This document provides information about veterinary diagnostics. It discusses the objectives of diagnostic testing, types of tests including screening and diagnostic tests, and key concepts in evaluating tests such as sensitivity, specificity, and the gold standard. Screening tests are meant to identify potential cases, while diagnostic tests confirm diagnoses. Sensitivity refers to a test's ability to identify true positives, and specificity refers to a test's ability to identify true negatives. The gold standard is the best available test to determine the true disease state.

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Veterinary
Diagnostics
Learning objectives:
At the end of this topic, the student should be able to:
1. Enumerate the types of diagnostic tests;
2. Describe, calculate and interpret the measures for test validity: sensitivity,
specificity, predictive value
3. Utilize the test’s sensitivity and specificity to determine the true and apparent
prevalence
4. Explain the use of predictive values, likelihood ratios and multiple testing.

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Issues in animal disease diagnosis
a. The presence of a pathogen does not always indicate a disease exists.
b. It is easier to make a diagnosis when a particular diagnostic test renders a
dichotomous result (e.g. positive or negative).
c. However, when the diagnostic procedure presents a continuous output (e.g. level of
antibody in the serum), the veterinarian must make a rational decision on setting
the cut-off point that will separate the healthy and the diseased.
d. Identifying the sick and the healthy animals using a test usually adds in a small
proportion of false positive and false negative cases. The number of the latter affects
the true prevalence of the disease in the population.
e. Some experts claim there is no perfect diagnostic test.

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Definition
“A test is any device or process designed to detect, or quantify a sign, substance, tissue
change, or body response in an animal. Tests can also be applied at the herd, or other level
of aggregation” (Dohoo, Martin, & Stryhn, 2003).
Tests for animal disease diagnosis
Examples
1. Clinical examination
a. Owner’s complaint
b. Signalment of the patient (detailed description of a person for purposes of
identification) includes the identification number, breed, age, sex, and color
and production class of animal. Some diseases are specific to some of these
groupings
c. History of the patient(s)
d. History of the farm
e. Observation of the environment
f. Observation of the animal at a distance

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g. Detailed observations of the animal
h. Examination of the animal
2. Laboratory test procedures
a. Whole blood analysis- e.g. complete blood count
b. Blood chemistry
c. Serological tests
d. Urine analysis
e. Fecalysis
3. Necropsy findings

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Categories
Tests used for clinically h___________ animals (apparently healthy or
asymptomatic).
These tests are generally cheap.
Qualitatively, the screening tests detect lots of possible cases of the disease but less
specific in identifying real cases.
The screening tests are meant to be fast and reasonably accurate.
They are not meant to supply complete information about the pathogen.
A positive screening test usually involves a follow-up diagnostic test in order to
correctly exclude those individuals who do not have the given disease.
Purposes:
 Identify subclinical cases (the suspects)
 Detect the presence of disease agents
 Quantify the seroprevalence
Examples
 Brucellosis: Rose Bengal Test
 Rabies: Direct Microscopic Examination-DME (Seller's staining for Negri bodies)
remains the standard screening test for rabies in most of the diagnostic
laboratories in the Philippines.
 Leptospirosis: Indirect hemagglutination assay, Leptospira Dip-S-Tick, IgM
ELISA and latex agglutination test
Tests for clinically d______________ animals.
After the screening test, a diagnostic test can be employed to confirm diagnosis or
classify disease status among animals suspected to have the disease.
Purposes:
1. Establish a diagnosis in symptomatic patients.
2. Provide prognostic information in animals with established disease.
3. Guide selection of treatment or control strategies
4. Monitor effects of selected type of treatment
Examples
 Brucellosis: Complement fixation test
 Rabies: The direct Fluorescent Antibody Test (FAT) is now the standard
rabies diagnostic test in North America as well as in Europe
 Leptospirosis: Isolation from blood or other clinical materials through
culture of pathogenic leptospires
A. SCREENING TEST
B. CONFIRMATORY TEST

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Differences between screening and diagnostic tests
(Adopted from Ruf & Morgan, 2008 and Sheringham, Kalim, & Crayford, 2008)
Screening tests Diagnostic tests
Purpose To detect potential disease
indicators
To establish
presence/absence of disease
Target population Large numbers of
asymptomatic, but
potentially at risk
individuals
Symptomatic individuals to
establish diagnosis, or
asymptomatic individuals
with a positive screening
test
Test method Simple, acceptable to
patients and staff
Maybe invasive, expensive
but justifiable as necessary
to establish diagnosis
Positive result threshold Generally chosen towards
high sensitivity not to miss
potential disease
Chosen towards high
specificity (true negatives).
More weight given to
accuracy and precision than
to patient acceptability
Positive result Essentially indicates
suspicion of disease (often
used in combination with
other risk factors) that
warrants confirmation
Result provides a definite
diagnosis
Cost Cheap, benefits should
justify the costs since large
numbers of people will need
to be screened to identify a
small number of potential
cases
Higher costs associated with
diagnostic test maybe
justified to establish
diagnosis.
Invasiveness Often non-invasive. May be invasive (ex. lumbar
puncture)

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Qualities of a TEST
“Accuracyistellingthe truth …Precision istellingthe same storyoverandover
again.”-YidingWang
 Precision refers to replication of test results when the same samples are re-
tested.
 Synonyms: Reproducibility, reliability (repeatability)
o Repeatability- the same results obtained when samples are tested
within the same laboratory.
o Reproducibility- - the same results obtained when samples are tested
between laboratories
 Factors that cause variation in test results
1. variation within animal subjects
2. variation in the reading of test results by the same reader
3. variation between those reading the test results
 The validity of a test is defined as its ability to distinguish between who has a
disease and who does not (Gordis, 2008).
 The term validity is commonly considered synonymous with accuracy
(Smith, 1995).
 However, Thrusfield (2007) distinguished accuracy as the property of a
single diagnosis (i.e., of one shot at the bull's-eye) while validity as a long-
term characteristic of a diagnostic technique (i.e., the average result of
several shots).
 Quantitatively, accuracy refers to the proportion of all correct test results, or
the extent of agreement of the test results with the true disease status of
animals. It is affected by the proportion infected in the population and
therefore applies only to the population for which the test is standardized
(Smith, 2003).
 Validity is expressed in four dimensions
1. Sensitivity
2. Specificity
3. Positive predictive value
4. Negative predictive value
QUALITY 1: Precision
QUALITY 2: Validity

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Validity and precisionin test procedures

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Techniques for the evaluation ofdiagnostic tests
Parameter How Measured How Expressed
Validity 2 x 2 contingency table Sensitivity
Specificity
Positive Predictive Value
Negative Predictive value
Accuracy
Optimum cut-off Response-operating
characteristic (ROC) curve
Positive/negative cutoff
value
Comparison of tests Fixed cutoff: Bayes’ graph
Continuous variable:
Response-operating
characteristic (ROC) curve
Posterior probability/prior
probability
Likelihood ration at different
levels of the test; area under
the curve
Modified from (Smith, 1995)
Data Lay-out for Test Evaluation
True Disease Status
(as measured by the Gold standard)
Row marginal
total
Diseased 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

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Gold Standard
Definition
1. An accepted test that is assumed to be able to determine the true disease state of a
patient regardless of positive or negative test findings or sensitivities or specificities
of other diagnostic tests used (Mosby, 2008)
2. The gold standard test refers to a diagnostic test or benchmark that is the best
available under reasonable conditions.
3. A clear, objective demarcation between disease and no disease ( (Szasz, 2005).
4. Other experts prefer to use the term C____________________ STANDARD. (See Glossary
of Methodological Terms of the Journal of the American Medical Association and the
AMA Manual of Style)
Characteristics
1. Available.
2. Slower (to allow time for disease to become detectable)
3. More difficult to perform (tissue samples from the patient may be required) .
4. More expensive
5. The most accurate. It has a sensitivity of 100 % and specificity of 100 % (In many
diseases, there are no true "gold standard" tests. Sometimes, it can merely be the
best performing test available).
6. They can be replaced. As new diagnostic tests are developed, the "gold standard"
test may change over time.

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Sensitivity
The sensitivity of a test is the probability of the test to generate positive results among
animals that actually possess the disease.
The sensitivity of a test can be determined by calculating
Sensitivity=
𝑎
𝑎+𝑐
=
𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒
𝑡𝑜𝑡𝑎𝑙 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑑
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.
The specificity of a test can be determined by calculating
Specificity =
𝑑
𝑏+𝑑
=
𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒
𝑡𝑜𝑡𝑎𝑙 𝑛𝑜𝑛−𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑑

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Reasons for positive and negative results from serological tests
(Stites, Stobo, Fundenberg, & Wells, 1982)
Diseased Non-diseases
Positive
Results
True positive
Actual infection
False positives
Group cross-reaction
Non-specific inhibitors
Non-specific agglutinins
Negative
Results
False negative
Natural/induced tolerance
Improper timing
Improper selection of test
Non-specific inhibitors
Toxic substances
Antibiotic induced
immunoglobulin suppression
Incomplete or blocking
antibody
Insensitive tests
True negative
Absence of infection

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Relationshipbetween sensitivityand specificity
A particular test with high sensitivity will be able to identify majority of the diseased
animals (these are the true positive cases). However, being an imperfect test, it would miss
some of the sick animals which, as a result of the test, would be assumed as healthy (these
are the false negative cases)
A particular test with high specificity will be able to identify majority of the non-diseased
animals (these are the true negative cases). However, being an imperfect test, it would miss
some of the healthy animals which, as a result of the test, would be assumed as diseased
(these are the false positive cases)
The cut-off value is a decision that has to be made by the health professional. There are
several considerations that affect this decision.
Moving the cut off limit to either direction will produce two effects: increase the ability of
one but decrease the other. Sensitivity and specificity are inversely related.
The choice of cut-off point to use will depend on the relative seriousness of either a
false negative or a false positive test result.

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Increasing the sensitivity ofthe test
Moving the cutoff limit of the test's value to the left will allow the test to catch all the sick
animals in a population. However, more healthy animals will be identified as having the
disease (false positive), when, in reality, they do not.
The test's sensitivity increased but its specificity decreased.
Effects of moving the cut-off point to the left
1. Increased sensitivity of the test
2. Reduced number of false negative cases.
3. Reduced specificity of the test
4. Increased number of false positive cases. Therefore, a test with high sensitivity
would not be adequate on its own to make a definitive diagnosis of the disease
When to select a test with very high sensitivity?
1. When missing a diseased animal would be costly and dangerous (for example,
missing a new animal harboring subclinical rinderpest- a terrible disease which is
absent in your country).
2. When screening for a disease or pathogen (typical situations where this is needed:
quarantine of imported animals; demonstration of absence of a disease in a disease-
free zone).

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Increasing the specificity ofthe test
Moving the cutoff limit of the test's value to the right will allow the test to catch all the
healthy animals in a population. However, more diseased animals will be identified as
healthy (false negative), when, in reality, they are not.
The test's specificity increased but its sensitivity decreased.
Effects of moving the cut-off point to the right
1. Reduced sensitivity of the test
2. Increased number of false negative cases.
3. Increased specificity of the test
4. Reduced number of false positive cases.

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When to select a test with very high specificity?
1. When confirming a diagnosis. We want to make sure that every test positive is
‘truly’ diseased. A highly specific test is seldom positive in the absence of disease.
Since it gives few false positive results, when it detects one, it will most likely be a
diseased animal.
2. When increased false positives would cause physical, emotional and financial
damages.
Think of what harm a false positive diagnosis can do to the following:
a. A nun diagnosed as HIV positive
b. A government employee advised to gather P1.2million for a cancer therapy
(also due to a false positive test)
c. The best racing horse that yielded positive in Equine Infectious Anemia test.
SnOUT and SpIN mnemonic:
1. SnOUT - Sensitivity rules _________________ Out
• The sensitivity describes the ability of a diagnostic test to identify true disease
without missing anyone by leaving the disease undiagnosed. Thus, a high
sensitivity test has few false negatives and is effective at ruling conditions “out”
(SnOut).
• Meaning that on a test with high sensitivity, if you test negative, chances are you
don't have the disease (s_________________)
2. SpIN- Specificity rules _________________ In
• The specificity describes the ability of a diagnostic test to be correctly negative
in the absence of disease without mislabeling anyone. Thus, a high specificity
test has few false positives and is effective in ruling conditions “in” (SpIn).
• In using a test with high specificity, if you test positive, chances are you got the
disease (c_____________________)
As a general rule of thumb, a test with at least 95% sensitivity and 75% specificity
should be used to rule out a disease and one with at least 95% specificity and 75%
sensitivity used to rule in a disease (Pfeiffer, 1998).

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Questions for Evaluating PublishedSensitivity and Specificity Values
Source: (Gardner & Blanchard, 2006)
1. Was the test evaluated under field conditions similar to those in which it will
be used?
2. Were test results evaluated with respect to the gold standard in a blinded
fashion?
3. Were the positive and negative gold standards appropriate choices given
existing technology?
4. Were adequate numbers of representative infected and noninfected pigs
included in the study so that sensitivity and specificity estimates are precise?
At least 100 infected and 100 noninfected pigs should be used in validation
studies, wherever possible.
5. Why was the chosen cutoff value selected, and are estimates of sensitivity
and specificity reported at other cutoff values?
6. Will the test be used for individual or herd diagnosis? A test that has low to
moderate sensitivity at an individual level might be perfectly appropriate for
herd diagnosis if adequate numbers of tests are done and the test has high
specificity.
7. Will the test be used by itself or in combination with other tests? If the test is
used in combination with other tests, how will multiple test results be
interpreted?
Confidence intervals
Confidence intervals can be calculated to reflect the statistical significance of each
measure. The smaller the interval the more precise a measurement is. Normally a
95% confidence interval is used. The calculation for a 95% confidence interval for
sensitivity and specificity are described below as:
p +_1.96 x √p(1-p)/n
p= sensitivityorspecificity(asaproportionnota percentage)
n=numberof testsperformedforinfectedpeople (sensitivity) orformuninfectedpeople
(specificity)

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Predictive Values
Positive predictive value
Sometimes expressed as “Predictive value for a positive result”
It refers to the proportion of animals actually with the disease among all of the animals
with positive test results.
It answers the question: “If the test result is positive what is the probability that the animal
actually has the disease?"
Positive predictive value =
𝑎
𝑎+𝑏
=
𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒
𝑡𝑜𝑡𝑎𝑙 𝑡𝑒𝑠𝑡 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒
Negative predictive value
Sometimes expressed as “Predictive value for a negative result”
It refers to the proportion of animals free of the disease among all of the animals with
negative test results.
It answers the question: “If the test result is negative what is the probability that the animal
does not have disease?"
Negative predictive value =
𝑑
𝑐+𝑑
=
𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒
𝑡𝑜𝑡𝑎𝑙 𝑡𝑒𝑠𝑡 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒

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Sample Exercise
Fill in the missing numbers:
Screening
Results
Diseased
Non-
diseased
Total
Test positive 140
Test negative 900
Total 170 943
Calculate the following
Sensitivity
Specificity
Positive predictive value of the test:
Negative predictive value of the test:

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Relationbetween SE,SP and prevalence
1. Sensitivity and specificity DO NOT depend on prevalence. PPV and NPV do.
2. The PPV is not intrinsic to the test. If the prevalence increases, positive predictive
value increases and negative predictive value decreases. If the prevalence decreases,
positive predictive value decreases and negative predictive value increases.
3. The more sensitive a test, the better its negative predictive value.
4. The more specific a test, the better its positive predictive value.
Note the effect of prevalence on predictive value (PV) with a hypothetical serological test
(Sensitivity and Specificity are both assumed at 0.95)
Prevalence (%) 5 10 20 30 40 50 75 95
Positive PV (%) 0 53 79 88 92 95 99 100
Negative PV (%) 100 100 99 98 97 95 84 0
The table shows that, at constant specificity and sensitivity, the positive predictive value of
the test increases as the disease prevalence in the population increases. At a very low
prevalence, the positive predictive value of the test is almost zero. In contrast, the negative
predictive value of the test improves at decreasing disease prevalence.
If the test has a low positive predictive value, it means the test is an inferior predictor of
disease occurrence and would be of inadequate in confirming suspected cases of disease.
PPV and NPV cannot be interpreted correctly without knowing the prevalence of disease in
the study sample.
The graph below shows the relationship between prevalence and positive predictive value
for tests of different sensitivities and specificities (Stevenson, 2005).

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Recommendations to improve the positive predictive value of a test (Baldock, 1996)
1. Testing of "high risk" groups (animals with clinical signs rather than normal
animals)
2. For the same test using a higher cut-off with higher specificity or use a
second test with a higher specificity)
3. Use of multiple tests for interpretation of results.

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Prevalence estimation with diagnostic
Tests
The estimate of disease prevalence determined on the basis of an imperfect test is called
the apparent prevalence. Apparent prevalence is the proportion of all animals that give
a positive test result. It can be more than, less than, or equal to the true prevalence.
Apparent Prevalence=
𝑎+𝑏
𝑎+𝑏+𝑐+𝑑
=
𝑡𝑜𝑡𝑎𝑙 𝑡𝑒𝑠𝑡 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒
𝑠𝑎𝑚𝑝𝑙𝑒 𝑡𝑜𝑡𝑎𝑙
True Prevalence=
𝑎+𝑐
𝑎+𝑏+𝑐+𝑑
=
𝑡𝑜𝑡𝑎𝑙 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑑
𝑠𝑎𝑚𝑝𝑙𝑒 𝑡𝑜𝑡𝑎𝑙
If sensitivity and specificity of a test are known, then the true prevalence and other values
can be calculated
True Disease Status
Total
Diseased Non-diseased
Test Result
Test
Positive
SE*TP
(True Positive)
(1-SP)*(1-TP)
(False positive)
(SE*TP)+ [(1-SP)*(1-TP)]
Test
Negative
(1-SE)*TP
(False negative)
SP*(1-TP)
(True negative)
[(1-SE)*TP]+ [SP*(1-TP)]
Total TP 1-TP 1
SE= sensitivity
SP= specificity
TP=True prevalence
True prevalence=
𝐴𝑃+𝑆𝑃−1
𝑆𝐸+𝑆𝑃−1
Apparent Prevalence= (SE*TP)+ (1-SP)*(1-TP)
Positive predictive value of the test=
𝑆𝐸∗𝑇𝑃
( 𝑆𝐸∗𝑇𝑃)+[(1−𝑆𝑃)∗(1−𝑇𝑃)]
Negative predictive value of the test=
𝑆𝑃 ∗(1−𝑇𝑃)
[(1−𝑆𝐸)∗𝑇𝑃]+[𝑆𝑃∗(1−𝑇𝑃)]

23. DIAGNOSTICS Page 23
Test Specificity and Prevalence
A table below shows the effect of decreasing test specificity on the true prevalence of the
disease:
Apparent Prevalence 50 50 50 50 50
Sensitivity 95 95 95 95 95
Specificity 95 80 70 60 50
True prevalence 50 40 31 18 0
As the test specificity decreases, the discrepancy between the apparent prevalence and the
true prevalence widens. Investigations on disease status should employ diagnostic tests
with high specificity and sensitivity.

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Accuracy
Accuracy is the proportion of all tests, both positive and negative, that are c_________________.
Accuracy=
𝑎+𝑑
𝑎+𝑏+𝑐+𝑑
=
𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 +𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠
𝑠𝑎𝑚𝑝𝑙𝑒 𝑡𝑜𝑡𝑎𝑙
When sensitivity, specificity and apparent prevalence are known, accuracy can be
calculated using the formula below:
Accuracy= (Apparent Prevalence x Sensitivity) + (1-Apparent Prevalence) x (Specificity)

25. DIAGNOSTICS Page 25
Likelihood ratios
The sensitivity and specificity of a test can be combined into one measure called the
likelihood ratio (LR).
LR provides a summary of how many times more (or less) likely patients with the disease
are to have that particular result than patients without the disease, and they can also be
used to calculate the probability of disease for individual patients (Deeks & Altman, 2004).
LR’s are not dependent on the prevalence of disease.
Definitions
 The likelihood ratio for a test result is defined as 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 (Halkin, Reichman, Schwaber, Paltiel, &
Brezis, 1998).
 The likelihood ratio is an index of diagnostic utility that expresses the odds that a
given finding on the history, physical, or laboratory examination would occur in an
animal with, as opposed to an animal without, the condition of interest (Sacket,
1992).
Review:
 Odds refers to the _________________ of two probabilities
 Probability refers to the ____________________ of animals possessing a particular
characteristic (ex. positive test). Sensitivity, specificity and predictive values are
expressed in probability.

26. DIAGNOSTICS Page 26
Formula for Likelihood ratio of a positive test result
Likelihood ratio ofa (+) testresult =
𝑇ℎ𝑒 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖 𝑡𝑦 𝑜𝑓 𝑎𝑛 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑤𝑖𝑡ℎ 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 ℎ𝑎𝑣𝑖𝑛𝑔 𝑎 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡
𝑇ℎ𝑒 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖 𝑡𝑦 𝑜𝑓 𝑎𝑛 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 ℎ𝑎𝑣𝑖𝑛𝑔 𝑎 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡
LR positive =
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦
1−𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦
=
𝑎 ÷(𝑎+𝑐)
1−[𝑑 ÷( 𝑏+𝑑)]
Theidealdiagnostictest wouldyieldanLR of infinity for a positivetest(1÷0).
Theidealdiagnostictest wouldyieldanLR of zero for a negativetest(0÷100).
InterpretationofLR positive
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
Generally,foranimalswith a positiveresult,LR positive values exceeding 10considerably
increasethe probabilityofdisease(‘rulein’ disease) whileLRpositive values below0.1 drastically
rule outthe chance that an animalhas the disease.
Thebesttest to usefor ruling in a diseaseis theone withthe largestlikelihoodratio of a
positivetest.
Hypothetical Test Results
Sensitivity of the test: 0.80
Specificity of the test: 0.95
LR positive =
0.80
1−0.95
=
0.80
0.05
= 16.00
Interpretation: This means that an
animal with Disease A is about 16 times
more likely to have a positive test than
an animal without Disease A.

27. DIAGNOSTICS Page 27
Formula for Likelihood ratio of a negative test result
Likelihood ratio ofa (-) test result =
𝑇ℎ𝑒 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙 𝑖𝑡𝑦 𝑜𝑓 𝑎𝑛 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑤𝑖𝑡ℎ 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 ℎ𝑎𝑣𝑖 𝑛 𝑔 𝑎 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡
𝑇ℎ𝑒 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙 𝑖𝑡𝑦 𝑜𝑓 𝑎𝑛 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 ℎ𝑎𝑣𝑖 𝑛 𝑔 𝑎 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡
LR negative =
1− 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦
𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦
=
1−[𝑎 ÷( 𝑎+𝑐)]
𝑑 ÷(𝑏+𝑑)
Theidealdiagnostictest wouldyieldanLR of infinity for a positivetest(1÷0).
Theidealdiagnostictest would yieldanLR of zero for a negativetest(0÷100).
InterpretationofLR negative
LR value Interpretation
LR negative > 1
A negative test is more likely to occur in animals with the
disease than in people without the disease.
LR negative < 1
A negative test is less likely to occur in animals with the
disease compared to animals without the disease.
LR negative = 1
Generally,foranimalswith a negativetest,LR negative values exceeding10considerablyincrease
the probabilityofdisease(‘rulein’disease) whileLR negative values below0.1drasticallyruleout
the chance that ananimal hasthe disease.
Hypothetical Test Results
Sensitivity of the test: 0.80
Specificity of the test: 0.95
LR negative =
1− 0.80
0.95
=
0.20
0.95
= 0.21
Interpretation: The probability of having a negative test for animals
With Disease A is 0.21 times or about one-fifth of that of those without
the disease. Put in another way, animals without the disease are about
five times more likely to have a negative test than animals with the
disease.

28. DIAGNOSTICS Page 28
Youden'sJ statistic
Synonym: Youden's index
What is it?
It is a single statistic that indicates the overall performance of a diagnostic test.
However, the use of this single index ALONE is not generally recommended. Other
measures must be considered.
In 1950, Youden presented his Youden Index as a measure of the goodness of a
diagnostic test, using alpha and beta errors:
Formula
J = 1 - (α + β) = (1 - α) + (1 - β) - 1
Y = Sensitivity + Specificity − 1
Interpretation
 If the test has no diagnostic value, the sensitivity = 1 - β equals the fnr = α, i.e., J = 0
(i.e., equal probability for disease among patients with positive and negative test
results).
Y = 0.5 + 0.5 – 1 = 1 – 1 = 0
 If the test is always correct, all errors equal 0 and J = 1.
Y = 1 + 1 – 1 = 2 - 1
 Negative values of J (between -1 and 0) occur if the test is misleading, that is, if test
results are negatively associated with the true diagnosis.

29. DIAGNOSTICS Page 29
Use of Multiple Tests
Animal diseases are sometimes diagnosed using two or more tests. The additional
procedures are meant to increase diagnostic accuracy. In practice, the veterinarian utilizes
several bases to come up with a credible diagnosis (physical examination, history,
laboratory testing, etc.). The sensitivity and specificity of the combined tests differ from the
individual sensitivity and specificity values.
Procedure
1. Parallel – the tests are performed at the same time and interpreted together.
2. 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. Obviously, this procedure saves on additional tests. Its disadvantage
lies on the longer time required to arrive at the right diagnosis.
Interpretation:
1. Parallel Testing Interpretation (“The OR rule”) – The animal case is given a positive
diagnosis if either test is positive and a negative diagnosis if both tests are negative.
The parallel testing, because it seeks to prove the animal is healthy, results in higher
sensitivity than any of the individual tests. Note also the higher negative predictive
value.
Test A Test B Diagnosis
(-) (-) Negative
(+) (-)
(-) (+)
Sensitivity of a Parallel Test = 1- (1 – SEA) x (1 – SEB)
Specificity of a Parallel Test = SPA x SPB

30. DIAGNOSTICS Page 30
2. Serial Testing Interpretation (“The AND rule” )- The animal case is given a positive
diagnosis only if both tests are positive and a negative diagnosis if either test is
negative. The serial testing, because it seeks to prove the test-positive animals are
really sick, results in higher specificity than any of the individual tests. Note also the
higher positive predictive value.
Test A Test B Diagnosis
(+) (-) Negative
(-) (+)
(+) (+)
Sensitivity of a Serial Test = SEA x SEB
Specificity of a Serial Test = 1- (1 – SPA) x (1 – SPB)
Effect of parallel and serial testing on sensitivity, specificity and predictive value of test
combinations.
Test Sensitivity
%
Specificity
%
Positive
Predictive
Value (%)*
Negative
Predictive
Value (%)*
A 80 60 33 92
B 90 90 69 92
A and B parallel 98 54 35 99
A and B serial 72 96 82 93
*For 20%prevalence
Source: (Smith, 1995)

31. DIAGNOSTICS Page 31
Assessing agreement indiagnostic tests:The Kappa Test
As mentioned earlier, oftentimes there is no perfect gold standard. The accepted
alternative is to evaluate the diagnosis by the level of agreement between different
techniques, with one of the tests being an accepted diagnostic method.
 The kappa test (or Cohen’s Kappa) is the most commonly used statistic to quantify
the level of agreement between two diagnostic methods measured on a
dichotomous scale.
 The kappa statistic lies within a range between -1 and 1.
o [Kappa= 1], interpretation: there is perfect agreement. The test always
correctly predicts the outcome. It is rare to get perfect agreement.
o [Kappa= 0], interpretation: there is no more agreement between test and
outcome then can be expected on the basis of chance. One test is likely better
than the other but the kappa test does not tell which one is better.
o [Kappa= -1], interpretation: the agreement is less than chance. This happens
rarely. It indicates a problem in the application of the test. There could be
potential systematic disagreement between the observers. In rare situations,
Kappa can be negative.
 Kappa is dependent on the prevalence of the disease in the population. Under the
same sensitivity and specificity, the agreement between test and outcome will
decrease with a decreasing prevalence.
 In Kappa terms a test will perform worse in low prevalence populations.
InterpretationofKappa
Source: (Viera & Garrett, 2005)
Poor Slight Fair Moderate Substantial
Almost
perfect
Kappa 0.0 0.2 0.4 0.6 0.8 1.0
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

32. DIAGNOSTICS Page 32
Calculationof the Kappa statistic
The calculation of the Kappa statistic requires estimation of the observed proportion of
agreement (OP) and the expected proportion of agreement (EP):
Diagnostic Test 1
Total
Test Positive Test Negative
Diagnostic
Test 2
Test Positive a b a + b
Test Negative c d c + d
Total a + c b + d n= a + b + c + d
Kappa=
(𝑂𝑃− 𝐸𝑃)
(1−𝐸𝑃)
Where
OP =
𝑎 + 𝑑
𝑛
EP =[(
𝑎 + 𝑏
𝑛
) 𝑥 (
𝑎 + 𝑐
𝑛
)]+ [(
𝑐 + 𝑑
𝑛
) 𝑥 (
𝑏 + 𝑑
𝑛
)]
Example calculationfor the kappa statistic
Diagnostic Test 1
Total
Test Positive Test Negative
Diagnostic
Test 2
Test Positive 45 15 60
Test Negative 30 210 240
Total 75 225 300
OP=
45 + 210
300
= 0.85
EP =[(
45 + 15
300
) 𝑥 (
45 + 30
300
)] + [(
30 + 210
300
) 𝑥 (
15 + 210
300
)] = 0.65
Kappa=
0.85−0.65
1−0.65
= 0.57 which indicates moderate agreementbetween the two tests
Note that the kappa value does not tell which of the tests is better and that a good agreement may
indicate that both tests are equally good or equally bad.

33. DIAGNOSTICS Page 33
Herd-Level Interpretation of Test Results
When applied to the herd, results of diagnostic tests must be interpreted differently from
individual tests. Making herd inferences from results of animal testing is often more
complicated and may magnify small errors especially when the tests used possess
qualitative imperfections.
Reasons for classifying the herd health status
1. Disease control programs
2. Farm certification
3. Risk analysis
4. Investigations on disease determinants or risk factors
Definition of a herd test
A herd test is 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).
Possible herdclassification
Strictly two categories only With consideration for false
test results
According to level of
infection
Positive
Negative
Positive
Inconclusive/unknown
Negative
Low
Moderate
High
Herd-levelSensitivity(HSE)
Herd-level sensitivity is the probability of a truly infected herd to be classified as infected
by the test (Noordhuizen, Frankena, Van Der Hoofd, & Graat, 1997)
Herd-levelSpecificity(HSP)
Herd-level specificity is the probability of a truly NON-infected herd to be classified as
NON-infected by the test (Noordhuizen, Frankena, Van Der Hoofd, & Graat, 1997)

34. DIAGNOSTICS Page 34
Factors affecting HSE and HSP
1. Test sensitivity and specificity. Using a test of high sensitivity increases the chance
of detecting infection in a herd, especially when the disease prevalence is low.
2. The prevalence of infection within infected herds.
3. Number of samples tested. Herd-level sensitivity increases when sample size
increases but specificity decreases. Attempts should be made to increase the sample
size when dealing with a low-prevalence disease.
4. The critical number of positives (1, 2, 3, etc.) at which the herd is declared positive.
Formula (when critical number= 1)
Apparent Prevalence= (SE*TP)+ (1-SP)*(1-TP)
HSP = SPn where n= sample size
Example: if the specificity (SP) of a test is 0.95, the probability that n=8
animals are negative, and thus the herd, is
HSP = 0.958 = 0.66
HSE = 1 – (1-AP)n where (1-AP) is the probability of testing one animal as
negative while (1-AP)n is the probability of testing all n
animals as negative. The equation 1 – (1-AP) n is therefore the
probability to test at least one animal out of n samples as
positive.
Formula (when critical number> 1)
HSP = ∑ 𝑛!
𝑖! ∗( 𝑛−𝑖)!
∗ 𝑝𝑓
𝑖𝑐−1
𝑖=0 ∗ 𝑞 𝑓
𝑛−𝑖
HSE = 1 − ∑
𝑛!
𝑖! ∗( 𝑛−𝑖)!
∗ 𝑝 𝑑
𝑖𝑐−1
𝑖=0 ∗ 𝑞 𝑑
𝑛−𝑖
Where:
n = sample size
c = critical number
pf = AP if the herd is truly free of the disease and
qf = 1- pf
pd = AP if the herd is truly positive
qd = 1- pd
(see HERDACC software for calculating HSE and HSP)

35. DIAGNOSTICS Page 35
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