Screening for diseases

If the test
results are
positive, what
is the
probability that
this patient has
the disease?
27 January 2020 Screenin for Diseases - Dr. JRC 1

Screening for Diseases
Dr. Jayaramachandran S
Associate Professor
Department of Community Medicine
MGMCRI
"Health should mean a lot more than escape from death or,
for that matter, escape from disease"

At the end of the session, the student shall be able to
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

At the end of the session, the student shall be able to
6. Toenumerate the types of screening
7. Todescribe the basic requirements of a screening test
8. Tocalculate the validity (sensitivity and specificity) of a screening
test and interpret them
9. Tocalculate the predicative accuracy of a screening test and
interpret them
10. Toset 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

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

Screening and diagnostic tests contrasted
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
• No. of symptoms, signs & lab investigations
• More accurate
• More expensive
• Basis for treatment
• Initiatives from a patient with a complaint

Concept of “lead time”

Aims and objectives

Uses of screening
A. Case detection
• Prescriptive screening
• Presumptive identification of unrecognized disease
• E.g. Breast cancer, cervical cancer, diabetes

Uses of screening
B. Control of disease
• Prospective screening
• For benefits of others
• E.g. screening of immigrants from infectious diseases

Uses of screening
C. Research Purposes
• More basic knowledge about natural history of diseases
• E.g. chronic diseases (cancer, hypertension)
D. Educational opportunities
• Creating public awareness and educating heath professionals
• E.g. screening for diabetes

Types of screening
Mass screening
• Whole
population
• Sub groups
High risk or
selective screening
• High risk groups
• Screening of
diabetes,
hypertension,
breast cancer in
other members
of family
Multiphasic
screening
• Two or more
screening tests at
one time

Criteria for screening
The
disease The test

Criteria for the disease – IATROGENIC
• Condition should be important (I)
• An acceptable treatment should be available for disease (A)
• Diagnostic and treatment facilities should be available (T)
• A recognizable early symptomatic stage is required (R)
• Opinions on who must be treated must be agreed (O)

Criteria for the disease – IATROGENIC
• The safety of the test is guaranteed (G)
• The test examination must be acceptable to the patient (E)
• The untreated natural history of the disease must be known (N)
• The test should be inexpensive (I)
• Screening must be continuous (C)

Criteria for screening test
• Simple
• Safe
• Cheap and
• Rapidly applied
• Acceptable by the people
• Reliable (repeatable or
reproducible)
• Acceptability
• Repeatability
• Validity
• Yield

Acceptability
• Since a high rate of cooperation is necessary, its important that the
test should be acceptable to the people at whom it is aimed.

Repeatability
• The test must give consistent results when repeated more than once
• It depends on 3 major factors:
a) Observer variation
b) Biological (Subject) variation
c) Errors relating to technical methods

Observer variations
• Intra-observer variations – If a
single observer takes two
measurements (e.g. BP & Chest
expansion) in the same subject,
at the same time and each time
he obtains a different result,
this is termed as intra-observer
or within-observer variation.
• Inter-Observer variation – this is
variation b/w diff. observers on
the same subject or material,
also known as Between-
observer variation.

Biological (subject) variations
• There is a biological availability associated with many physiological
variables such as BP, Blood sugar, Serum cholesterol etc.
• The fluctuation in the variate measured in the same individual may
be due to:
1. Changes in the parameters observed. E.g. Cervical smears taken
from the same woman may be normal one day, and abnormal on
other day.
2. Variations in the way patients perceive their symptoms and
answers.

Biological (subject) variations
3. Regression to mean : There is tendency for values at the extreme of
distribution, to regress toward the mean or average on repeat
measurements.
• E.g. elevated serum cholesterol is associated with high risk of
developing coronary heart disease.
• In this way preventive measures can be applied before the disease
occurs.

Errors relating to technical methods
• Repeatability may be affected by variations inherent in the method.
• e.g.
• -- Defective instruments
• -- Erroneous calibration
• -- Faulty reagents
• -- Test inappropriate or unreliable

Validity (accuracy)
• The term Validity refers to what extent the test accurately measures
which it purports to measure.
• In other words, validity expresses the ability of a test to separate or
distinguish those who have the disease from those who do not.

Validity has two components
• Sensitivity
• Specificity
• When assessing the accuracy of a diagnostic test, one must consider
both these tests
• Sensitivity & Specificity are usually determined by applying the test,
to one group of persons having disease, and to a reference group not
having the disease.
• Both measurements are expressed in percentages.

Evaluation of screening test
Sensitivity Specificity
Predictive value of
positive test
Predictive value of
negative test
Percentage of false
negatives
Percentage of false
positives

Diagnosis (screening test results)
Test Diseased Not diseased Total
Positive
(True positive )
a
(False positive)
b
(Total positive)
a + b
Negative
(False negative)
c
(True negative)
d
(Total negative)
c + d
Total
(Total disease)
a + c
(Total non-diseased)
b + d
(Grand total)
a + b + c + d

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
Diseased
Not
diseased
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

Sensitivity
• 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
Diseased
Not
diseased
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

Specificity
• 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%

Predictive accuracy
• Performance of screening test is measured by its “predictive value”
which reflects diagnostic power of test.
• Predictive accuracy depends upon sensitivity, specificity and disease
prevalence.
• More prevalent a disease in a given population, more accurate will be
the predictive value of a positive screening test.
• Two measures : Predictive value of a positive test and 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
27 January 2020 Screenin for Diseases - Dr. JRC 338/12/2012
Screening
test
Diagnosis
Total
Diseased
Not
diseased
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
27 January 2020 Screenin for Diseases - Dr. JRC 348/12/2012
Screening
test
Diagnosis
Total
Diseased
Not
diseased
Positive
a
(True
positives)
b
(False
positives)
a + b
Negative
c
(False
negatives)
d
(True
negatives)
c + d

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
Diseased
Not
diseased
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
Diseased
Not
diseased
Positive
a
(True
positives)
b
(False
positives)
a + b
Negative
c
(False
negatives)
d
(True
negatives)
c + d

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
8/12/201227 January 2020 Screenin for Diseases - Dr. JRC 37

Likelihood ratio (LR): Is test useful?
• 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.
• LR+ = [a/(a+c)] / 1- [b/(b+d)]
D+ D-
T+ a+b
T-
a b
c d c+d
a+c b+d a+b+c
8/12/2012

Likelihood Ratios Negative
• Likelihood 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.
• LR(-) = [1 - c/(a+c)] / [d/(b+d)]
D+ D-
T+ a+b
T-
a b
c d c+d
a+c b+d a+b+c+d
8/12/2012

Combination of tests
• Two or more tests can be used in combination to enhance specificity
or sensitivity of screening.
• For example syphilis screening (RPR test) has high sensitivity, yet will
yield false positives.
• However, all those positive to RPR are then submitted to FTA-ABS
which is more specific test and the resultant positives now truly have
syphilis

Yield
• It is the amount of previously unrecognized disease that is diagnosed
as a result of screening effort.
• It depend on sensitivity and specificity of the test, prevalence of the
disease and participation of individuals in the detection program.

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

8/12/2012
Trade-off b/n sensitivity & 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
High sensitivity
low cutoff values
• When the disease is
– Lethal
– Early detection
improves the prognosis
(E.g. cervical cancer, breast cancer) Tolerable FP
• When the disease
High specificity
high cutoff values
– Tx not change much
– Need to limit FP
(E.g. diabetes)

How to choose the best cut-off 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
0
10
20
30
40
50
60
70
80
90
100
0 20 80
50
100
10000
25000
50000
10
1000 (meanrlu)
S
e
n
s
i
t
i
v
i
t
y
40 60
1- specificity
100

Receiver Operator Characteristic (ROC) Curve
• The area under the curve
represent overall accuracy of
the test useful to compare two
tests
27 January 2020 Screening for Diseases - Dr. JRC 49
ROC curve to determine best cutoff point for Wilsom Risksum
scoring to detect difficulty of endotrachealintubation
0
100
90
80
0 20 60 80 100
70
2
60
503
40
30
205
10
0
1
sensitivity
40
1- specificity
37

Some Screening Tests
Pregnancy Infancy
•Anemia
•Hypertension
•Toxemia
•Rh status
•Syphilis
•Cardiovascular disease
•Neural tube defects
•Down syndrome
•HIV
•Congenital heart disease
•Spina bifida
•visual defects
•Hypothyroidism
•Haemoglobinopathies
•Sickle cell anemia

Some Screening Tests
Middle-aged men and women Elderly
•Hypertension
•Cancer
•Diabetes mellitus
•Serum cholesterol
•Nutritional disorders
•Cancer
•Glaucoma
•Cataract

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

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

Screening for diseases

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