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KCUMB Epidemiology > Exam 1 Material > Flashcards

Exam 1 Material Flashcards

1
Q

Crude morbidity rate

A

diseased people / # total population

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2
Q

Crude mortality rate

A

deaths (all causes) / # total population

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3
Q

Incidence / risk/ attach rate

A

of new cases of a disease / # of people at risk for that disease

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4
Q

Prevalence

A

(# of existing + new cases of a disease) / total population

~morbidity

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5
Q

3 factors of descriptive epidemiology

A

Who / When / Where

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6
Q

Passive surveillance system

A

Relies on public healthcare system to bring in information by following regulations concerning required reportable disease

Must passively wait for information to be collected

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7
Q

Active surveillance system

A

Public health officials go into communities to actively search for new disease/condition cases

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8
Q

Syndromic surveillance system

A

Officials look for pre-defined signs/symptoms related to a trackable but rare disease/occurrence
CON: can be non-specific

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9
Q

Biosurveillance

A

Mix of passive surveillance and syndromic surveillance

Look for specific symptoms/signs in collected data for a population

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10
Q

Induction / incubation

A

Time between exposure and onset of symptoms of a disease

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11
Q

Latency period

A

Time between onset of symptoms of a disease and disease detection (via symptoms or diagnosis)

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12
Q

What is the most critical element that must be defined before any descriptive epidemiology information can be acquired?

A

A case definition

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13
Q

Why are case definitions important?

A

They define how a disease is detected/diagnosed and are guidelines for how we can accurately verify (confirmed or probable) cases of a disease

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14
Q

Epidemic

A

A “more than normal” occurrence of disease

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15
Q

Outbreak

A

A localized epidemic; limited by size of geographical area or population

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16
Q

Endemic

A

The constant presence of disease within a given area or population; the “normal” amount of disease

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17
Q

Pandemic

A

A world-wide/multi-national/multi-continent epidemic

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18
Q

Emergency of international concern

A

An epidemic requiring high vigilance

Pre-pandemic labeling

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19
Q

Cause-specific morbidity

A

of diseased people with a specific disease / # total population

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20
Q

3 factors in comparing measures of disease frequency between groups

A
  1. # of people affected
  2. # of people at risk
  3. Period of time that people are followed
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21
Q

Proportion

A

Part / whole

Division of 2 related numbers

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22
Q

Ratio

A

Whole / whole

Division of 2 unrelated numbers

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23
Q

Rate

A

Part / [whole*time]

A proportion over time

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24
Q

Cumulative incidence

A

Sum of incidence over (multiple time periods?)

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25
Q

Incidence density

A

Sum of incidence rate (over multiple time periods?)

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26
Q

Cause-specific mortality

A

deaths specifically from a disease / # total population

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27
Q

Case-fatality rate

A

deaths specifically cause by a disease / # cases of that disease

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28
Q

Cause-specific survival rate

A

survival cases from disease / # cases of the disease

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29
Q

Proportional mortality rate

A

cause-specific deaths / # total deaths in population

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30
Q

Live birth rate

A

live births / 1,000 population

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31
Q

Fertility rate

A

live births / 1,000 women who are fertile

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32
Q

Neonatal mortality rate

A

neonatal deaths / 1,000 live births

Neonatal: <28 days of age

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33
Q

Postnatal mortality rate

A

postnatal deaths / 1,000 live births

Postnatal: 28days < x < 1year of age

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34
Q

Infant mortality rate

A

infant deaths / 1,000 live births

Infant: <1yr of age

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35
Q

Maternal mortality ratio

A

maternal deaths / 100,000 live births

Maternal deaths: any death relating to pregnancy

36
Q

Counter factual theory

A

It would be ideal, but impossible, to have the same person placed in different study groups for a disease.

In order for disease to be studied, we assume exchangeability (comparability) between subjects - that populations can generally be standardized, or made equal, in all else except the exposure.

37
Q

Absolute differences

A

The highest difference between frequencies

A subtraction between 2 frequencies

38
Q

Relative difference

A

Division between 2 frequencies or proportions

Typically larger than absolute differences; better for pharmacy

39
Q

Probability outcome / incidence risk (IR)

A

Percentage / proportion of risk

40
Q

Absolute risk reduction (ARR)

A

Risk difference attributed to exposure

A subtraction

41
Q

Relative risk reduction (RRR)

A

(ARR) / R unexposed

Division of the difference between 2 risks by a baseline risk

42
Q

Number needed to Treat (NNT)

A

Number of patients you need to treat before 1 person gets the benefit; smaller NNT (larger ARR) means less people need to be treated in order for a benefit to occur.

1 / ARR

43
Q

Risk Ratio

A

Risk of outcome (exposed pop.) / Risk of outcome (non exposed pop.)

=1 … outcome is equally likely to occur
>1 … outcome is more likely to occur in comparison group (exposed)
<1 … outcome is less likely to occur in comparison group (exposed)

44
Q

How do you interpret ratios for measures of association?

A
  1. Find difference from 1.0
  2. Convert difference to percent
  3. Interpret difference
45
Q

What are 3 things to look for in interpreting measures of association?

A
  1. Comparison groups (comparing x to y is x / y)
  2. Direction of comparison (increase, decrease, _ times greater, etc.)
  3. Magnitude (# value)
46
Q

Confounding

A

Type of bias where there is a distortion of the association between an exposure and outcome

47
Q

What 3 aspects must be studied before inferring a real, true association?

A

Confounding and effect modification / Bias / Statistical significance

48
Q

3 aspects of a confounder

A
  1. Independently associated with the exposure
  2. Independently associated with the outcome
  3. Not linked to causal pathway between the exposure and outcome
49
Q

Steps for calculating presence of confounding variable or effect modification (*difference between the two)

A
  1. Calculate CRUDE value
  2. Calculate for individual strata of the proposed “confounder” (adjusted value)
  3. Compare (look for 15% difference)

Confounding variable compares CRUDE vs ADJUSTED
Effect modification compares between different strata (ADJUSTED)

50
Q

What effects doe confounder have on a value?

A

Can change magnitude (strength) of association

Can change direction of association

51
Q

Ways to control for confounding at study design stage?

A

Randomization
Restriction
Matching

52
Q

Ways to control for confounding at the analysis of data stage?

A

Stratification

Multivariate statistical analysis

53
Q

Randomization

A

Way to control for confounding in study design stage

Ideally equal allotment of subjects with known confounder (and unassessed) into each group

S-good if large population
W-bad if small popultion; used mainly for interventional studies

54
Q

Restriction

A

Way to control for confounding in study design stage

Study participants are restricted based on if they present a confounding variable

S-no impact on internal validity
W-negatively impacts external validity (limits generalizability)

55
Q

Matching

A

Way to control for confounding in study design stage

Subjects are selected in matched-pairs in relation to the confounding variable

S-greater analytical efficiency
W-difficult to do; can mask effect of other cofounders

56
Q

Stratification

A

Way to control for confounding in analysis of data stage

Separating out strata bases on confounding variable

S-enhances understanding of data
W-impractical for simultaneous control of multiple confounders

57
Q

Multivariate analysis

A

Way to control for confounding in analysis of data stage

Mathematically factoring out effects of confounding variable

S-simultaneous control of multiple confounding variables; interpret OR
W-time consuming; researcher must have good understanding of data

58
Q

Effect modification

A

A 3rd variable that modifies the effect of a true association by variations within strata

If present, must be described at each level of the strata

59
Q

Bias

A

Systematic, non-random error in study design or conduct that distorts an association

Cannot be “fixed” once study has been complete

60
Q

3 elements that bias impact

A
  1. Source/type
  2. Magnitude/strength
  3. Direction
61
Q

Selection-related bias

A

Bias as a result of the way in which researchers select their study subjects, thereby creating systematic differences between the groups

62
Q

Measurement-related bias

A

Bias as a result of the way in which data is collected/observed that would lead to systematic difference between the groups

Can be subject, observer, or screening related

63
Q

Healthy worker bias

A

A form of selection bias in which subjects are taken from a population restricted to healthy individuals (ignores the sick)

Easily seen in prospective Cohort studies

64
Q

Self-selection/participation bias

A

A form of selection bias in which voluntary involvement dictates sampling group; there may be a difference between subjects that volunteer and those that do not wish to volunteer

65
Q

Recall (reporting) bias

A

A form of (subject-related) measurement bias in which subjects memory/recollection impacts study results; diseased subjects tend to have a higher sensitivity for recollection or may exaggerate

66
Q

Hawthorne (observation) effect

A

A form of (subject-related) measurement bias in which study subjects act differently from how they would normally because they know they are part of a study

67
Q

Contamination bias

A

A form of (subject-related) measurement bias in which subjects in the control group are exposed to the exposure being studied

68
Q

Compliance/adherence bias

A

A form of (subject-related) measurement bias in which subjects do not adhere to the protocols of the study

69
Q

Lost to follow-up bias

A

A form of (subject-related) measurement bias in which subjects are lost track of or drop out of a study; aka. Differential attribution bias

Ex. Subject moves away

70
Q

Interviewer bias

A

A form of (observer-related) measurement bias in which researchers solicit, record, or interpret data unequally;

includes how the treatment is administered and how research treat subjects during data collection

71
Q

Diagnosis/surveillance bias

A

A form of (observer-related) measurement bias in which the researcher evaluates data while being affected by preconceived notions

“Hawthorne effect” for researchers

72
Q

Lead-time bias

A

A form of (screening-related) measurement bias in which early detection causes beneficial outcome, not necessarily the exposure

73
Q

Misclassification bias

A

A source of measurement bias in which exposure or outcome/disease status is erroneously attributed

Can lead to differential or non-differential error

74
Q

Non-differential bias

A

Misclassification bias that lessens the association between variable so that the OR/RR/HR are closer to (attenuates to) 1.0

Error is in both groups equally; misclassification shifts value to make exposure seem UNRELATED to outcome

75
Q

Differential bias

A

Misclassification bias in which association is amplified

Error is in one group over another; misclassification shifts value to make exposure seem even more RELATED outcome

76
Q

Controlling for bias

A
  • blinding/masking
  • using multiple sources to gather info
  • random allocation of observers/interviewers
  • minimize loss-to-follow up bias
77
Q

Cause

A

Precursor event required for the outcome of a disease

78
Q

3 types of associations

A
  1. Artifactual (false)
  2. Non-causal
  3. Causal
79
Q

Artifactual (false) association

A

An effect of confounding and effect modification

80
Q

Non-causal association

A

A. Disease may cause the exposure, instead of other way around
B. Disease and exposure both affected by 3rd factor

81
Q

Causal association

A

Exposure causes the outcome

82
Q

Sufficient cause

A

A causal association in which a minimum condition leads to a disease

83
Q

Necessary cause

A

A causal association in which the cause must be present for the disease to occur, but can also exist without leading to the disease

Ex. TB

84
Q

Component cause

A

A causal association in which the cause increases the likelihood of disease; aka risk factor

85
Q

Synergism

A

When factors work together so that the combined measure of effect is greater than the sum of their individual measures of effect

86
Q

Parallelism

A

When factors interact so that the measure of effect is greater if either is present

87
Q

Explain Hill’s Criteria

A

Set of guidelines that help determine if an exposure is a CAUSE for an outcome (strong association)

  • Strength = large magnitude/value means stronger association
  • Consistency = high reproducability means strong association
  • Temporality = timing of effect (cause precedes outcome)
  • Biological gradient = more of an exposure leads to more of an outcome
  • Plausibility = does the relationship make physiological/biological sense

KCUMB Epidemiology (3 decks)

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