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First midterm content Flashcards

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1
Q

vector disease transmission

A

a living carrier transports an infectious agent from an infected individual to a susceptible individual

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2
Q

zoonotic disease

A

infectious agents are transmitted from non human animals to humans
- can be spread by any route of transmission

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3
Q

timeline of disease progression

A
  1. exposure to sufficient cause
  2. pathologic process detectable
  3. clinical disease evident
  4. outcome (chronic or recovery)
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4
Q

what is the communicability period

A

the time which a pathogen can be transmitted from an infected individual to another individual

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5
Q

types of prevention

A

primary: alter susceptibility, reduce exposure, health promotion
secondary: early detection, screening, case-finding
tertiary: psychosocial, medical, vocational and physical rehabilitation

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6
Q

where in disease timeline do types of prevention occur

A

primary: before exposure
secondary prevention: before clinical disease is evident
tertiary: after clinical disease is evident

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7
Q

“qualifiers” to herd immunity

A
  1. infectious agent restricted to one host species - transmission is direct
  2. infection/vaccination must induce solid immunity
  3. need random mixing of individuals
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8
Q

what is systems thinking

A

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

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9
Q

what is transdisciplinarity

A

an approach that brings together and integrates different perspectives and knowledges to generate new ideas

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10
Q

strengths of one health

A
  • 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
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11
Q

analytical vs descriptive study

A

descriptive: describe characteristics of a population
analytical: assess specific associations between risk factors and disease

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12
Q

target, source and study population

A

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

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13
Q

sampling frame

A

the list of all the members I the source population

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14
Q

external vs internal validity

A

external: how well can the study results extrapolated to the target population
internal validity: how well dies the study relate to the source population

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15
Q

convenience sampling

A

sampling units are chosen because they are easy to get

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16
Q

judgement sampling

A

the investigator chooses what they deem to be units representative of the population

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17
Q

purposive sampling

A

sampling units are chosen on purpose because of their exposure or disease status

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18
Q

types of non-probability sampling

A
  • convenience sampling
  • judgement sampling
  • purposive sampling
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19
Q

types of probability sampling

A
  • simple random sampling
  • systematic random sampling
  • stratified random sampling
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20
Q

simple random sample

A

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

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21
Q

systematic random sampling

A

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

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22
Q

stratified random sampling

A

sampling frame is broken into strata based on some factor and then simple or systematic random sampling is conducted within each strata

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23
Q

cluster sampling

A
  • the sampling unit is a GROUP, but the unit of concern is the INDIVIDUAL
  • all individuals in the sampling units are selected
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24
Q

multistage sampling

A

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

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25
what is precision
how tight the confidence interval is around your estimate - i.e. the allowable error
26
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
27
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
28
screening tests
- focused on populations - individuals are "healthy" - early detection of a pathological process - sub-clinical disease
29
diagnostic tests
- focused on individuals - individuals are "sick" - confirm, guide teartment or aid in prognosis of clinical disease
30
what is true prevalence
the actual level of disease present in the population TP = (a+c)/n - individuals that are truly disease positive
31
apparent prevalence
what the prevalence appears to be AP = (a+b)/n - individuals that test disease positive
32
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?
33
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
34
sensitivity
the proportion of those individuals that actually HAVE THE DISEASE who TEST POSITIVE Sn = a/(a+c)
35
specificity
the proportion of those individuals who DON'T HAVE DISEASE that TEST NEGATIVE Sp = d/(b+d)
36
what does sensitivity tell you about
sensitivity = fasle negatives 1- Sn = % of false negatives - highly sensitive tests rule out disease
37
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
38
what don't Sn and Sp tell us
don't tell us how useful the test might be
39
positive predictive value
probability that given a positive test result the individual actually has disease PPV = a/(a+b)
40
negative predictive value
probability that given a negative test result, the individual actually doesn't have the disease NPV = d/(c+d)
41
how does prevalence affect predictive values
decreasing prevalence of disease... decreases PPV increases NPV
42
2 ways to combine tests to improve Sn and Sp
series interpretation and parallel interpretation
43
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
44
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
45
logistics of series testing
- increases specificity - lower chance of false positives - decreases sensitivity
46
logistics of parallel testing
- increases sensitivity - more false negatives - decreases specificity
47
steps in completing a series test
1. complete 2x2 table for test 1 2. do the second test only on positives from test 1 and complete a 2x2 for Test 2 3. calculate net sensitivity and net specificity from this table
48
steps in completing a parallel test
1. complete a 2x2 table for test 1 on the whole population 2. complete a 2x2 table for test 2 on the whole population 3. calculate net sensitivity and specificity
49
what is net sensitivity
how many individuals were correctly diagnosed as disease positive using the two tests
50
what is net specificity
how Manu individuals were correctly diagnosed as disease negative using the two tests
51
how to calculate net specificity and net sensitivity for series
Net Sp: (d1+d2)/(c1+d1) Net Sn: a2/a1
52
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)
53
what is validity
ability to distinguish between who has the disease and who doesn't - more true - sensitivity and specificity
54
what is reliability
ability of a test to give repeatable results - more consistant
55
3 sources of variation in reliability
intra-subject intra-observer inter-observer
56
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
57
what is an association
an identifiable measure between an exposure and outcome - does not necessarily mean relation is causal
58
what is bias
systematic errors (deviation from the truth) that result in an incorrect estimate of the association between exposure and outcome
59
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
60
3 main types of bias
selection bias information bias confounding
61
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
62
types of selection bias
1. non-response/volunteer bias 2 healthy worker effect/selective entry 3. detection/surevillance biace 4. loss to follow-up
63
what is information bias
incorrect classification or measuring of exposure, outcome of other factors
64
types of information bias
1. measurement error (continuous variables) 2. misclassification bias (categorical variables) - split into differential and non-differential
65
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
66
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
67
what is required for a variable to be a confounder
- associated with the outcome - associated with the exposure - not a consequence of the exposure
68
controlling for confounding
design stage: randomization, exclusion, matching analysis stage: stratification, multivariable modelling
69
causation vs association
association: implies E might cause O causation: implies there is a true mechanism that leads from E to O
70
ways to determine causality
1. statistical association 2. epidemiological association 3. casual inference
71
Causal inference methods
1. Bradford-hill criteria 2. component-cause model
72
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
73
what is a component cause
one of a number of factors that, in combination, constitutes a sufficient cause

Epidemiology (3 decks)

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