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10 marks
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I now have all the material needed. Here is a complete, exam-ready 10-mark answer on Cohort Study:
Cohort Study (10 Marks)
(Source: Park's Textbook of Preventive and Social Medicine)
a) Study Design
A cohort study is an observational analytical epidemiological study in which a group of exposed and a group of non-exposed individuals (free from the disease under study) are identified and followed over a period of time to determine and compare the frequency (incidence) of disease in both groups.
Distinguishing features:
- The cohorts are identified before the appearance of the disease under investigation
- Study groups are observed over time to determine frequency of disease
- The study proceeds forward from cause to effect (unlike case-control which goes from effect to cause)
Other names: Prospective study, longitudinal study, incidence study, forward-looking study
Framework (2×2 table):
| Cohort | Disease: Yes | Disease: No | Total |
|---|---|---|---|
| Exposed to factor | a | b | a+b |
| Not exposed to factor | c | d | c+d |
- Incidence in exposed = a/(a+b)
- Incidence in unexposed = c/(c+d)
- Relative Risk (RR) = [a/(a+b)] / [c/(c+d)]
If incidence in exposed is significantly higher than in unexposed, it suggests an association between the suspected cause and the disease.
Key assembly criteria:
- Both cohorts must be free from the disease under study at the start
- Both groups should be equally susceptible and comparable for all confounding variables
- Diagnostic and eligibility criteria must be defined beforehand
- Both groups followed under identical conditions
Classic example: Doll & Hill's study (1951) on British doctors - smoking and lung cancer (a prospective cohort study)
b) Steps (Elements) of a Cohort Study
Step 1 - Selection of Study Subjects
Subjects are assembled from:
- General population (e.g., Framingham Heart Study) - when exposure is common
- Special groups - professional groups (doctors, nurses), insured persons, military veterans (e.g., Dorn's study on 2,93,658 veterans)
- Exposure groups - workers in high-risk industries, radiologists exposed to X-rays (when exposure is rare)
Step 2 - Obtaining Data on Exposure
Information collected via:
- Personal interviews or mailed questionnaires (e.g., Doll & Hill used questionnaires for smoking history)
- Review of medical records (dose of radiation, surgical details)
- Medical examination / special tests (blood pressure, serum cholesterol, ECG)
- Environmental surveys (exposure levels in workplace/environment)
Step 3 - Selection of Comparison Groups
- The comparison (control) cohort = persons not exposed to the suspected factor
- May be drawn from the general population, or another cohort with minimal exposure
- Must be comparable to the study cohort in all other respects
Step 4 - Follow-up
- Both cohorts followed over time (may be 20-30 years for cancer studies)
- All new cases (incidence) in both exposed and unexposed groups are recorded
- Loss to follow-up must be minimized
- Outcome may be disease onset, disability, or death
Step 5 - Analysis
- Calculate incidence rates in both groups
- Calculate Relative Risk (RR) = ratio of incidence in exposed to incidence in unexposed
- Calculate attributable risk and dose-response relationships
c) Pros and Cons
Advantages (Pros)
| # | Advantage |
|---|---|
| 1 | Incidence can be calculated directly from cohort data |
| 2 | Multiple outcomes can be studied simultaneously (e.g., smoking cohort studies revealed associations with lung cancer, CHD, peptic ulcer, oesophageal cancer) |
| 3 | Provides a direct estimate of Relative Risk (RR) |
| 4 | Dose-response relationships can be calculated |
| 5 | Temporal sequence is established (exposure precedes disease) - stronger causal inference |
| 6 | Bias is minimized - comparison groups formed before disease develops, so misclassification of exposed/unexposed is reduced |
| 7 | Considered the most reliable observational study design for showing causal association |
Disadvantages (Cons)
| # | Disadvantage |
|---|---|
| 1 | Involves large numbers of people - unsuitable for rare diseases or diseases with low incidence |
| 2 | Long time to complete (20-30 years for cancer) - results delayed; investigators may die or participants change classification |
| 3 | Administrative problems - loss of staff, loss of funding, extensive record-keeping |
| 4 | Loss to follow-up is substantial - participants may migrate, lose interest, or refuse to provide information |
| 5 | Selection of representative comparison groups is difficult |
| 6 | Diagnostic criteria may change over prolonged follow-up; difficult to incorporate new tests into an established protocol |
| 7 | Expensive to conduct |
| 8 | Hawthorne effect - study itself may alter behaviour (e.g., subjects may quit smoking, altering exposure status) |
| 9 | Ethical problems - as evidence of harm accumulates, investigators are obliged to intervene |
| 10 | Limited to studying a finite number of factors related to disease outcome |
Summary: The cohort study is the gold standard among observational designs because it clearly establishes the temporal relationship between exposure and disease and allows direct calculation of incidence and relative risk. Its main limitations are cost, time, and unsuitability for rare diseases. - Park's Textbook of Preventive and Social Medicine
Dose response relationship means
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Here is a clear explanation:
Dose-Response Relationship
Definition:
A dose-response relationship means that as the level (dose/amount/duration) of exposure to a risk factor increases, the incidence (frequency) of the disease also increases proportionally. It shows a biological gradient between cause and effect.
In epidemiology, it is formally defined as:
"The amount of the cause needed to lead to a stated incidence of the effect."
- Park's Textbook of Preventive and Social Medicine
In the Context of a Cohort Study
In a cohort study, since you follow people with different levels of exposure, you can calculate incidence at each exposure level. This lets you directly measure whether more exposure = more disease.
Classic Example - Smoking and Lung Cancer
This is described as "one of the most impressive demonstrations of a dose-response relationship in all of epidemiology":
| Cigarettes smoked/day | Death rate per 1000 | Relative Risk |
|---|---|---|
| Non-smoker | 0.07 | 1.0 (baseline) |
| 1-14 | 0.47 | 6.7x |
| 15-24 | 0.86 | 12.3x |
| 25+ | 1.66 | 23.7x |
As the dose (number of cigarettes) goes up, the risk of lung cancer goes up - that is a dose-response relationship.
Why It Matters
- It is one of Bradford Hill's criteria for causation - if a dose-response relationship exists, it strongly supports that the association is truly causal (not just coincidence)
- Conversely, if no dose-response relationship is found, that is an argument against the relationship being causal
- In the smoking-lung cancer example: the fact that moderate smokers had death rates intermediate between light and heavy smokers was a key piece of evidence cementing the causal link
Simple Way to Remember
More dose → More disease = Dose-response relationship
It is like saying: the more you smoke, the higher your chance of lung cancer. The more radiation you're exposed to, the higher the cancer risk. The relationship goes in one direction and is proportional.
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