Screening tests are performed to identify diseases in asymptomatic individuals to improve outcomes. An ideal screening test is cheap, easy to use, reliable, and valid. Screening test accuracy is evaluated using a 2x2 table to calculate sensitivity, specificity, positive predictive value, and negative predictive value. An example evaluates a diabetic retinopathy screening test and finds it has high sensitivity (96%) and specificity (95%) but low positive predictive value (70%), indicating many false positives.

1. Screening tests
Dr Mathias Tumwebaze PhD
Senior Lecturer. And Consultant
MPH Programs- 2020
DR MT/LECTURE NOTES/MPH

2. Screening
• Screening is performed in order to identify whether people have
a disease for which they currently have no symptoms
• Screening is not performed to diagnose illness. Instead, it aims to
improve the outcomes of those who are affected, by detecting a
disease before its symptoms have developed.
• screening test should be able to detect disease in the period
between the time when it can be detected using a screening test
and the time when symptoms develop.
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3. Con’t screening
• In practice, screening tests are never completely accurate
• There will always be a number of false-positive results (in which the
test indicates that a subject has the infection when in reality they
do not).
• False-negative results can also occur (in which the test indicates that
there is no infection present, when in reality the subject does have
the disease)
• good screening test should keep false-positive and false-negative
results to an absolute minimum.
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4. Screening test
• The screening test itself must be cheap, easy to apply,
acceptable to the public, reliable and valid.
• A test is reliable if it provides consistent results, and
valid if it correctly categorizes people into groups
with and without disease, as measured by its
sensitivity and specificity.
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5. Evaluating the accuracy of
screening tests
• A screening test can be evaluated using a 2x2 table,
Present in table
• how many subjects with a positive result actually have the
disease (true positive) (cell a)
• how many subjects with a positive result do not have the
disease (false positive) (b)
• how many subjects have a positive result (a + b)
• how many subjects have a negative result (c + d)
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6. Con’t evaluation
• how many subjects with a negative result actually have the
disease (false negative) (c)
• how many subjects with a negative result do not have the
disease (true negative) (d)
• how many subjects actually have the disease (a + c)
• how many subjects do not have the disease (b + d)
• the total number of subjects (a + b + c + d).
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7. A 2 x 2 table for evaluating a screening test
Disease
Present
Disease
absent
Total
Positive a
True Positive
b
False Positive
A+b
Negative c
False Negative
d
True Negative
C+d
Total a+c b+d A+b+c+d
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8. Ways to measure the accuracy of a
screening test.
Sensitivity
• This is the proportion of subjects who really have the
disease, and who have been identified as diseased by the test.
• The formula for calculating sensitivity is a/ (a + c)
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9. Specificity
• This is the proportion of subjects who really do not have the disease,
and who have been identified as non-diseased by the test.
• The formula for calculating specificity is d/(b + d).
• Sensitivity and specificity both indicate how accurately the test can
detect whether or not a subject has the disease (this is known as the
test's validity}.
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10. Positive predictive value (PPV)
• This is the probability that a subject with a positive test result
really has the disease.
• The formula for calculating PPV is a/ (a+b)
• Negative predictive value (NPV)
• This is the probability that a subject with a negative test result
really does not have the disease.
• The formula for calculating NPV is d/(c + d}.
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11. Prevalence
• This is the proportion of diseased subjects in a screened
population (also called the pre-test probability), and it is the
probability of having the disease before the screening test is
performed.
• It can be especially useful when evaluating screening tests for
groups of people who may have different prevalences (e.g.
different genders, age groups or ethnic groups).
• The formula for calculating prevalence in screening is (a+c)/
(a+b+c+d)
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12. Example
• Suppose that a new screening test has been
developed for diabetic retinopathy. We carry out a
study to find out how effective it is in a population
of 33750 patients with diabetes, all aged over 55
years. Use data to evaluate the test.
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13. A 2 x 2 table for evaluating a diabetic retinopathy
screening test
Diabetic retinopathy
Disease
Present
Adisease
bsent
Total
Positive (a)
3200
(b)
1400
A+b
4600
Negative (c)
150
(d)
29,000
(C+d)
29150
Total (a+c)
3350
(b+d)
30400
(A+b+c+d)
33750
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14. • Compute the sensitivity of this test
• Interpret your finding
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15. Sensitivity
• Sensitivity = a/(a + c)
= 3200/3350 = 0.9552 = 96%.
• This means that 96% of subjects who actually have
diabetic retinopathy will be correctly identified by the
test.
• This result indicates that only 4% of subjects with
diabetic retinopathy will be wrongly identified as
being disease-free.
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16. Compute specificity of the test,
• What is the specificity of this test.
• What is the interpretation of your finding.
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17. Specificity
• Specificity = d/(b + d}
= 29000/30400 = 0.9539 = 95%.
• This means that 95% of subjects who do not have
diabetic retinopathy will be correctly identified by the
test. This result indicates that only 5% of subjects
without the disease will be wrongly identified as
having
• diabetic retinopathy.
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18. Compute the positive predictive
value of this test
• What is the PPV of this test.
• What is the interpretation of this +PPV
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19. Compute PPV
• Positive predictive value = a/(a + b) =
3200/4600 = 0.6957 = 70%.
• This means that there is a 70% chance that someone
who tests positive does have diabetic retinopathy.
• This is poor, as there is a 30% chance that someone
with a positive test result is actually disease-free.
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20. Compute the Negative predictive
value
• What is the NPV of this test.
• What is the interpretation of this Negative
predicti?ve value
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21. Compute the NPV of the test.
• Negative predictive value = d/(c + d)
= 29000/29150 = 0.9949 = 99%.
• This means that there is a 99% chance that someone
who tests negative does not have diabetic retinopathy.
• This is good, as there is only a 1% chance that
someone with a negative test result will actually have
the disease.
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22. Prevalence
• Prevalence = (a + c)/(a + b + c + d)
= 3350/33 750 = 0.0993 = 10%.
• This means that 10% of the screened population have diabetic
retinopathy.
• We can conclude that although this screening test appears to be
generally
• very good, the disappointing positive predictive value of only
70% would
• limit its overall usefulness.DR MT/LECTURE NOTES/MPH