First midterm content Flashcards
vector disease transmission
a living carrier transports an infectious agent from an infected individual to a susceptible individual
zoonotic disease
infectious agents are transmitted from non human animals to humans
- can be spread by any route of transmission
timeline of disease progression
- exposure to sufficient cause
- pathologic process detectable
- clinical disease evident
- outcome (chronic or recovery)
what is the communicability period
the time which a pathogen can be transmitted from an infected individual to another individual
types of prevention
primary: alter susceptibility, reduce exposure, health promotion
secondary: early detection, screening, case-finding
tertiary: psychosocial, medical, vocational and physical rehabilitation
where in disease timeline do types of prevention occur
primary: before exposure
secondary prevention: before clinical disease is evident
tertiary: after clinical disease is evident
“qualifiers” to herd immunity
- infectious agent restricted to one host species - transmission is direct
- infection/vaccination must induce solid immunity
- need random mixing of individuals
what is systems thinking
an approach to examining a system that includes how the individual parts are interconnected and how that system is a part of the broader context
what is transdisciplinarity
an approach that brings together and integrates different perspectives and knowledges to generate new ideas
strengths of one health
- more fulsome understanding of current issues
- reduce risks and faster recognition of problems
- increased collaboration between stakeholders
- more effective interventions
- enhanced resiliency and sustainability of ecosystems
- improved human and animal health and wellbeing
analytical vs descriptive study
descriptive: describe characteristics of a population
analytical: assess specific associations between risk factors and disease
target, source and study population
target: population which it might be possible to extrapolate results
source: population from which the study subjects are drawn, can list all its members
study: the individuals included in the study
sampling frame
the list of all the members I the source population
external vs internal validity
external: how well can the study results extrapolated to the target population
internal validity: how well dies the study relate to the source population
convenience sampling
sampling units are chosen because they are easy to get
judgement sampling
the investigator chooses what they deem to be units representative of the population
purposive sampling
sampling units are chosen on purpose because of their exposure or disease status
types of non-probability sampling
- convenience sampling
- judgement sampling
- purposive sampling
types of probability sampling
- simple random sampling
- systematic random sampling
- stratified random sampling
simple random sample
a fixed percentage of the source population is randomly chosen
- need to know the sampling frame (therefore total # of individuals in the population) to use this method
systematic random sampling
use when you don’t have a complete list of individuals in the population to be sampled
- determine a sampling interval and randomly select your starting point them sample every j^th person
stratified random sampling
sampling frame is broken into strata based on some factor and then simple or systematic random sampling is conducted within each strata
cluster sampling
- the sampling unit is a GROUP, but the unit of concern is the INDIVIDUAL
- all individuals in the sampling units are selected
multistage sampling
takes place at both the individual and the cluster level - convenient when too many individuals in a cluster or when individuals in the cluster are very similar
what is precision
how tight the confidence interval is around your estimate
- i.e. the allowable error
type I vs type II error
type I: outcomes in groups being compared are proven to be different, when they are actually not
type II: outcomes in groups being compared are not proven to be different, when they actually are
required sampling size increases as…
- the size of the difference between 2 means or proportions decreases
- the level of power to detect a difference between the groups increases
- the number of confounders you’re controlling increases
- the number of hypotheses being tested increases
screening tests
- focused on populations
- individuals are “healthy”
- early detection of a pathological process
- sub-clinical disease
diagnostic tests
- focused on individuals
- individuals are “sick”
- confirm, guide teartment or aid in prognosis of clinical disease
what is true prevalence
the actual level of disease present in the population
TP = (a+c)/n
- individuals that are truly disease positive
apparent prevalence
what the prevalence appears to be
AP = (a+b)/n
- individuals that test disease positive
how do you measure if a test is a good test
sensitivity and specificity
- if the individual is diseased, will the test identify them as so?
how do you measure if a test is a useful test?
predictive values (positive and negative)
- given that a test says the individual is positive, what is the probability that they actually have the disease
sensitivity
the proportion of those individuals that actually HAVE THE DISEASE who TEST POSITIVE
Sn = a/(a+c)
specificity
the proportion of those individuals who DON’T HAVE DISEASE that TEST NEGATIVE
Sp = d/(b+d)
what does sensitivity tell you about
sensitivity = fasle negatives
1- Sn = % of false negatives
- highly sensitive tests rule out disease
what does specificity tell you about
fasle positives
1- Sp = % of false positives you can expect with the test
- highly specific tests rule diseases in
what don’t Sn and Sp tell us
don’t tell us how useful the test might be
positive predictive value
probability that given a positive test result the individual actually has disease
PPV = a/(a+b)
negative predictive value
probability that given a negative test result, the individual actually doesn’t have the disease
NPV = d/(c+d)
how does prevalence affect predictive values
decreasing prevalence of disease…
decreases PPV
increases NPV
2 ways to combine tests to improve Sn and Sp
series interpretation and parallel interpretation
what is series interpretation
we call a test positive only if an individual tests positive on BOTH test
- 1st test is cheaper/less invasive
- 2nd test is more expensive/invasive
what is parallel interpretation
we call a test positive if the individual test positive on at least ONE test
- both tests must be negative to be called negative
logistics of series testing
- increases specificity
- lower chance of false positives
- decreases sensitivity
logistics of parallel testing
- increases sensitivity
- more false negatives
- decreases specificity
steps in completing a series test
- complete 2x2 table for test 1
- do the second test only on positives from test 1 and complete a 2x2 for Test 2
- calculate net sensitivity and net specificity from this table
steps in completing a parallel test
- complete a 2x2 table for test 1 on the whole population
- complete a 2x2 table for test 2 on the whole population
- calculate net sensitivity and specificity
what is net sensitivity
how many individuals were correctly diagnosed as disease positive using the two tests
what is net specificity
how Manu individuals were correctly diagnosed as disease negative using the two tests
how to calculate net specificity and net sensitivity for series
Net Sp:
(d1+d2)/(c1+d1)
Net Sn:
a2/a1
how to calculate net specificity and sensitivity for parallel
Net Sp:
(c1 x sp2) / (c1+d1)
Net Sn:
step 1: a1 x sn2 = Y
step 2: ((a1-Y) + (a2-Y) +Y) / (a + b)
what is validity
ability to distinguish between who has the disease and who doesn’t
- more true
- sensitivity and specificity
what is reliability
ability of a test to give repeatable results
- more consistant
3 sources of variation in reliability
intra-subject
intra-observer
inter-observer
what is Kappa
a measure of agreement beyond what would be due to chance alone
- the closer Kappa is to 1, the better the agreement
what is an association
an identifiable measure between an exposure and outcome
- does not necessarily mean relation is causal
what is bias
systematic errors (deviation from the truth) that result in an incorrect estimate of the association between exposure and outcome
random vs systematic error
random error: fluctuations around the true value due to chance - solve by increasing sample size
systematic error: deviations that disproportionately affect the data (not due to chance) - can NOT be fixed by increasing sample size
3 main types of bias
selection bias
information bias
confounding
what is selection bias
arises from the way subjects are enrolled in the study
- the relationship between E and O among those in the study differs from that among those who were potentially eligible
types of selection bias
- non-response/volunteer bias
2 healthy worker effect/selective entry - detection/surevillance biace
- loss to follow-up
what is information bias
incorrect classification or measuring of exposure, outcome of other factors
types of information bias
- measurement error (continuous variables)
- misclassification bias (categorical variables) - split into differential and non-differential
misclassification bias (type of information bias)
error in classifying the exposure or outcome
non-differential: magnitude and direction of error between the 2 groups is the same, estimate is biased towards the null value
differential: magnitude and direction of the misclassification of E or O is different in the 2 groups being compared, bias can be toward or away from null
confounding bias
mixing together of the effects of 2 or more factors
- the observed association between the exposure and outcome is affected by a third factor
what is required for a variable to be a confounder
- associated with the outcome
- associated with the exposure
- not a consequence of the exposure
controlling for confounding
design stage: randomization, exclusion, matching
analysis stage: stratification, multivariable modelling
causation vs association
association: implies E might cause O
causation: implies there is a true mechanism that leads from E to O
ways to determine causality
- statistical association
- epidemiological association
- casual inference
Causal inference methods
- Bradford-hill criteria
- component-cause model
component cause model of causal inference
determines if a cause is necessary or sufficient
necessary cause: if not present disease cannot occur, always present if disease is present
sufficient cause: precede the disease, if present the disease always occurs
what is a component cause
one of a number of factors that, in combination, constitutes a sufficient cause
