Screening is the testing of apparently healthy populations to identify previously undiagnosed diseases or people at high risk of developing a disease.  Screening aims to detect early disease before it becomes symptomatic. Screening is an important aspect of prevention, but not all diseases are suitable for screening. 

1. SCREENING
.PhD
Gujranwala Medical College Gujranwala

2. What is Screening
• Screening is the testing of apparently healthy
populations to identify previously undiagnosed diseases
or people at high risk of developing a disease.
• Screening aims to detect early disease before it
becomes symptomatic.
• Screening is an important aspect of prevention, but not
all diseases are suitable for screening. 6/9/2023
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Prof Dr M Tauseef Jawaid GMC

3. Definitions
1. Screening program -- comprehensive disease control
activity based on the identification and treatment of
persons with either unrecognized disease or
unrecognized risk factors for disease.
2. Screening test -- specific technology (survey
questionnaire, physical observation or measurement,
laboratory test, radiological procedure, etc.) used to help
identify persons with unrecognized disease or
unrecognized risk factors for disease.
Definitions
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Prof Dr M Tauseef Jawaid GMC

4. Definitions
3. Primary prevention -- disease control approach based on the
elimination or reduction of risk factors for disease. Primary
prevention aims to prevent the occurrence of disease. Primary
prevention may use screening tests to identify persons with risk
factors.
4. Secondary prevention -- disease control approach based on the
active identification and treatment of persons with unrecognized
disease. Secondary prevention aims to prevent the occurrence of
adverse outcomes from disease (such as fatal outcomes), without
necessarily reducing the occurrence of disease. Secondary
prevention must screen to identify persons with unrecognized
disease
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Prof Dr M Tauseef Jawaid GMC

5. Generalities
1. Screening often implies a public health related
activity involving asymptomatic or healthy
subjects coming from the general population.
2. Case-finding refers to special clinical efforts to
recognize disease among persons who
consult a health professional.
Generalities
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Prof Dr M Tauseef Jawaid GMC

6. Screening, Case finding and Diagnostic test
Terminology
for testing
Target Persons
Screening Apparently healthy individuals who
are not seeking health care
Case-finding To detect disease in individuals
seeking health care for other
reasons
Diagnostic
tests
To confirm or disprove the existence
of disease in patients presenting
with complaints (Symptoms & signs
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Prof Dr M Tauseef Jawaid GMC

7. The Principles of Screening
• The choice of disease for which
to screen;
• There should be longer latent or
early a symptomatic stage
• Facilities for confirmation of
diagnosis must be available
• The availability of a treatment
for those found to have the
disease;
• The relative costs of the
screening.
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Prof Dr M Tauseef Jawaid GMC

8. • The disease must be an important health problem.
• There should be a recognizable latent or early symptomatic
stage.
• The natural history of the disease, including latent to
declared disease, should be adequately understood.
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Prof Dr M Tauseef Jawaid GMC

9. 6/9/2023
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DR Muhammad Tauseef Javed SIMS
2017

10. When to screen?
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11. 6/9/2023
Prof Dr M Tauseef Jawaid GMC
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12. • There should be a suitable test or examination.
• The test should be acceptable to the population.
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13. Examples of screening
• Screening the healthy people for hypertension
• Screening healthy adults for diabetes
• Screening of high-risk population for HIV/AIDS and Hepatitis
• Screening of pregnant ladies for anemia/ Cervical cancers
etc
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14. Screening and diagnostic tests
Screening tests Diagnostic tests
Conducted on apparently
health population
Conducted on sick or with
some indications
Applied to groups or
communities
Applied to the patients under
consideration
The initiative comes from the
investigator or some agency
Initiative based on patient
complaints
The objectives are
predominantly preventive
The objective is to modify the
treatment on basis of tests
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Prof Dr M Tauseef Jawaid GMC

15. Screening and diagnostic tests
Screening tests Diagnostic tests
Based on one criterion or cut-
off point
Based on clinical evaluation of
signs and symptoms
Less expensive More expensive
Less accurate More accurate
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16. True Disease Status
Screening
Test
Positive Negative Total
Positive True Positives
(TP)
False Positives
(FP)
TP+FP
Negative False Negatives
(FN)
True Negatives
(TN)
FN+TN
Total TP+FN FP+TN TP+FP+FN+TN
Outcomes of a Screening Test
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17. • There should be an acceptable treatment for the patients
with recognized disease.
• There should be facilities for diagnosis
and treatment should be available.
• There should be an agreed policy on whom to treat as
patients.
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Prof Dr M Tauseef Jawaid GMC

18. • The cost of case finding (including diagnosis and treatment of
patients diagnosed) should be economically balanced in
relation to possible expenditure on medical care as a whole.
• Case finding should be a continuing process and not a "once
for all" project.
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Prof Dr M Tauseef Jawaid GMC

19. Uses of Screening
Case detection Objectively done to identify the
unrecognized diseases e.g.
neonatal screening
Control of disease Objectively done to identify the
diseases to prevent transmission
in the community
Epidemiology /
Research
Initial screening to identify the
prevalence subsequent for
research purpose
Educational
Opportunities
Objectively done for health
education purposes e.g. screening
of diabetics
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Prof Dr M Tauseef Jawaid GMC

20. Screening Strategies
Mass
Screening
Screening of whole population or
subgroups of population e.g. Screening
of all adults for tuberculosis
High risk or
Selective
Screening is applied to selectively to
high-risk for a particular health problem
or disease
Multiphase
Screening
The people are subjected to more than
one screening test. First screening for
identification of suspect and second for
confirmation of diseases
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Prof Dr M Tauseef Jawaid GMC

21. Latent or Incubation period
Time period lapse between the start of the
disease process up to the appearance of
sign and symptoms of disease.
Disease
onset
Possible
detectio
n
Final
critical
point
Usual time
of
diagnosis
Latent/ incubation
period
outcom
e
A B C D
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22. • Time between possible detection and the usual time of
diagnosis by signs and symptoms is the “Lead Time”
• Time between first possible detection and the final critical
detection is the “Screening Time”
Screening time and lead time
Disease
onset
Possible
detection
Final
critical
point
Usual time
of
diagnosis
outcom
e
Screening
time
Lead time
A B C D
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Prof Dr M Tauseef Jawaid GMC

23. Concept of Latent period, Screening
time and Lead time
Disease
onset
Possible
detection
Final critical
point
Usual time
of
diagnosis
Latent/ incubation
period
outcome
Disease
onset
Possible
detection
Final
critical
point
Usual time of
diagnosis
outcome
Screening
time
Lead time
A B C D
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24. Summary
• Screening is the testing of apparently healthy populations
to identify previously undiagnosed diseases or people at
high risk of developing a disease.
• Principles of Screening: disease, test, treatment and cost.
What is the next step?
Define the validity of the screening test and
put screening to use in the population.
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25. Terms Related to
Screening Tests
• Validity - relates to accuracy (correctness)
• Reliability - repeatability
• Yield - the # of tests that can be done in a time period
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26. Terms Related to Screening Tests (cont’d)
• Sensitivity - ability of a test to identify those who
have disease
• Specificity - ability of a test to exclude those who
don’t have disease
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Prof Dr M Tauseef Jawaid GMC

27. Terms Related to Screening Tests
(cont’d)
• Tests with dichotomous results – tests that give either
positive or negative results
• Tests of continuous variables – tests that do not yield
obvious “positive” or “negative” results, but require a
cutoff level to be established as criteria for
distinguishing between “positive” and “negative”
groups
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Prof Dr M Tauseef Jawaid GMC

28. How will you test the accuracy of
screening test?
• Identify the screening test to be evaluated
• Identify the confirmatory test for counter testing also
known as “Gold Standard Test”
• Screened the population of interest by screening test
• Apply counter test or Gold Standard Test to all the
positive and negative identify by screening test
• Determine the accuracy by 2x2 Table analysis
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29. Examples of Screening and Gold
Standard
Disease Screening test Gold Standard or
Counter test
Diabetes Blood Glucose Glucose tolerance
test
Brain tumor EEG CT Scan
Breast
cancer
Mammography FNA
(histopathology)
Tuberculosi
s
Tuberculin test Sputum for AFB
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30. Sensitivity
Dis. Yes Dis. No Total
Dis.
yes
a
(True
positive)
b
(False
Positive)
a + b
Dis. No c
(False
Negative)
d
(True
Negative)
c + d
Total a + c b + d Grand total
Sensitivity =
a
a + c
True positive
True positive + False
Negative
X 100
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Prof Dr M Tauseef Jawaid GMC

31. Specificity
X 100
Dis. Yes Dis. No Total
Dis.
yes
a
(True
positive)
b
(False
Positive)
a + b
Dis. No c
(False
Negative)
d
(True
Negative)
c + d
Total a + c b + d Grand total
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Prof Dr M Tauseef Jawaid GMC

32. Percentage of false Positive
Percentage false
positive =
b
b + d
Dis. Yes Dis. No Total
Dis.
yes
a
(True
positive)
b
(False
Positive)
a + b
Dis. No c
(False
Negative)
d
(True
Negative)
c + d
Total a + c b + d Grand total
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Prof Dr M Tauseef Jawaid GMC

33. Percentage of false negative
X 100
Dis. Yes Dis. No Total
Dis.
yes
a
(True
positive)
b
(False
Positive)
a + b
Dis. No c
(False
Negative)
d
(True
Negative)
c + d
Total a + c b + d Grand total
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Prof Dr M Tauseef Jawaid GMC

34. Predictive value of positive test (PPV)
a
a + b
True Positive X 100
Dis. Yes Dis. No Total
Dis.
yes
a
(True
positive)
b
(False
Positive)
a + b
Dis. No c
(False
Negative)
d
(True
Negative)
c + d
Total a + c b + d Grand total
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Prof Dr M Tauseef Jawaid GMC

35. Predictive value of Negative test (NPV)
Dis. Yes Dis. No Total
Dis.
yes
a
(True
positive)
b
(False
Positive)
a + b
Dis. No c
(False
Negative)
d
(True
Negative)
c + d
Total a + c b + d Grand total
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Prof Dr M Tauseef Jawaid GMC

36. Apparent or false prevalence
False/apparent
prevalence =
a +
G. total
Dis. Yes Dis. No Total
Dis.
yes
a
(True
positive)
b
(False
Positive)
a + b
Dis. No c
(False
Negative)
d
(True
Negative)
c + d
Total a + c b + d Grand total
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Prof Dr M Tauseef Jawaid GMC

37. True Prevalence
a +c
G. total Total patient Screened
X
Dis. Yes Dis. No Total
Dis.
yes
a
(True
positive)
b
(False
Positive)
a + b
Dis. No c
(False
Negative)
d
(True
Negative)
c + d
Total a + c b + d Grand total
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Prof Dr M Tauseef Jawaid GMC

38. Accuracy of the test
Dis. Yes Dis. No Total
Dis.
yes
a
(True
positive)
b
(False
Positive)
a + b
Dis. No c
(False
Negative)
d
(True
Negative)
c + d
Total a + c b + d Grand total
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Prof Dr M Tauseef Jawaid GMC

39. Sensitivity and Specificity
• Sensitivity and specificity has reciprocal
relationship with each other
• If we increase the sensitivity of a test
specificity will be decreased
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40. Reliability of the Screening tests
What are the factors that determine the
reliability of screening tests?
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41. Three type of factors effect the
reliability of test
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42. Observational Variation
1. Intra-observer Variations (variation in
observation when a single observer repeat
the same observation)
2. Inter-observer Variation (Different observers
when the same observation is repeated by
different observers.
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Prof Dr M Tauseef Jawaid GMC

43. Intra-Observer
Also called Within Observer Variation
Same Observer
2 measurements
Same Person
Same Time
Each Time
Different Results
Minimized by - taking average of all measurements
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Prof Dr M Tauseef Jawaid GMC

44. +
+
=
=
A B
A B
X
Y
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Prof Dr M Tauseef Jawaid GMC

45. Inter - Observer
Also called as “Between - observer variation”
Different observers
Same subject
Ex: Examination of blood
smear for malarial parasite by 2observers
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Prof Dr M Tauseef Jawaid GMC

46. +
+
=
=
A
C X
Y
C
B
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47. •Use of Multiple Screening Tests
Sequential (Two-stage) Testing
Simultaneous Testing
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Prof Dr M Tauseef Jawaid GMC

48. Study designs for screening
1. Correlation Studies
Use:
Description of population
Strength:
Suggest possibility of benefit
Limitation:
Can’t test hypothesis
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Prof Dr M Tauseef Jawaid GMC

49. Study designs for screening
2. Analytical Studies
Types:
Case-control
Cohorts
Use:
Comparison
of rates
Advantage:
 Test hypothesis
Limitation:
Selection
Lead time
 length 6/9/2023
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Prof Dr M Tauseef Jawaid GMC

50. Study designs for screening
3. Randomized Trials
Use:
Comparison of rates
Strength:
Most valid test of hypothesis
Limitation:
Cost, ethics & feasibility
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Prof Dr M Tauseef Jawaid GMC

51. Review Questions (Developed by the
Supercourse team)
• What is screening and what types of screening can you name?
• What are the objectives of screening?
• For what type of diseases would it be appropriate to set up
screening programs? List characteristics.
• How is screening program evaluated?
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52. 6/9/2023
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53. Sensitivity, Specificity and
Likelihood Ratios
1
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Prof Dr M Tauseef Jawaid GMC

54. Sensitivity
5
4
• Proportion of patients with disease who are
tested positive with a test
• A 100% sensitive test will not have any false
negative results (although it may have a
high rate of false positive results)
• Therefore, a negative result of a highly
sensitive test means it is likely to be a true
negative (it rules out the disease)
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Prof Dr M Tauseef Jawaid GMC

55. Specificity
5
5
• Proportion of patients without disease
who are tested negative with a test
• A 100% specific test will not have false
positive results (although it may have high
rate of false negative results)
• Therefore, a positive result of a highly
specific test means it is likely to be true
positive (it rules in the disease)
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Prof Dr M Tauseef Jawaid GMC

56. Sensitivity
5
6
Disease
+ve
Disease -ve
Test +ve a (TP) b (FP)
Test –ve c (FN) d (TN)
TP = True
positive FN =
False negative
FP = False
positive TN =
True negative
Sensitivity =
(a)/(a+c)
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Prof Dr M Tauseef Jawaid GMC

57. Positive PredictiveValue
5
7
Disease
+ve
Disease -ve
Test +ve a (TP) b (FP)
Test –ve c (FN) d (TN)
TP = True positive
FN = False
negative FP =
False positive TN
= True negative
Positive PV =
(a)/(a+b)
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Prof Dr M Tauseef Jawaid GMC

58. Specificity
5
8
Disease
+ve
Disease -ve
Test +ve a (TP) b (FP)
Test –ve c (FN) d (TN)
TP = True positive
FN = False
negative FP =
False positive TN
= True negative
Specificity =
(d)/(b+d)
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Prof Dr M Tauseef Jawaid GMC

59. Negative Predictive
Value
5
9
Disease
+ve
Disease -ve
Test +ve a (TP) b (FP)
Test –ve c (FN) d (TN)
TP = True positive
FN = False
negative FP =
False positive TN
= True negative
Negative PV =
(d)/(c+d)
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Prof Dr M Tauseef Jawaid GMC

60. Sensitivity and Specificity
To increase sensitivity,
shift to the left (purple
line)
But by shifting to
the left, it
increases
proportion of false
positive, which
means reduced
specificity
Image taken from:
http://library.med.utah.edu/WebPath/TUTORIAL/BIOSTATS/BIOSTATS
.html
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Prof Dr M Tauseef Jawaid GMC

61. Sensitivity and Specificity
To increase specificity, shift
to the right
(purple line)
But by shifting to
the right, it
increases
proportion of
false negative,
which means
reduced
sensitivity
Image taken from:
http://library.med.utah.edu/WebPath/TUTORIAL/BIOSTATS/BIOSTATS
.html
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Prof Dr M Tauseef Jawaid GMC

62. Validity
It is the extent to which a test measures what it is supposed to
measure; in other words, it is the accuracy of the test. Validity is
measured by sensitivity and specificity. These terms, as well as other
jargon, are best illustrated using a conventional two- by-two (2 x 2)
table.
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Prof Dr M Tauseef Jawaid GMC

63. Sensitivity (positive in disease)
Sensitivity is the ability of a test to correctly classify an individual as
′diseased
Sensitivity = a / a+c
= a (true positive) / a+c (true positive + false negative)
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64. 6/9/2023
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65. ANSWER
•75 / 100 = 75%.Sen
•85 / 100 = 85%.Sp
• Sensitivity and specificity are inversely proportional, meaning that as the sensitivity increases, the specificity
decreases and vice versa.
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Prof Dr M Tauseef Jawaid GMC

66. Positive Predictive Value (PPV)
• It is the percentage of patients with a positive test who actually have the
disease. In a 2 x 2 table [Table ], cell ′a′ is ′true positives′ and cell ′b′ is ′false
positives.′ In real life situation, we do the new test first and we do not have
results of ′gold standard′ available. We want to know how this new test is
doing. PPV tells us about this – how many of test positives are true positives;
and if this number is higher (as close to 100 as possible), then it suggests
that this new test is doing as good as ′gold standard.′
• PPV: = a / a+b
• = a (true positive) / a+b (true positive + false positive)
• = Probability (patient having disease when test is positive)
• Example: We will use sensitivity and specificity provided in Table to
calculate positive predictive value.
• PPV = a (true positive) / a+b (true positive + false positive)
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Prof Dr M Tauseef Jawaid GMC

67. • = 75 / 75 + 15 = 75 / 90 = 83.3%
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Prof Dr M Tauseef Jawaid GMC

68. Negative Predictive Value (NPV)
• It is the percentage of patients with a negative test who do not
have the disease. In 2 x 2 table [Table], cell ′d′ is ′true negatives′
and cell ′c′ is ′false negatives.′ NPV tells us how many of test
negatives are true negatives; and if this number is higher
(should be close to 100), then it suggests that this new test is
doing as good as ′gold standard.′
• NPV: = d / c+d
• = d (true negative) / c+d (false negative + true negative)
• = Probability (patient not having disease when test is negative)
• Example: We will use sensitivity and specificity provided
in Table to calculate negative predictive value.
• NPV = a (true negatives) / c+d (false negative + true negative)
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Prof Dr M Tauseef Jawaid GMC

69. • NPV = a (true negatives) / c+d (false negative + true negative)
• = 85 / 85 + 25 = 85 / 110 = 77.3%
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Prof Dr M Tauseef Jawaid GMC

70. • Positive and negative predictive values are directly related to
the prevalence of the disease in the population [Fig. 1].
Assuming all other factors remain constant, the PPV will
increase with increasing prevalence; and NPV decreases with
increase in prevalence. This is illustrated by the following
example.
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71. Figure 1
As the disease prevalence increases, the positive predictive value also increases6/9/2023
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72. Showing example of calculation of predictive value at 50% prevalence
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73. Showing example of calculation of predictive values at 1%
prevalence
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Prof Dr M Tauseef Jawaid GMC

74. Thank You