Introduction to Cohort Studies
Facilitator
G. Kwesigabo
Department of Epidemiology and Biostatistics,
SPHSS, MUHAS
Session objectives
At the end of this session, the participants
should be able to:
• Know the history and understand the meaning and
important elements of cohort studies
• Comprehend the key issues in the design of cohort
studies including;
–
–
–
–
•
Assembling the study populations,
Ascertainment of exposures,
Ascertainment of outcomes,
Data analysis and assessing associations between the
exposure and disease/outcomes.
Discuss the strength and limitations of cohort
studies.
Cohort Studies
• One of the observational studies that
investigates the relation between exposures
and outcomes (diseases)
• Through implementation of studies that
answer a specific research question regarding
risk factors, it is possible to identify risk and
protective factors and eventually causes of
diseases.
What is a cohort?
• Cohort: Latin word for 1 of the 10 divisions of a
Roman legion (1/10 of a legion)
In research methods:
• A group of individuals
– Sharing a common experience
– Followed-up for a specified period of time
• Examples
– Birth cohort
– Workers at a chemical plant
– 2020/2021 students at the School of Law UDSM
Design Principle
• Individuals / subjects are defined on the basis
of presence or absence of exposure to a
suspected risk factor for the outcome / disease
being studied
• At the time exposure status is defined, all
potential subjects must be free from the
disease/outcome under investigation
• All eligible participants are then followed over
a period of time to assess the occurrence of
that outcome
• The Incidence of outcome in the exposed group
is compared to that in the unexposed group to
determine the risk of being exposed
Cohort study
Population
at risk
Exposed
Disease among
exposed?
Usually prospective
and
Not Exposed
Disease among
non-exposed?
Controls
Exposure
Types of cohort studies
Exposed
Retrospective cohort study
Prospective cohort study
Unexposed
Determine
PAST
Incidence
NOW
Ambidirectional
FUTURE
Types of cohort studies
• Classified as either prospective or
retrospective depending on the temporal
relationship between initiation of the study
and occurrence of disease or outcome
• Retrospective: Both exposure and
outcome have already
occurred
Retrospective Cohort Study
Exposure
occurrence
Disease
occurrence
Study
starts
ill
+ +
exp
-
Selection based
on exposure
+
exp
rétrospectives assessment
of disease
Chernobyl, Industrial accidents, Flood victims
Prospective:
Exposure is present but not the
outcome Thus after the selection of the
cohort, participants are followed into
the future to assess incidence rates of
disease among both groups i.e. the
exposed and unexposed
Prospective Cohort Study
Study
starts
Exposure
occurrence
Disease
occurrence
ill
+
+
exp
exp
Selection of
population
Prospective assessment
of exposure and disease
-
+
-
Time
Ambidirectional:
• Data are collected both retrospectively and
prospectively on the same cohort
• This design is useful for exposures having
short and long term effects eg chemicals that
may increase the risk of birth defects within a
few years of exposure and cancer risk after
one or two decades
• Another example could be exposure to
Poisonous gases or chemicals e.g. in mines,
can study immediate effects eg paralysis by a
retrospective design and follow up the
exposed for long term effects
Selection of an exposed population
Things to consider:
• Common exposures: use a random sample of population
• For rare exposures: use specific occupational groups –
miners, asbestos workers, workers in cotton processing
plants etc
• Need to obtain accurate exposure and follow-up
information from the study participants – lack of accurate
and complete information affects the validity of the study
• Feasibility of follow-up of the population to be studied ?
High loss to follow up
• The cohort selected should assure sufficient number of
outcomes
Comparison group, is necessary
in order to:
Allow the evaluation of whether
the frequency of disease or
outcome in the exposed group is
different from that which would
have been expected based on the
experience of a comparable group
of individuals who are not
exposed to the factor under study
Selection of a comparison group
• The exposed and none exposed group should be as
similar as possible with respect to factors that may be
related to the disease (outcome) except the exposure
under investigation
• So that if there is no association between exposure
and disease, the disease rates in the populations
being compared will essentially be the same
• Also ensure that the information that can be obtained
from the non-exposed group is adequate for
comparison with the exposed population
Issues in the Design of Cohort Studies
Sources of Data
Exposure Information
• Pre-existing records
–
–
–
–
Consider availability for much of cohort
May be Inexpensive
Objective, bias-free categorisation of exposure status
But – insufficient detail and no information on potential
confounders – information may have been kept for
another purpose
• Information from study subjects
– Information of data not routinely collected
– Questionnaires/interviews
– Ascertainment of exposure must be comparable for all
Issues in Design of Cohort Studies
Sources of Data
Outcome Information
• Obtain complete, comparable, unbiased information
• Death certificates (potential bias when cause-specific
mortality)
• Medical records, Medical Aid schemes, etc.
• From study subjects
• Periodic direct medical examinations
Apply equally to exposed and non-exposed
Bias in Cohort studies
1. Loss to follow up (Attrition) can not get in
touch with some study participants
• Failure to ascertain outcome data is the
major source of potential bias
• The longer the follow-up period the more
difficult to ensure complete data
• If lost to follow-up is large (eg, 30-40%) ?
Validity ?
• Loss to follow-up may be differential – more
on one arm compared to the other
How to minimize the loss to follow up (Attrition)
During enrollment
(i) Include populations/subjects that are less
mobile, Exclude subjects likely to be lost i.e
• Planning to move
• Non committal
(ii) Obtain information to allow future tracking:– Collect subjects contact information (email
address, phone numbers, GPS, and mailing
address)
– Identification number and tracking tags.
During follow up
Maintain periodic contact by telephone,
physical visiting etc.(cost implication)
2. Participation bias
Accepting participants may differ from nonparticipants
3. Misclassification bias
• Misclassification due to exposure status is
common (smoker may be reported as non
smoker)
• Can be random (equally for exposed and
unexposed) or non-random
4. Ascertainment bias
• Bias in ascertaining the outcome.
• Outcomes should be ascertained
equally in both the exposed and non
– exposed groups
Measure of effect (effect of exposure)
• In order to establish the association between
exposure and disease, one has to estimate
and compare measures of disease frequency
among the exposed compared to the nonexposed group
• The association is determined by calculating
the Relative Risk of developing the disease
being investigated
Distribution of illness according to
exposure in a cohort study
ILL
Exposed
a
Not exposed
c
NOT ILL
b
Cumulative
Incidence
a+b
a
a+b
Relative risk
d
c+d
Incidence exposed
= _____________________
Incidence not exposed
c
c+d
Cohort study: Exercise and heart disease
No exercise
(exposed)
>Half hr exercise
daily
ILL
NOT ILL
30
10
5
25
Relative Risk (RR) = 30 /40
5 /30
Risk
40
30
40
30
5
30
= 4.5
Cohort study: Exercise and heart disease
ILL
>Half hr
exercise
daily
Exposed
No exercise
NOT ILL
Risk
5
30
25
10
Relative Risk (RR) = 5 /30
30/40
30
40
5
30
30
40
= 0.2
(80% reduction in risk of heart disease)
1-0.2
Incidence density
Events / Person time of observation
Usually expressed in person years of
observation (per 1000 yrs of observation)
Relative risk
Incidence density in the exposed pop
-------------------------------------------------Incidence density in the Non exposed pop
Interpretation of the RR
•
•
•
•
1 Null value
> 1 may be associated with risk
< 1 may be protective
Rule out the possibility of chance by
calculating the 95% CI around the RR
Interpretation of Relative Risk cont..
• The individuals who do not exercise are at 4.5 times at
risk of developing Heart disease compared to those
who exercise.
• Test of significance for the estimated association – 95%
Confidence Interval around the RR. Interpret the
following:
a. 0.4 (0.1-0.8)
b. 1.6 (0.9-1.8)
c. 2.2 (1.5-2.9)
(If the confidence interval includes the Null value of No
Association – implies a statistically non significant
association)
Rate difference
• Rate difference is the difference between the
incidences rates in the exposed and
unexposed groups
RD= Incidence exposed
-
Incidence unexposed
• RD - Measure excess risk of outcome (disease)
attributable to the exposure
Attributable risk percent (AR%)
• Gives proportion of disease
attributable to the exposure
• AR%= (Incidence exposed - Incidence unexposed )
_______________________________
Incidence (exposed)
• Incidence of lung cancer among smokers
70/7000 = 10 per 1000
• Incidence of lung cancer among non-smokers
3/3000 = 1 per thousand
RR = 10 / 1 = 10
(Smokers are at 10 times at risk of developing lung
cancer compared to people who do not smoke)
AR = 10 – 1 / 10 X 100
= 90 %
(90% of the cases of lung cancer among smokers are
attributed to their habit of smoking)
If smoking is eliminated, 90% of the cases will be
eliminated
Advantages of cohort studies:
• Can elucidate temporal relationship between
exposure and disease. Exposure comes before
the disease develops.
• Permits direct measurements of exposure
specific incidence of the disease.
• Allows for evaluation of multiple outcomes of
the same exposure.
• Particularly useful when exposure is rare.
• Less prone to selection bias for prospective
cohort studies.
Limitations of cohort studies:
• Expensive and time consuming and
therefore takes a long time before
completion.
• Liable to attrition or loss to follow-up
among subjects.
• Inefficient for rare diseases, need for huge
sample sizes
• Not suitable for diseases with long latency
period such as most cancers.