Epi Terms

Question 1

Accuracy

Answer 1

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

Question 2

Analytical Epi

Answer 2

examines how an exposure relates to a disease.

Question 3

Attack rate

Answer 3

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

Question 4

Risk difference / Attributable risk

Answer 4

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

Question 5

Measures of Effect

Answer 5

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)

Question 6

Baseline level of risk

Answer 6

Incidence of O in non-exposed group

Question 7

Attributable risk of being exposed (Afe)

Answer 7

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

Question 8

What do Afe and RD quantify?

Answer 8

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)

Question 9

MOE pop

Answer 9

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

Question 10

population Attributable Risk

Answer 10

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)

Question 11

population fraction Risk

Answer 11

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

Question 12

What MOEs and MoAs cannot be used in case control?

Answer 12

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

Question 13

Standard Error

Answer 13

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.

Question 14

Standard Deviation

Answer 14

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

Question 15

confidence Intervals

Answer 15

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

Question 16

p value

Answer 16

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

Question 17

Why should we use caution when using p-values?

Answer 17

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

Question 18

Necessary cause

Answer 18

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

Question 19

Sufficient cause

Answer 19

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

Question 20

causal complements

Answer 20

Additional factors that combine with exposure to form sufficent cause.

Question 21

What can a component cause Model not tell us?

Answer 21

confounding and intervening variables

Strength of association

Question 22

Bias

Answer 22

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

Question 23

cause

Answer 23

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

Question 24

Descriptive epi

Answer 24

examines distribution or patterns of Disease in pop

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

Question 25

Determinant

Answer 25

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

Question 26

Diagnostic test

Answer 26

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

Question 27

Incubation period

Answer 27

period between exposure to Clinical disease

Question 28

Latent period

Answer 28

period between exposure to detectable pathological changeldisease

Question 29

lead time Bias

Answer 29

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

Question 30

Negative predictive value

Answer 30

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

Question 31

positive predictive value

Answer 31

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

Question 32

non-differential Misclassification

Answer 32

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

Question 33

prevalence

Answer 33

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

Question 34

Issues with prevalence

Answer 34

Does not tell us when disease was obtained

Question 35

Issues with prevalence

Answer 35

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

Question 36

precision

Answer 36

consistency

Question 37

Sensitivity (Sn)

Answer 37

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

Question 38

specificity (Sp)

Answer 38

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

Question 39

If you want to confirm disease ...

Answer 39

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

Question 40

if you want to rule out disease ...

Answer 40

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

Question 41

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

Answer 41

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

Question 42

ROC curve

Answer 42

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

Question 43

multiple test- parallel

Answer 43

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

Question 44

Multiple test-series

Answer 44

Test positive to both tests to be desease positive | nigh Sp, low sn

Question 45

Name 2 memods to compare tests on a continuous scale

Answer 45

limits of agreement plot: Aka bland-Altman Plot : | concordance correlation coefficient : perfect agreement is perfect 45 degree line

Question 46

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

Answer 46

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

Question 47

proportion

Answer 47

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

Question 48

point prevalence

Answer 48

number of cases at one point in time / .

Question 49

period prevalence

Answer 49

#Of people who are identified as cases during specified time period

Question 50

Risk

Answer 50

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

Question 51

Rate

Answer 51

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.

Question 52

incidence

Answer 52

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

Question 53

incidence Risk calculation .

Answer 53

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

Question 54

Incidence Rate calculation

Answer 54

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)

Question 55

case Fatality

Answer 55

#deaths among cases /# of cases

Question 56

proportional Morbidity

Answer 56

#casesordeaths from specific disease /cases or deaths from all diseases

Question 57

Standardization

Answer 57

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

Question 58

indirect Standardization

Answer 58

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

Question 59

SMR

Answer 59

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

Question 60

Direct Standardization

Answer 60

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

Question 61

What are Hills criteria for causation?

Answer 61

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

Question 62

consistency (Hills criteria)

Answer 62

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?

Question 63

specificity (Hills Criteria)

Answer 63

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

Question 64

Temporality (Hills Criteria)

Answer 64

Does Exposure preceded outcome?

Question 65

Biological Gradient /dose response (Hills Criteria)

Answer 65

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?

Question 66

plausibility. (Hills Criteria)

Answer 66

is the relationship possible based on current knowledge? | Does the association make sense ?

Question 67

coherence (Hills Criteria)

Answer 67

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?

Question 68

Experiment (Hills Criteria)

Answer 68

Strongest support for causation can come from experiment | does treatment X really effect the outcome ?

Question 69

Analogy (Hills Criteria)

Answer 69

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 ?

Question 70

Strength of association (Hills Criteria)

Answer 70

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

Question 71

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

Answer 71

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

Question 72

poor choice of comparison groups

Answer 72

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.

Question 73

non-response bias

Answer 73

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

Question 74

selective Entry/survival bias

Answer 74

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

Question 75

Follow-up Bias

Answer 75

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

Question 76

Detection Bias

Answer 76

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.

Question 77

Admission Risk Bras

Answer 77

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

Question 78

How to deal with confounding

Answer 78

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

Question 79

simple Antecedent

Answer 79

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.

Question 80

Exposure-Independent variable

Answer 80

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

Question 81

Explanatory Antecedent: complete confounding

Answer 81

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

Question 82

Explanatory Antecedent: Incomplete confunding

Answer 82

extraneous variable causes both E and O but E also causes O | including extraneous reduces residual variance

Question 83

Intervening variable

Answer 83

is on the causal or temporal path | DO not control for.

Question 84

Distorter variable

Answer 84

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).

Question 85

suppressor variable

Answer 85

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

Question 86

Moderator variable

Answer 86

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

Question 87

False Negative / positive fraction

Answer 87

quantify errors in diagnostic tests | ex: if someone has a disease there is a10% chance that the test will be negative (false negative fraction).