Tutorial 2 - The Use of Data Flashcards
what proportion of people consult their GP about their health complaints
20%
what proportion of patients visiting their GP are referred onto secondary care
3%
define disease
symptoms, signs ⇒ diagnosis
(bio-medical perspective)
define illness
ideas, concerns, expectations – experience
patients perspective
what proportion of GP appointments involve no disease
up to 50%
list several medical and non-medical factors which may influence an individuals desire to seek medical attention
Medical factors - new/visible symptoms, increasing severity, duration (e.g long)
Non medical factors – crisis, peer pressure “wife sent me”, patient beliefs, lay refferal, expectations, social class, economic, psychological, environmental, cultural, ethnic, age, gender, GP practice leaflet, NHS website, media: internet, TV, newspaper
who seeks medical attention more often - males or females?
females
give 3 reasons a patient that feels well may not want to accept treatment
- believe themselves to be healthy.
- is physically fit.
- proud not to be using tablets.
define epidemiology
study of how often diseases occur in different groups of people and why
what are the 3 main aims of epidemiology
- description
- explanation
- disease control
regarding epidemiology, define description
to describe the amount and distribution of disease in human populations
regarding epidemiology, define explanation
to elucidate (find out) the natural history and identify aetiological factors
usually by combining epidemiological data with data from other disciplines such as biochemistry, occupational health and genetics
regarding epidemiology, define disease control
data can provide basis for
- preventive measures
- (develop new/modify) public health practices
- therapeutic strategies
to be developed, implemented, monitored and evaluated to help control disease.
epidemiology compares groups (study populations) in order to detect differences.
describe 3 things that may be detected/discovered
- aetiological clues
- scope (capacity) for prevention
- identification of high risk/priority groups in society
in epidemiology, give some examples of different study populations (based on: )
the study population may be defined by:
- age
- sex
- location
(or even be the same group over time)
we then compare how often an event appears in one group with another
data in epidemiology is converted into ratios - what does the numerator consist of?
number of events e.g death / [population at risk]
data in epidemiology is converted into ratios - what does the denominator consist of?
[number of events] / population at risk
minor illnesses (e.g. a cold) typically have a
(high/low) ______ incidence
(high/low) ______ prevalence
high incidence
low prevalence
chronic illnesses (e.g. diabetes) typically have a
(high/low) ______ incidence
(high/low) ______ prevalence
low incidence
high prevalence
incidence is useful for investigating what aspect of a disease?
aetiology of disease
what does the prevalence of a disease tell us?
prevalence tells us something about the amount of disease in a population
define relative risk
Relative risk (RR) =
incidence of disease in exposed group
/
incidence of disease in unexposed group
what is relative risk (RR) a measure of?
the strength of association between a suspected risk factor and the disease
give 10 sources of epidemiological data
- mortality data
- hospital activity statistics
- reproductive health statistics
- cancer statistics
- accident statistics
- general practice morbidity
- health and household surveys
- social security statistics
- drug misuse databases
- expenditure data from NHS
define health literacy
having the knowledge, skills, understanding and confidence to use health information, to be active partners in their care, and to navigate health and social care systems.
why is health literacy relevant?
health literacy is being increasingly recognised as a significant health concern around the world
describe descriptive studies
descriptive studies attempt to describe the amount and distribution of a disease in a given population
describe the advantages and limitations of descriptive studies
limitation
- does not provide definitive conclusions about disease causation,
advantages
- may give clues to possible risk factors and candidate aetiologies.
- usually cheap, quick and give a valuable initial overview of a problem.
what is a cross-sectional study?
in cross-sectional studies, observations are made at a single point in time
what conclusions are drawn from cross-sectional studies?
relationship between diseases and other variables of interest in a defined population.
one strenght of a cross-sectional study
provide results quickly
one negative of a cross-sectional study
usually impossible to infer causation
what is a case control study?
two groups of people are compared:
- Cases - those who have the disease of interest
- Controls - those who do not
how are conclusions drawn from case-controlled studies (process)?
- both groups have exposure to a suspected aetiological factor measured
- amount (of disease?) is compared to identify significant differences
- to give clues as to what factors elevate/reduce risk of disease being studied
what are the results in a case controlled study expressed as?
relative risk (RR)
what is a cohort study?
- baseline data on exposure collected from a group who do not have the disease under study
- group is then followed through time until a sufficient number have developed the disease
- original group is separated into subgroups according to original exposure status and compared to determine incidence of disease according to exposure.
cohort studies allow the calculation of cumulative incidence, allowing for differences in follow up time.
what are trials?
trials are experiments used to test ideas about aetiology or to evaluate interventions
what is the definitive method of assessing any new treatment in medicine?
the “randomised controlled trial”
how is a trial assessing aetiology conducted?
two groups at risk of developing a disease are assembled
- intervention group - alteration made (eg suspected causative factor removed)
- control group - no alteration is made
data on subsequent outcomes (eg disease incidence) are collected from both groups, and the relative risk is calculated.
how is a trial assessing a new treatment conducted?
- intervention group receive new therapy
- control group receive current standard therapy (or placebo)
⇒ treatment outcomes (eg reduction in symptoms) compared in both groups
what is meant by standardisation?
a set of techniques used to remove (or adjust for) the effects of differences in age, gender or other confounding variables, when comparing two or more populations.
what is the standardised mortality ratio (SMR)?
a special kind of standardisation
it is simply a standardised death rate converted into a ratio for easy comparison
the figure for a standard reference population (eg, Scotland) is taken to be 100 and the standardised death rates for the comparison (study) populations (eg, Grampian) are expressed as a proportion of 100. A figure below one hundred means fewer than expected deaths, and above 100 means more. For example, an SMR of 120 means that 20% more deaths occurred than expected in the study population, allowing for differences in the age and sex structure of the standard and study populations and an SMR of 83 means 17% fewer deaths occurred.
describe why the quality of data is an important factor to consider in interpreting results
must ensure data is trustworthy.
explain what ‘case defintion’ means
the purpose of it is to decide whether an individual has the condition of interest or not.
It is important because not all doctors or investigators mean the same thing when they use medical terms. Differences in incidence of disease over time or in different populations may be artefact, due to differences in case definition, rather than differences in true incidence.
Artefact definition: something observed in a scientific investigation or experiment that is not naturally present but occurs as a result of the preparative or investigative procedure.
describe ‘coding and classification’
when data is being collected routinely (eg death certificates), it is normal to convert disease information to a set of codes, to assist in data storage and analysis.
rules are drawn up to dictate how clinical information is converted to a code. If these rules change, it sometimes appears that a disease has become more common, or less common, when in fact it has just been coded under a new heading.
describe ‘ascertainment’ aka ascertainment bias
Is the data complete - are any subjects missing?
If researchers in one country look harder for cases of a given disease than researchers in any other, it might not be surprising that they come up with higher incidence rates.
Ascertainment bias arises when data for a study or analysis is collected such that some members of the intended population are less/more likely to be included than others
define bias
any trend in the collection, analysis, interpretation, publication or review of data that can lead to conclusions that are systematically different from the truth
name 4 types of bias
- Selection Bias
- Information Bias
- Follow up Bias
- Systematic Error
describe selection bias
Selection bias
Occurs when the study sample is not truly representative of the whole study population about which conclusions are to be drawn.
For example, in a randomised controlled trial of a new drug, subjects should be allocated to the intervention (study) group and control group using a random method. If certain types of people (eg, older, more ill) were deliberately allocated to one of these groups then the results of the trial would reflect these differences, not just the effect of the drug.
describe information bias
Information bias
arises from systematic errors in measuring exposure or disease. For example, in a case control study, a researcher who was aware of whether the patient being interviewed was a ‘case’ or a ‘control’ might encourage cases more than controls to think hard about past exposures to the factors of interest. Any differences in exposure would then reflect the enthusiasm of the researcher as well as any true difference in exposure between the two groups.
describe follow up bias
arises when one group of subjects is followed up more diligently than another to measure disease incidence or other relevant outcome.
For example, in cohort studies, subjects sometimes move address or fail to reply to questionnaires sent out by the researchers. If greater attempts are made to trace these missing subjects from the group with greater initial exposure to a factor of interest than from the group with less exposure, the resulting relative risk would be based on a (relative) underestimate of the incidence in the less exposed group compared with the more exposed group.
describe systematic error
tendency for measurements to always fall on one side of the true value.
It may be because the instrument (eg blood pressure machine) is calibrated wrongly, or because of the way a person uses an instrument. This problem may occur with interviews, questionnaires etc, as well as with medical instruments.
what is a confounding factor?
factor associated independently with both the disease and with the exposure under investigation
⇒ distorts relationship between exposure and disease.
(in some cases the confounding factor may be the true causal factor, and not the exposure that is under consideration)
give 3 examples of confounding factors
- age
- sex
- social class
describe 5 ways around confounding factors
- randomisation
- restriction of eligibility criteria to only certain kinds of study subjects
- subjects in different groups can be matched for likely confounding factors.
- results can be stratified (arranged) according to confounding factors.
- results can be adjusted (using multivariate analysis techniques) to take account of suspected confounding factors.
describe 9 factors in the criteria for causality
(It is difficult to prove causation between an exposure and disease. Often the best that can be achieved is to demonstrate a weight of evidence in favour of a causal relationship. A number of criteria have been devised to help investigators assess the available evidence, known as the criteria for causality)
- Strength of association: the larger the association, the more likely that it is causal.
- Consistency: Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
- Specificity : Causation is likely if there is a very specific population at a specific site and disease with no other likely explanation.
- Temporality: The effect has to occur after the cause
- Biological gradient: Greater exposure should generally lead to greater incidence of the effect
- Biological plausibility: A plausible mechanism between cause and effect is helpful
- Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
- Analogy : The effect of similar factors may be considered.
- Experiment: experimental evidence
list the 3 intended aims of sign guidelines
- Help healthcare professionals + patients understand medical evidence and use it to make decisions about healthcare.
- Reduce unwarranted variations in practice + make sure patients get best care available, no matter where they live
- Improve healthcare across Scotland by focusing on patient-important outcomes
What does SIGN stand for and what do they do
- The Scottish Intercollegiate Guidelines Network (SIGN)
- Develop evidence based clinical practice guidelines for the NHS in Scotland.
