This document discusses concepts related to diagnostic testing in animal disease. It defines what a diagnostic test is and discusses some key issues like the presence of false positives and negatives. It describes different categories of tests, including screening tests for healthy animals and confirmatory tests for diseased animals. Key metrics for evaluating tests are explained, such as sensitivity, specificity, predictive values, and accuracy. Factors that can impact test results like cut-off points and prevalence are also covered. The document provides examples of specific tests and discusses the trade-offs of optimizing tests for sensitivity versus specificity.

1. Presented by Dr. Clarissa Yvonne J. Domingo
Professor, CVSM-CLSU

2. Definition
“A diagnostic test is any
device or process designed
to detect, or quantify a sign,
substance, tissue change, or
body response in an animal.

3. Issues in animal disease
diagnosis
• INFECTION ≠ DISEASE
• What is the cut-off point in
disease diagnosis?
• Who decides on the cut-off
point that will separate the
healthy and the diseased?

5. Issues in animal
disease diagnosis
• Presence of false positive and
false negative cases.
• Some experts claim there is
no perfect diagnostic test.

6. HPT
(Home Pregnancy Test kit)

7. Ideal vs Actual

8. How do you diagnose a
disease?

10. Test Categories
1. Screening Tests- Tests used
for clinically healthy
animals
2. Confirmatory/Diagnostic
Tests- Tests for clinically
diseased animals.

11. Screening Tests
• For clinically healthy
animals.
• Generally cheap.
• Detect lots of possible
cases of the disease but
less specific in identifying
real cases.

12. For use in the early detection of
Brucella specific agglutinins
(Brucella melitensis, abortus and suis)
Rose Bengal Kit

13. Screening Tests
• Fast and reasonably
accurate.
• Not meant to supply
complete information
about the pathogen.
• Need follow-up
confirmatory/diagnostic
test

14. Negri bodies (intracytoplasmic inclusion bodies)
http://intranet.tdmu.edu.ua/auth.php
Result in 2 HOURS?
RABIES

15. Examples
1. Brucellosis: Rose Bengal Test
2. Rabies: Direct Microscopic
Examination-DME (Seller's
staining for Negri bodies).
3. Leptospira Dip-S-Tick

16. Confirmatory/
Diagnostic Tests
• Tests for clinically diseased
animals.
• To confirm diagnosis or
classify disease status
among animals suspected
to have the disease.

17. Positive rabies virus direct fluorescent antibody
test on a Big Brown Bat brain. Rabies antigen
stains bright green (Oregon state University).
POSITIVE NEGATIVE

18. Rabies Diagnosis
( Johnson, H. W., and R.W.Emmons.1980 )
Tests Accuracy %
FA Test 99.4
Mouse inoculation 98.3
Sellers stain for the
detection of Negri bodies
65.8

19. Purposes
1. Establish a diagnosis in
symptomatic patients.
2. Provide prognostic information
3. Guide selection of treatment or
control strategies
4. Monitor effects of treatment

20. Examples
• Brucellosis: Complement fixation
test
• Rabies: The direct Fluorescent
Antibody Test (FAT)
• Leptospirosis: Isolation from blood
or other clinical materials through
culture of pathogenic leptospires

21. Rabies- FAT
QSMI, Thailand

22. Qualities of a TEST
Precision Validity
replication of test
results when the same
samples are re-tested.
ability to distinguish
between who has a
disease and who does
not .
Synonyms:
Reproducibility,
reliability (repeatability)
Synonym: accuracy

23. Validity and
precision in
test
procedures

24. Factors that cause
variation in test results
• Variation within animal
subjects
• Variation in the reading of
test results by the same
reader (measuring tool)
• Variation between those
reading the test results
(lab.diagnostician)

25. Data Lay-out for
Test Evaluation
True Disease Status
(as measured by the Gold standard)
Row marginal
totalDiseased
+
Non-diseased
-
Diagnostic
Test
Test Positive
+
a
(True Positive)
b
(False positive)
a + b
Test Negative
-
c
(False negative)
d
(True negative)
c + d
Column marginal total a + c b + d a + b + c + d

26. Gold Standard
• A diagnostic test or benchmark
that is the best available under
reasonable conditions.
• The CRITERION STANDARD

27. Validity is expressed in
four dimensions
1. Sensitivity
2. Specificity
3. Positive predictive value
4. Negative predictive value
Test Quality
Precision
Validity

28. Sensitive TestSpecific Test

30. Sensitivity
• The sensitivity of a test is the
probability of the test to
generate positive results among
animals that actually possess the
disease.

31. Data Lay-out for
Test Evaluation
True Disease Status
(as measured by the Gold standard)
Row marginal
totalDiseased
+
Non-diseased
-
Diagnostic
Test
Test Positive
+
a
(True Positive)
b
(False positive)
a + b
Test Negative
-
c
(False negative)
d
(True negative)
c + d
Column marginal total a + c b + d a + b + c + d

32. Specificity
• The specificity of the test is the
probability of a test to generate
negative results among animals
that are genuinely free of the
disease.

33. Data Lay-out for
Test Evaluation
True Disease Status
(as measured by the Gold standard)
Row marginal
totalDiseased
+
Non-diseased
-
Diagnostic
Test
Test Positive
+
a
(True Positive)
b
(False positive)
a + b
Test Negative
-
c
(False negative)
d
(True negative)
c + d
Column marginal total a + c b + d a + b + c + d

34. Reasons for positive and negative
results from serological tests

35. Hypothetical Values
Screening
Results
Diseased
Non-
diseased
Total
Test positive 28 8
Test negative 2 62
Total
Sensitivity = 93 %
Specificity = 88.7 %
There is 93 % probability that a truly infected
animal to be classified as infected by the test
There is 88.7% probability that a truly non-
infected animal to be classified as non-infected
by the test.

36. Increasing the sensitivity of the test
1. Catches all the sick animals
2. But more healthy animals will
be identified as false positive
3. Specificity decreased.
(to the left)

37. Relationship between
sensitivity and specificity

38. When to select a test with very
high sensitivity?
1. When missing a diseased animal would be
costly and dangerous. (ex. FMD, CAE)
2. When screening for a disease or pathogen
in imported animals
3. Demonstration of absence of a disease in
a disease-free zone

39. Increasing the specificity of
the test
1. Catches all the healthy animals.
2. But more diseased animals will be
identified as false negative
3. Sensitivity decreased.
(to the right)

40. Relationship between
sensitivity and specificity

41. When to select a test with
very high specificity?
• When confirming a diagnosis.
• When increased false
positives would cause
physical, emotional and
financial damages.

42. Diagnosis is brain cancer!

43. Sorry, but
you are
HIV
positive.

44. SnOUT & SpIN mnemonic:
SnOUT – sensitivity rules
negatives OUT
• ability of the test to be correctly
positive in the presence of disease
• a high sensitivity has few false
negatives and effective in ruling
negative conditions OUT
• If you test negative, you are truly
free of the disease

45. SpIN – specificity rules
positives IN
• ability of the test to be correctly
negative in the absence of disease
• a high specificity has few false
positives and effective in ruling
positive conditions IN
• If you test positive, you truly
possess the disease

46. Categories
At least 95%
sensitivity and
75% specificity
At least 75%
sensitivity and
95% specificity
1. Screening Tests- Tests
used for clinically
healthy animals
2. Confirmatory/
Diagnostic Tests- Tests
for clinically diseased
animals

47. Validity is expressed in
four dimensions
3. Positive predictive value
4. Negative predictive value
Test Quality
Precision
Validity
1. Sensitivity
2. Specificity

48. Positive predictive value
• It answers the question: “If the test
result is positive what is the
probability that the animal actually has
the disease?"

49. Negative predictive value
• It answers the question: “If the test
result is negative what is the
probability that the animal does not
have disease?"

50. Screening
Results
Diseased
Non-
diseased
Total
Test positive 28 8
Test negative 2 62
Total
Hypothetical Values
PPV = 77.8 %
NPV = 97 %
There is 77.8% probability that the animal has
the disease if the test result is positive.
There is 97% probability that the animal does
not have the disease if the test result is
negative.

51. Sensitivity
and
Specificity
PPV and
NPV
Apparent
Prevalence
or True
Prevalence

52. Apparent vs True
Prevalence
• Apparent prevalence= the
estimate of disease prevalence
determined on the basis of an
imperfect test

53. Formula

54. Screening
Results
Diseased
Non-
diseased
Total
Test positive 28 8
Test negative 2 62
Total
Hypothetical Values
AP = 36% TP = 30%

55. TRUE prevalence
AP=30% SP= 95% SE= 95%
0.3+0.95−1
0.95+0.95−1
= 0.27
AP=30% SP= 90% SE= 95%
0.3+0.90−1
0.95+0.90−1
= 0.23
AP=30% SP= 75% SE= 95%
0.3+0.75−1
0.95+0.75−1
= 0.07

56. Sensitivity
and
Specificity
PPV and
NPV
Apparent
Prevalence
or True
Prevalence
Accuracy

57. Accuracy
• Accuracy is the proportion of all
tests, both positive and negative,
that are correct.

58. Screening
Results
Diseased
Non-
diseased
Total
Test positive 28 8
Test negative 2 62
Total
Hypothetical Values
Accuracy = 90%

59. Sensitivity
and
Specificity
PPV and
NPV
Apparent
Prevalence
or True
Prevalence
Accuracy
Multiple
Tests

60. Use of Multiple Tests
• Parallel – the tests are performed
at the same time and interpreted
together.
• Serial – the tests are performed
sequentially. The results of the
first test usually determine
whether the second test is still
necessary or not. Only the
positive cases are retested.

61. Parallel Testing Interpretation
(“The OR rule”)
Test A Test B Diagnosis
(-) (-) Negative
(+) (-) Positive
(-) (+) Positive
• It seeks to prove the animal is healthy,
• It results in higher sensitivity than any of the individual tests

62. Serial Testing Interpretation
(“The AND rule” )
Test A Test B Diagnosis
(+) (-) Negative
(-) Not done Negative
(+) (+) Positive
It seeks to prove the test-positive animals are really sick
It results in higher specificity than any of the individual tests.

63. Sensitivity
and
Specificity
PPV and
NPV
Apparent
Prevalence
or True
Prevalence
Accuracy
Likelihood
ratio

64. The likelihood ratio for a
test result
• The ratio between the
probability of observing
that result in patients with
the disease in question,
and the probability of that
result in patients without
the disease

65. LR Interpretation
LR value Interpretation
LR positive > 1
A positive test is more likely to
occur in animals with the disease
than in animals without the
disease
LR positive < 1
A positive test is less likely to occur
in animals with the disease than in
animals without the disease
LR positive = 1
The test result conveys no
information

67. What if there is no gold
standard?
• Answer: use two or more
tests
• Use the kappa test (or
Cohen’s Kappa) to
quantify the level of
agreement between the
two

68. Formula

69. Quick Method: Winepi
• Copy Win Episcope software from
group 3
• Click “Test”, then click “agreement”
• Sample problem:
LAMP + LAMP-
PCR+ 36 9
PCR- 12 400
Calculate level of agreement (kappa value)

70. Answer: 0.75

71. Interpretation of Kappa
• Kappa Agreement
• <0.00 Less than chance agreement
• 0.01- 0.20 Slight agreement
• 0.21- 0.40 Fair agreement
• 0.41- 0.60 Moderate agreement
• 0.61- 0.80 Substantial agreement
• 0.81- 0.99 Almost perfect agreement

72. Herd test
• An evaluation of a sample of (or all)
animals from a herd and the
application of decision rules that
classify the herd as positive or
negative based on the test results from
individual animals (Christensen &
Gardner, 2000).

73. Herd test: reasons
• Disease control programs
• Farm certification
• Risk analysis
• Investigations on disease determinants
or risk factors

74. Options for herd classification
Strictly two
categories only
With
consideration
for false test
results
According to
level of
infection
Positive
Negative
Positive
Inconclusive
Negative
Low
Moderate
High

75. Herd-level Sensitivity (HSE)
• The probability of a truly infected herd
to be classified as infected by the test

76. Herd-level Specificity (HSP)
• The probability of a truly NON-infected
herd to be classified as NON-infected
by the test

77. Example

78. THANKS