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POPLHLTH 111 > Module 1 (+ Module 3-1) > Flashcards

Module 1 (+ Module 3-1) Flashcards

1
Q

Epidemiology

A

Study of frequency/occurrence of dis-ease in populations

- differences/similarities in frequency between populations helps identify causes

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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

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3
Q

Need for age standardisation/adjustment

A

Can only compare ‘like with like’ (confounding)

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4
Q

Numerical values

A
  • convert into categorical measures

- use mean or median level of outcome

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5
Q

Cohort study

A

Allocation into EG and CG: measured exposures

Measurement of outcome: followed over a period of time

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6
Q

Cross-sectional study

A

Allocation into EG and CG: measured exposures

Measurement of outcomes: at the same time as measurement of exposures

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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
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8
Q

Incidence type of study and data

A

ONLY cohort, ONLY categorical

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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
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10
Q

Prevalence types of study and data

A

Both cross-sectional and cohort (one point during the study)

Categorical and numerical

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11
Q

Change in prevalence

A

Difference between prevalence measured at two points in time

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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

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13
Q

If most people died rapidly or were cured

A

High incidence disease has low prevalence

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14
Q

If few people died or were cured

A

Low incidence disease has high prevalence

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15
Q

Incidence strengths

A
  • determined only by disease risk - clean measure

- includes N, D AND time - more info

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16
Q

Incidence weaknesses

A
  • can be difficult to measure

- must be observed over time

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17
Q

Prevalence strengths

A

Relatively easy to measure

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18
Q

Prevalence weaknesses

A
  • determined by incidence, cure rate and death rate - dirty measure
  • doesn’t include time - less info
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19
Q

Ecological studies

A

Populations allocated to EG and CG (exposures are an average of a group of people)

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20
Q

Individual participant studies

A

Individuals are allocated to EG and CG

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21
Q

Reasons against RCT

A

Unethical, impractical

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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

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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

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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

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25
Q

Relative mean

A

Disease occurrence measures are calculated as averages and RR is comparison between the means

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26
Q

RR no-effect value

A

RR = 1.0

- closer RR is to 1.0 smaller the difference

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27
Q

RRR

A

= (1-RR) x 100%
EGO < CGO
RR < 1.0

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28
Q

RRI

A

= (RR - 1) x 100%
EGO > CGO
RR > 1.0

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29
Q

RR range and units

A

RR > 0

No units

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30
Q

RD description

A

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

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31
Q

RD no-effect value

A

RD = 0 units

- closer RD is to 0 less effective drug/difference in outcome between EG and CG

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32
Q

ARR

A

EGO < CGO

RD < 0

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33
Q

ARI

A

EGO > CGO

RD > 0

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34
Q

RD range and units

A

-infinity < RD < infinity

Same units as EGO and CGO - events per n people per T

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35
Q

RR vs RD

A
  • 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
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36
Q

Occasional study

A

Don’t have comparison groups though implicitly present by subdividing EG by age, gender etc.

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37
Q

Error

A

Incorrect EGO/CGO due to:

  • wrong people recruited
  • right people put in wrong category (EG/CG)
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38
Q

Non-random error

A

Due to poor study design, processes or measurement

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39
Q

Valid study

A

Only has small amount of random and non-random error

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40
Q

RAMBOM

A

Recruitment, allocation (+-adjustment in analyses), maintenance, blind or objective measurement

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41
Q

Recruitment

A
  • representative?
  • sufficient info about process to apply?
  • recruitment error aka external validity error
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42
Q

Response rate

A

No. Who took part / no. Eligible

- if < ~70%, could cause significant recruitment error

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43
Q

Allocation methods

A

By measurement/observation

Random allocation

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44
Q

Types of allocation error

A

Measurement error

Confounding

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45
Q

Allocation measurement error

A
  • exposures measured incorrectly

- participants tell the truth?

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46
Q

Allocation measurement error solution

A

Use well-designed, validated questionnaires or biological tests measuring chemicals

47
Q

Confounding

A

When exposure mixed with another factor (confounded) that is also associated with outcome
- EG and CG similar at the beginning of the study?

48
Q

Confounding solution

A
  • adjustment
  • RCT
  • concealment of allocation
49
Q

Baseline comparison

A

Checking for differences between EG & CG at start of study

50
Q

Selection bias

A

Causes confounding

- equivalent to two separate/overlapping triangles

51
Q

Maintenance error

A
  • 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
Q

Maintenance error solution

A
  • blind studies - minimise difference in degree of error between EG and CG
  • checking periodically especially in long term observational studies
53
Q

BOM

A

1) objectivity

2) blind - helps reduce effect/error from subjective measures

54
Q

Analyses

A

Confounding adjusted for?

  • stratified analysis
  • adjusted analysis (standardisation)
55
Q

Stratified analysis

A

Diving participants into ‘strata’ and analysing data as if they were two sub-studies

  • similar results: strata combined
  • different result: reported separately
56
Q

Confounding in ecological studies

A

Common but difficult to measure and adjust for

57
Q

Crude death rate

A

No. Deaths from disease / size of pop.

58
Q

Age specific death rate

A

No. Deaths from disease in age group / no. People in that age group

59
Q

Expected deaths

A

Age specific death rate x no. People in age group in standard pop.

60
Q

Age-standardised death rate

A

Sum of expected deaths / standard population

61
Q

Age standardisation

A

Process of converting different age structures in each population into one (standard) population age structure then working out death rates

62
Q

Random errors

A
  • Occur due to chance

- present in every measurement in every study

63
Q

Regression to(wards) the mean

A

Repeating measurements/studies with extreme results (often chance events) usually give less extreme results
- more measurements = less randomness = closer to middle

64
Q

Random error solutions

A
  • increase study size (allocation)
  • increase no. Times a factor is measured (measurement, biological)
  • objective measuring instrument (measurement)
65
Q

Types of random error

A

Sampling, measurement/assessment, inherent in biological phenomena, allocation

66
Q

Random sampling error

A

Unrealistic to study entire pop. Thus each sample from pop. Will be different each time
- results are ‘estimates’

67
Q

Random allocation error

A

Differences due to chance in RCTs esp. in smaller studies

68
Q

Measures of random error

A

Unrealistic to estimate all error thus measures generally underestimate total random error
- confidence intervals
(- P-values)

69
Q

Confidence intervals

A

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
Q

95% CI acceptable def.

A

About 95% chance that true value in pop. Lies within 95% CI

71
Q

95% CI accurate def.

A

In 100 identical studies using samples from same pop. 95/100 of 95% CIs will include true value for pop.

72
Q

CI can be calculated for

A

Both categorical and numerical variables

73
Q

Point estimates

A

Estimated value from study

- represented by square

74
Q

CI interpretation

A

Width: wider interval, more random error in measure

  • estimates degree of uncertainty
  • upper/lower confidence limits
75
Q

Statistical significance

A

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
Q

Not statistically significant

A

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
Q

Clinical/practical significance

A

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
Q

Meta-analyses

A

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
Q

Study classification by allocation

A

1) experimental: allocated by investigators

2) observational: allocated by measurement

80
Q

Study classification by measurement of outcomes

A

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
Q

Recall bias

A

Common in retrospective studies

82
Q

Systematic reviews

A

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
Q

Determinants for individuals

A
  • 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
Q

Determinants for groups

A
  • 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
Q

Downstream interventions

A

at micro (proximal) level

86
Q

Proximal

A

Near to change in its health status

- any that’s readily and directly associated with change in health status

87
Q

Upstream interventions

A

At macro (distal) level

88
Q

Distal

A

Distant in time and/or place from change in health status

89
Q

Effects of discrimination/inequities on health

A

Indirect: biased/limited healthcare
Direct: mental health struggles

90
Q

Importance of considering social inequities

A
  • 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
Q

Dahlgren and whitehead model levels

A

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
Q

Single gene disorders

A

Rare among pop.

93
Q

Polygenic inheritance

A

Influences likelihood of offspring developing a disease

94
Q

Individual lifestyle factors and attitude

A

= environment

  • certain degree of choice as an individual - impacts health
  • ability to change behaviour(s) may vary by social group
95
Q

Social capital

A

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
Q

Community level

A

Attitudes/behaviours of families, friends, people living/working in local community influence perception of ‘normative’ behaviours

97
Q

Major structural environment level

A

Require political actions at national or international levels
- physical, built, cultural, biological, political environs and ecosystem

98
Q

Caution about level of Dahlgren and whitehead model

A

Permeability between factors - no arch operates in isolation from others

  • synergetic effects = more effective
  • action at each can impact others - offset
99
Q

Four capitals

A

Natural, human, social, financial/physical

100
Q

Structure

A

Social and physical environmental conditions/patterns (social determinants) that influence choices and opportunities available

101
Q

Agency

A

Sociological concept of the capacity of an individual to act independently and make free choices

102
Q

RCT uses

A

investigating effects of interventions (therapies, treatments)

103
Q

cohort uses

A

causal associations

104
Q

cross-sectional uses

A

measure disease prevalence

105
Q

ecologial uses

A
  • prevalence in different populations
  • when majority of some pop exposed but not others
  • rare outcomes
106
Q

RCT strengths

A

minimise confounding

107
Q

cohort strengths

A
  • ethical
  • cheaper than RCT
  • clear time sequence
  • avoid recall bias
  • participants more likely to be representative of general pop than RCT
108
Q

cross-sectional strengths

A
  • cheaper + quicker than cohort and RCT

- no maintenance error

109
Q

ecological strengths

A
  • use already collected data => cheaper + quicker

- large size => low random error

110
Q

RCT weaknesses

A
  • 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
Q

cohort weaknesses

A
  • confounding
  • maintenance error
  • not good for investigating interventions
112
Q

cross-sectional weaknesses

A
  • uncertain time sequence => reverse causality (not good for causal associations)
  • confounding
  • not good for investigating interventions
113
Q

ecological weaknesses

A
  • confounding common, difficult to measure and adjust for

- measurement error

POPLHLTH 111 (5 decks)

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