Advance concept of screening nabaraj paudel

1. ADVANCE CONCEPT OF
SCREENING
Presented By
Nabaraj Paudel
A- 1986 Jan PH
BPH-SHAS, PU
MPH (Health Service Management)
SHAS, PU

2. OUTLINE OF THE PRESENTATION
 Advance concepts in screening
 Sequential and parallel tests
 Net gain and net loss
 ROC curve in screening and its interpretation
 Youden Index in screening
 Cost consideration in screening
2

3. SCREENING
 Screening test is used to search for an unrecognized
disease or defects, in apparently healthy individuals,
by means of rapidly applied tests, examinations or other
procedures.
 Test done in individuals with no symptom or sign of an
illness are referred to as screening tests.
 Secondary level of prevention.
3

4. BASIS FOR SCREENING
Iceberg phenomenon of disease
 Floating tip: clinical cases
 Submerged portion: hidden mass of diseases
( latent, inapparent, pre symptomatic and undiagnosed
cases and carriers)
Screening is done for the hidden portion of an Iceberg.
4

5. SCREENING
 Screening is public health approach.
 Lead time : Advantage gained by screening i.e the
period between diagnosis by early detection and
diagnosis by other means.
 Yield of screening test: it is the amount of previously
unrecognized disease that is diagnosed as a result of
screening effort.
5

6. SCREENING VS DIAGNOSIS TEST
Screening test Diagnosis Test
Done on Apparently Healthy Cases(signs/Symptoms)
Applied on Groups, Populations Individuals
Based on One criteria Signs, symptoms, lab
findings
Accuracy Relatively inaccurate Accurate
Basis for treatment Cannot be used as basis Useful basis
Cost Cheaper Expensive
Initiate from Investigator Case with complain
6

7. SCREENING TEST AND DISEASE SCREENED
Screening test Disease screened
Papanicolaou (Pap ) smear test Cervical cancer
BSE/ Mammography Brest Cancer
ELISA HIV
7

8. CRITERIA FOR SCREENING TEST
 Disease should be important public health problem.
 Effective treatment should be available.
 Facilities for diagnosis and treatment should be
available.
 Should be a latent or early asymptomatic stage.
 The natural history of disease should be adequately
understood.
8

9. TYPES OF SCREENING
Prescriptive
screening
Prospective
screening
Definition People screened for
owns benefits
People screened for
others benefits
Essential purpose Case detection Disease control
Request for screening Non specific request Special request from
authority
Eg. Neonatal screening
Pap smear
Urine for sugar
Screening of
immigrants,
HIV screening among
sex workers
9

10. RESULTS OF SCREENING TEST
Results of a screening
test for a disease
Disease
Present Absent
Screening
test
Positive a (TP) b (FP) a+b
Negative c (FN) d (TN) c+d
Total a+c b+d
10

11. RESULTS OF SCREENING TEST/ PROPERTIES OF
SCREENING TEST
Sensitivity
 Ability of screening test to identify correctly all those
who have disease.
 Sensitivity = a*100/(a+c) %
 Usefullness of screening test.
Specificity
 Ability of screening test to identify correctly all those
who do not have disease.
 Specificity = d*100/ (b+d) %
11

12. RESULTS OF SCREENING TEST/ PROPERTIES OF
SCREENING TEST
 Diagnostic power of screening test: predictive accuracy
Positive Predictive Value
 Ability of screening test to identify correctly all those
who have disease, out of all those who test positive on a
screening test
 PPV= a*100/(a+b) %
Negative Predictive Value
 Ability of screening test to identify correctly all those
who do not have disease, out of all those who test
negative on a screening test
 NPV=d*100/(c+d) % 12

13. SCREENING TEST IN SERIES AND PARALLEL
13

14. 14

15. SCREENING TEST USED IN SERIES
15

16. SCREENING TEST USED IN SERIES
 Combined sensitivity of 2 tests A and B in series
= Sensitivity A* Sensitivity B
 Combined specificity of 2 tests A and B in series
= (Specificity A+ Specificity B)-(Specificity A* Specificity B)
16

17. 17

18. 18

19. SCREENING TEST USED IN PARALLEL
19

20. SCREENING TEST USED IN PARALLEL
20

21. SCREENING TEST USED IN PARALLEL
 A population is subjected to two or more screening tests at a
same time; each of individuals is subjective to all screening tests
 Combined sensitivity of 2 tests A and B in Parallel
= {(Sensitivity A+ Sensitivity B)-(Sensitivity A* Sensitivity B) }
 Combined specificity of 2 tests A and B in parallel
= Specificity A* Specificity B
21

22. SCREENING TEST USED IN PARALLEL
22

23. 23

24. OUTCOME OF SCREENING TEST
Test in series Tests in parallel
Combined Sensitivity Decreases Increases
Combined Specificity Increases Decreases
Combined PPV Increases Decreases
Combined NPP Decreases Increases
24

25. CUT OFF VALUE WITH SENSITIVITY AND SPECIFICITY
25

26. CUT OFF VALUE WITH SENSITIVITY AND
SPECIFICITY
26

27. CUT OFF VALUE WITH SENSITIVITY AND
SPECIFICITY
27

28. ROC SPACE AND AUC
28

29. RECEIVER OPERATING CHARACTERISTIC
(ROC) CURVE
 ROC curve is defined as a plot of test sensitivity or True
positive rate (TPR) as the y coordinate versus its
(1-specificity) or false positive rate (FPR) as the x
coordinate, is an effective method of evaluating the
performance of diagnostic tests.
29

30. 30

31. ROC CURVE ANALYSIS
o Area under the ROC curve
(AUC) is considered as an
effective measure of
inherent validity of a
diagnostic test.
31

32. ROC CURVE ANALYSIS
 The diagonal joining the
point (0, 0) to (1,1) divides
the square in two equal
parts and each has an area
equal to 0.5.
 When ROC is this line,
overall there is 50-50
chances that test will
correctly discriminate the
diseased and non-diseased
subjects.
32

33. ROC CURVE ANALYSIS
 The minimum value of AUC
should be considered 0.5
instead of 0 because AUC=0
means test incorrectly
classified all subjects with
disease as negative and all
non-disease subjects as
positive.
 If the test results are reversed
then area=0 is transformed to
area=1; thus a perfectly
inaccurate test can be
transformed into a perfectly
accurate test!
33

34. ROC CURVE ANALYSIS
 The area under the curve (AUC) is an effective and
combined measure of sensitivity and specificity for
assessing inherent validity of a diagnostic test.
 Maximum AUC=1 and it means diagnostic test is perfect
in differentiating diseased with non-diseased subjects.
This implies both sensitivity and specificity are one and
both errors (false positive and false negative) are zero.
 This can happen when the distribution of diseased and
non diseased test values do not overlap.
 This is extremely unlikely to happen in practice. The
AUC closer to 1 indicates better performance of the test.
34

35. 35
Cut point Sensitivity Specificity
5 0.56 0.99
7 0.78 0.81
9 0.91 0.42
Cut point True Positives False Positives
5 0.56 0.01
7 0.78 0.19
9 0.91 0.58

36. 36

37. ADVANTAGES
 The ROC curve displays all possible cut-off points,
and one can read the optimal cut-off for correctly
identifying diseased or non-diseased subjects as per the
procedure.
 The ROC curve is independent of prevalence of
disease since it is based on sensitivity and specificity
which are known to be independent of prevalence of
disease.
 Two or more diagnostic tests can be visually
compared simultaneously in one figure.
 Sometimes sensitivity is more important than
specificity or vice versa, ROC curve helps in finding the
required value of sensitivity at fixed value of specificity. 37

38. AREA UNDER THE CURVE (AUC)
This curve is useful in
I. Evaluating the discriminatory ability of a test to
correctly pick up diseased and non-diseased subjects.
II. Finding optimal cut-off point to least misclassify
diseased and non-diseased subjects.
III. Comparing efficacy of two or more medical tests for
assessing the same disease.
IV. Comparing two or more observers measuring the same
test (inter-observer variability).
38

39. METHOD TO FIND THE OPTIMUM CUT OFF POINT OF
A SCREENING TEST
 Optimal threshold is the point that gives maximum correct
classification. Three criteria are used to find optimal threshold
point from ROC curve.
1. Points on curve closest to the (0, 1)
2. Youden index and
3. Minimize cost criterion
 First two methods give equal weight to sensitivity and
specificity and impose no ethical, cost, and no prevalence
constraints.
 The third criterion considers cost which mainly includes
financial cost for correct and false diagnosis, cost of discomfort
to person caused by treatment, and cost of further investigation
when needed. This method is rarely used in medical literature
because it is difficult to implement.
39

40. METHOD TO FIND THE OPTIMUM CUT – OFF POINT OF A
SCREENING TEST
40

41. 41
J=Youden index

42. 1. POINTS ON CURVE CLOSEST TO THE (0, 1)
 The distance between the point (0, 1) and any point on
the ROC curve is
d2 =[(1–SN)2 + (1 – Sp)2].
 To obtain the optimal cut-off point to discriminate the
disease with non-disease subject, calculate this distance
for each observed cut-off point, and locate the point
where the distance is minimum.
42

43. 1. DISTANCE TO CORNER
 The distance to the top-left corner of the ROC curve for
each cutoff value is given by
 Lower distances to the corner are better than higher
distances.
43

44. 2. YOUDEN INDEX
 Youden index that maximizes the vertical distance from
line of equality to the point [x, y].
 The x-axis represents (1- specificity) and y-axis
represents sensitivity.
 In other words, the Youden index J is the point on the
ROC curve which is farthest from line of equality
(diagonal line).
 It is a single statistics that capture the performance of
the test
 Y= Sensitivity +Specificity-1
44

45. 45

46. YOUDEN INDEX
 Conceptually, the Youden index is the vertical distance
between the 45 degree line and the point on the ROC
curve.
 Higher values of the Youden index are better than lower
values.
46

47. 3. COST APPROACH
 Seeking to determine the optimal cutoff value.
 This approach is based on an analysis of the costs of the
four possible outcomes of a diagnostic test: true positive
(TP), true negative (TN), false positive (FP), and false
negative (FN).
 If the cost of each of these outcomes is known. The
average overall cost C of performing a test at a given
cutoff is given by
47

48. 3. COST APPROACH
 Here, C0 is the fixed cost of performing the test,
 CTP is the cost associated with a true positive,
 P(TP) is the proportion of TP’s in the population, and so
on.
48

49. 3. COST APPROACH
 Metz (1978) showed that the slope of the ROC
curve at the optimal cutoff value is
49

50. 3. COST APPROACH
 Zweig and Campbell (1993) showed that the point
along the ROC curve where the average cost is
minimum corresponds to the cutoff value where
is maximized.
 We refer to fm as the Cost Index.
 In order to make these cost calculations, known
prevalence and cost values (or cost ratio) must be
supplied.
50

51. REFERENCES
 Kumar R, Indrayan A. Receiver Operating
Characteristic (ROC) Curve for Medical
Researchers. Indian Pediatrics.2011;48:277-87.
 Kanchanaraksa S:Evaluation of Diagnostic and
Screening Tests: Validity and Reliability, Johns
Hopkins University; lecture given 2008.
 Hanley JA, McNeil BJ. The meaning and use of the
area under a receiver operating characteristic
(ROC) curve. Radiology 1982;143:29-36.
51