Epidemiology Revision

Front 1

What are the 5 factors determining causality?

Back 1

Temporal relationship.
Plausibility.
Consistency.
Strength of association.
Dose response relationship.
Reversability.

Front 2

What does sufficient mean?

Back 2

If the cause is present the disease is always there.
When a disease has a number of causes then the causes are sufficient but not necessary.

Front 3

What does necessary mean?

Back 3

If the cause is absent the disease is never there.

Front 4

What is confounding?

Back 4

A confounding factor changes the rate of developing the disease and is also linked to the exposure under consideration.

Front 5

What is prevalence?

Back 5

Number of existing cases at a defined point in time

Front 6

What is incidence? What are the two ways of measuring incidence?

Back 6

Number of new cases. Risk (probability of a disease occurring in a disease free population during a specifice time period). Rate (A measure of frequency of new cases) - these measurements are usually quite similar.

Front 7

What is relative risk?

Back 7

Estimates the magnitude of the effect of a risk factor on incidence. If RR = 1 then the risk factor does not increase or decrease the likelihood of the condition.

Front 8

Risk difference

Back 8

RD= Re-Ru

Front 9

Risk difference percentage

Back 9

RD%= (Re - Ru) / Re

Front 10

Disability adjusted life years (DALY)

Back 10

Loss of time and function due to disease.
=Years lost due to premature mortality + Years lost due to disability.

Front 11

Reliability

Back 11

Does it consistently produce the same results?
Stability: will people give consistent answers to the questions over time?
Reproducibility: Will different interviewers using the same questionnaire obtain equivalent responses?

Front 12

Validity

Back 12

Does it measure what it is supposed to measure?
Content validity? Do the questions express what they were supposed to?
Criterion validity? Do the responses agree with the gold standard?
Construct validity? Is it measuring what is it claiming to measure?

Front 13

Probability designs

Back 13

• Simple random sample
• Systematic random sample
• Stratified sample
• Cluster sample

Front 14

Standard deviation

Back 14

A measure of variability b/w individuals in the level of the factor being investigated.

Front 15

Standard error

Back 15

A measure of the uncertainty in a sample statistic

Front 16

95% Confidence interval

Back 16

(sample statistic) ± 1.96 x (standard error)

Front 17

Types of study design

Back 17

Ecological studies.

Front 18

Ecological Fallacy (or Ecological Bias):

Back 18

Assumes that an association that’s apparent in population-level analyses will hold true for any individual person in that population. An ecological association will not necessarily represent the association that exists at the individual level.

Front 19

Case control

Back 19

Always retrospective. Looking at people with the disease. There are no controls.

Front 20

Prospective cohort study

Back 20

Group who are exposed, group who aren’t (control). Followed up over many yrs to assess the development of the disease.

Front 21

Retrospective cohort study

Back 21

Assemble a group who’ve been exposed in the past and assess whether they’ve developed any disease in the intervening yrs. Often must rely on past medical records. May be less certainty about the temporal r/ship.

Front 22

What would you use to study a rare disease?

Back 22

Case-control study.

Front 23

What would you use to study rare exposures?

Back 23

Cohort study.

Front 24

What would you use to study multiple outcomes?

Back 24

Cohort study

Front 25

What would you use to study multiple exposures?

Back 25

Case-study

Front 26

What would you use to study conditions where there is a long time between exposure of disease onset?

Back 26

Case control

Front 27

Systematic review

Back 27

All available evidence from different studies (whether published or not) are collected and put together for review via the statistical approach of meta-analysis.

Front 28

How do you reduce for confounding?

Back 28

• Randomisation – randomly assignment into treatment and control groups (basis of RCT). It should equally distribute unknown confounders b/w the 2 groups.
• Restriction – restricting the participants. E.g. only choosing non-smokers if testing for a r/ship b/w coffee and pancreatic cancer.
• Matching – matching participants on potential confounding factors so that they’re evenly distributed b/w the cases and controls, esp. in case-control studies. Is often expensive though.

Front 29

Null hypothesis

Back 29

You expect this to be disproved → i.e. RR = 1

Front 30

Alternative hypothesis

Back 30

You expect this to be proven → i.e. RR ≠ 1

Front 31

p-value

Back 31

The probability of null hypothesis being true.

Front 32

2 sample t-test: Comparison of means of 2 groups.

Back 32

Null hypothesis is that the 2 groups have the same mean. Alternate hypothesis is that they are different.
p-value is taken from this t statistic, using those probability tables for a normal distribution

Front 33

When is odds ratio used?

Back 33

Case-controls

Odds ratio = (exposed cases x unexposed controls) / (exposed control x unexposed cases)

Odds ratio = 1 → exposure not associated with increased or decreased risk
Odds ratio > 1 → exposure is associated with increased risk
Odds ratio < 1 → exposure is associated with decreased risk

Front 34

Chi-squared test

Back 34

Increase in difference b/w observed and expected frequencies → Increase in chi-square statistic → Decrease in likelihood difference is due to chance, Increase it was due to a real difference → Decrease in likelihood of H0 being true!

Front 35

The 10 steps for conducting a field investigation.

Back 35

1. Determine the existence of the epidemic
2. Confirm the diagnosis
3. Define the cases and count the cases
4. Orient the data in terms of time, place and person
5. Determine who is at risk of becoming ill
6. Develop a hypothesis and then test it
7. Compare hypothesis with established facts
8. Plan a more systematic study
9. Prepare a written report
10. Execute control and prevention measures

Front 36

Sensitivity:

Back 36

How good the test is at finding those who really have the disease and have been labelled correctly.
Sensitivity = a / (a + c) = the proportion of those with the disease who test positive

Front 37

Specificity:

Back 37

How good the test is at finding those who really don’t have the disease and have been labelled correctly

Specificity = d / (b + d) = the proportion of those w/out the disease who test negative

Front 38

Positive predictive value:

Back 38

The proportion of all test positives that are true positives.
Positive predictive value = a / (a + b) = the proportion of those who have a positive test and have the disease

Front 39

Negative predictive value:

Back 39

Negative predictive value = d / (c + d) = the proportion of those who test negative and don’t have the disease