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Pop Health 111 > Epidemiology, Gate frames & Incidence/Prevalence > Flashcards

Epidemiology, Gate frames & Incidence/Prevalence Flashcards

1
Q

What is the goal of epidemiology & basic equation used

A

Studying disease occurance in a population over time

(no. of ppl with disease) / (No. of ppl in the population)

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2
Q

What does numerator and denominator represent

A

Numerator is those with disease and prevalence is the study population sharing similar characteristics

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3
Q

Components of the Gate frame

A

Triangle - study population
Circle - EG and CG
Square - EGO and CGO
O - Outcome in square
Horizontal - One time measurement
Vertical arrow - Overtime measurement

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4
Q

What do you do when a numerical measure isn’tn converted into a categorical?

A

Calcultae the mean or median excluding c or d (No disease people from both EG and CG) as this assumes everyone has a disease

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5
Q

EGO and CGO acronym meaning

A

Exposure group occurance and Comparison group occurance

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6
Q

Incidence components

A

Easy to observe disease events measured over time via categorical measurments e.g. death

Clean measure but hard to measure as observation occurs overtime

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7
Q

Prevalence components

A

Disease occurance measured at one point in time when transition from non diseased to diseased is blurry viz categorical and numerical measures

Dirty measure (Affected by incidence, death and cure)

Easy to measure as it happens at one point in time

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8
Q

Prevalence pool

A

Leftover drizzle from incidence and size depends on drizzle rate and loss to death and cure

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9
Q

Prevalence pool

A

Leftover drizzle from incidence and size depends on drizzle rate and loss to death and cure

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10
Q

Direction of arrows for one point in time measurement

A

Horizontal

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11
Q

Direction of arrow for longitudinal study

A

Vertical

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12
Q

Direction of arrow for timeframe from the past - Prevalence study

A

Top left corner

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13
Q

Association (Comparing disease measures)

A

Intitialdisease occurance should be estimates of association

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14
Q

Relative risk

A

Ratio of occurance with no units

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15
Q

Relative risk = 1

A

No effect value - there’s no difference in the effect of E and C on outcome

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16
Q

Equation for Relative risk > 1

A

Relative risk increase = (RR -1) x 100

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17
Q

Equation for relative risk < 1

A

Relative risk reduction = (1-RR) x 100

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18
Q

Absolute risk difference

A

Difference in disease occurance with units and contains more information than RR

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19
Q

Absolute risk difference = zero

A

No risk difference thus no difference between EGO and CGO

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20
Q

Absolute risk difference < 0

A

Ideal risk difference

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21
Q

Absolute risk reduction

A

Absolute risk is lower in EG

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22
Q

Absolute risk increase

A

Absolute risk is greater in EG

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23
Q

Randomised control trial key components

A

Study the effects of ethical and practical treatments
by random allocation into EG and CG via experiments

Rarely used to discover risk factors instead more in placebo studies

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24
Q

Long term RCT

A

Harmful treatment can be used as the test is now only observational but difficult to do well due to maintenance

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25
Q

Randomised control trial strengths

A
  • minimise cofounding
  • measures incidence and prevalence
  • ensures participants in EG and CG are similar
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26
Q

Randomised control trial weaknesses

A
  • Hard to recruit representative eager participants
  • A-lot of maintenance error
  • Very expensive and too small
  • Too small produce insufficient results due to expensive conduct
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27
Q

Non random error

A

Errors in study design, poor ramboman

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28
Q

Recruitment error

A

Study population representativness/elgibility - May be over or under representation

29
Q

Selection bias

A

Recruitment error

EG is recruited from a very different group to CG

30
Q

Cofounding error

A

Recruitment error

Studies exposures mixed with external factors effecting outcome causing a bias

31
Q

Non response bias

A

Recruitment error

Large proportion of eligible population don’t respond and if 70% of the responders are significantly different to population

32
Q

Allocation error

A

How participants were allocated into EG and CG - measurment or randomly

33
Q

Measurement error

A

Allocation error

EG and CG is measured incorrectly leading to wrong allocation

34
Q

Allocation by measurment

A

Participants are measured objectively or subjectively to be allocated

35
Q

Maintenance error

A

Mainitaining participants in alloacted groups - occurs more often in long term studies as patients can be lost to cure, death, leaving study etc

36
Q

Compliance

A

Maintenance error

Percentage of participants remaining in allocated group

37
Q

Contamination

A

Maintenance error

Percentage of participants crossing over allocatedn groups

38
Q

Co- intervention

A

Maintenance error

Did EG and CG recieve unequal interventions in follow up

39
Q

Blinding

A

Reduces cofounding by preventing personal interpretation - single or double

Objective outcomes don’t need blinding

40
Q

Adjustment

A

Dividie participants into strata and compare differing groups to reduce cofounding

41
Q

Random allocation

A

randomly allocate into EG and CG

42
Q

Random error

A

Biological variation causes participants to be moving targets

43
Q

Random sampling error

A

Smaller sample the less representative of population

Every sample is the best estimate of the truth of a population

44
Q

Where does random error occur

A

EGO, CGO , RR and RD

45
Q

95% confidence interval

A

Measure of the range and size of random error

95% chance the true value of population lies within the CI

The wider the interval the more random error = 99% CI

46
Q

Statistical signifiance

A

When EGO and CGO confidence intervals dont overlap the no effect line

47
Q

RD doesn’t cross no effect line

A

RD is statisticaly significant

48
Q

RD overlaps no effect line

A

No statsitical significance in EGO AND CGO usually due too to much random error to determine a real difference

49
Q

RR overlaps 1

A

No statistical signficance

50
Q

Reducing random error

A

Get bigger samples to narrow the Confidence interval

51
Q

Regression to the mean

A

Extreme events are often due to chance thus repeating them eventually gives less extreme results closer to the mean

52
Q

Meta anaysis

A

Combine many small good studies to form one big meta study - Used especially in RCT when too much random error blurs effects of study

53
Q

Cohort study

A

For unethical and unpractical studies done overtime to measure incidence and prevalence

54
Q

Cohort study weakness

A

Cofounding and maintenance error

55
Q

Cohort study strengths

A
  • Easier to recruit representative sample
  • Cheaper, larger and easier
  • Little measurment and random error
56
Q

Cross sectional study

A

Exposures and outcome measured at once for prevalence observational study

57
Q

Cross sectional study strengths

A
  • Easiest to recruit representative sample
  • More cheap and large
  • Ethical to study harmful treatment
  • No maintenenance error
58
Q

Cross sectional study weaknesses

A
  • Cfounding common
  • Reverse causality - Did outcome or exposure come first
59
Q

Ecological study

A

Participants are countries thus many EG

DOne via surveys usually

Longitudinal or cross sectional

60
Q

Ecological study strengths

A
  • Large thus low random error
  • Very cheap - Based on already published study
61
Q

Ecological study weaknesses

A
  • Often unadjustable cofounding
62
Q

Descriptive study

A

Dsecribes frequency of health behaviour, risks and outcomes

63
Q

Analytical study

A

Analyses descriptive study results

64
Q

Measures incidence AND prevalence

A

RCT and cohort studies

65
Q

Only measures prevalence

A

Cross sectional studies

66
Q

Individual participation studies

A
  • RCT
  • Cohort
  • Cross sectional
67
Q

Ecological studies

A
  • RCT
  • Cohort
  • Cross sectional
68
Q

SImilarties in study designs

A
  • GATE and PECOT
  • Rambom
  • Random error
69
Q

Differences in study desgins

A
  • Not all involve EGO and CGO calculation

Pop Health 111 (5 decks)

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