Screening for diseases by Dr. San

Screening for Diseases

Dr San San Oo

Learning outcomes
1. To describe the concept of screening
2. To differentiate between screening test and
diagnostic test
3. To explain the concept of “lead time”
4. To understand aims and objectives of
screening
5. To list the uses of screening

8/12/2012 Dr.san san oo_commed 2

6. To enumerate the types of screening
7. To describe the basic requirements of a
screening test
8. To calculate the validity (sensitivity and
specificity) of a screening test and interpret
them
9. To calculate the predicative accuracy of a
screening test and interpret them
10.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 Diagnostic test
• Apparently healthy • With indications or sick
• Groups • Single patients
• Test results are arbitrary and • Diagnosis not final, the sum of
final all evidence
• One criterion or cut-off • Numbers of symptoms, signs
and lab investigations
• Less accurate • More accurate
• Less expensive • More expensive
• Not a basis for treatment • Basis for treatment
• Initiatives from investigators or • Initiatives from a patient with
agency 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
Apparently abnormal
(Periodic re screening)

Normal
Intermediate Abnormal
(Periodic re-
(Surveillance) (Treatment)
screening)


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 considerations
1. The disease
2. 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 Infancy
• Anaemia • Hearing defects
• Hypertension toxaemia • Visual defects
• Rh status • Haemoglobinopathies
• Syphilis (VDRL) • Spina bifida
• Diabetes
• HIV
• Neural tube defects
• Down’s syndrome


Middle aged men and women Elderly
• Hypertension • Cancer
• Cancer • Glaucoma
• Diabetes mellitus • Cataract
• Serum cholesterol • Chronic bronchitis
• obesity • 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. Sensitivity
2. Specificity


Test with dichotomous results
(positive or negative)


Two by two table
Screening 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. Sensitivity
2. Specificity
3. Predictive value of a positive test
4. Predictive value of a negative test
5. Percentage of false negatives
6. Percentage of false positives


Sensitivity
• The ability of a test to Screening Diagnosis Total
test
identify correctly those
who have the disease Diseased Not
diseased
• Proportion of
individuals with the Positive a b a+b
(True (False
disease who are positives) positives)
correctly identified by
the test Negative c d c+d
(False (True
• True positives negatives) negatives)

• a/a+c
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 Screening Diagnosis Total
test
identify correctly those
who do not have the Diseased Not
diseased
disease
Positive a b a+b
• Proportion of individuals (True (False
without the disease who positives) positives)

are correctly identified by Negative c d c+d
the test (False (True
negatives) negatives)
• True negatives
• d/b+d Total a+c b+d a+b+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 Screening Diagnosis Total
have the disease are told test
that they do not have the
disease Diseased Not
diseased
• c/a + c
• False reassurance Positive a b a+b
(True (False
• Ignore the development positives) positives)
of symptoms and signs
• Critical Negative c d c+d
(False (True
– if effective intervention is negatives) negatives)
available (e.g. cancer)
• Very sensitive test has Total a+c b+d a+b+c+d
fewer FN


False positives
• Patients who do not Screening Diagnosis Total
have the disease are test
told that they have Diseased Not
• b/b+d diseased
• Further tests Positive a b a+b
• Expenses (True (False
positives) positives)
• Anxiety and worry
• Limitation in Negative c d c+d
employment (False
negatives)
(True
negatives)
• A high specificity
screening test has fewer Total a+c b+d a+b+c+d
FP


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


8/12/2012 Dr.san san oo_commed Downloaded from: StudentConsult (on 27 November 2010 02:13 AM)
31
© 2005 Elsevier

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

• Plot true positive rate 100
(sensitivity) against 90
50 10

false positive rate
100

80
(1-specificity) for several s 70
1000 (mean rlu)

choice of positively
e
n 60
criterion
10000
s
i 50

• choose closest to top left ti 40
25000
50000

corner to maximized the vi 30

discriminative ability of y t 20

the test 10

0
0 20 40 60 80 100
8/12/2012 Dr.san san oo_commed 1- specificity 36

Receiver Operator Characteristic (ROC) Curve

ROC curve to determine best cutoff point for Wilsom Risk sum
• The area under the curve scoring to detect difficulty of endotracheal intubation

represent overall
100
0
1
90
accuracy of the test 80
• useful to compare two 70 2

test
sensitivity
60
3
50

40

30

20
5

10

0
0 20 40 60 80 100
1- specificity

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

38
© 2005 Elsevier

Predictive accuracy
• Diagnostic power of the test
• Depend upon
1. Sensitivity
2. Specificity
3. Prevalence of disease
• Two measures
1. Predictive value of a positive test
2. Predictive value of a negative test


Predictive value of a positive test
• The probability that an Screening Diagnosis Total
test
individual with a
Diseased Not
positive test result has diseased
the disease
Positive a b a+b
• a/a+b (True
positives)
(False
positives)
• A 44% PPV means
Negative c d c+d
• 44% of the people with (False (True
positive test result have the negatives) negative)
disease in question
Total a+c b+d a+b+c+d


Predictive value of a negative test
• The probability that an Screening Diagnosis Total
test
individual with a
Diseased Not
negative test result diseased
does not have the Positive a b a+b
disease (True (False
positives) positives)
• d/c+d
Negative c d c+d
• A 98% NPV means (False (True
negatives) negatives)
• 98% of the people with
negative test result do not Total a+c b+d a+b+c+d
have the disease in question


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?


Calculation for community with
50/1,000 pop
Breast No breast Totals
cancer D+ cancer D-

Test T+ 76(step 4) 228(step 7) 304(step8)
sensitivity

Test T - 4(step 6) 1292(step 5) 1296(step 5)
specificity

Totals 80(step 2) 1520(step 3) 1,600(step 1)
prevalence

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


Calculation for community with
30/1,000 pop
Breast No breast Totals
cancer D+ cancer D-

Test T+ 45.6(step 4) 232.8(step 7) 278.4(step8)
sensitivity

Test T - 2.4(step 6) 1319.2(step 5) 1321.6(step 5)
specificity

Totals 48(step 2) 1552(step 3) 1,600(step 1)
prevalence

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


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
D+ D-
(LR+) is the ratio of the
sensitivity of a test to the false
T+ a b a+b
positive error rate of the test
(1- specificity) T- c d c+d
The higher the ratio is the
better the test. a+c b+d a+b+c+

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

Likelihood Ratios Negative
Likelihood ratio negative
(LR-) is the ratio of the
false negative error rate of D+ D-
a test (1- sensitivity )to the
specificity of the test T+ a b a+b
The closer the ratio is to 0 the
better the test. 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


Assignment
Pelvic scan Ovarian cancer Total (n)
Present Absent
abnormal 15 20 35
normal 5 60 65
Total 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. Sensitivity
2. Specificity
3. False negatives
4. False positives
5. Positive Predictive value
6. 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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