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Epidemiology > First midterm content > Flashcards

First midterm content Flashcards

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
Q

what is precision

A

how tight the confidence interval is around your estimate
- i.e. the allowable error

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26
Q

type I vs type II error

A

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

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27
Q

required sampling size increases as…

A
  • 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
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28
Q

screening tests

A
  • focused on populations
  • individuals are “healthy”
  • early detection of a pathological process
  • sub-clinical disease
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29
Q

diagnostic tests

A
  • focused on individuals
  • individuals are “sick”
  • confirm, guide teartment or aid in prognosis of clinical disease
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30
Q

what is true prevalence

A

the actual level of disease present in the population
TP = (a+c)/n
- individuals that are truly disease positive

31
Q

apparent prevalence

A

what the prevalence appears to be
AP = (a+b)/n
- individuals that test disease positive

32
Q

how do you measure if a test is a good test

A

sensitivity and specificity
- if the individual is diseased, will the test identify them as so?

33
Q

how do you measure if a test is a useful test?

A

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
Q

sensitivity

A

the proportion of those individuals that actually HAVE THE DISEASE who TEST POSITIVE
Sn = a/(a+c)

35
Q

specificity

A

the proportion of those individuals who DON’T HAVE DISEASE that TEST NEGATIVE
Sp = d/(b+d)

36
Q

what does sensitivity tell you about

A

sensitivity = fasle negatives
1- Sn = % of false negatives
- highly sensitive tests rule out disease

37
Q

what does specificity tell you about

A

fasle positives
1- Sp = % of false positives you can expect with the test
- highly specific tests rule diseases in

38
Q

what don’t Sn and Sp tell us

A

don’t tell us how useful the test might be

39
Q

positive predictive value

A

probability that given a positive test result the individual actually has disease
PPV = a/(a+b)

40
Q

negative predictive value

A

probability that given a negative test result, the individual actually doesn’t have the disease
NPV = d/(c+d)

41
Q

how does prevalence affect predictive values

A

decreasing prevalence of disease…
decreases PPV
increases NPV

42
Q

2 ways to combine tests to improve Sn and Sp

A

series interpretation and parallel interpretation

43
Q

what is series interpretation

A

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
Q

what is parallel interpretation

A

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
Q

logistics of series testing

A
  • increases specificity
  • lower chance of false positives
  • decreases sensitivity
46
Q

logistics of parallel testing

A
  • increases sensitivity
  • more false negatives
  • decreases specificity
47
Q

steps in completing a series test

A
  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
Q

steps in completing a parallel test

A
  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
Q

what is net sensitivity

A

how many individuals were correctly diagnosed as disease positive using the two tests

50
Q

what is net specificity

A

how Manu individuals were correctly diagnosed as disease negative using the two tests

51
Q

how to calculate net specificity and net sensitivity for series

A

Net Sp:
(d1+d2)/(c1+d1)

Net Sn:
a2/a1

52
Q

how to calculate net specificity and sensitivity for parallel

A

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
Q

what is validity

A

ability to distinguish between who has the disease and who doesn’t
- more true
- sensitivity and specificity

54
Q

what is reliability

A

ability of a test to give repeatable results
- more consistant

55
Q

3 sources of variation in reliability

A

intra-subject
intra-observer
inter-observer

56
Q

what is Kappa

A

a measure of agreement beyond what would be due to chance alone
- the closer Kappa is to 1, the better the agreement

57
Q

what is an association

A

an identifiable measure between an exposure and outcome
- does not necessarily mean relation is causal

58
Q

what is bias

A

systematic errors (deviation from the truth) that result in an incorrect estimate of the association between exposure and outcome

59
Q

random vs systematic error

A

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
Q

3 main types of bias

A

selection bias
information bias
confounding

61
Q

what is selection bias

A

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
Q

types of selection bias

A
  1. non-response/volunteer bias
    2 healthy worker effect/selective entry
  2. detection/surevillance biace
  3. loss to follow-up
63
Q

what is information bias

A

incorrect classification or measuring of exposure, outcome of other factors

64
Q

types of information bias

A
  1. measurement error (continuous variables)
  2. misclassification bias (categorical variables) - split into differential and non-differential
65
Q

misclassification bias (type of information bias)

A

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
Q

confounding bias

A

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
Q

what is required for a variable to be a confounder

A
  • associated with the outcome
  • associated with the exposure
  • not a consequence of the exposure
68
Q

controlling for confounding

A

design stage: randomization, exclusion, matching
analysis stage: stratification, multivariable modelling

69
Q

causation vs association

A

association: implies E might cause O
causation: implies there is a true mechanism that leads from E to O

70
Q

ways to determine causality

A
  1. statistical association
  2. epidemiological association
  3. casual inference
71
Q

Causal inference methods

A
  1. Bradford-hill criteria
  2. component-cause model
72
Q

component cause model of causal inference

A

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
Q

what is a component cause

A

one of a number of factors that, in combination, constitutes a sufficient cause

Epidemiology (3 decks)

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