1. From association to causation

2. Objectives of lecture:
• what is the cause
concept of Association and Causation
• types of association
• does association implies causation
• what are the types of causal factors
What are the Hill’s criteria for causation.

3. - Purpose of Epidemiology? - Basic Question in Analytic Epidemiology
Exposure
Disease

4. Research Questions/Hypotheses
Is there an association between Exposure (E) & Disease (D)?
Hypothesis: Do persons with exposure have higher levels of disease than persons without exposure?

5. Incidence Ratio per 100,000 Women Per Capita Supply of Fat Calories
Figure Correlation between dietary fat intake and breast cancer by country.
USA
250
Switzerland
Canada
Fed. Repub.
Of Germany
200
Italy
Israel UK
Denmark
Sweden
France
New Zealand
Australia
150
Norway
Finland
Yugoslavia
Spain
Incidence Ratio per 100,000 Women
Poland
100
Romania
Hong Kong
Hungary 50
Japan
Per Capita Supply of Fat Calories
600
800
1000
1200
1400
1600
Prentice RL, Kakar F, Hursting S, et al: Aspects of the rationale for the Women’s Health Trial. J Natl Cancer Inst 80: , 1988.)

6. DOES CORRELATION IMPLY CAUSATION?
Correlation: it indicates degree of association.
Association……
Do both invoke causation?

7. Types of Association:
A real association (causal) is present if we succeed in reducing the risk of disease by lowering the exposure level.
Spurious associations refer to non-causal associations due to chance, bias, failure to control (confounding), etc. e.g;
Neonatal deaths at Home vs hosp delivery,

8. Interpreting Associations - Causal and Non-Causal
Non-Causal (due to confounding)
Causal
Characteristic Under Study
Characteristic Under Study
Factor X
Disease
Disease

9. Types of real association:
A) Negative Association (Inverse Relationship):
higher levels of the risk factor are protective against the outcome.
e.g. regular physical exercise and ischemic heart diseases
B) Positive Association:
The magnitudes of both variables appear to move together up or down.
e.g. hypertension and ischemic heart diseases

10. Pyramid of Associations
Causal
Non-causal
Confounded
Spurious / artefact
Chance

11. Association :
the rate of disease in a persons with a specific exposure.
statistical dependence between two variables,
• • A causal association is one in which a change in the
frequency or quality of the exposure results in a corresponding change in the frequency of the disease or outcome of interest.

12. What is a Cause? Causative factor:
An event, condition, or characteristic without which the disease would not have occurred.
Causative factor:

13. One pragmatic definition of a cause of a disease is an exposure which produces a regular and predictable change in the risk of the disease.

14. A good epidemiologic exposure variable should….
Have an impact on health
Be measureable
Differentiate populations
Generate testable hypotheses
Help to prevent or control disease

15. ASSOCIATION VS CAUSATION
To decide whether exposure A causes disease B,
we must first find out whether the two variables are associated?

16. Clinical observations
Available data
(Ecological or Cross-sectional Studies)
Case-control studies
Cohort studies
Randomized trials
(only used to confirm causation

17. Ecologic study of per capita smoking and lung cancer incidence
Surgeon Alton Ochsner observed that almost all lung cancer patients were smokers
Ecologic study of per capita smoking and lung cancer incidence
Case-control study of lung cancer patients versus those without lung cancer
39 cohort studies ?

18. Types of causal pathway:
1. Direct causation (with out any intermediate step).
2. Indirect causation (a factor causes a disease, but only through an intermediate steps).
- Which one is always present in human biology?
disease
factor
Step 1
Step 2
factor
disease

19. Causal Factors in general:
Necessary cause: The cause must be present for the outcome to happen.
Sufficient cause: If the cause is present the outcome must occur.
Rare or Common?
Isolation/inclusion

20. Types of causal factors
1. Necessary and sufficient
Without factor, disease does not develop
Its presence is sufficient to initiate disease

21. 2. ‘necessary but not sufficient’:
causative factor, and enabling factors required
Each factor is necessary but not sufficient to cause the disease.
e.g: initiator + promoter
Factor A
Disease
Factor B
Factor C
Carcinogenesis

22. 3. “Sufficient but not necessary’:
the factor alone can produce the disease, but so can other factors that are acting alone.
Factor A OR
Factor B
DISEASE OR
Factor C

23. 4.Neither sufficient nor necessary
Factor A
Factor B OR
Factor C
Factor D
DISEASE OR
Factor E
Factor F

24. CAUSAL INFERENCE 1. DETERMINISTIC CAUSALITY:
as in necessary and sufficient causes.
AR%= ?
2. PROBABILISTIC CAUSALITY :
the web of causation, or chain of causation
Characteristics??

25. Web of Causation Disease social organization phenotype behaviour
microbes
Disease
genes
environment
Unknown factors
workplace

26. genetic susceptibility
Web of Causation - CHD
stress
medications
genetic susceptibility
smoking
lipids
CHD
gender
physical activity
Unknown factors
inflammation
blood pressure

27. An attempt to discover WHICH THE CAUSE, OR whether TWO VARIABLES are associated, WE NEED:
EPIDEMIOLOGICAL STUDIES, to??
Statistics, to?
Does Statistical analysis alone can constitute proof of a causal relationship? NO

28. MAKING CAUSAL INFERENCES
The use of Hill’s causal criteria in making inferences from data.

29. Hill’s criteria for judging whether an association is causal
Consideration of alternate explanations
Reversibility of exposure
Specificity of the association
Study design
Temporal relationship
Strength of association
Dose response relationship
Consistency of the findings
Biologic plausibility and Coherence with other knowledge

30. Temporal Sequence It means? The length of exposure is also important,
Which study designs?

31. STRENGTH Is the association strong? Heavy smoking with lung cancer,
Heavy smoking with CHD.
The stronger the association the more likely it is to be truly causal.
WHY??
small associations can be causal?

32. dose-response relationship
relative risk increases with higher exposure dose.
Years of exposure?
Not necessary. Why? … threshold effect… unascertained exposure level … irrelevant to all exposures.

33. Consistency of Findings of Effect
Does mean exact replication?
Look for consistent findings
across different populations
in differing circumstances
with different study designs
Relationships that are demonstrated in multiple studies are more likely to be causal.
Why?
Smoking with lung cancer.
contraceptives and breast cancer.

34. Plausibility and Coherence (ie., biologic credibility)
• existence of biologic or social mechanistic model to explain the association
e.g. Trinitrate → headache
Fitness .
• does it a provision?

35. EXAMPLE:
Serological marker of hepatitis B infection is associated with elevated rates of liver cancer.
Is supported by viral genome in many liver cancers.
So… Hepatitis B infection is a true cause of liver cancer

36. By contrast,
Reserpine was thought to be a cause of breast cancer.
But, no supporting biological mechanism.
Subsequent larger studies (no consistancy).
So .. Causation not true.

37. Consideration of alternate explanations
- review of literatures for analogue.
- depend on depth of knowledge.

38. Reversibility (cessation of exposure) :
Removal of the exposure leads to decreased risk of outcome.
e.g. . stop smoking leads to decreased risk of CHD.
HOWEVER, in certain cases, the damage may be irreversible.

39. Specificity of the Association
Is it a Provision?
- H. Pylori?
- smoking & lung cancer?

40. Study design : All of the study designs are important.
The best evidence comes from?.
Example: Smoking cessation programs result in lower lung cancer rates.

41. To put the studies in order according to power:
Ability to " prove causation
Type of study
Strong
Randomized controlled trial
strong
Other intervention study
Moderate
prospective cohort
moderate
Retrospective cohort
Case-control
Poor
Cross-sectional study
Ecological
Very Poor
Parametric study

42. Judging Causality Weigh quality Weigh weaknesses of science and
results of causal
models
Weigh weaknesses
in data and other
explanations

43. Framework for the Interpretation of an Epidemiologic Study I
Framework for the Interpretation of an Epidemiologic Study I. Is there a valid statistical association? • Due to chance? • Due to bias? • Due to confounding? II. Can this valid statistical association be judged to be one of cause and effect? • Is there a strong association? • Is there consistency with other studies? • Is the time sequence compatible? • Is there biologic credibility to the hypothesis? • Is there evidence of a dose-response relationship?

44. Why was it relatively easy to determine that smoking was a cause of lung cancer?
Measurable.
Used to classify persons accordingly.
Lung cancer incidence in smokers is much greater than in nonsmokers.

45. Why will it be relatively hard to determine if community air pollution is a cause of lung cancer?