With relevant examples, explain the validity and reliability of diagnostic and screening tests.

1. CAT 1: WITH RELEVANT EXAMPLES, EXPLAIN THE VALIDITY AND
RELIABILITY OF DIAGNOSTIC AND SCREENING TESTS
MPH 5101- FOUNDATIONS OF EPIDEMIOLOGY
GROUP MEMBERS:
• KARANJA EUNICE WAIRURI – MPH/2022/49470
• JAMA SHAFICI ISMAIL – MPH/2022/48923
• BENJAMIN WELU
1

2. Introduction
• To provide effective health services, it is important to distinguish populations of people with and
without diseases.
• A screening test is a medical test or procedure performed on members of a defined asymptomatic
population or population subgroup to assess the likelihood of their members having a particular
disease
• A major objective of most screening tests is to reduce morbidity or mortality in the population
group being screened for the disease by early detection, when treatment may be more successful
(Rafferty et al, 2013)
• Accurate diagnosis of diseases is important for delivering the appropriate treatments, implementing
preventive programs in the community, and finding the causes and etiology of diseases.

3. Validity
• The validity of a screening test is defined as its ability to distinguish between who have
disease and who do not i.e. a test measures what it is supposed to measure and how much
the test result is close to real value.
• The sensitivity of the test is defined as the ability of a test to identify correctly those who
are actually suffering from the disease.
• The specificity of the test is defined as the ability of a test to identify correctly those who
do not have the disease (Wayne W, 2016).

4. Gold Standard
• The validity of a screening test is based on its accuracy in identifying diseased and non-
diseased persons, and this can only be determined if the accuracy of the screening test can
be compared to some "gold standard" that establishes the true disease status (LM Troy et
al, 1996)
• The gold standard might be a very accurate but more expensive diagnostic test.
• Alternatively, it might be the final diagnosis based on a series of diagnostic tests.
• Example od a Gold standard is Gene expert test for TB or PCR for HIV.

5. Testing the Validity of screening tests
• A 2 x 2 table, or contingency table, is used when
testing the validity of a screening test.
• The contingency table for evaluating a
screening test lists the true disease status in the
columns, and the observed screening test results
are listed in the rows.
• Example: screening test for breast cancer using
mammogram
Diseased
Not
Diseased
Total
Test
Positive
132 983 1,115
Test
Negative
45 63,650 63,695
Column
Totals
177 64,633 64,810

6. Contingency Table Cont’d
• There were 177 women who were ultimately found
to have had breast cancer, and 64,633 women
remained free of breast cancer during the study.
• Among the 177 women with breast cancer, 132
had a positive screening test (true positives), but
45 had negative tests (false negatives).
• Among the 64,633 women without breast cancer,
63,650 appropriately had negative screening tests
(true negatives), but 983 incorrectly had positive
screening tests (false positives).

7. Contingency Table Cont’d
• If we focus on the rows, we find that 1,115
subjects had a positive screening disease,
i.e., the test results were abnormal and
suggested disease.
• However, only 132 of these were found to
actually have disease, based on the gold
standard test.
• Also note that 63,695 people had a negative
screening test, suggesting that they did not
have the disease, BUT, in fact 45 of these
people were actually diseased.
Diseased
Not
Diseased
Total
Test
Positive
132 983 1,115
Test
Negative
45 63,650 63,695
Column
Totals
177 64,633 64,810

8. Sensitivity
• How accurate the screening test is in identifying disease
in people who truly have the disease.
• What was the probability that the screening test would
correctly indicate disease in this subset?
• Sensitivity = True Positive Fraction = P(Screen
Positive | Disease) = a/(a+c)
• The probability is simply the percentage of diseased
people who had a positive screening test, i.e., 132/177 =
74.6%.
• We could interpret this by saying, "The probability of
the screening test correctly identifying diseased
subjects was 74.6%."
Diseased
Not
Diseased
Total
Test
Positive
132 983 1,115
Test
Negative
45 63,650 63,695
Column
Totals
177 64,633 64,810

9. Specificity
• Focuses on the accuracy of the screening test in
correctly classifying truly non-diseased people.
• In this example, the specificity is 63,650/64,633 =
98.5%.
• Specificity = True Negative Fraction = P(Screen
Negative | Disease Free) = d/(b+d)
We could interpret this by saying, "The probability of
the screening test correctly identifying non-diseased
subjects was 98.5%."
Diseased
Not
Diseased
Total
Test
Positive
132 983 1,115
Test
Negative
45 63,650 63,695
Column
Totals
177 64,633 64,810

10. Sensitivity and Specificity
• Sensitivity = True Positive Fraction =
P(Screen Positive | Disease) = a/(a+c)
• Specificity = True Negative Fraction =
P(Screen Negative | Disease Free) =
d/(b+d)
• True Positive(A) = Those actually
suffering from disease and test positive
• True Negative (D) = Those free from
disease and test negative
• False Positive (B) = who do not have the
disease and test positive
• False Negative (C)= who actually
suffering from disease and test negative
Diseased Not Diseased
Test
Positive
True
Positive(A)
False Positive
(B)
Test
Negative
False
Negative (C)
True Negative
(D)

11. Predictive value of a Test (Diagnostic power)
Positive predictive value (PPV)
• is the probability that subjects with a positive screening test truly have the disease.
• Proportion of the patients who test positive, actually suffering from the disease OR
diagnostic power of a test to correctly detect the disease.
• PPV = A /A+B
Negative predictive value (NPV)
• is the probability that subjects with a negative screening test truly don't have the disease.
• Proportion of the patients who test negative, actually free from the disease OR diagnostic
power of a test to correctly exclude the disease.
• NPV = D /C+D

12. Positive and Negative Predictive Value
• 1,115 subjects whose screening test was positive, but
only 132 of these actually had the disease, according
to the gold standard diagnosis.
• Therefore, if a subject's screening test was positive,
the probability of disease was 132/1,115 = 11.8%.
• Interpretation: Among those who had a positive
screening test, the probability of disease was 11.8%.
• Consequently, the negative predictive value of the
test was 63,650/63,695 = 99.9%.
• Interpretation: Among those who had a negative
screening test, the probability of being disease-free
was 99.9%.
Diseased
Not
Diseased
Total
Test
Positive
132 983 1,115
Test
Negative
45 63,650 63,695
Column
Totals
177 64,633 64,810

13. References
• https://sphweb.bumc.bu.edu/otlt/mph-modules/ep/ep713_screening/index.html
Retrieved on 7th March 2021
• Rafferty EA, Park JM, Philpotts LE, et al. Assessing radiologist performance
using combined digital mammography and breast tomosynthesis compared with
digital mammography alone: results of a multicenter multireader
trial. Radiology. 2013
• LM Troy, KB Michels, DJ Hunter, D Spiegelman (1996). "Self-reported
birthweight and history of having been breastfed among younger women: an
assessment of validity
• Gordis Epidedimiology, 6th edition
• Gjørup T. (1997). Reliability of diagnostic tests. Acta obstetricia et gynecologica
Scandinavica. Supplement, 166, 9–14.
• https://hrguide.com/Testing_and_Assessment/Reliability_and_Validity.htm#:~:text
=reliability%20and%20validity.-
,Test%20reliability,to%20measure%20a%20characteristic%20reliably.