EPI 2 Flashcards
What are the 2 main things diagnostic tests must classify and the measure of these
- Correctly class diseased animals as diseased
- Diagnostic Sensitivity - Correctly class non-diseased animals as non-diseased
Diagnostic Specificity i
What does high sensitivity and high specificity imply
High Sensitivity implies a low number of false negatives
High Specificity implies a low number of false positives
Define sensitivity and specificity
Sensitivity -> number of true positives within the disease animals
Specificity -> the number of true negatives within the non-diseased animals
What are the 2 important questions when applying diagnostic tests and what equations answer this
- An animal has come up test positive to a particular condition, what is the probability of the animal really having the condition?
- Positive Predictive Value (PPV) - An animal has come up test negative, what is the probability that the animal is really free from the condition?
- Negative Predictive Value (NPV)
How to make a diagnosis and therefore how diagnostic tests work and their downfall
- Aim is to correctly identify abnormalities -> Normal Vs Abnormal -> for this wee need to define boundaries around normality - THE TEST
How to you separate the populations uninfected and infected?
Almost impossible for most tests always going to have false positives and false negatives
Define a test and list some examples
Any procedure that reduces uncertainty about the state of disease
- questions posed during history taking,
- clinical signs (examination and measurements) - CLINICAL EXAMINATION IS A TEST
- lab findings (haematology, serology, biochemistry, histopath),
- post mortem findings
- Gold standard is what we are sure will give us the result
What are 3 general types of tests and how to know which on to use and if choose wrong what will this affect
Categorized by what they measure
1) physiological or production parameter
2) agent, antigens or nucleic acid
3) animals immune response to challenge (antibodies)
Depends on the point of disease you are testing, unlikely to see antibodies at the start of disease but will see virus and vice versa
Effects SENSITIVITY - may not pick up the disease
What are the two general categories for things measured by diseases and which harder
- Binary outcome (+/-, pregnant versus not pregnant)
- Continuous outcome (heart rate, blood albumin, ab titre)
• need to set a cut-off to define what is normal versus not
What is the perfect diagnostic test, do they exists and how to measure the validity of the test
Perfect -> Would allow us to differentiate between disease positive and disease-negative individuals without error
• Does not exist – the tests that we use are imperfect
• To know how good they are we need to evaluate them against a reference
List and describe 7 characteristics that make for a valid diagnostic test
1) fitness for intended purpose -> accurate and precise
2) robustness (reliability) -> unaffected by small changes in test situation
3) repeatable -> within and between runs in one lab
4) reproducible -> between runs in different labs
5) Diagnostic sensitivity -> few false negatives
6) Diagnostic specificity -> few false positives
5 and 6 -> when tested in animal of known infection status in target population
7) thresholds -> cut-off selection
When is high sensitivity important
- a high cost from calling a diseased animal negative:
- Screening -> testing all animals -> pick up most as positive then later may do a high specific test to ensure that it is a true positive
- quarantine/testing new stock entering a herd)
- testing for something rare (<1% prevalence)
What is need high sensitivity and specificity what tests would you do in what order
When screening for a disease
1) Capture all animals that is positive -> don’t want to miss any -> High sensitivity
2) Confirmation -> follow-up work -> ensure all are true positive - High specificity
When is high specificty important
- a high cost from calling a non-diseased animal positive:
○ confirmatory testing
○ Depopulating (killing) based on test results
○ trade impacts when you get false positive results - ruling-in (confirmation):
What are 2 main ways of improving accuracy, the types within and what does this increase and therefore decrease
- Use several tests instead of a single test:
- Parallel testing(OR) –the animal is considered infected if it is positive to ANY test.
○ ↑ SENSITIVITY, ↓ false negatives -> decrease specific
- Serial testing (AND) –the animal must be positive to ALL test to be considered infected.
○ ↑ SPECIFICITY, ↓ false positives -> decrease sensitivity - Use a different cut-off point for the context
- But always a trade-off
○ Improving sensitivity will reduce specificity and vice versa.
Predicative valves what d they vary with and the 3 things needed to calculate
- PREDICTIVE VALUES VARY WITH PROBABILITY (PREVALENCE) OF THE CONDITION -not a constant trait of the test!
3 estimates: - Sensitivity - set for the test
- Specificity - set for the test
- Pre-test probability - variable
○ the probability of the animal having the condition PRIOR to testing (an estimate/guestimate of prevalence can be used)
How does positive and negative predictive values change with change in prevalence
- PPV goes down with low prevalence
- NPV goes down with high prevalence
what is post-test predicative value and how to get with negative and positive predictive value
Post-test predictive value is the value of the cow being infected given the predictive value
= positive predictive value and the remaining percent with the negative predictive value
What are surveys used for and what get when have a perfect test
- Surveys may be conducted to estimate the prevalence of a condition in a population
- What can be directly estimated is apparent prevalence, the % animals that test positive, not the real (true) prevalence
- True prevalence is the estimate you’d get with a perfect test
Define an outbreak
series of disease events clustered in space and time
List the 5 steps in problem solving
1) Evaluate solution
2) define and analyses problem
3) gather information
4) identify solutions
5) choose and implement solution
What are the 3 aims of an outbreak investigations and what type of approach is needed
- To identify the problem/cause -> problem solving, structured process
- To control it
- To prevent it
Systematic approach - Applied (field) epidemiology involves collecting information for action!
Aim to identify modifiable causal factors AND prevent further cases
List 3 contexts for investigations and what approaches are needed
1) Single agent clear cut outbreak (anthrax in Northern Victoria) -> diagnose, treat and prevent
2) Multi-agent/confused picture -> systematic investigation required (the 7 steps)
3) Emergency animal disease – 1 case is an outbreak -> contain and investigate (again the 7 steps)
With a single agent clear cut outbreak is occurring what are the 4 things success depends on
- true diagnosis being considered
- only one disease being present
- the results of examinations being definitive for the disease
- traditionally assumed risk factors apply
With a multi-agent outbreak what are 4 main challenges
•New, emerging, exotic or unfamiliar diseases may not be considered on the differential diagnosis list
○ Fear, unsure of how to proceed, higher stakes if zoonotic
•Misinformation, uncertainty, fear
•Time pressure
•Multifactorial disease complexes (e.g. bovine respiratory disease)