Diagnostic tests

Question 1

Screening vs. Diagnostic Tests

Question 2

Screening

Answer 2

• preventative
• measures “healthy”
• sub-clinical signs
• early detection
• early treatment & increased prognosis of disease
• focus: population
• during incubation & latent period
• important to have it pick up signs earlier

Question 3

diagnostic

Answer 3

• clinical signs showing
• identify disease faster
• measures “sick”
• guide treatment & prognosis of disease
• take more reactive approach
• focus: individual
• from clinical disease evident to outcome (after incubation period)

Question 4

valid & reliable

Answer 4

narrow curve w/ true value

Question 5

sensitivity & specificity

Answer 5

inversely proportional

Question 6

2x2 tables

Question 7

True disease status: +
Test result: +

Answer 7

true positive (want these!)
- truly sick

Question 8

True disease status: +
Test result: -

Answer 8

False negative
- not truly healthy

• timing: diurnal variation in what test is measuring
• factors suppressing body’s reaction to pathogen: little production of antibodies, early in subclinical stage
• lab/test error (too early)

Question 9

True disease status: -
Test result: +

Answer 9

False positive
- not truly sick

• similar disease agent
• previous exposure
• lab/test error

Question 10

True disease status: -
Test result: -

Answer 10

true negative
- truly healthy

Question 11

proportion exposed

Answer 11

P = (a+b)/n

Question 12

proportion diseased

Answer 12

P = (a+c)/n

Question 13

proportion diseased & exposed

Answer 13

P = a / n

Question 14

what do we look for in epidemiology

Answer 14

disease risk in exposed group & diseased risk in unexposed group

Question 15

True prevalence

Answer 15

• actual level of disease present in population

TP = (a+c)/n

Question 16

apparent prevalence

Answer 16

• what the prevalence “appears to be” if you use this particular test
• positive test results

AP = (a+b)/n

Question 17

sensitivity

Answer 17

• proportion of diseased that test positive
• never have perfect sensitivity

Sn = a / (a+c)

Question 18

specificity

Answer 18

• the proportion of nondiseased that test negative

Sp = d / (b+d)

Question 19

sensitivity => false negatives

Answer 19

• animals that test negative but actually have disease

1 - Sn = % of false negatives

• “Snout” - if test is highly SeNsitive, you can rule out a Negative test result, you can be confident in ruling the disease OUT
• highly sensitive test = few false negatives
• highly infectious diseases that cause serious illness/death

Question 20

specificity => false positives

Answer 20

• animals that test positive but aren’t diseased

1 - Sp = % of false positive

• “Spin” - if test is highly SPecific, and you get a Positive test result, you can be confident in ruling disease IN
• disease w/ costly treatments, treatments cause suffering, etc.

Question 21

What if apparent prevalence if higher than true prevalence?

Answer 21

• poor specificity, lots of false positive so non-diseased animals are falsely diagnosed

Question 22

If true prevalence is higher than apparent

Answer 22

good sensitivity, more false negatives so diseased animals are missed by test

Question 23

Predictive Value

Answer 23

probability test results being currect depend on Sn & Sp, prevalence

Question 24

predictive value of positive test (PPV)

Answer 24

probability of TEST POSITIVES are actually DISEASED

PPV = a / (a+b)

• usually aim for 90%

1 - PPV = % false positives (disease free)

Question 25

predictive value of negative test (NPV)

Answer 25

probability of TEST NEGATIVES are truly negatives

NPV = d / (c+d)

• aim for 90% or higher

1 - NPV = % false negatives (actually be diseased)

Question 26

Prevalence affects predictive values

Answer 26

prevalence increase, PPV increase, NPV decrease

prevalence decrease, PPV decreased, NPV increase