1. Association & Causation

2. Framework Definitions Introduction
Historical theories of causation of disease
Current concepts
Factors in causation
From association to causation
How to establish the cause of a disease?
Analytical approach
Modern concept of causation

3. Definitions
Association: define as occurrence of two variable more often than would be expected by chance
Causal association: when cause and effect relation is seen.

4. Historical Theories “Supernatural causes”& Karma
Theory of humors (humor means fluid)
The miasmatic theory of disease
Theory of contagion
Germ theory
Koch’s postulates

5. Koch’s postulates
The organism must be present in every case of the disease;
The organism must be able to be isolated and grown in pure culture;
The organism must, when inoculated into a susceptible animal, cause the specific disease;
The organism must then be recovered from the animal and identified.

6. Limitations of Koch postulate
Non communicable disease
One to one relation are rare biology.
Disease production may require co cofactors.
Always it is not possible to isolate organism from disease person
Viruses cannot be cultured like bacteria because viruses need living cells in which to grow.
Always infection does not produce disease
Pathogenic microbes may be present without clinical disease (sub sub-clinical infections, carrier states).

7. Single or Multiple cause?
One to one association Epidemiological triad Sufficient & Necessary cause
(Specificity) Multi factorial causation
Web of causation
Interaction

8. Epidemiological triad

9. Epidemiological triad
Agent Factors
Physical Agents
Chemical Agents
Biological Agents
Nutritional agents
Agent Factors
Physical Agents : (as heat, cold, vibrations, electricity,
mechanical forces etc.).
Chemical Agents : (as acids, alkalies, heavy metals, allergens,
etc.).
Biological Agents : (as viruses, bacteria, parasites, etc.).
Nutritional agents : These are truly a part of chemical agents
but often described as a separate category because of the
Host Factors
Socio-demographic Factors : Such as age, sex, occupation,
education, marital status, etc.
Psycho-social Factors : Such as attitudes, practices,
behavioural patterns, life style, etc.
Intrinsic Characteristics : Intrinsic Characteristics or the
“biological, Immunological and Genetic factors” in a human
being - e.g. genetic factors, HLA types, biochemical and
physiological characteristics, etc.
Environmental Factors
Physical Environment : Such as seasons, climate, altitude,
rainfall, etc.
Biological Environment : e.g. arthropod vectors of diseases
like mosquitoes, animal reservoirs like canines and rodents,
etc.
Social Environment : e.g. community attitudes, beliefs,
practices and cultural factors affecting disease; level of socioeconomic
development; availability of health services, etc.
Environmental Factors
Physical Environment
Biological Environment
Social Environment
Host Factors
Socio-demographic Factors
Psycho-social Factors
Intrinsic Characteristics

10. From association to causation
Spurious association
B. Indirect association
C. Direct (Causal) association
1. One –to- one causal association
2. Multifactorial causation
Sufficient & necessary cause
Web of causation (Interaction)

11. Spurious association Not real
e.g. More perinatal deaths in hospital delivery than home delivery.
The cause of spurious association is poor control of Biases in study.

12. Direct Vs indirect cause
F508 Polymorphism
High cholesterol
Artery thickening
Hemostatic factors
Myocardial infarction
Cystic Fibrosis

13. Indirect association:
Statistical association due to presence of another factor, known or unknown that is common both the characteristics & disease i.e. Confounding factors.
Example
Smoking
Pancreatic cancer
e.g. 1. Altitude & endemic goiter
2. Sucrose & CHD
Coffee drinking

14. Direct ( Causal) association
One –to- one causal association
2. Multifactorial causation
Sufficient & necessary cause
Web of causation (Interaction)

15. One-to-one causal association
A causing B
e.g. Measles
Critics
Streptococal tonsilitis
Scarlet fever
Erysipelas
Haemolytic Streptococci

16. ii) Multifactorial causation
Multiple factor leads to the diseases
Common in non-communicable diseases
e.g.
Smoking
Air pollution Reaction at cellular level Lung cancer
Exposure to asbestos

17. b. Interaction of multiple individual causes
Smoking +
Air pollution Reaction at cellular level Lung cancer
Exposure to asbestos
Table 1: Age-standardized lung cancer death rates (per population) in
relation to tobacco use and occupational exposure to asbestos dust

18. Web of causation Change in life style Stress
Abundance of food Smoking Emotional Aging & D Disturbance other factor
Obesity Lack of physical activity Hypertension
Hyperlidemia Increase catacholamine Changes in walls of arteries
thrombotic activity
Coronory atherosclerosis
Coronary occlusion
Myocardial Infarction

19. INTERACTION Positive: Synergistic, Transadditivity,
Negative: Antagonist,
Subadditivity
Disease
Factor A +
Factor B - ?
I- +
I+ -
I- -

20. I11 I10 I01 I00 No interaction INTERACTION I11= I01 I11=I10
0 – Factor absent
1 – Factor present
Lung cancer
Asbestos
I11
I10
I01
I00
I11= I01
I11=I10
No interaction 1
Smoking

21. Sufficient & necessary cause
Necessary cause is without this disease/outcome never develops.
Sufficient cause: presence of this factor disease always develops.
Component cause: Supporting causes, per se they can not develop ds
Necessary causes + Component causes = Sufficient cause
Necessary cause: necessary is factor without this disease/outcome never develops.
Each sufficient cause has a necessary cause as a component.
Sufficient cause: A cause is termed sufficient when it inevitably produces or initiates an outcome, that is in the presence of this factor disease always develops.

22. Sufficient & necessary cause
U B
C N
Known components (causes) – A, B, C, N
Unknown component (cause)- U
N – Necessary cause
Known components + Unknown component cause + Necessary cause = Sufficient cause

23. Figure 1: Causes of tuberculosis
Susceptible host
Infection
Tubercu-losis

24. There may be number of sufficient causes for single disease in various combination of component causes, necessary causes A
U B
E N A
U D
C N A
U B
C N U
A B U
A B U
A E
Disease

25. How to establish the cause of a disease?
OBSERVED ASSOCIATION?
Could it be due to selection or measurement bias? No
Could it be due to confounding? No
Could it be a result of chance?
Probably not
Could it be causal?
Apply guidelines and make judgment

26. Appling guidelines (Hills criteria/Guidelines for causation) and making judgment regarding causation
Temporal relation
Does the cause precede the effect? (essential)
Plausibility
Is the association consistent with other knowledge? (mechanism of action; evidence from experimental animals)
Consistency
Have similar results been shown in other studies?
Strength
What is the strength of the association between the cause and the effect? (relative risk)
Dose–response relationship
Is increased exposure to the possible cause associated with increased effect?
Reversibility
Does the removal of a possible cause lead to reduction of disease risk?
Study design
Is the evidence based on a strong study design?
Judging the evidence
How many lines of evidence lead to the conclusion?

27. 1.Temporal relationship (Relationship with time)
Cause must precede the effect. (Essential)
Which is cart & Which hourse?
Drinking contaminated water occurrence of diarrhea
However many chronic cases, because of insidious onset and ignorance of precise induction period, it become hard to establish a temporal sequence as which comes first -the suspected agent or disease.

28. 2. . Plausibility ( Biological plausibility)
Consistent with biological knowledge of day
Smoking causing lung cancer
Smoking causes skin cancer?
Lack of plausibility may simply reflect lack of scientific knowledge

29. 3. Consistency of association
Different persons, in Different places, in Different circumstances & times by Different method (by various types studies) is established the Same result by several studies.
Cigarette smoking and lung cancer. More than 50 retrospective studies and at least nine prospective studies

30. Meta-analysis of the relative risk of cleft palate in the offspring of mothers who smoked during pregnancy compared with the offspring of mothers who did not smoke

31. 4 . Strength of association
Relative risks/Odds ratio greater than 2 can be considered strong
Risk ratio
Interpretation
< 1
Protective
No association
Weak Causal association
moderate causal association
>2.6
Strong causal association

32. 5. Dose – response relationship ( The Biological gradient )
Death rates from lung cancer (per 1000) by number of cigarettes smoked, British male doctors, –1961

33. 6. Specificity One to one association Critics
Haemolytic Streptococal tonsilitis
Streptococci Scarlet fever
Erysipelas

34. 7. Reversibility
Fig 7: Stopping works: cumulative risk of lung cancer mortality
Critics
eg Infection of HIV/ AIDS

35. 8. Study design
Relative ability of different types of study to “prove” causation

36. 9. Analogy (= Similarity, = reasoning from parallel cases)
Judging by analogy
known effect of drug thalidomide & rubella in pregnancy
accepting slighter but similar evidence with another drug or another viral disease

37. 10. Coherence of association & Judging the evidence
Based on available evidence or should be coherence with known facts that are thought to be relevant: uncertainty always remains
Correct temporal relationship is essential; then greatest weight may be given to plausibility, consistency and the dose–response relationship. The likelihood of a causal association is heightened when many different types of evidence lead to the same conclusion.

38. Critics on Hill’s guideline on causation
Criteria Vs Guidelines Vs consideration
Except for temporality, none of the Hill’s criteria is absolute for establishing a causal relation

39. Measures of association /strength of association
Analytical Methods
Measures of association /strength of association
Testing hypothesis of association
Controlling confounders

40. Measures of association / strength of association
1. Ratio measures
- Relative risk
- Odds ratio
2. Difference measures
-Attributable risk
-Population Attributable risk

41. Testing hypothesis of association
Null Hypothesis
Rejecting Accepted
Causal association Not causal association

42. Controlling confounders
At time designing of epidemiological study or while carrying study
Randomization
Restriction
Matching
At analysis stage
Stratification
Adjustment
Statistical modeling

43. Modern concepts in causation
Counterfactual Model
Causal diagram

44. Counterfactual model (Potential outcome model)
When we are interested to measure effect of a particular cause, we measure
effect in a population who are exposed
Imagine amount of effect which would have been observed, if the same population would not have been exposed to that cause, all other conditions remaining identical.
We calculate risk ratios & risk differences based on this model
The difference of the two effect measures is the effect due the cause we are interested in.

45. Example
Will smoking ban decrease the rate of lung cancer in 10 yrs beyond what can be expected in absence of ban?

46. R11 R10 R01 R00 R11-R10 R11/R10 R01-R00 R01/R00 Risk difference
1 – Factor present/ Ds present
0 – Factor absent /Ds absent
Disease
R11
R10
R01
R00
Risk difference
Risk ratio
R11-R10
R11/R10
R01-R00
R01/R00 1
Exp
Risk difference
R11-R01
R10-R00
Risk ratio
R11/R00
R01/R00

47. Causal Diagram
Confounding is complex phenomenon – Need to understand relation
Useful for analysis of confounders
Conceptual definition of variable involved
Directionality of causal association
Need some level of understanding (Knowledge & hypothetical) – relation between risk factor, confounders & outcome.
Directed Acyclic Graph (DAG)

48. Causal Diagram X
Y Z U

49. ? EXAMPLE Collider ASPIRIN PLATELET AGGREGATION CHD GENETIC VARIENT
Backdoor path

50. X Y Z U Causal Assumption Independency Marginal association
Conditional association
X & Y are each direct cause of Y
(Direct with respect to other variable in Diagram)
X & Y are independent (only path between them is blocked by the collider)
X & Y are associated
X & U are associated conditional on Y
(Conditional on a collider unblocks the path)
Y is direct cause of Z
X & Y are independent conditioned on Y (Conditioning on Y blocks the path between X & Z)
U & Y are associated
X & U are associated conditional on Z (Z is a descendent of collider)
X is not a direct cause of Z, but X is an indirect cause of Z via Y
U & Z are independent conditional on Y
Y & Z are associated
No 2 variable in diagram(X,U, Y, Z) share prior cause not shown in the diagram eg. No variable causes both X & Y, or both X & U
X & Z are associated
U & Z are associated

51. References :
Hill AB. The environment and disease: association or causation? Proc R Soc Med
1965;58:
Hill AB. Bradford Hill’s Principle of Medical statistics. Ed first Indian addition New
Delhi: B. I. Publication pvt limited.
Detels R, McEwen J, Beaglhole R, Tanaka H. Oxford textbook of public health. 4th ed.
New York: Oxford university press; 2004.
Beaglehole R, Bonita R. Basic epidemiology. Delhi: AITBS publisher & distributor;
2006.
Park K. Park’s textbook of preventive & social medicine. 19th ed. Jabalpur: M/s
Bhanarsidas Bhanot publishers; 2007.
Galea S, Riddle M, Kaplan GA. Causal thinking & complex system approach in
epidemiology. International journal of epidemiology Feb; 39(1):
Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. New Delhi:
Wolter kluwar (India) pvt; 2009.