Screening

1. Screening test and evaluation of screening test.
By Dr Nilesh Kucha
By Dr. Soujannya Kundu Chowdhury

2. DEFINITION OF SCREENING
The search for unrecognized disease or defect
applied tests, examinations or other procedures
individuals.*
by means of rapidly
in apparently healthy
The presumptive identification of unrecognized disease or defect by
the application of tests, exams or other procedures which can be
applied rapidly to sort out apparently well persons who probably have
a disease from those who probably do not.*
Tests done in individuals with no symptom or sign of an illness are
referred to as screening tests.*
21st
(* K. Park Textbook of PSM edition
; 4th
* J M Last Dictionary of Epidemiology
Papers 1968;
edition, WHO Public
Health
* J H Abramson and Z H Abramson Survey methods in Community
5th
Medicine edition)

3. BASIS OF SCREENING
Iceberg phenomenon
disease
tip of the iceberg
CLINICAL DISEASE
of
submerged portion
HIDDEN BURDEN OF DISEASE

4. BASIS OF SCREENING
Screening is 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.
It has both diagnostic (?) and therapeutic components.

5. SCREENING TEST VERSUS DIAGNOSTIC TEST
Screening test Diagnostic test
1. Done on apparently 1. Done on sick or ill
healthy individuals
Applied to groups
individuals
Applied on single patient
Diagnosis is not final
2.
3.
2.
3.
4.
Results
final
Based
are arbitrary and
Based on evaluation of a
&
4. on one criteria no. of signs/symptoms
and cut-off lab findings
5.
6.
7.
Less
Less
Not
accurate
expensive
5.
6.
7.
More
More
Used
accurate
expensive
a basis for as a basis for
treatment
Initiative
treatment
Initiative
patient
8. comes from 8. comes from a
investigator

6. TYPES OF SCREENING
1. MASS SCREENING
Application of screening test to large, unselected population.
Everyone in the group is screened regardless of the probability of
having the disease or condition.
Example: A) mammography in women aged 40 years or less

7. 2. HIGH RISK / SELECTIVE / TARGETED SCREENING
The screening of selected high-risk groups in the population.
3. OPPORTUNISTIC / CASE FINDING
SCREENING
There is no accurate or precise diagnostic test for the disease
and where the frequency of its occurrence in the population is
small. The main objective is to detect disease and bring patients
to treatment.

8. 4. MULTIPURPOSE SCREENING
The screening of a population by more than one test done
simultaneously to detect more than one disease
5. MULTIPHASIC SCREENING
The screening in which various diagnostic procedures are
employed during the same screening program.

9. USES OF SCREENING
1. CASE DETECTION prescriptive screening, people are
screened for their own benefit
(cancer, diabetes, hypertension)
2. CONTROL OF DISEASE prospective screening, people are
screened for the benefit of others
(HIV, STI)
3. RESEARCH to know the natural history of a
disease
4. EDUCATION public awareness

10. CRITERIA FOR CHOOSING A SCREENING TEST
1. DISEASE
a) Significant burden of disease
b) Detectable and long preclinical stage of disease
c) Adequately understood natural history of disease
d) Appropriate test available for early detection of disease
e) Facilities for diagnosis of disease
f) Early detection of disease has outcome benefit
g) Effective treatment available for disease
h) Policy of screening program for disease

11. 2. SCREENING TEST
a)
b)
c)
d)
e)
Inexpensive
Acceptable
Valid
Reliable
Yielding

12. GOLD STANDARD
• GOLD STANDARD: an external source of truth regarding the
disease status of each individual in the population. Gold
standard is a benchmark
definitive.
Infections – CULTURE
and its results are considered
•
• Cancers – BIOPSY
• Drug testing – RANDOMIZED CONTROLLED TRIAL
• Cause of death – AUTOPSY

13. WHAT IS VALID AND RELIABLE?
VALIDITY IS THE ACCURACY OF A TEST.
RELIABILITY IS THE PRECISION OF A TEST.
ACCURACY: “how close is result of a test to its true value?”
PRECISION: “how close are the results of a test on repetition?”

15. COMPONENTS OF VALIDITY
Ds present Ds absent
Test positive
Test negative

16. SENSITIVITY
The ability of a test to correctly identify
disease.
a/ (a + c) expressed as percentage.
thosewhohavethe
Ds present Ds absent
Test positive
Test negative

17. SPECIFICITY
The ability of a test to correctly identify those
the disease.
d/ (b + d) expressed as percentage.
whodonothave
Ds present Ds absent
Test positive
Test negative

18. ACCURACY
ACCURACY =TP+TN/TP+FP+TN+FN

19. PROBLEM OF FN AND FP
FN: false reassurance
ignoring of disease signs and symptoms
postponement of treatment
detrimental to overall health
FP: further testing
discomfort, inconvenience,
anxiety
burden on health facilities
emotional trauma
difficulty in “de-labeling”

20. 80 of those having disease came positive and 800 of those
without disease came negative.

23. SENSITIVTY AND SPECIFICITY OF IMPORTANT TESTS
1. Mammography: Sensitivity 75 – 95%
Specificity 83 – 98%
2. PAP test: Sensitivity 29 – 56%
Specificity 94 – 100%
3. PSA test (4
ng/ml):
Sensitivity 20 – 32%
Specificity 94 – 97%
4. FBS (5mmol/L): Sensitivity 85 – 89%
Specificity 70 – 77%
5. RAPID ELISA: Sensitivity 99.5%
Specificity 98%

24. USE OF MULTIPLE TESTS
•Sequential Testing (Two-Stage Screening)
After the first (screening) test is conducted, thosewho tested
positive are brought back for the second test to further reduce
false positives.
Consequently, the overall process will increase
specificity but with reduced sensitivity.

31. 80 of those having disease came positive and 800 of those
without disease came negative.
Calculate positive and negative predictive value

32. PPV = 44.4%. NPV= 97.5%
Calculate positive and negative predictive value

34. LIKELIHOOD RATIO
The percentage of sick people with a given test result divided by
the percentage of well individuals with the same result.
Positive LR: The relative probability of a positive test in a
diseased individual in comparison to a disease-
free individual.
TP / FP = SENSITIVITY / 1 – SPECIFICITY

35. Negative LR: The relative probability of a negative test in a
diseased individual in comparison to a disease-
free individual.
FN / TN = 1 – SENSITIVITY / SPECIFICITY

36. WHY DO WE NEED LIKELIHOOD RATIO?
• Many tests in clinical medicine have continuous results or
multiple ordinal levels. Putting multiple categories into either
positive or negative test causes loss
ratios enable clinicians to interpret
diagnostic test results.
of information. Likelihood
and use the full range of
• While predictive values relate test characteristics to
populations,
patient.
likelihood ratios can be applied to a specific
• Likelihood ratios refine clinical judgment. Application of a
likelihood ratio to a working diagnosis generally changes the
diagnostic probability—sometimes radically.

40. EXAMPLE OF INTER-OBSERVER AND INTRA-OBSERVER
VARIATION
• INTRA-OBSERVER VARIATION:
two readings of blood pressure measurement
• INTER-OBSERVER VARIATION:
chest x-ray films by different radiologists

41. YIELD
YIELD is the amount of unrecognized disease that is detected
brought to treatment as a result of screening.
and
YIELD =TP+FP/TP+FP+TN+FN
It depends on prevalence of the disease and
screening test.
sensitivity of the
Hence, yield of a screening test is high in high – risk screening.

42. ISSUES WITH SCREENING
1. Lead time bias - the systematic error of apparent increased
survival from detecting disease in an early stage.

50. 2. LENGTH TIME BIAS
Diseases with a long pre-clinical phase are more likely to be
detected during screening. Moreover, pre-clinical phase for the
same disease may be variable in different individuals.

51. 3. SELECTION BIAS
Not everyone will take part in a screening program. There are
factors that differ between those willing to get tested and
those who are not.
Willingness
of patient.
Example:
outcome if
outcomes if
depends on perceived risk of disease and intelligence
a) breast cancer screening – more positive
only intelligent people participate. More
negative
only high risk patients participate.

52. 4. OVERDIAGNOSIS
Screening may identify abnormalities that would never cause a
problem in a person's lifetime. Causes overestimate of disease
well as survival.
as
Example
:
a) PAP testing and Cervical CIS
b) PSA testing and low grade prostate cancer
c) mammography and DCIS

53. EVALUATIONOF SCREENING TEST
1.METHODS
a) Experimental: conduct an RCT of the screening test to
compare the disease specific cumulative
mortality rate between the intervention
and control group.
this also eliminates confounding.
allows study of distribution of lead time,
effects of early treatment and
identification of prognostic factors.

54. b) Non – experimental:
cohort
study
(comparison of advanced
disease or death rates in those
who choose to screen and those
who do not)
case - control study (comparison of
screening history in those who have
advanced disease
healthy)
and those who are
ecological study (correlation of
pattern and disease experience
populations)
screening
of several

55. 2.MEASURESOFEFFECT
a) Comparison of survival experience
b) Comparison of disease specific mortality rate

56. BAYES THEOREM
A prior (pre-test) probability is an initial probability value originally
obtained before any additional information is obtained.
A posterior (post-test) probability is a probability value that has
been revisedby using additional information that is later obtained.
Example
:
Road traffic accident patients in a hospital are
listed and one of the patients is randomly
selected.
a) what is the probability that he will have
cerebral hemorrhage?
b) during the later analysis of this patient it was
found that he has CT scan positive for cerebral
hemorrhage. Now, what is the probability that
he actually has cerebral hemorrhage?

57. For the first question, we know that
Probability of cerebral hemorrhage in a RTA patient is 28%.
For the second question, we know that
CT scan has a sensitivity of 90% and specificity of 95% for
detecting cerebral hemorrhage.
Bayes theorem gives us a way of calculating probability of an
event in the light of presence of a second event, which itself
occurs in the presence of the first one.

58. Probability that our patient has cerebral hemorrhage, in
an already positive CT scan for cerebral hemorrhage, is…
view of
Pre-test probability of event (P1) X sensitivity of test (Sn)
--------------------------------------------------------------------------- X 100
(P1X Sn) + (1 – P1)X (1 – Sp)
Here,
P1 = 0.28
Sn = 0.90
Sp = 0.95
So,
Post-test probability (P2)= 0.875 or 87.5%

59. THANKYOU…