GATE Frame Flashcards
(81 cards)
1
Q
Numerator
A
Number of people from study population in whom dis-ease occurs
2
Q
Denominator
A
Number of people in a study
3
Q
Population
A
Any group of people who share a common factor
4
Q
Quantified
A
Data that can be counted
5
Q
Two categories of quantified data?
A
Categorical
Numerical
6
Q
Non-observable event
A
Can’t easily observe so you measure at a point in time
7
Q
Event
A
Counting as it occurs (eg road traffic accidents)
8
Q
Occurrence
A
The transition from a non-diseases state to a diseased state
9
Q
GATE
A
Graphic approach to epidemiology
10
Q
What is dis-ease?
A
Any health-related event (death/ill health easier to measure than well-being)
11
Q
What is epidemiology?
A
The study of how much disease
12
Q
Occurrence of disease calculation?
A
Numerator/denominator
13
Q
Explain gate diagram
A
Triangle= participants Circle= EG/CG Square= numerators (disease outcomes) Time= vertical and horizontal
14
Q
Exposure group occurrence equation
A
EGO = a/EG
15
Q
Comparison group occurrence equation?
A
B/CG
16
Q
Can numerical measures be recorded as categories?
A
Yes
17
Q
Example of categorical measures?
A
High vs low salt intake
18
Q
Examples of numerical measures
A
Average (mean)/ EG
19
Q
Incidence?
A
Number of onsets of dis-ease occurring during a period of time (eg raindrops)
20
Q
Can incidence be observed as it occurs?
A
Yes
21
Q
What type of quantified measure is incidence?
A
Categorical
22
Q
How is incidence normally written?
A
As a percentage of people with the disease in a specific period
23
Q
Equation of incidence
A
A/EG during time
24
Q
Example of high incidence, low prevalence?
A
Flu
25
Example of low incidence, high prevalence
Obesity
26
Prevalence?
The number of people with a disease at one point in time
27
When is prevalence used?
When the transition from non-diseases to diseased is not easily observable
28
How do people leave the prevalence pool?
By death or cure
29
What does prevalence assess?
The amount of disease (burden) at a point in time
30
What is prevalence not useful for?
Identifying the cause
31
Prevalence calculation?
A/EG at a point in time
32
Two types of prevalence?
Point (at one point)
| Period (in a set time frame looking back)
33
Calculations to compare disease between groups?
Risk ratio
| Risk difference
34
Relative risk?
Risk ratio (ratio of occurrence)
35
Relative risk/risk ratio/RR equation?
EGO/CGO
36
Absolute risk?
Risk difference (difference between occurrences)
37
Risk difference equation?
EGO-CGO with units
38
No effect value RR?
1
39
No effect value RD?
0
40
Relative risk increase/reduction?
+\- from 1 x 100
41
Odds ratio?
A/b /c/d
42
When is odds ratio more appropriate?
When disease becomes more common as two relative estimates increase
43
Types of error?
Random
| Non-random
44
Other names for non-random error
Bias
Systematic errors
Validity problems
45
Define RAMBOMAN
```
Recruitment
Allocation
Maintenance
Blind
Objective
Measurement
Analysis
```
46
Two types of recruitment errors?
External validity error: study findings are not applicable
| Selection bias
47
Example of allocation error?
Confounding
48
What study avoids confounding?
RCTs
49
What do analyses involve?
Adjusting for confounding etc, with stratified analysis, and checking risk analyses
50
What causes random error?
Chance
51
Three types of random error
Random sampling error (will never be 100% representative)
Random measurement study (our ability to measure factors in the same way is subject to difference
Randomness in human nature
52
How do we estimate random error?
With a 95% confidence interval
53
Definition of a 95% confidence interval?
A range of values of a particular measure derived from a single study that is likely to include the true value in the underlying population
54
What does a wider CI mean?
More random error
55
Will a 99% or 95% CI have a wider interval?
99%
56
How would you write a 95% CI?
There is a 95% probability that the true value of EGO in the whole population from which the study participants were recruited lies between 8 and 10
57
If EGO and CGO confidence intervals do not overlap we call it?
Statistical significance
58
What happens is the CI for RR or RD crosses the no effect line?
There's too much random error to determine if there's a real difference between EGO and CGO
59
What does width of CI depend on?
Number of events in the study
60
What is meta analysis?
Combining studies to generate a summary estimate of effect (and alternative to a large study)
61
What type of study often uses meta analysis?
RTCs
62
Study objective of RCTs?
effects of different interventions (exposure) on disease incidence in different groups
63
Main application of RCTs?
Studying the effect of interventions (ie new therapies)
64
Main design features of RCTs?
Longitudinal
Experimental
Participants randomly allocated to either study exposure or comparison exposure and dis-ease outcomes measured during a follow-up period
65
Main strength of RCTs
Randomisation minimises confounding
66
Main weaknesses of RCTs
Ethical limitations
Logistically difficult, long term follow up difficult and costly
Large studies expensive so usually too small (random error is an issue)
Maintenance error common
67
Study objective of cohort studies
To investigate associations (effects) between risk/prognostic factors (exposures) and disease incidence in different groups of individuals
68
Main application of cohort studies
Studying cause of dis-ease incidence or the effects of interventions
69
Main design features of a cohort study
Longitudinal
Observational (non-experimental)
Participants allocated to exposure and comparison by measurement and disease outcomes measured during follow up
70
Main strength of cohort study
Cheaper than RCTs
Exposure measured before outcome
Avoids recall bias
Provides clear time sequence between exposure and disease outcome
71
Main weakness of cohort study
Confounding
| Maintenance error common in long term situation
72
usual objective of cross sectional studies
To measure disease prevalence in defined populations. To investigate associations between exposure and disease prevalence
73
Main application of cross sectional studies
Measuring burden of disease in different populations
74
Main design features of cross sectional study
Observational
| Participants located to EG/CG by measurement and outcome is measured at the same time
75
Main strengths of cross sectional study
Cheap
Quick
Best for assessing burden/prevalence of a population
No maintenance error as no follow up
76
Main weaknesses of cross sectional study?
Uncertain time sequence (possible reverse causality) limits interpretation of cause and effect
Confounding common
77
Study objective of ecological study
To investigate associations between exposures and disease prevalence or incidence in different populations
78
Main application ecological study
Studying the causes of disease incidence and prevalence
79
Main design feature of ecological study
Longitudinal or cross-sectional
Non-experimental or experimental
Exposure and comparison allocated to groups rather than individuals
80
Main strengths of ecological studies
Cheap and quick
Useful when majority of some populations are exposed and others aren't
Efficient for rare outcomes
81
Main weakness of ecological studies
Confounding very common
