1
Q
Epidemiology
A
Study of frequency/occurrence of dis-ease in populations
- differences/similarities in frequency between populations helps identify causes
2
Q
Process of epidemiology
A
1) describe a population
2) count total population
3) count number of cases of dis-ease
Formula:
E = N/D/T
3
Q
Need for age standardisation/adjustment
A
Can only compare ‘like with like’ (confounding)
4
Q
Numerical values
A
- convert into categorical measures
- use mean or median level of outcome
5
Q
Cohort study
A
Allocation into EG and CG: measured exposures
Measurement of outcome: followed over a period of time
6
Q
Cross-sectional study
A
Allocation into EG and CG: measured exposures
Measurement of outcomes: at the same time as measurement of exposures
7
Q
Incidence
A
- Outcome events counted forward from starting point, over a period of time
- time included in calculation
- rate
- preferred method if easy to observe events
- depends ONLY on no. Events during specified time period
8
Q
Incidence type of study and data
A
ONLY cohort, ONLY categorical
9
Q
Prevalence
A
- no. People with disease counted at ONE POINT in time
- time not included in calculation but when counted is mentioned
- state
- depends on incidence, deaths and cures
10
Q
Prevalence types of study and data
A
Both cross-sectional and cohort (one point during the study)
Categorical and numerical
11
Q
Change in prevalence
A
Difference between prevalence measured at two points in time
12
Q
Retrospective info
A
If events come and go frequently
1) use incidence over a retrospective time period to group people into categories
2) total number of episodes for each group is outcome (no. Episodes for each individual person is not used in calculation)
Measures prevalence because deaths/cures are lost
Cohort/cross-sectional study depending on when exposures are measured in relation to outcomes
13
Q
If most people died rapidly or were cured
A
High incidence disease has low prevalence
14
Q
If few people died or were cured
A
Low incidence disease has high prevalence
15
Q
Incidence strengths
A
- determined only by disease risk - clean measure
- includes N, D AND time - more info
16
Q
Incidence weaknesses
A
- can be difficult to measure
- must be observed over time
17
Q
Prevalence strengths
A
Relatively easy to measure
18
Q
Prevalence weaknesses
A
- determined by incidence, cure rate and death rate - dirty measure
- doesn’t include time - less info
19
Q
Ecological studies
A
Populations allocated to EG and CG (exposures are an average of a group of people)
20
Q
Individual participant studies
A
Individuals are allocated to EG and CG
21
Q
Reasons against RCT
A
Unethical, impractical
22
Q
Blinding
A
Double-blind: neither participants nor investigators know which intervention was given to which participant
Single-blind: participants don’t know which intervention was given to which participant but investigators do
Only really works for experimental studies
23
Q
Estimates of effect
A
Comparisons of disease occurrence in EG and CG
- gives idea of size of effect of study exposure on disease outcome
24
Q
RR description
A
The risk of x in A is n times higher/lower than in B
- use RRI if higher, RRR if lower
25
Relative mean
Disease occurrence measures are calculated as averages and RR is comparison between the means
26
RR no-effect value
RR = 1.0
| - closer RR is to 1.0 smaller the difference
27
RRR
= (1-RR) x 100%
EGO < CGO
RR < 1.0
28
RRI
= (RR - 1) x 100%
EGO > CGO
RR > 1.0
29
RR range and units
RR > 0
| No units
30
RD description
Observational:
- there are y fewer/more x per n A than per n B
Experimental:
- if n people are treated rather than not treated, there will be y fewer/more events/occurrences
31
RD no-effect value
RD = 0 units
| - closer RD is to 0 less effective drug/difference in outcome between EG and CG
32
ARR
EGO < CGO
| RD < 0
33
ARI
EGO > CGO
| RD > 0
34
RD range and units
-infinity < RD < infinity
| Same units as EGO and CGO - events per n people per T
35
RR vs RD
- Decisions should be based on RD as this depends on original risk and so do benefits of treatments whereas RR doesn’t (only a ratio)
- RD gives more info (CGO must be known) - groups can have same RR but diff. RD
- beware of large RR but small RD
36
Occasional study
Don’t have comparison groups though implicitly present by subdividing EG by age, gender etc.
37
Error
Incorrect EGO/CGO due to:
- wrong people recruited
- right people put in wrong category (EG/CG)
38
Non-random error
Due to poor study design, processes or measurement
39
Valid study
Only has small amount of random and non-random error
40
RAMBOM
Recruitment, allocation (+-adjustment in analyses), maintenance, blind or objective measurement
41
Recruitment
- representative?
- sufficient info about process to apply?
- recruitment error aka external validity error
42
Response rate
No. Who took part / no. Eligible
| - if < ~70%, could cause significant recruitment error
43
Allocation methods
By measurement/observation
| Random allocation
44
Types of allocation error
Measurement error
| Confounding
45
Allocation measurement error
- exposures measured incorrectly
| - participants tell the truth?
46
Allocation measurement error solution
Use well-designed, validated questionnaires or biological tests measuring chemicals
47
Confounding
When exposure mixed with another factor (confounded) that is also associated with outcome
- EG and CG similar at the beginning of the study?
48
Confounding solution
- adjustment
- RCT
- concealment of allocation
49
Baseline comparison
Checking for differences between EG & CG at start of study
50
Selection bias
Causes confounding
| - equivalent to two separate/overlapping triangles
51
Maintenance error
- remain in allocated groups by maintaining exposure and not being exposed to other factors?
- lost to follow-up? More lost from EG that CG, vice versa? (Not unusual to lose 30%)
52
Maintenance error solution
- blind studies - minimise difference in degree of error between EG and CG
- checking periodically especially in long term observational studies
53
BOM
1) objectivity
| 2) blind - helps reduce effect/error from subjective measures
54
Analyses
Confounding adjusted for?
- stratified analysis
- adjusted analysis (standardisation)
55
Stratified analysis
Diving participants into ‘strata’ and analysing data as if they were two sub-studies
- similar results: strata combined
- different result: reported separately
56
Confounding in ecological studies
Common but difficult to measure and adjust for
57
Crude death rate
No. Deaths from disease / size of pop.
58
Age specific death rate
No. Deaths from disease in age group / no. People in that age group
59
Expected deaths
Age specific death rate x no. People in age group in standard pop.
60
Age-standardised death rate
Sum of expected deaths / standard population
61
Age standardisation
Process of converting different age structures in each population into one (standard) population age structure then working out death rates
62
Random errors
- Occur due to chance
| - present in every measurement in every study
63
Regression to(wards) the mean
Repeating measurements/studies with extreme results (often chance events) usually give less extreme results
- more measurements = less randomness = closer to middle
64
Random error solutions
- increase study size (allocation)
- increase no. Times a factor is measured (measurement, biological)
- objective measuring instrument (measurement)
65
Types of random error
Sampling, measurement/assessment, inherent in biological phenomena, allocation
66
Random sampling error
Unrealistic to study entire pop. Thus each sample from pop. Will be different each time
- results are ‘estimates’
67
Random allocation error
Differences due to chance in RCTs esp. in smaller studies
68
Measures of random error
Unrealistic to estimate all error thus measures generally underestimate total random error
- confidence intervals
(- P-values)
69
Confidence intervals
Measure amount of random error in estimates of EGO, CGO, RR, RD in whole pop. When only one study has been done
- describes range of results likely to include true result in whole pop.
70
95% CI acceptable def.
About 95% chance that true value in pop. Lies within 95% CI
71
95% CI accurate def.
In 100 identical studies using samples from same pop. 95/100 of 95% CIs will include true value for pop.
72
CI can be calculated for
Both categorical and numerical variables
73
Point estimates
Estimated value from study
| - represented by square
74
CI interpretation
Width: wider interval, more random error in measure
- estimates degree of uncertainty
- upper/lower confidence limits
75
Statistical significance
No overlap between CIs of EGO and CGO
CI of RD/RR doesn’t cross no-effect line
- reasonable to assume EGO and CGO are truly different in underlying pop.
76
Not statistically significant
Wider CIs => large overlap between CIs of EGO and CGO
CI of RD/RR crosses no-effect line
- study unable to determine if EGO truly different from CGO (actually no difference or just too much random error)
77
Clinical/practical significance
If clinician would make similar clinical decision on whether the true result was near one end of CI or the other of statistically significant results
78
Meta-analyses
Mathematical combo of results (usually multiple studies that are too small) which generates summary of estimate of effect
- alternative to conducting one large study
79
Study classification by allocation
1) experimental: allocated by investigators
| 2) observational: allocated by measurement
80
Study classification by measurement of outcomes
1) longitudinal: followed over time (allocated EITHER randomly or by measurement)
2) cross-sectional: outcomes measured at same time as exposures (allocated by measurement)
81
Recall bias
Common in retrospective studies
82
Systematic reviews
1) review literature systematically (rigorously) to find all relevant studies
2) assess quality of studies and only keep good ones
3) combine results in meta-analysis if similar enough
- studies in review must be valid for valid review
83
Determinants for individuals
- any event, characteristic or other definable entity that brings about a change for better or worse in health
- may vary at different life stages
84
Determinants for groups
- concepts similar as for individuals but nature of determinants is often different
- includes characteristics of population itself + context in which it exists
- population health is greater than the sum of its parts
85
Downstream interventions
at micro (proximal) level
86
Proximal
Near to change in its health status
| - any that’s readily and directly associated with change in health status
87
Upstream interventions
At macro (distal) level
88
Distal
Distant in time and/or place from change in health status
89
Effects of discrimination/inequities on health
Indirect: biased/limited healthcare
Direct: mental health struggles
90
Importance of considering social inequities
- urge to actions
- indicate possibilities to improve health conditions for groups at particular risk
- otherwise both ethically unsound and inefficient in a health development perspective
91
Dahlgren and whitehead model levels
1) individual level (micro)
- non-modifiable/fixed determinants
- individual lifestyle factors and attitude
2) community level (meso)
- social/community networks
- living and working conditions
3) major structural environment level (macro)
92
Single gene disorders
Rare among pop.
93
Polygenic inheritance
Influences likelihood of offspring developing a disease
94
Individual lifestyle factors and attitude
= environment
- certain degree of choice as an individual - impacts health
- ability to change behaviour(s) may vary by social group
95
Social capital
Value of social networks that facilitate bonds between similar groups of people
- inclusive environ. Diverse backgrounds
- mutual support
- strengthen defence against health hazards
- civic participation, volunteerism, supportive communities
96
Community level
Attitudes/behaviours of families, friends, people living/working in local community influence perception of ‘normative’ behaviours
97
Major structural environment level
Require political actions at national or international levels
- physical, built, cultural, biological, political environs and ecosystem
98
Caution about level of Dahlgren and whitehead model
Permeability between factors - no arch operates in isolation from others
- synergetic effects = more effective
- action at each can impact others - offset
99
Four capitals
Natural, human, social, financial/physical
100
Structure
Social and physical environmental conditions/patterns (social determinants) that influence choices and opportunities available
101
Agency
Sociological concept of the capacity of an individual to act independently and make free choices
102
RCT uses
investigating effects of interventions (therapies, treatments)
103
cohort uses
causal associations
104
cross-sectional uses
measure disease prevalence
105
ecologial uses
- prevalence in different populations
- when majority of some pop exposed but not others
- rare outcomes
106
RCT strengths
minimise confounding
107
cohort strengths
- ethical
- cheaper than RCT
- clear time sequence
- avoid recall bias
- participants more likely to be representative of general pop than RCT
108
cross-sectional strengths
- cheaper + quicker than cohort and RCT
| - no maintenance error
109
ecological strengths
- use already collected data => cheaper + quicker
| - large size => low random error
110
RCT weaknesses
- ethical limitations
- logistically difficult
- expensive
- small => too much random error
- participants often not representative of general pop (motivated volunteers)
- maintenance error
- random allocation error
111
cohort weaknesses
- confounding
- maintenance error
- not good for investigating interventions
112
cross-sectional weaknesses
- uncertain time sequence => reverse causality (not good for causal associations)
- confounding
- not good for investigating interventions
113
ecological weaknesses
- confounding common, difficult to measure and adjust for
| - measurement error
POPLHLTH 111
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