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General Epidemiology > Epi Terms > Flashcards

Epi Terms Flashcards

1
Q

Accuracy

A

Ability to give a true measure of the substance being measured.

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2
Q

Analytical Epi

A

examines how an exposure relates to a disease.

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3
Q

Attack rate

A

used in epidemics- risk of becoming affected ( #of people sick / # of people at risk )

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4
Q

Risk difference / Attributable risk

A

RD = risk of O in E+ minus risk of O in E-
RD: p ( D+ I E +) - P (D + I E- )
Indicates the increased risk of outcome ifyou are exposure positive ,beyond.
baseline
the absolute difference btwn E+ and E - groups
Interpretation: for every 100 exposed )# had it due to exposure

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5
Q

Measures of Effect

A

The impact of a risk factor (E) on a disease
expressed using absolute effect and is computed as the difference btwn 2 measures of disease freq.
more closley relates to the #of cases exposure causes (or prevents) than MoAs
Effect of E on O (literal #of cases)

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6
Q

Baseline level of risk

A

Incidence of O in non-exposed group

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7
Q

Attributable risk of being exposed (Afe)

A

isthe proportion of O in the exposed group That is due to the exposure , assuming causal relationship
Interpretation: 88 percent Of diseased cases among exposed are due to exposure
Afe also for vaccine efficacy and you treat non-vaccinated as exposure positive
Afe: RD / P(D + | E + )
OR
Afe= (RR -1 ) / RR

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8
Q

What do Afe and RD quantify?

A

The effect of an exposure in an exposed grOup but do not reflect effect of E on pop.
example: there may be a strong association btwn IV use and HIV (MOE exposed) but if IV use is rare in a Specific pup, then it will not contribute much to HIV prevalence (MOEpop)

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9
Q

MOE pop

A

used in public Health
A relatively weak risk factor that is
common may be more important in determinants of Disecise in public pop

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10
Q

population Attributable Risk

A

Analogus to RD- Simple difference btwn 2 groups
PAR isthe increased risk of outcome in entire pop due to exposure
Is influenced by strength of association and frequency of exposure to risk factor
PAR= P(D + ) - P (D+ I E- )
PAR= RD* P (E + )
interpretation: For every 100 people in population 13.2 have HIV due to exposure (IV use)

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11
Q

population fraction Risk

A

Analogous to Afe
Reflects the effect of The disease in entire pop rather than just E+ group
AFP is the proportion of disease in pop that would be avoided if exposure were removed
Afp =PAR /P (D+)
interpretation: 66 percent of HIV Cases in pop are due to exposure

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12
Q

What MOEs and MoAs cannot be used in case control?

A

Relative risk, Incidence risk, Risk difference, population Attributable risk
AFP and AFe can be estimated

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13
Q

Standard Error

A

precision of estimate oF a sample mean as a measure of the pop mean
A measure of accuracy ofthe estimate
SE= SD/ sqr n
SE tells us how accurate the mean of any gives sample from that population is likely to be, compared the the the mean. when SE increases it becomes more likely that any given mean is an inaccurate representation of the the mean.

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14
Q

Standard Deviation

A

tells us how spread out thedata is
It is a measureof how far each observation is from the mean
SD= sqr variance

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15
Q

confidence Intervals

A

a range of values which contain a population parameter with a given probability that is likely to encompass the valve
The level of uncertainty of an estimate

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16
Q

p value

A

probability of obtaining The observed result (or more extreme) if null- hypo is true, based on a predetermined Cutpoint

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17
Q

Why should we use caution when using p-values?

A

knowing a result was significant does not provide any information . about the magnitude of The effect observed

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18
Q

Necessary cause

A

precedes the outcome and must always be present for disease to occur.
le : exposure will always be present if disease occurs

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19
Q

Sufficient cause

A

proceeds the outcome , is often multifactorial , and will always produce disease
le: if the exposure is present, the outcome will follow.

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20
Q

causal complements

A

Additional factors that combine with exposure to form sufficent cause.

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21
Q

What can a component cause Model not tell us?

A

confounding and intervening variables

Strength of association

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22
Q

Bias

A

reason why study sample and true population differ, beyond random variation.

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23
Q

cause

A

Any factor that produces change in severity or frequency of an outcome.

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24
Q

Descriptive epi

A

examines distribution or patterns of Disease in pop

useful for hypothesis generating surveillance Implementation of control (prevention ) Strategies,

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25
Q

Determinant

A

Any factor that when altered produces a change in freq or characteristics of disease

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26
Q

Diagnostic test

A

used to confirm or deny or classify disease . used to guide treatment or aid in prognosis

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27
Q

Incubation period

A

period between exposure to Clinical disease

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28
Q

Latent period

A

period between exposure to detectable pathological changeldisease

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29
Q

lead time Bias

A

screening bias that makes it look like it improves survival only because we detected disease eany , but the person would have lived same amount of time
Makes screening program look better than it is

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30
Q

Negative predictive value

A

probability of truly being disease negative given a negative test result
p (D- I T - )
d/ (Ctd)

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31
Q

positive predictive value

A

probability of truly being disease positive given a positive test result
p( D + I T + )
a/a+ b

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32
Q

non-differential Misclassification

A

Magnitude and direction of the misclassification are similar in 2 groups being compared.
usually bias towards null aslong as Sn +Sp >1 → less likely to detect a difference of there is one
ex. Scale is offby 1 Ib ) will be same in both g groups

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33
Q

prevalence

A

number of cases (new and existing) in a specified pop at a given point in time .
conceptually prev= Incidence* duration
prev decreases when ppl die or getbetter (Short disease)
“How much disease is present”
measures burden of disease in a pop at any given time

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34
Q

Issues with prevalence

A

Does not tell us when disease was obtained

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35
Q

Issues with prevalence

A

temporal issues for causal inference
Magnitude’ May vary drastically since prev is a function of I and D… le if disease freq is low but has long duration, prev will increase

36
Q

precision

A

consistency

37
Q

Sensitivity (Sn)

A

Ability of a test to detect true diseased Ppl correctly
a /a+ c
p(T + I D + )
Snout → False neg fraction = l-sn

38
Q

specificity (Sp)

A

Ability of a test to correctly classify non-diseased people
Spin-> false positive fraction =1- Sp
p( T- I D - )
Sp= b/ b+ d

39
Q

If you want to confirm disease …

A

use test with high Sp because there would be few false positives
ex. scary test

40
Q

if you want to rule out disease …

A

Use test with high sn because There would be few false negatives

ex. cost for false negative is high, ie. highly infectious disease ‘ you want to minimize false negatives
* decrease catpoint

41
Q

Why are predictive valves not a good measure of test performance?

A

pVs vary with prevalence increased prev = increased predictive value of test

42
Q

ROC curve

A

isa plot of Sn 1-SP( false positive fraction)
Area under curve measures overall ability of test
left corner best for Sp and Sn

43
Q

multiple test- parallel

A

test positive with one or both and you are considered disease positive
high sn , low sp

44
Q

Multiple test-series

A

Test positive to both tests to be desease positive

nigh Sp, low sn

45
Q

Name 2 memods to compare tests on a continuous scale

A

limits of agreement plot: Aka bland-Altman Plot :

concordance correlation coefficient : perfect agreement is perfect 45 degree line

46
Q

Name a test to compare tests on a dichotomous scale… .

A

KAPPA: removes agreement that would happen by chance alone. O would mean no agreement 0.8-1is almost perfect.

47
Q

proportion

A

numerator is a subset of the denominator
ex: 188 ppl are tested for COVID and 82 test positive , me eqn is 82/188
prev and risk are proportions

48
Q

point prevalence

A

number of cases at one point in time / .

49
Q

period prevalence

A

Of people who are identified as cases during specified time period

50
Q

Risk

A

numerator is #of new cases over a period of time
is a proportion
probability that an individual will contract anatcome in a defined time period
since it’s a probability it’s dimensionless
only first case counts in numerator since it’s a proportion
AKA: culminative incidence
used in studies where we want an individual prediction
closed populations with short disease period

51
Q

Rate

A

of new cases taking into account time at risk (person time at risk)
not a proportion
probability Of new cases occuring in a time period.
Has dimensions
AKA: incidence density
Rate focuses on cases
open pop with long risk period.

52
Q

incidence

A 
#of NEW events in a defined population within a specific time period.
associated with studies about BECOMING ill
53
Q

incidence Risk calculation .

A

I risk = # of new cases in time period / initial NAR -1/2 W D

54
Q

Incidence Rate calculation

A

Exact method:
- used when we know exact amount of person time contributed by each member , preferred but often info is not available
#new cases/ person-time @ risk

estimated method:
- if only I case per person:
I Rate= cases / (#@start - 1/2 sick - 1/2WD - ‘/2 Add) x time

  • if multiple cases per person included :
    (Rate= cases/ (#@start -1/2 WD-1/2 Add )xtime
  • if individual movements are unknown:
    Irate: cases/ 1/2 (# disease free@ start- # disease free @ end)
55
Q

case Fatality

A

deaths among cases /# of cases

56
Q

proportional Morbidity

A

casesordeaths from specific disease /cases or deaths from all diseases

57
Q

Standardization

A

A way to control confounding by standardizing risk or rates-le dividing pop into Strata based on confounding factors
The goal is to make Inferences about the factors which affect freq of disease

58
Q

indirect Standardization

A

using a set of Standard rates from a referent pop

  1. compute Stratum specific rates of referent pop
  2. Multiply SSR oF referent pop by # of ppl or proportion in each strata from Study pop (Strata specific rate)
  3. Add to getexpected rate
  4. calculateSMR= crude rate/ observed rate
  5. Multiply SMR by referent pop Crude rate to get standard rate

Rates usedfrom referent pop / no SSR are available or pop is small

59
Q

SMR

A

Standardized Mortality Rate
percent increase or decrease immortality relative to referent pop
SMR < 1 means study pop has lower risk

60
Q

Direct Standardization

A

used when #of events or rate in each Strata is known, i e we know age Specific Mortality rates in City 1 and 2
STeps :
1. calculate study pop “Stratum specific rate” =SSR ref pop # in each Stratum* study pop Stratum specific rate
2. #Stratum specific rate (study pop) * referent pop proportion (SSR x #ppl in each Strata )
3. Add product for final Standard rate

61
Q

What are Hills criteria for causation?

A
  1. Strength of association 2. consistency 3. specificity 4. Temporality 5. Biological gradient/dose response 6. Biological plausibility 7. coherence 8. Experiment 9. Analogy
62
Q

consistency (Hills criteria)

A

The same results have been found by different persons, in different places, circumstances and time (ie are the results due to random error, chance or fallacy? )
Havethe effects been seen by others?

63
Q

specificity (Hills Criteria)

A

one cause one outcome
→ usually not useful as many disease is multifactorial
-→ if specificity exists we may be able to draw conclusions without hesitation

64
Q

Temporality (Hills Criteria)

A

Does Exposure preceded outcome?

65
Q

Biological Gradient /dose response (Hills Criteria)

A

If the association is one which can reveal a dose response relationship then weshould look closely for this.
→ A change in exposure causes a change in association to outcome
does increase in exposure lead to increase in outcome?

66
Q

plausibility. (Hills Criteria)

A

is the relationship possible based on current knowledge?

Does the association make sense ?

67
Q

coherence (Hills Criteria)

A

Similar to plausibility_ the cause. effect relationship should not interfere with what is generally known about the natural history of disease
is the association consistent with available evidence?

68
Q

Experiment (Hills Criteria)

A

Strongest support for causation can come from experiment

does treatment X really effect the outcome ?

69
Q

Analogy (Hills Criteria)

A

when there is strong evidence of a causal relationship bowman exposure and specific atcome, we should be more accepting of weaker evidence that a similar agent may cause a similar outcome.
Is the association similar to others ?

70
Q

Strength of association (Hills Criteria)

A

The larger the association btwn exposure and disease the more nicely it is causal.
→we should not disregard smaller associations
measured in Risk ratio or odds ratio

71
Q

Why doesn’t Hill include Statistical association as a causal criteria?

A

correlation does not equal causation. correlation may occur statistically due to a counfounding variable that causes a spurious relationship . correlation is fine if yourpurposes are just prediction. However if the goal isto understand Why something happens or Manipulate variables to change outcome then you need to determine causality

72
Q

poor choice of comparison groups

A

groups not counterfactual

example: in cohort study E negative group must be comparable to E positive group with respect to risk factors for atcome related to exposure of interest , le CFV’s
example: c-c studies , control group must reflect prevalence of exposure (risk based) or proportion of exposed person-time at risk (rate based) in The non-case members of source pop.

73
Q

non-response bias

A

association between E and O differs in responders from non- responders
who isn’t answering and why?

74
Q

selective Entry/survival bias

A

“healthy worker effect”

Individuals are highly selected because removal of possible sample Units from Original pop may be highly correlated with exposure and outcome

75
Q

Follow-up Bias

A

Differential loss to follow up that is related to exposure status and outcome.
Also includes Hawthorne effect.

76
Q

Detection Bias

A

sampling Bias in case control studies : occurs when exposed are screened for disease more frequently than non-exposed.
Misclassification (info) Bias in cohort studies: occurs if those assessing outcome know exposure Status and this Influences how they classify outcome.

77
Q

Admission Risk Bras

A

AKA Berkson’s Bias
occurs in C-C studies- secondary base
probability of admission is related to both disease and exposure
results in controls having and excess or deficit of exposure relative to the source pop

78
Q

How to deal with confounding

A
  1. Restricted sampling
  2. Matching
    → frequency matching : same freq of potential Cfr in both groups
    → pair Matching: one or more controls 1s individually matched ( matched analysis required)
  3. Statistically
    → Mantel-Haenzsel
    →Matche 1:1- McNemar’s Chi- Square
    → Regression
79
Q

simple Antecedent

A

occurs temporally before exposure and is causally associated with outcome only through exposure variable
when this variable is controlled it does not change association between E and O.

80
Q

Exposure-Independent variable

A

Exposure and extraneous variable have independent relationship to outcome but no relationship to each other.
occurs when matching in Cohorts thus do not bias estimate and do not need to be controlled for

81
Q

Explanatory Antecedent: complete confounding

A

extraneous variable proceeds and causes (predicts) bom exposure and outcome.
when extraneous variable added to model association between E and O becomes notsignificant because extraneous explains the original association Y

82
Q

Explanatory Antecedent: Incomplete confunding

A

extraneous variable causes both E and O but E also causes O

including extraneous reduces residual variance

83
Q

Intervening variable

A

is on the causal or temporal path

DO not control for.

84
Q

Distorter variable

A

Structurally the same as explanatory antecedent except at least One causal effects is a different sign than the other ( le causal arrows reflect prevention causation).
need to contro l extraneous variable
Distorterscan reverse association ( a significant positive association can become significantly negative).

85
Q

suppressor variable

A

Occurs when exposure and extraneous variable are part of a global variable. The effect of exposure onatcome is lost with Other variables

86
Q

Moderator variable

A

produces Statistical Interaction The causal structure is that exposure causes atcome but this depends on extraneous variable.
Interaction

87
Q

False Negative / positive fraction

A

quantify errors in diagnostic tests

ex: if someone has a disease there is a10% chance that the test will be negative (false negative fraction).

General Epidemiology (4 decks)

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