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Public Health > Epidemiology 1-3 > Flashcards

Epidemiology 1-3 Flashcards

1
Q

Definition of prevalence

A

Cross-section

Number of cases/sample at a given time point

Expressed as % or fraction

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2
Q

Incidence risk

A

Number of new cases / sample of people at risk

in a given time period

Expressed as % or fraction per unit time

aka cumulative incidence aka risk

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3
Q

Utility of prevalence estimates

A

Idea of risk factors

Allows planning of services

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4
Q

Incidence density

A

Number of new cases / person-years of observation

Only consider duration person is at risk

More accurate for larger studies with different followup times

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5
Q

Factors affecting disease prevalence

A

Reporting rate

Length of disease (shorter due to cure or longer due to better treatment)

Incidence

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6
Q

Relationship between incidence and prevalence

A

Prevalence = incidence * duration of disease

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7
Q

Sources of routine health information

A

Mortality - death registries

Morbidity - hospital episode data (coded)

morbidity- primary care registries

Incidence - notifiable diseases

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8
Q

Definition of ecological study

A

Study of aggregates - not individuals!

Units can be e.g. school, region, healthcare facility

Correlation between exposure/disease at aggregate level

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9
Q

What is the ecological fallacy?

A

Not possible to draw conclusion about individual risk based on aggregate data, as within-unit association may be reerse (e.g. CVD risk and wealth)

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10
Q

Infant mortality rate

A

Deaths under 1 year old / number of live births

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11
Q

Neonatal mortality rate

A

Number of deaths under 28d / number of live births

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12
Q

Perinatal mortality rate

A

Number of stillbirths and deaths <7d / total number of live and still births

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13
Q

Age-standardisation process

A

Multiply population age structure by age-specific mortality rate in a reference population –> calculate expected deaths for age

Divide observed mortality by expected –> standardised mortality ratio

This is the indirect method

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14
Q

Features of cohort studies

A

Populations defined on basis of exposure

Exposure measurement precedes disease measurement - free from disease at this point!

Population followed up (regular) over time (may be retrospective)

Assess for changes in outcome between different exposure groups

Can measure multiple outcomes (c.f. case-control)

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15
Q

Features of a case-control study

A

Define a set of cases with outcome of interest

Define population of controls without outcome of interest

Compare exposures in the past/future between groups

Useful in rare outcomes

Can study multiple exposures

CANNOT CALCULATE RISK - artificially inflated cases

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16
Q

Features of ideal control in case-control study

A

Drawn from same population as cases

Would be eligible to be cases if they had the outcome of interest

Not overmatched - weakens study

  • but matched enough* - e.g. age, sex
    2: 1 or more ratio of controls to cases

Similarly accurate measurements to those in cases

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17
Q

Features of a cross-sectional study

A

Snapshot measure of population - can look at prevalence of outcomes or exposures

Difficult to interpret relationship between outcome and exposure

Not suitable for rare outcomes/exposures (need very large sample)

18
Q

Difference between odds and risk

A

Odds is a ratio

Risk is a proportion

Risk can only be obtained from cohort studies that follow patients up over time (ones that give an incidence!)

Odds indicate association, do not interpret magnitude as risk (except if rare disease assumption)

19
Q

What odds ratios can be calculated from cohort and case control studies?

A

Case-control: Odds of exposure given disease (a/c divided by b/d)

Cohort: Odds of disease given exposure (a/b divided by c/d

Same mathematically!

20
Q

What is attributable risk?

A

Excess risk in a population caused by exposure

Equal to risk in exposed group - risk in unexposed group

Only from cohort studies!

21
Q

What is attributable fraction

A

Attributable risk / incidence in exposed individuals

Assumes causal relationship (c.f. ratio measures)

22
Q

How to calculate population attributable risk

A

Need to know either true population incidence

OR

population exposure prevalence

PAR = population incedence - unexposed incidence from study

OR

PAR = AR x population prevalence of exposure

23
Q

What is power?

A

Probability of a study detecting a true difference if there is one

Usually set at 80%

24
Q

Precision

A

Degree of random error in a study

Described by confidence intervals

Reduced by larger sample sizes

25
Q

Bias

A

Systematic error in result arising from study design

e.g. selection, information

26
Q

Confounders

A

Independently associated with both outcome and exposure

Can be accounted for in:

Design: e.g. RCT, case-control matching

Standardisation: e.g. age-standardisation

Regression: Multivariate, useful for multiple confounders

27
Q

Interpretation of ‘risk factors’ arising from cohort studies

A

Markers of disease

Confounders

Causal in disease

28
Q

NNH/NNT

A

1/attributable risk or absolute risk reduction

Assume causal relationship

Need a specified period of time!

29
Q

Forms of informaiton bias

A

Misclassificaiton of exposure: recall bias, interviewer tools

Misclassification of disease status: loss to follow up, diagnostic bias (non-blinding)

30
Q

Relating exposure OR, disease OR, and relative risk

A

Exposure OR = disease OR if controls selected in unbiased way

Disease OR approx to disease relative risk if disease/outcome is rare

More prevalent disease –> greater divergence of OR and RR

31
Q

Relationship between OR and RR

A

More common –> OR exagerrates RR (positively or negatively)

Rare disease b aprox = to a+b –> OR and RR similar

Cannot verify rare disease assumption from case control studies

32
Q

Which studies can give measures of disease frequency

A

Cohort (incidence)

Cross-sectional (prevalence)

NOT CASE-CONTROL

33
Q

Factors increasing disease incidence

A

Improved detection/diagnosis

Changes in coding/recording

Increased exposure to risk factors (true rise in incidence)

Changes in population structure (e.g. ageing)

34
Q

Benefits of 2:1 or higher control:case ratios

A

Increase power and precision of case-control study

35
Q

Type one error

A

False positive (reject null hypothesis despite being true)

36
Q

Type two error

A

False negative

Due to insufficient power

37
Q

Which studies are best suited to study rare diseases?

A

Ecological

Case-control

38
Q

Which studies are best suited to study multiple exposures

A

Cross-sectional

Case-control

Cohort

39
Q

Which studies are best suited to study multiple outcomes?

A

Cohort

40
Q

Which studies are best suited for rare exposures?

A

Cohort

41
Q

Study designs for long latent periods

A

Cohort

Case-control

Public Health (7 decks)

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