Lecture Notes for Master of Public Health Class at Bishop Stuart University. Ugaganda

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