoff ?• Only have 2 groups

– Test positives– Test negatives

• Depend on the relative importance of– False positives– False negatives

Decision

• When the disease is – Lethal High sensitivity– Early detection low cutoff values

improves the prognosis(E.g. cervical cancer, breast cancer)– Tolerable FP

• When the disease– Tx not change much High specificity– Need to limit FP high cutoff values(E.g. diabetes)

How to choose the best cutoff points

• The Receiver operator curve (ROC)

Receiver Operator Characteristic (ROC) Curve

• Plot true positive rate (sensitivity) against false positive rate (1-specificity) for several choice of positively criterion

• choose closest to top left corner to maximized the discriminative ability of the test

ROC curve to determine best cutoff point for scc by means of meanrlu

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

50100

1000

10000

2500050000

10

(mean rlu)sensitivity

1- specificity

Receiver Operator Characteristic (ROC) Curve

• The area under the curve represent overall accuracy of the test

• useful to compare two test

ROC curve to determine best cutoff point for Wilsom Risk sum scoring to detect difficulty of endotracheal intubation

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

2

3

5

01

sensitivity

1- specificity


If the test results are positive, what is the probability that this patient has the disease?

Predictive accuracy

• Diagnostic power of the test• Depend upon

1. Sensitivity2. Specificity3. Prevalence of disease

• Two measures1. Predictive value of a positive test2. Predictive value of a negative test

Predictive value of a positive test

• The probability that an individual with a positive test result has the disease

• a / a + b• A 44% PPV means

• 44% of the people with positive test result have the disease in question

Screening test

Diagnosis Total


Positive a (True

positives)

b (False

positives)

a + b

Negative c (False

negatives)

d (True

negative)

c + d


Predictive value of a negative test

• The probability that an individual with a negative test result does not have the disease

• d / c + d• A 98% NPV means

• 98% of the people with negative test result do not have the disease in question

Screening test

Diagnosis Total


Positive a (True

positives)

b (False

positives)

a + b

Negative c (False

negatives)

d (True

negatives)

c + d


Example (2)Screening test Diagnosis (cervical biopsy) Total

Pap smear Diseased Not diseased

Positive 160 240 400

Negative 40 560 600

Total 200 800 1,000

PPV = 160/400 * 100 = 40% •40% of women with positive Pap smear result suffered from Ca cervix.

NPV = 560/600 * 100 = 93%•93% of women with negative Pap smear result do not suffer from Ca cervix.

Relationship between Predictive value and Disease Prevalence

• There are two community with different breast cancer prevalence; – 50/1,000pop and 30/1,000pop.

• Both community has total population of 1,600• If we are going to apply a screening test with

95% sensitivity and 85% specificity • what will be the predictive value of positive

and negative in that communities?

Breast cancer D+

No breast cancer D-

Totals

Test T+ 76(step 4) sensitivity

228(step 7) 304(step8)

Test T - 4(step 6) 1292(step 5)specificity

1296(step 5)

Totals 80(step 2) prevalence

1520(step 3) 1,600(step 1)

Calculation for community with 50/1,000 pop

PVP=76/304= 0.25PVN=1292/1296=.0.997

Breast cancer D+

No breast cancer D-

Totals

Test T+ 45.6(step 4) sensitivity

232.8(step 7) 278.4(step8)

Test T - 2.4(step 6) 1319.2(step 5)specificity

1321.6(step 5)

Totals 48(step 2) prevalence

1552(step 3) 1,600(step 1)

Calculation for community with 30/1,000 pop

PVP=45.6/278.4= 0.16PVN=1319.2/1321.6=.0.998


The higher the prevalence the greater the predictive value of positive

Why should we be concerned ?

• Directed to – High risk target population

• Most productive and efficient• More motivated to participate• More likely to take recommended action

Efficiency of a test

– The percentage of all true positive and true negative results

– a+d / a+b+c+d– The higher the value, the more efficient the

measure

Is test useful?

• Likelihood ratio (LR)– The likelihood that the test result would be

expected in a patient with the condition compared to the likelihood that the same result would be expected in a patient without the condition

– Unlike predictive values, likelihood ratios are not influenced by prevalence of the disease

• Likelihood ratio (Positive)– Divide the sensitivity by 1- specificity

• Likelihood ratio (Negative)– Divide the 1- sensitivity by specificity

Likelihood Ratios Positive

Likelihood ratio positive (LR+) is the ratio of the sensitivity of a test to the false positive error rate of the test (1- specificity)

The higher the ratio is the better the test.

D+ D-

T+ a b a+b

T- c d c+d

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

LR+ = [a/(a+c)] / [b/(b+d)]

Likelihood Ratios NegativeLikelihood ratio negative

(LR-) is the ratio of the false negative error rate of a test (1- sensitivity )to the specificity of the test

The closer the ratio is to 0 the better the test.

D+ D-

T+ a b a+b

T- c d c+d

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

LR- = [c/(a+c)] / [d/(b+d)]

Summary

• Concept of a screening test• How good is a screening test? (Validity)• Question for physician (Predictive accuracy)• Cutoff values• Is test useful? (LR)

References

1. Park. K., 2009. Park’s Textbook of Preventive and Social Medicine. pp 123-130. 20th Edition.

2. Gordis. L., 2009. Epidemiology. pp 85-108. 4th Edition

3. Petrie. A., and Sabin. C.,2000. Medical Statistics at a Glance. pp 90-92

AssignmentPelvic scan Ovarian cancer Total (n)

Present Absent abnormal 15 20 35normal 5 60 65Total 20 80 100

A hundred women at high risk of ovarian carcinoma have a pelvic ultrasound scan. The findings after scan and surgery are shown in the table. Calculate the following measures and interpret them.1. Sensitivity2. Specificity3. False negatives4. False positives5. Positive Predictive value6. Negative Predictive value

• A new screening test with sensitivity of 80% and specificity of 90% was performed on 1,000 persons for detection of avian influenza H5N1 infection. The prevalence of disease was 20% in the general population. Compute the following and interpret them.– Construct 2x2 table.– Calculate positive predictive value of the test.– Calculate false positive of positive test.

screening for diseases by dr. san

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