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

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
a (true positive) b 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
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
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. a + b (all subjects with testing positive) Negative
What we get from the Test Results
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 –

20. 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.
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
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%
8910/8915= 99.9%
-ve 5
8910
8915
100
9900
10000 5%
475
950
1425
475/1425= 33.3%
8550/8575= 99.7% 25
8550
8575
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
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.