• Understanding Research: Cohort Studies

    by Cian O'Brien. Last modified: 16/05/14

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    • Evaluate a possible association between exposure and outcome by following a group of exposed individuals over a period of time to see whether they develop the disease/outcome
    • A cohort is a group of individuals who share a common characteristic (workers in the same factory)
    • Participants are selected on the basis of exposure and should be free of the outcome at the start of the study
    • The incidence of disease in the exposed group is then compared to the incidence of disease in an unexposed group
    • Relative risk is calculated to assess whether the exposure and disease are causally linked
    • Cohort studies can be prospective or retrospective – both types define the cohort on the basis of EXPOSURE, not outcome

    Prospective cohort studies

    • Participants are identified and followed up over time until the outcome of interest has occurred
    • A temporal relationship between exposure and outcome can be established

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    Retrospective cohort studies

    • Exposure and outcome have already occurred at the start of the study
    • Pre-existing data (medical notes) can be used to assess any causal links
    • Less time consuming and costly but is more susceptible to Bias
      • The exposure may have occurred some years previously and adequate reliable data on exposure may be unavailable or incomplete

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    Issues in the design of cohort studies

    Selection of study groups

    • The aim of a cohort study is to select study participants who are identical with the exception of their exposure status
    • All study participants must be free of the outcome under investigation and have the potential to develop the outcome under investigation.
    • If the exposure is common, the study population can be selected before classifying individuals as exposed or unexposed
    • An occupational cohort includes individuals who share the same workplace, which may make it easier to follow the population up.
    • If the exposure is rare, the study population may be chosen on the basis of exposure, to ensure sufficient exposed individuals are enrolled.
      • workers at a particular factory who regularly handle a chemical of interest. The comparison group might be workers at the same factory whose roles do not bring them into contact with the chemical. If a control group is chosen from the general population, there is a risk of bias due to the ‘healthy worker effect’: the general population is usually less healthy than the workforce because it includes those unable to work due to illness.

    Measuring exposure

    • Levels of exposure (e.g. packs of cigarettes smoked per year) are measured for each individual at baseline at the beginning of study and assessed at intervals during the period of follow-up
    • When several exposures are being considered simultaneously, the non-exposed group should comprise all those with none of the risk factors under investigation
    • A particular problem occurring in cohort studies is whether individuals in the control group are truly unexposed.
      • study participants may start smoking or they may fail to correctly recall past exposure. Similarly, those in the exposed group may change their behaviour in relation to the exposure such as diet, smoking or alcohol consumption
      • Exposure data may be obtained from a number of sources including medical or employment records, standardized questionnaires, interviews and by physical examination.

    Methods of follow up

    • The follow up of study participants in a cohort study is a major challenge, and it may take many years for a sufficient proportion of participants to have reached an outcome
    • A great deal of cost and time is required to ensure adequate follow-up of cohort members and to update measures of exposures and confounders, as well as monitoring participants’ health outcomes
    • Failure to collect outcome data for all members of the cohort will affect the validity of study results.

    Potential sources of bias in cohort studies

    • A major source of potential bias in cohort studies is due to losses to follow up.
    • Cohort members may die, migrate, change jobs or refuse to continue to participate in the study.
    • losses to follow up may be related to the exposure, outcome or both
      • individuals who develop the outcome may be less likely to continue to participate in the study
      • The degree to which losses to follow up are correlated with either exposure or outcome can lead to serious bias in the measurement of effect of exposure and outcome
      • Another major source of potential bias in cohort studies arises from the degree of accuracy with which subjects have been classified with respect to their exposure or disease status
      • Differential misclassification, when one group of participants is more likely to have been misclassified than the other, can lead to an over- or underestimate of the effect between exposure and outcome

    Analysis of cohort studies

    • Analysis of a cohort study uses the ratio of either the risk or rate of disease in the exposed cohort, compared with the rate or risk in the unexposed cohort
    • If follow-up times differ markedly between participants, a rate may be more appropriate
    • The risk ratio uses as a denominator the entire group recruited at the start of the study while the rate ratio uses as a denominator the person years which takes account of losses to follow-up

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    The relative risk of 15 indicates that the risk of cancer of the pancreas is 15 times higher among smokers than non-smokers.

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    The attributable risk of cancer of the pancreas due to smoking is 1.4 cases per 1000 per year

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    This can be interpreted as: smoking accounts for 93% of all cases of cancer of the pancreas among smokers.

    Strengths

    • Multiple outcomes can be measured for any one exposure.
    • Can look at multiple exposures.
    • Exposure is measured before the onset of disease (in prospective cohort studies).
    • Good for measuring rare exposures, for example among different occupations.
    • Demonstrate direction of causality.
    • Can measure incidence and prevalence. 

    Weaknesses

    • Costly and time consuming.
    • Prone to bias due to loss to follow-up.
    • Knowledge of exposure status may bias classification of the outcome.
    • Being in the study may alter participant’s behaviour (Hawethorn, Healthy Workers)
    • Poor choice for the study of a rare disease.
    • Classification of individuals (exposure or outcome status) can be affected by changes in diagnostic procedures.

    Examples of cohort studies

    • One of the most famous examples of a cohort study is Sir Richard Doll’s study of the hazards of cigarette smoking in a cohort of nearly 35,000 British doctors. Baseline information about their smoking habits was obtained in 1951, and periodically thereafter. Cause-specific mortality was then monitored for 50 years and the results showed an excess mortality associated with smoking, chiefly due to vascular, neoplastic, and respiratory diseases.
    • Other cohorts include the Framingham cardiovascular studies, following people living in the town of Framingham, Massachusetts. Much of our current knowledge about heart disease, such as the effects of diet, exercise, and common medications such as aspirin, is based on this longitudinal cohort study. In the UK, the Whitehall studies have followed cohorts of British civil servants, demonstrating how groups in the cohort with differing levels of a characteristic such as cholesterol subsequently have different rates of ischaemic heart disease.
    • Cohort studies are also useful to study what happens if something unusual happens – a rare exposure – to a group of people. The most famous example of this is the cohort of people who were survived the atomic bomb explosions at Hiroshima and Nagasaki.
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    Cian O'Brien

    Cian O'Brien

    Cian is an Irish trained Emergency Medical Technician, Registered General Nurse and holds a Masters degree in Public Health from University College Cork, Ireland. His research interests include prehospital care and marathon medicine.

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