Screening of Diseases_Community Medicine Slides may be referred by both undergraduate and postgraduate students and anyone affiliated to Public health. Any comments or doubts may be addressed to vineeta1992@gmail.com

1. SCREENING
Dr. Vineeta
Shukla
st

2. Outline
 Definition and Concept
 Iceberg Phenomenon of Disease
 Screening vs Diagnosis
 Concept of Lead Time
 Examples of Screening
 Types of Screening
 Characteristics of a Screening Test
 Evaluating a Screening Test
 Baye’s Theorem
 Screening in Series and Parallel
 Problem of the Borderline
 Kappa Statistics
 ROC Curve

3. Definition
 Defined as “ the search for
unrecognized disease or defect
by means of rapidly applied tests,
examinations or other procedures
in apparently healthy individuals.”

4. Iceberg Phenomenon of
Disease
•Biggest Challenge in Preventive Medicine

5. Screening : A form of
Secondary Prevention
•Earlier done to conserve physician’s time for
diagnosis , administer inexpensive lab tests etc
•Now-a-days Screening is
considered a form of
Secondary prevention
•It detects disease in its early
Asymptomatic phase whereby
early treatment can be given and
disease can be cured or its
progression can be delayed

6. SCREENING DIAGNOSIS
Done on Apparently healthy Done on Cases
Applied on Population groups Applied on individual basis
Test result is finalDiagnosis and
confirmation
Diagnostic Test result is not final
Based on one criterion or cut off
point
Based on Signs and Symptoms
Less accurate More accurate
Less expensive More expensive
Rapid test,faster Time Consuming
Not a basis for Treatment Basis for Treatment
Initiative comes from the
investigator
Initiative comes from patient

7. Concept of Lead time

8. How much we are ‘leading’ the
time of detection of the disease (
The Advantage gained by
Screening)

9. Examples of Screening
SCREENING TEST(S) DISEASE SCREENED
VIA , Pap Smear Cervical Cancer
Breast Self Examination(BSE) Breast Cancer
Mammography Breast Cancer
Bimanual Oral Palpation Oral Cancer
ELISA HIV
Urine for Sugar , RBS Diabetes mellitus
AFP ( Alpha Feto Protein) Developmental anomalies in
fetus
Prostate Specific
Antigen(PSE)
Prostate Cancer
Fecal Occult Blood Test Colorectal Cancer

12. Uses of Screening
1. CASE DETECTION : Presumptive Screening
“ Presumptive identification of unrecognized
disease
which does not arise from a patient’s request
.”
Example : Neonatal Screening
The people are screened
primarily for their own
benefit.

13. 2. CONTROL OF DISEASE : Prospective
Screening
People are examined for the Benefit of others
Example :
-Screening of Immigrants to protect home population
-Screening for HIV,
STIs etc

14. 3. RESEARCH PURPOSES :
•To know the Natural History of Chronic diseases

15. 4. EDUCATIONAL OPPORTUNITIES :
-Acquisition of Information of Public Health
relevance
-Providing opportunities for creating Public
awareness
-For educating Health Professionals

16. Types of Screening
1. MASS SCREENING :
Applied generally on large unselected
populations regardless of the probability
of having the disease or condition.
Example :
-Visual defects in all school children
-Chest Xray in elderly
for detection of
Lung Cancer
-Mammography
For breast cancer
Among women

17. 2. HIGH RISK/SELECTIVE
SCREENING : Applied on high risk groups.
Example :
-Screening for HIV in commercial sex workers.
-Screening fetus for Down’s Syndrome in a
mother who has already had a Down’s
Syndrome baby in the past
-Screening for Ca Cervix
In low SE status women

18. 3. MULTIPHASIC SCREENING :
Various Screening tests are applied during
the same screening program.
Example :
-Annual Health Check up(DM, Lipid
profile, LFT, KFT)
4. MULTIPURPOSE SCREENING :
When >1 Test is applied simultaneously to
detect >1 Disease.
Example :
-Screening of Pregnant women for VDRL ,
HIV , HBV by serological tests

19. 5. OPPORTUNISTIC SCREENING :
Example – Screening for Rheumatic
Heart disease in school children

20. CRITERIA FOR SCREENING
 Before initiating a Screening
Programme , a decision must be
made whether it abides to all the
ethical, scientific and financial
justification.
 The priniciples that should govern a
Screening Programme were first
enunciated by Wilson and
Jugner(1968)
 Criteria based on – The Disease

21. Wilson and Jugner Criteria(1968)
A : The Disease
 Knowledge of the Disease
(i)The condition should be important
(ii)There must be a recognisable latent or
early symptomatic stage
(iii)Natural course of condition,including
development from latent to declared
disease, should be adequately
understood
 Knowledge of Test
(iv)Suitable test or examination
(v)Test acceptable to population
(vi)Case finding should be continuous , not
just ‘once and for all’ project

22.  Treatment for Disease
(vii)Accepted treatment for patients with
recognised disease
(viii)Facilities for diagnosis and treatment
available
(ix)Agreed policy concerning whom to
treat as patients
 Cost Considerations
(x) Cost of case finding should be
economically balanced in relation to
possible expenditure on medical care as
a whole

23. B : The Screening Test
 Simple
 Acceptable to Subjects and Providers
 Reliable : Precision, Reproducible,
Repeatability – Observer Variation
Biological Variation
Technical error
 Valid : Accuracy
 Safe
 Cost effective
 Yielding : the amount of previously
unrecognized disease that is
diagnosed and brought to treatment
as a result of the screening programme.

25. Components of Validity
 Sensitivity
 Specificity
 Predictive Accuracy

26. Result of a Screening Test
DISEASE
PRESENT
DISEASE
ABSENT
TOTAL
Screening Test
POSITIVE
True Positive
( TP) (a)
False
Positive (FP)
(b)
Total Positive
by Test = a +
b
Screening Test
NEGATIVE
False
Negative
(FN) (c)
True
Negative
(TN) (d)
Total
Negative by
Test = c + d
Total
Diseased = a
+ c
Total Non-
diseased =
b + d
Total
population=
a+b+c+d

27. Sensitivity
 Ability of a Test to identify correctly all
those who have the disease ( True
Positive)
 Also called as Positivity in Disease
X 100
=
X 100
X 100

28. Specificity
 Ability of a Test to identify correctly those
who do not have the disease( True
Negative)
X
100
=
=
X 100
X 100

29. Example :
Q : ELISA was used as a Screening Test for HIV
among 1000 healthy blood donors. The results of
the test are as follows. Calculate Sensitivity and
Specificity
HIV Positive HIV Negative
ELISA
Positive
95 (a) 45 (b) a + b = 140
ELISA
Negative
5 (c) 855 (d) c + d = 860
a + c = 100 b + d = 900

30. Sensitivity =
= (95 / 100) x 100
= 95%
Specificity =
= (855/900) x 100
= 95%
X 100
X 100

31. Problems with FP and FN
 False Positives :
-Further testing with long , expensive tests
-Discomfort , inconvenience , anxiety
-Burden on health facilities
-Emotional trauma
-Difficulty in de-labelling
 False Negatives :
-False Reassurance
-Ignores any disease signs and symptoms
-Postponement of treatment
-Detrimental to overall health

32. Positive Predictive Value
 Ability of a Screening Test to identify
correctly all those who have the
disease , out of all those who test
positive on a screening test
 Also called Post Test Probability
PPV = [a /(a+b) ] x 100
X 100

33. Negative Predictive Value
 Ability of a Screening Test to identify
correctly all those who do not have the
disease , out of all those who test
negative on a screening test
NPV = [ d/(c+d) ] x 100
X 100

34. Calculate PPV and NPV
HIV Positive HIV Negative
ELISA
Positive
95 (a) 45 (b) a + b = 140
ELISA
Negative
5 (c) 855 (d) c + d = 860
a + c = 100 b + d = 900

35. PPV =
= (95/140) x 100
= 68 %
NPV =
= (855/860) x 100
= 99 %

36. Problems with PPV and NPV :
 While Sensitivity and Specificity of a
particular Screening test are constant
PPV and NPV are largely dependent on
the Prevalence of the disease in the
Population
 Example :
Q:The sensitivity and specificity of ELISA
in diagnosing HIV infection is 99% and
90% respectively, prevalence of HIV =
40%. Total Population (Commercial sex
workers) is 10,000.Calculate PPV and

37. Prevalence = 40%
Total HIV positive = (40/100) X 10,000=
4000.
and HIV negative = 10,000 - 4,000 = 6000.
Sensitivity of ELISA is 99%
TP : (99 /100) x 4000 = 3960
FN : 4000 - 3960 = 40
Specificity is 90%, it will correctly call, as
negative,
TN : (90/100) of 6000= 5400
FP : 6000 - 5400 = 600

38. HIV Positive HIV Negative
ELISA
Positive
3960 (a) 600 (b) a + b =
4560
ELISA
Negative
40 (c) 5400 (d) c + d =
5440
Total = 4000 Total = 6000
 PPV = [ a /(a+b)] x 100
= (3960/4560) x100
= 0.86 x 100 = 86%
 NPV = [ d/(c+d)] x 100
= (5400/5440) x 100
= 0.99 x 100 = 99%

39. Baye’s Theorem
 If the test results are +ve , what is the
probability that the patient has the
disease?
 If the test is negative , what is the
probability that the person doesn’t
have the disease?
 Baye’s Theorem provides answer
 It was first described by Clergyman

40. Formula :

41. Baye’s Theorem
Relationship between PPV of a Screening Test
and Sensitivity, Specificity and Prevalence of
disease in a population:
PPV
NPV [ ]
[ ]

42. Likelihood Ratio
 Incorporates both the Sensitivity and
Specificity of the test and provides a
direct estimate of how much a test
result will change the chances of
having a disease
 Sensitivity (TP) = 1- FN
or FN = 1 – Sensitivity
 Specificity (TN) = 1- FP
or FP = 1 - Specificity

43. Likelihood Ratio Positive (LR
+)
Example : If the sensitivity of ELISA is 99% (i.e.
0.99) and specificity is 90% (i.e. 0.90), then
A positive result on ELISA for HIV is 9.9 times
more likely to occur in a subject with HIV infection
as compared to a subject who does not have HIV
infection

44. Likelihood Ratio Negative (LR
-)
Example : If the sensitivity of ELISA is 99%
(i.e. 0.99 and specificity is 90% (i.e. 0.90),
then
The interpretation is that a negative result is only
one
hundredth times likely to occur in a person who
really has HIV infection as compared to a person

45. Tests in Series and Parallel
• SERIES : One Test after Another
2nd Test is applied only after 1st Test is
Positive
• PARALLEL : Both Tests are applied together
Series Parallel
Sensitivity Decreases Increases
Specificity Increases Decreases
PPV Increases Decreases
NPV Decreases Increases

46.  STa( Sn= 90%) in 100 patients
+ve in 90/100 patients
STb( Sn=90%) in 90 patients
+ve in 90% of 90 = 81
patients

47. STa STb
90/100 90/100
+ve cases +ve cases
So, Sensitivity Increases in Parallel

48. In Series :
 Combined Sensitivity of 2 Tests A & B
in series = Sn(A) x Sn(B)
 Combined Specificity of 2 Tests A & B
in series = [ Sp(A) + Sp(B)] – [Sp (A) x
Sp(B)]

49. In Parallel :
 Combined Sensitivity of 2 tests A & B in
Parallel = [ Sn(A) + Sn(B)] – [Sn (A) x
Sn(B)]
 Combined Specificity of 2 Tests A & B in
Parallel = Sp(A) x Sp(B)

50. Unimodal & Bimodal Distribution
(problem of the borderline)

51. Problem of the Borderline
Which of the two Qualities ( Sensitivity or
Specificity) is more important in Screening?

56. Kappa Statistics
 Reliability can be statistically assessed
by estimating the degree of agreement
between two measurements
 Example : Consider a study on
pulmonary TB, with AFB positivity on
sputum smear as the method of
measurement. For assessing the inter -
observer reliability,(between the
microbiologist and Lab technician), we
took 200 stained slides and each slide
was examined by both of them.The
results are given as :

57. POSITIVE NEGATIVE TOTAL
TECHNICIAN’s
DIAGNOSIS
POSITIVE 13 (a) 7 (b) 20 (p1)
NEGATIVE 24 (c) 156 (d) 180 (q1)
TOTAL 37 (p2) 163 (q2) n=200
MICROBIOLOGIST’S DIAGNOSIS

58.  K
O : Observed Agreement
E : Expected Agreement(expected in
chance)

59.  O = (a+d) / n
= (13 + 156) / 200 = 169/200 = 0.85
 E = [(20 x 37) + (180 x 163)] /
(200x200)
= (740 + 29340)/40000
= 30080/40000=0.75
 K =
= (0.85 – 0.75) / (1 – 0.75)
=0.10/0.25
= 0.40

60. Kappa Coefficient
 Value between 0 and 1
 0 : The 2 measurements agree simply
because of chance
 1 : The 2 measurements agree perfectly
irrespective of chance
 Nearer the K to 1 better the agreement
 K between
0 - 0.25 : Mild agreement
0.25 - 0.50 : Moderately strong
0.50 - 0.75 :Strong agreement
0.75 – 1.00 : Very strong / excellent

61. Interpretation :
 Moderately Strong agreement
 Kappa coeff is 0.4 means that the
observed agreement between the 2
observers of assessing AFB positivity is
0.40
or 40% of the way between a
coincidental
agreement purely by chance and a
perfect

62. Bias in Screening
1.Lead Time Bias :
-Over estimation of survival duration due to
earlier detection by screening than clinical
presentation
2. Length Bias
-Over estimation of survival duration due to the
relative excess of cases detected that are
slowly progressing
3. Self Selection Bias
-If the groups which are offered screening are
not constituted by random allocation but

63. Receiver Operator
Characteristic Curve
(Relationship between
Sensitivity & Specificity)

64. History
 The name ‘ Receiver Operator
Characteristic’ came from “ Signal
Detection Theory” developed during
World War II for analysis of Radar
Images
 Radar operators had to decide whether
a blip on the screen
represented an enemy
target, a friendly ship
or just noise

65.  True Positive: Correct early warning of
enemy ships crossing the English Channel
 False Positive : When Radar operator sent
out an alarm but no enemy ships appeared
 False Negative: When enemy ships
appeared without previous warning from the
radar operator

69. More the area of the Curve , Better the Test

71. Area Under ROC Curve
Perfect = 1 Random = 0.5

72. Conclusion
 Screening , despite its flaws is a major
Public health determinant
 Establishing appropriate criteria
requires considerable knowledge of
the Natural history of disease,
adequate facilities for follow up and
treatment
 It is necessary to ensure that the
program is continuously monitored to
confirm that effectiveness is
maintained (benefits>cost)

73. ThankYou 