Block 3: Week 1: Epidemiology

Question 1

What is Epidimeology?

Answer 1

Study of disease in populations

Question 2

Define Prevalance

Answer 2

Number of people with a problem in a defined population at one time

Question 3

Define Indicidence

Answer 3

Number of new cases of a problem in a defined population in a defined period of time

Question 4

Define mortality rate

Answer 4

Number of people dying in a defined population in a defined period of time

Question 5

What is causality?

Answer 5

Differentiating association from causation

Question 6

Concepts of causality: what are the two types?

Answer 6

Deterministic approach (A causes B)

and

Stochastic approach (likelihood/risk)

Question 7

Concepts of causality: Deterministic approach

Answer 7

Deterministic inevitability

Validation of hypothesis by systematic observations to predict with certainty future events

Question 8

Concepts of causality: Stochastic approach

Answer 8

Ax of hypothesis by systematic observations to give risk of future events

Question 9

Concept of causality: Deterministic approach- what are some of the features? What is it useful for?

Answer 9

Newtonian thinking (A leads to B)

Mechanistic, can take apart to study

Objective, quantifiable and certain

Whole is the sum of the parts

Useful:

Single cause for a single disease

Question 10

Concept of causality: Stochastic approach- what are the features?

Answer 10

Quantum thinking (Hollistic approach, not just linear)

Whole greater than sum of parts

Whole not predictable from knowledge of parts

Probabilities of certainties

Systems theory; complexity theory

• The observer influences the observed
• Emergent phenomena

Question 11

Confouding: What are confounding factors?

Answer 11

Something that is associated with both the exposure & the outcome

Eg: Smoking is associated with high blood pressure (exposure) and MI (outcome)

Question 12

Confouding: What is exposure?

Answer 12

An exposure is independently associated with the outcomes even after taking confounding factors into account

(Eg: Smoking causes MI by itself. It can also cause increased BP which can calso cause MI.

Smoking = confouding factor

BP = Exposure)

Question 13

Confouding: What is a mediating variable?

Answer 13

A variable through which an exposure wholly or partially exerts its effect

eg: Eating sugar (exposure) causes obesity (mediator) which causes heart disease

Question 14

Confouding: What is reverse causality?

Answer 14

Both factors can cause each other

Eg: Loosing a job causes mental illness

BUT

People with mental illness can cause unemployment

Question 15

What is Causality: Aetiology?

Answer 15

Epidemiology is very good at Ax the disease risk associated with individual agents

In assess probability, epidemiological studies CANNOT prove causality but can make a case ‘beyond reasonable doubt’

Question 16

What is Bradford Hill’s Criteria for Inferring Causality: Assoication factors? (1965)

Answer 16

1) Strength of association: A causal link is more likely with strong associations (measured by rate ratio OR odds ratio) & are unlikely to be explained by bias or confounding factors.

Eg: heavy smokers x20 likely of mortality from laryngeal ca tha non-smokers. Weak association can be causal.

2) Specificity of association: A causal link is more likely when a disease is assoicated with a specific factor & vice-versa. Eg: asbestos –> mesothelioma. Lack of specificity does not necessarily weaken the case. Current models of disease are multi-factoral.
3) Consistency of association: A causal link is more likely if the assoication is observed in different studies & subgroups.

• Less likely to be due to bias/ confounding factor
• Lack of consistency can be due to study design

Question 17

Bradford Hill’s Criteria for Inferring Causality (1965): What are the exposure and outcome features?

Answer 17

1) Temporal Sequence: A causal link is more likely if the exposure to the putative cause has been shown to precede the outcome.

• Converse: causal link cannot exsist if the outcome precedes the esposure to putative factor
• Optimal study deigns: Prospective cohort/ RCT
• Weak study designs: Cross-sectional/ Case control study

2) Dose response (biological gradient): A causal link is more likely if different levels of exposure to the putative factor lead to different risk of acquiring the outcome. Eg: 5 a day.
* Unlikely to be due to unknown confounding or bias
3) Reversibility: A causal link is very likely if removal or prevention of the putative factor leads to a reduced or non-existent risk of acquiring the out comes.

• Probably strongest evidence for causal link but dificult to demonstrate
  
  • Disease: long time lags
  • Ethical issues for RTC
  • Public health programme require action by society

Question 18

Bradford Hill’s: What are the three other evidences?

Answer 18

1) Coherence of Theory: A causal link more likely if the observed association conforms with current knowledge- paradigms/ constructs/ theories

• Lack does not rule out causal link
• Inapproriate rejection of ‘unfavoured’ associations

8) Biological Plausibility: A causal link is more likely if a biological plausibility mechanism is likely or demonstrated
* eg: babies sleeping on tummy
9) Analogy: If an analogy exists with other disease, species or settings
* Easier to infer than a biologically plausible mechanism

Question 19

Epidemiological study designs: What are the observational studies, what are they used for?

Answer 19

1) Cross sectional Survey: May be set up for specific purpose

• Prevalance of specific disease
• Investigate distribution of a specific disease in population
• Monitoring health over time
• Medium cost
  2) Case control Studies: Almost always set up for speicifc purpose
• Investigate suspected determinants eg: Outbreak investigation/ determinants of rare conditions
• Quick & Cheap
• Recall and selection bias an issue

3) Cohort: May be for specific purpose. Often multipurpose

• Eg: Effects of smoking, asbestos
• Determinants of common conditions
• Relative importance of different factors
• (Very) Slow & (very) Expensive
• Issues due to confounding with unknown risk factors

Question 20

Epidemiological study design: Experimental studies- what are they and what are they used for?

Answer 20

1) Uncontrolled Studies

• Easy to do
• Selection bias a problem
• Don’t know what happens without intervention

2) Natural Experiments:
* May be only way to investigate some things due to unkown confounding factors
3) Controlled studies
4) RTC

Question 21

What is the hierarchy of evidence?

Answer 21

Systematic reviews

Experimental studies

• Randomised Controlled Trials
• Controlled trials

Observational studies

• Cohort studies
• Case control studies

Descriptive studies

• Cross sectional
• (Qualitative studies)

Question 22

What is bias?

Answer 22

Any trend in the collection, analysis, interpretation, publication of review of data than can lead to conclusions that are systematically different from the truth

Question 23

What are the types of bias in epidemoilogical studies?

Answer 23

1) Selection (design plus execution)

Admission, prevalence/ incidence, detection, volunteer, loss to follow up

2) Information (data collection)

Interviewer, questionaire, recall, diagnostic suspicion & recall

3) Confounding

Question 24

What are some of the epidemiological issues?

Answer 24

They provide information of the average effect

Average effect hides individual level

For some patients better not to do what is best of average- patient preference support these

Question 25

When are epidemiological studies best, good and more limited?

Answer 25

Best:

• Single agent causes disease
• Single Tx reverses disease

Good:

• Primary factor causes disease with several secondary influences

Limited:

• Many different factors interact in complex pathways to create the conditions in which multiple diseases are likey to arise eg: social inequalities & health