HADPOP

Question 1

Define crude birth rate

Answer 1

Number of live births per 1000 population

Describes impact of birth on size of population

Question 2

Define general fertility rate

Answer 2

Number of live births per 1000 females aged 15-44

compares fertility of fertile female populations
Affected by age-specific fertility rate & age distribution

Question 3

Define total period fertility rate

Answer 3

Average number of children born to a hypothetical woman in her life

Compares fertility of fertile females without being influenced by age or group stucture

Question 4

What are the determinants of fertility

Answer 4

Fecundity: physical ability to reproduce
Fertility: realisation of this potential as births

Question 5

Define crude death rate

Answer 5

Number of deaths per 1000 population

Question 6

Define age-specific death rate

Answer 6

Number of deaths per 1000 people in a given age group

Question 7

What is standardised mortality ratio

How do you calculate it

What are its advantages

Answer 7

Compares number of deaths with expected number of deaths (if age-sex distribution of the population in identical)

Observed number of deaths / expected (x 100)

Provides overall mortality ratio adjusted for different distributions of factors e.g. Age, sex in populations
Compares death rates by applying same population age structure

Question 8

How do you calculate incidence / incidence rate

What are its advantages

Answer 8

Number of new cases / person-years (no of people x no of yrs)

Number of cases / population number x 1000

Focuses on new events, monitors epidemics

Question 9

What is prevalence
How do you calculate it
What are its advantages

Answer 9

A proportion not a rate

Number of people affected (old & new) / total population number
(X 100 for percentage)

Describes burden
Measure service needs

Question 10

What is incidence rate ratio
How do you calculate it
How do you interpret it

Answer 10

Compares number of cases per population per unit time

Rate b (exposed) / rate a (unexposed)

Compare IR between groups with different levels of exposure
Make sure person years are the same for both

If rate b higher = difference in exposures associated with different rates of disease
b > a = IR > 1
b

Question 11

What figures do you use in error factor calculation for:
IR / prevalence
IRR
SMR
OR

Answer 11

d= no of events observed (not a rate)

d1 & 2 = no of events in each population

O = observed no of cases

a= cases exposed, b= controls exposed, c= cases unexposed, d= controls unexposed

Question 12

Interpreting confidence intervals:
If range doesnt include null hypothesis (1)
If range does include null hypothesis (1)

Answer 12

Can reject null
Data inconsistent with hypothesis

Cannot reject null
Data consistent with hypothesis

Question 13

Describe:
Systematic variation
Random variation

Answer 13

Variation attributed to particular factors

Variation cant be explained (e.g. By chance)

Question 14

Cohort studies:
Describe characteristics
Advantages
Disadvantages

Answer 14

Recruit disease free individuals
Follow them over time & count how many develop disease (person yrs)
Comparative: risk one group vs another
Prospective or historical

Study rare exposures/characteristics
More detailed info on outcomes & exposures
Collect additional info on potential confounders

Large/resource intensive
Time consuming
Risk loss to follow up
Not good for rare outcomes
Confounding (unknown)

Question 15

Cohort studies:
How is risk estimated
How can comparisons be made

Answer 15

Incidence rate in each group

Exposed & unexposed:
Subdivide levels of exposure & compare with relative risk (IRR)
Compare to external reference population (SMR)

(Error factor for internal comparison larger than for external)
(External comparison usually have less random variation)

Question 16

Case control studies:
Characteristics
Advantages
Disadvantages

Answer 16

Identify group already ill (cases) & group that are not (controls)
Look back to compare exposure status (generally retrospecitve)
Identify exposure & compare disease outcome
Prevalence fixed by study design/number of participants;
Estimates relative (not absolute) risk

Good for rare diseases
Can look @ different exposures once, for single outcome/disease
Quicker, cheaper

Not good for rare exposures
Selection bias: recall, finding representative comparison population
Info bias: info on exposure, randomly inaccurate measurement, systematic (e.g. Different data collection), interviewer bias
Confounding: minimise with matching for confounders & logistic reg

Question 17

Odds ratio (for case control & cohort):
How to calculate
What it shows

Effects on OR if:
Cases underestimate more than controls
Controls underestimate more than cases
Both cases & controls underestimate randomly

Answer 17

Table: cases & controls across top, exposed & unexposed down side
(axd) / (bxc)

Estimates relative risk (like IRR)
compare oods of being exposed in cases vs odds of being exposed in controls
Increase precision by increasing number of controls (reduced EF); usually 5 x number of cases

Bias OR towards underestimation of affect of outcome
Bias OR towards overestimation of affect of outcome
Shrinkage towards null

Question 18

Define chance: 95% confidence interval & P value

Answer 18

95% chance that true value lies within range

How likely results would have occurred by chance if the null was true

Question 19

Define bias

What are the types of bias

Answer 19

Deviation of results / inferences from truth; invalidates conclusins

Selection bias:
correct info, wrong people
Characteristics of groups & the way they are selected

Information bias:
Incorrect info, right people
Measurement/classification into groups

Recall bias

Each may be:
Systematic (results skewed in particular direction)
Non-systematic (random errors, CI wider, IR towards 1)

Question 20

Define confounding

Answer 20

Characteristic of the population, linked to exposure & outcome

Measure of effect of exposure on risk of outcome being distorted

Question 21

Define reverse causality

Answer 21

Cause-effect relationship exists in the opposite direction

Question 22

How do you distinguish causal from non-causal association

Answer 22

Hypothesis: from observation / clinical practice & studies

Analytical study: test hypothesis

Observed association: test validity, exclude alternative explanations (chance, bias, confounding)

Does statistical association represent cause-effect relationship?:
Infer beyond data in light of current knowledge

Question 23

Census: definition

Answer 23

Simultaneous recording 
Of demographic data
By government
@ particular time
Re: all people with live in a particular country

Question 24

Cause-effect relationship:
What are Henle-Koch’s Postulates
What are their limitations

Answer 24

Agent present in every case by isolation in pure culture
Agent not found in any other diseases
Agent able to reproduce disease in experimental conditions in experimental animals (once isolated)

Diseases are multifactorial; exposure can cause many diseases

Question 25

Evaluating strength of evidence in the cause-effect relationship:
What are the Bradford Hill criteria for inferring causality

Answer 25

Strength of association
Specificity of association
Consistency of association
Temporal sequence
Dose response (biological gradient)
Reversibility (strongest criterion)
Coherence of theory
Biological plausibility
Analogy

Question 26

Randomised control trials:
What is the purpose of a clinical trial
Why do you not use historical controls

Answer 26

Fair, controlled, comparative, reproducible study to clarify the most appropriate treatment of future patients

Selection criteria less well defined/rigorous
May have been treated in different/other ways
Less information about patients, therefore difficult to adjust for confounders

Question 27

Randomised control trials:
What are the steps involved
Why are participants randomly allocated to groups
Why use blinding (& what are the types of blinding)

Answer 27

Answer question: is new treatment better of worse than the usual/standard treatment?
Identify, recruit, consent & maintain 2 comparable groups
Allocate, follow up, assess & compare

Minimise allocation bias & confounding (known & unknown)

Minimise non treatment effect & measurement bias
(Single or double blind)

Question 28

Randomised control trials:
What is the definition of placebo
Why is it used

Answer 28

Inert substance that looks/tastes/is packaged identically

Eliminate placebo effect (psychological benefit of being treated)

Question 29

Randomised control trials:

Features of suitable outcome measures

Answer 29

Measured by assessor
Blind to treatment status
Relevant, objective, valid, sensitive, reliable, practical

Question 30

Randomised control trials:
How do you minimise losses to follow up
How do you maximise compliance

Answer 30

Sufficient/clear info on what is involved
Follow up not too onerous

Clear, simple instruction
Check on compliance

Question 31

Randomised control trials:

What are the types of analysis

Answer 31

As treated: but no longer randomly allocated / immune to follow up (e.g. Assess drug physiology)

Intention to treat: realistic indication of impact of treatment
Randomisation preserved & confounding avoided

Question 32

Randomised control trials:
What are the ethical principals of research
What are the issues in making a clinical trial ethical
What is the role of the Research & Ethics committee

Answer 32

Declaration of Helsinki: health of patient 1st consideration
Collective ethic: all patients should have treatment tested for efficacy/safety
Individual ethic: RCTs for benefit of future patients

Voluntariness
Valid consent
Ethical recruitment
Scientifically robust
Clinical equipoise (uncertainty about better treatment)

Involved NHS trust & PCT research & development office
Focus on: scientific design/conduct, recruitment, care & protection, confidentiality, informed consent, community consideration