The usefulness of diagnostic tests, that is their ability to detect a person with disease or exclude a person without disease, is usually described by terms such as sensitivity, specificity, positive predictive value and negative predictive value (NPV). Many clinicians are frequently unclear about the practical application of these terms (1). The traditional method for teaching these concepts is based on the 2 × 2 table (Table 1). A 2 × 2 table shows results after both a diagnostic test and a definitive test (gold standard) have been performed on a pre-determined population consisting of people with the disease and those without the disease. The definitions of sensitivity, specificity, positive predictive value and NPV as expressed by letters are provided in Table 1. While 2 × 2 tables allow the calculations of sensitivity, specificity and predictive values, many clinicians find it too abstract and it is difficult to apply what it tries to teach into clinical practice as patients do not present as ‘having disease’ and ‘not having disease’. The use of the 2 × 2 table to teach these concepts also frequently creates the erroneous impression that the positive and NPVs calculated from such tables could be generalized to other populations without regard being paid to different disease prevalence. New ways of teaching these concepts have therefore been suggested.

1. Dr. POONAM KUMARI &
Dr. BHOJ R. SINGH
Division of Epidemiology, ICAR-
Indian Veterinary Research
Institute, Izatnagar-243122, India.
Email: brs1762@gmail.com
EPIDEMIOLOGICAL METHOD TO
DETERMINE UTILITY OF A
DIAGNOSTIC TEST

2. Diagnostic Test and Screening Test
 „ A diagnostic test is used to determine the
presence or absence of a disease when a subject
shows signs or symptoms of the disease.
 „ A screening test identifies asymptomatic
individuals who may have the disease.
 „The diagnostic test is performed after a positive
screening test to establish a definitive diagnosis.

3. Some Common Screening Tests
 Pap smear for cervical dysplasia or cervical
cancer
 „Fasting blood cholesterol for heart disease
 „Fasting blood sugar for diabetes
 „Blood pressure for hypertension
 „Mammography for breast cancer
 „PSA test for prostate cancer
 „MRT for brucellosis
 „Ocular pressure for glaucoma
 „TSH for hypothyroid and hyperthyroid

4. Diagnostic tests categorisation
 The ‘prescribed tests’ are those which are considered
optimal for determining the health status of animals before
shipment or reporting a disease.
 ‘Alternative tests’ do not demonstrate the absence of
infection in the tested animals with the same level of
confidence as the prescribed tests do.
 However, the OIE Terrestrial Animal Health Standards
Commission considers that an ‘alternative test’, chosen by
mutual agreement between the importing and exporting
countries, can provide valuable information for evaluating
the risks of any proposed trade in animals or animal
products.

5. Selection of diagnostic tests
 The selection of an appropriate diagnostic test
depends upon the intended use of the result.
 If the intention is to rule out a disease, reliable
negative results are required for which a test with
high sensitivity (i.e., few false negative ) is used.
 If it is desired to confirm a diagnosis or find
evidence of disease (i.e. to "rule in" the disease)
we require a test with reliable positive results (i.e.,
high specificity) .

6. CONTINUE……
 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).

7. Evaluation of diagnostic techniques
 Evaluation of diagnostic techniques requires
some independent, valid measure of the true
condition of the animal (the 'gold standard')
 The 'gold standard' may be a single unequivocal
test (histological or post-mortem demonstration of
the disease, for example) or a combination of
alternative tests which,when simultaneously
positive, identify animals which are true positives.

8. Continue…
 However, no 'gold standard' exists for a particular condition and
it is necessary to evaluate the diagnosis by the level of
agreement between different tests.
 This assumes that agreement between tests is evidence of
validity, whereas disagreement suggests that the tests are not
reliable.
 The kappa test can be used to measure the level of agreement
beyond that which may be obtained by chance. The kappa
statistic lies within a range between -1 and +1.
 The kappa test uses the same table as for calculation of
epidemiological values with the observed agreement given by
the formula:
OA = (a + d)/(a + b + c + d )
 Kappa is the agreement greater than that expected by chance
divided by the potential excess

9. CONTINUE…..
 The assessment or comparison of diagnostic
tests requires their application, with the 'gold
standard', to a sample of animals with a typical
disease spectrum.
 The characteristics of the test are compared with
the gold standard in terms of their sensitivity and
specificity.

10. Sensitivity and Specificity of a
diagnostic test
 Sensitivity− The ability of the test to identify
correctly those who have the disease.
 „ Specificity− The ability of the test to identify
correctly those who do not have the disease.

11. Determining the Sensitivity,
Specificity of a New Test
 Must know the correct disease status prior to
calculation
 „Gold standard test is the best test available
− It is often invasive or expensive
 „A new test is, for example, a new screening test
or a less expensive diagnostic test.
 „Use a 2 x 2 table to compare the performance of
the new test to the gold standard test.

12. Gold Standard Test
Disease
Positive with the test Negative with the test
a+b (all animals with the
disease)
c+d (all animals without the
disease)

13. Comparison of Disease Status:
Gold Standard Test and New Test
Disease diagnosed
with New test
Disease diagnosed with Gold standard test
Positive Negative
Positive a (true positive) b
Negative c d (true negative)

14. Sensitivity
 Sensitivity is the ability of the test to identify correctly
those who have the disease (a) from all individuals with
the disease (a+c)
 Sensitivity = a/a+c = true positive/disease+
 Sensitivity is a fixed characteristic of the test.

15. Specificity
 Specificity is the ability of the test to identify correctly
those who do not have the disease (d) from all
individuals free from the disease (b+d)
 Specificity = d/b+d = true negative/disease-
 Specificity is also a fixed characteristic of the test.

16. Applying Concept of Sensitivity and
Specificity to a Screening Test
 Assume a population of 1,000 people
 200 have a disease
 800 do not have the disease
 A screening test is used to identify the 200 people with
the disease
 „The results of the screening appears in this table
Results of
screening test
True status of disease in the
population
Total
Disease No disease
Positive 150 100 250
Negative 50 700 750
Total 200 800 1000

17. Calculating Sensitivity and Specificity
Results of
screening test
True status of disease in the
population
Total
Disease No disease
Positive 150 100 250
Negative 50 700 750
Total 200 800 1000
Calculating Sensitivity and Specificity
Sensitivity= 150*100/200= 75%
Specificity= 700*100/800= 87.5%

18. Predictive Values of diagnostic tests
 Positive predictive value (PPV)
− The proportion of patients who test positive
who actually have the disease.
 „Negative predictive value (NPV)
− The proportion of patients who test negative
who are actually free of the disease.

19. Test
Results
Disease
Present Absent
Positive a (true positive) b (false
positive)
Negative c (false negative) d (true
negative)
Test
Results
Disease
Present Absent
Positive a + b (all subjects with testing positive)
Negative c + d (all subjects with testing negative)
What the Test Shows
Predictive Value
Positive predictive value = a/ a+ b = true positive/test +
Negative predictive value =d/c +d = true negative /test –
What we get from the Test Results

20. Results of the
screening test
The status of the disease in
the test population
Total
Disease No disease
Tested
positive
150 100 250
Tested
negative
50 700 750
200 800 1000
Applying Concept of Predictive Values to
Screening Test
Assume a population of 1,000 people, 200 have a disease, 800 do not
have the disease. A screening test is used to identify the 200 people with
the Disease. The results of the screening appear in this table.
Calculating Predictive Values
Positive Predictive value (PPV) of the test= 100*150/250= 60%
Negative Predictive value (NPV) of the test= 100*700/750=93.3%

21. Relationship of Disease Prevalence
to Predictive Value
Suppose sensitivity is 95% and Specificity is 90*
Disease
Prevalence
Test
results
With
Disease
Without
Disease
Total PPV NPV
1%
+ve 95 90 185 95/185=
51.35%
8910/8
915=
99.9%
-ve 5 8910 8915
Total 100 9900 10000
5%
+ve 475 950 1425 475/1425=
33.3%
8550/8
575=
99.7%
-ve 25 8550 8575
Total 500 9500 1000

22. Positive Predictive Value (PPV)
Primarily Depends On
 The prevalence of the disease in the population
tested, and the test itself (sensitivity and
specificity)
 − In general, it depends more on the specificity
(and less on the sensitivity) of the test (if the
disease prevalence is low)

23. PPV Improvement
 The PPV of a particular test can be improved by
appropriate selection strategies
 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.
(Baldock, 1996):

24. Reproducibility, Repeatability,
Reliability of a diagnostic test
 Reproducibility, repeatability, reliability all mean that
the results of a test or measure are identical or
closely similar each time it is conducted
 „Because of variation in laboratory procedures,
observers, or changing conditions of test subjects
(such as time, location), a test may not consistently
yield the same result when repeated
Different types of variation
 − Intra-subject variation
 − Intra-observer variation
 − Inter-observer variation

25.  Intra-subject variation is a variation in the results of a
test conducted over (a short period of) time on the same
individual
 „ The difference is due to the changes (such as
physiological, environmental, etc.) occurring to that
individual over that time period.
 Inter-observer variation is a variation in the result of a
test due to multiple observers examining the result
(inter=between)
 „Intra-observer variation is a variation in the result of a
test due to the same observer examining the result at
different times (intra = within)
 „The difference is due to the extent to which observer(s)
agree or disagree when interpreting the same test result

26. Conclusions
 The interpretation of diagnostic tests depends upon
the definition of clinical disease and its distinction
from the presence of the pathogen.
 Ideally, a diagnostic test can be evaluated based on
a clear relationship with an unequivocal "gold
standard" diagnosis.
 The use of epidemiological methods in the planning
and analysis of diagnosis, or better still, a greater co-
operation between pathologists and epidemiologists,
will assist greatly in the development and
interpretation of better diagnostic tests.

27. References
1. Steurer J, Fischer JE, Bachmann LM, Koller M, ter Riet G.
Communicating accuracy of tests to general practitioners: a controlled
study. BMJ 2002; 324: 824–6.
2. Waisman Y, Zerem E, Amir L, Mimouni M. The validity of the uriscreen
test for early detection of urinary tract infection in children. Pediatrics 1999;
104: e41.
3. Anthony K Akobeng .(tony.akobeng@cmmc.nhs.uk)
Department of Paediatric Gastroenterology, Booth Hall Children’s Hospital,
Central Manchester and Manchester Children’s University Hospitals,
Manchester, UK
4.Thrusfield. M. (1995). Veterinary Epidemiology 2nd Edition. Publ.
Blackwell Science Ltd., Oxford, UK.
5.Baldock, C. (1996). Course notes from the Australian Centre for
International Agricultural Research Workshop on "Epidemiology in Tropical
Aquaculure" Bangkok, 1-12 July, 1996.
6..Pfeiffer, D. (1998). Veterinary Epidemiology. An Introduction. Institute of
Veterinary, Animal and Biomedical Sciences. Massey University,
Palmerston, New Zealand.