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Y1 FOCP Epidemiology > Definitions > Flashcards

Flashcards in Definitions Deck (32)
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1
Q

Q: Attributable risk.

A

A: a measure of exposure effect that indicates, on an absolute scale, how much greater the frequency of disease in the exposed group is compared with the unexposed, assuming the relationship between exposure and disease is causal (an important assumption).

It is the difference between the incidence rate in the exposed and non exposed groups, i.e. it represents the risk attributable to the exposure of interest.

2
Q

Q: Case.

A

A: an individual with the outcome under study (in a case-control study).

This could be a person who has the disease, health disorder, or suffers the event of interest (by “event” we mean a change in health status, e.g. death in studies of mortality or becoming pregnant in fertility studies).

The epidemiological definition of a case is not necessarily the same as the clinical definition.

3
Q

Q: Case-control study. What’s explored? Always results in?

A

A: study in which individuals are selected on the basis of whether or not they have the outcome of interest; usually some relatively rare outcome.

Exposure (risk factor) status is explored to establish whether the exposure is more common in the case (those that have the outcome) or control (those that do not have the outcome) group.

This type of study always results in an odds ratio, for example comparing the odds of being exposed (e.g. a smoker) in those who had the outcome (e.g. pancreatic cancer), with the odds of being a smoker in those who did not have pancreatic cancer.

4
Q

Q: Count.

A

A: the most basic measure of disease frequency is a
simple count of affected individuals. The number (count) of cases that occurred in a particular population is of little use in comparing populations and groups.

5
Q

Q: Chi squared test.

A

A: a statistical procedure for testing whether two proportions are similar (e.g. whether the proportion of lung cancer cases in males who smoke is significantly different to the proportion of lung cancer cases in males who do not smoke).

6
Q

Q: Cohort study. Method? Usually results in?

A

A: study in which individuals are selected on the basis of exposure status and are followed over a period of time to allow the frequency of occurrence of the outcome of interest in the exposed and non exposed groups to be compared.

Take a group of people, note whether they’ve been exposed or not, observed them over time and wait for them to get ill, to die etc.

This type of study typically produces a relative risk.

7
Q

Q: (95%) Confidence interval. Example?

A

A: an estimated range of values calculated from a given set of sample data which are likely to contain the ‘true’ population value.

E.g. a range of values around a relative risk measure which would, in 95% of such studies, contain the ‘true’ risk (the true risk being the relative risk that would be obtained if the study had included the entire population of patients).

By “contain (or ‘span’) the true value”, we mean that the true value lies above the lower value of the confidence interval but below the upper values of the confidence interval.

For example, for a 95% confidence interval of 1.2 – 3.4, we can say that we are 95% confident that the true value of risk will not be lower than 1.2 and will not be higher than 3.4.

8
Q

Q: Confounding.

A

A: a possible explanation for the study finding if confounding variables have not been taken into account in the study.

9
Q

Q: Confounding variable.

A

A: a factor that is associated with both the exposure and outcome of interest. Common confounders include age, smoking, socio-economic deprivation.

10
Q

Q: Control.

A

A: (as opposed to a case) – a person without the outcome under study (in a case-control study), or a person not receiving the intervention (in a clinical trial).

11
Q

Q: Exposure.

A

A: when people have been ‘exposed’, they have been in contact with something that is hypothesised to have an effect on health e.g. tobacco, nuclear radiation, pesticides in food, HRT. These are typically called ‘risk factors’ of disease.

12
Q

Q: Incidence. Not same as? Measures? Calculation?

Interpreted as?

A

A: the number of new cases of the outcome of interest occurring in a defined population over a define period of time. Note that this is not the same as prevalence, which includes new and old cases.

Incidence measures events (a change from a healthy state to a diseased state).

number of disease free people at beginning of time period

This measure of incidence can be interpreted as the probability, or risk, that an individual will develop the disease during a specific time period.

13
Q

Q: Matching.

A

A: method for “controlling for” (i.e. effectively removing) the effect of confounding at the design stage of a case-control study; controls are selected to have a similar distribution of potentially confounding variables to the cases, e.g. they are said to be “matched” for sex if there are similar proportions of men and women in both groups.

14
Q

Q: Normal distribution.

A

A: a set of values and frequencies that describe many things in nature, at least approximately, e.g. height, weight, blood pressure. This symmetrical distribution (see Figure 1) is the basis of many statistical tests because, if you know the average value (usually called the mean) and the standard deviation, then you can draw every point of a normal distribution and you know what proportion of values are greater than (or less than) any given point

15
Q

Q: Null hypothesis. Comparing rates? Case-control studies? Normally distributed variables?

A

A: the statistical test tries to disprove is that there is no difference between the two groups in the measure being tested.

If we are comparing rates, then the null hypothesis would be that rate A equals rate B, which means that the relative risk (rate A divided by rate B) equals 1.

For case-control studies, the null hypothesis would be that the odds of exposure for group A equal the odds of exposure for group B, i.e. the odds ratio (odds of exposure for A divided by the odds of exposure for B)
equals 1.

However, for normally distributed variables such as blood pressure (BP) in Question 4, the null hypothesis would be that the average BP for group A equals the average BP for group B, i.e. the difference between the two average BPs equals 0.

16
Q

Q: Odds. Mathematical relationship?

A

A: the odds is another way to express probability, e.g. the odds of exposure is the number of people who have been exposed divided by the number of people who have not been exposed.

The mathematical relationship between odds and probability is: Odds = probability / (1 – probability)

17
Q

Q: Odds ratio. Calculation?

A

A: odds ratio (of exposure) is the ratio between two odds

odds of exposure in the controls

This ratio is the measure reported in case-control studies instead of the relative risk. It can be mathematically shown that the odds ratio of exposure is generally a good estimate of the relative risk.

18
Q

Q: Why is it not possible to calculate incidence of disease/relative risk in case-control studies? But can calculate?

A

A: the subjects are selected on the basis of their disease status (sample of subjects with a particular disease (cases) and sample of subjects without that disease (controls)), not on the basis of exposure. Therefore, it is not possible to calculate the incidence of disease in the exposed and non-exposed individuals.

It is, however, possible to calculate the odds of exposure. (odds ratio)

19
Q

Q: Outcome.

A

A: the event or main quantity of interest in a particular study, e.g. death, contracting a disease, blood pressure.

20
Q

Q: Population attributable risk.

A

A: (also known as the population excess risk) – a measure of the risk of outcome in the study population which is attributable to the exposure of interest.

21
Q

Q: Population excess fraction.

A

A: (also known as the population attributable fraction) – a measure of the proportion (fraction) of the cases observed in the study population attributable to the exposure of interest.

22
Q

Q: Prevalence. Calculation?

A

A: the number of cases of an outcome of interest in a defined population at a particular point of time, hence it is often called point prevalence. This includes both new (also called “incident”) cases and existing cases.

number of persons in defined population at same point in time

23
Q

Q: p-value. Interpretation?

A

A: the probability of obtaining the study result (relative risk, odds ratio etc) if the null hypothesis is true. The smaller the p-value, the easier it is for us to reject the null hypothesis and accept that the result was not just due to chance.

A p-value of <0.05 means that there is only a very small chance of obtaining the study result if the null hypothesis is true, and so we would usually reject the null. Such as result is commonly called “statistically significant”.

A p-value of >0.05 is usually
seen as providing insufficient evidence against the null
hypothesis, so we accept the null.

24
Q

Q: Randomisation.

A

A: a method for ensuring that both groups in a clinical trial (i.e. those receiving the intervention and those not receiving the intervention (controls)), have similar proportions of confounding variables, such as age.

25
Q

Q: Rate and risk. Calculation?

A

A: these words are often taken to mean the same thing (though to some epidemiological purists they are not always the same). We talk of someone’s risk/chance/probability of getting a disease (or getting pregnant or dying etc.) and a population having a disease rate. Both terms imply a proportion

the total number of people at risk of the outcome.

26
Q

Q: Regression.

A

A: a method for controlling the effect of confounding at the analysis stage of a study - statistical modelling is used to control for one or many confounding variables.

27
Q

Q: Relative risk. Calculation? Interpretation?

A

A: used as a measure of association between an exposure and disease.

incidence rate in the non-exposed group

A value of 1.0 indicates that the incidence of disease in the exposed and the unexposed are identical and thus the data shows no association between the exposure and the disease.

A value greater than 1.0 indicates a positive association or an increased risk among those exposed to a factor.

Similarly, a relative risk less than 1.0 means there is an inverse association or a decreased risk among those
exposed, i.e. the exposure is protective.

28
Q

Q: Restriction.

A

A: a method for controlling the effect of confounding at the design stage of a study, e.g. by including patients in a clinical trial only between the ages of 18 and 65 without pre-existing illness so that the results of the trial are not confused (‘confounded’) by different levels of age or morbidity in the two treatment groups.

29
Q

Q: Sample.

A

A: a relatively small number of observations (or patients) from which we try to describe the whole population from which the sample has been taken.

30
Q

Q: Standardisation. Used to produce?

A

A: a method for controlling the effect of
confounding at the analysis stage of a study. Used to produce a Standardised Mortality Ratio, a commonly used measure in epidemiology.

31
Q

Q: Statistical test.

A

A: the only way to decide whether the results of your analysis, e.g. your measure for group A compared with your measure for group B, are likely to be due to chance or could be real. The procedure for doing a statistical test is to take one value representing the observed difference in your study between groups A and B and compare that value against tables of an appropriate mathematical distribution such as the normal distribution to see how extreme it is

32
Q

Q: Stratification.

A

A: a method for controlling the effect of
confounding at the analysis stage of a study - risks are
calculated separately for each category of confounding
variable, e.g. each age group and each sex separately.