Association & Causation
Framework
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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
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.
Historical Theories
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“Supernatural causes”& Karma
Theory of humors (humor means fluid)
The miasmatic theory of disease
Theory of contagion
Germ theory
Koch’s postulates
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.
Limitations of Koch postulate
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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).
Single or Multiple cause?
One to one association
(Specificity)
Epidemiological triad
Sufficient & Necessary cause
Multi factorial causation
Web of causation
Interaction
Epidemiological triad
Epidemiological triad
Agent Factors
Physical Agents
Chemical Agents
Biological Agents
Nutritional agents
Environmental Factors
Physical Environment
Biological Environment
Social Environment
Host Factors
Socio-demographic Factors
Psycho-social Factors
Intrinsic Characteristics
From association to causation
A. 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)
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.
Direct Vs indirect cause
Direct
F508 Polymorphism
Indirect
High cholesterol
Artery thickening
Hemostatic factors
Cystic Fibrosis
Myocardial infarction
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
Coffee drinking
Pancreatic cancer
e.g. 1. Altitude & endemic goiter
2. Sucrose & CHD
Direct ( Causal) association
1. One –to- one causal association
2. Multifactorial causation
Sufficient & necessary cause
Web of causation (Interaction)
One-to-one causal
association
• A causing B
• e.g. Measles
• Critics
Haemolytic
Streptococci
Streptococal tonsilitis
Scarlet fever
Erysipelas
ii) Multifactorial causation
• Multiple factor leads to the diseases
• Common in non-communicable diseases
e.g.
Smoking
Air pollution
Exposure to asbestos
Reaction at cellular level
Lung cancer
b. Interaction of multiple individual
causes
Smoking
+
Air pollution
Reaction at cellular level
+
Exposure to asbestos
Lung cancer
Table 1: Age-standardized lung cancer death rates (per 100 000 population) in
relation to tobacco use and occupational exposure to asbestos dust
Web of causation
Change in life style
Abundance of food
D
Obesity
Stress
Smoking
Lack of physical activity
Hyperlidemia
Emotional
Disturbance
Aging &
other factor
Hypertension
Increase catacholamine
Changes in walls of
arteries
thrombotic activity
Coronory atherosclerosis
Coronary occlusion
Myocardial Infarction
Myocardial Infarction
INTERACTION
Positive: Synergistic,
Transadditivity,
Negative: Antagonist,
Subadditivity
Disease
Factor A
+
+
Factor B
-
?
I+ -
-
I- +
I- -
INTERACTION
0 – Factor absent
1 – Factor present
Lung cancer
Asbestos
1
1
Smoking
0
0
I11
I01
I10
I11= I01
I11=I10
I00
No
interaction
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
Sufficient & necessary cause
Known components (causes) – A, B, C, N
A
U
B
C
N
Unknown component (cause)- U
N – Necessary cause
Known components + Unknown component
cause + Necessary cause = Sufficient cause
Figure 1: Causes of tuberculosis
Susceptible
host
Infection
Tubercu
-losis
There may be number of sufficient causes for
single disease in various combination of
component causes, necessary causes
A
U
B
E
U
A
A
N
B
A
U
D
C
U
A
N
E
Disease
U
U
AN
B
C
B
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
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
Is increased exposure to the possible cause associated
relationship
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 How many lines of evidence lead to the conclusion?
evidence
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.
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
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
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
4 . Strength of association
• Relative risks/Odds ratio greater than 2 can be
considered strong
Risk ratio
<1
0.9-1.1
1.2- 1.6
1.7- 2.5
>2.6
Interpretation
Protective
No association
Weak Causal association
moderate causal association
Strong causal association
5. Dose – response relationship
( The Biological gradient )
Death rates from lung cancer (per 1000) by number of cigarettes
smoked, British male doctors, 1951 –1961
6. Specificity
• One to one association
• Critics
Haemolytic
Streptococci
Streptococal tonsilitis
Scarlet fever
Erysipelas
7. Reversibility
• Fig 7: Stopping works: cumulative risk of lung cancer mortality
Critics
• eg Infection of HIV/ AIDS
8. Study design
• Relative ability of different types of study to “prove”
causation
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
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.
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
Analytical Methods
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Measures of association /strength of association
• Testing hypothesis of association
• Controlling confounders
Measures of association / strength of association
1. Ratio measures
- Relative risk
- Odds ratio
2. Difference measures
-Attributable risk
-Population Attributable risk
Testing hypothesis of association
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Null Hypothesis
Rejecting
Causal association
Accepted
Not causal association
Controlling confounders
At time designing of epidemiological study or while carrying study
• Randomization
• Restriction
• Matching
At analysis stage
•Stratification
•Adjustment
•Statistical modeling
Modern concepts in causation
• Counterfactual Model
• Causal diagram
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.
Example
• Will smoking ban decrease the rate of lung
cancer in 10 yrs beyond what can be
expected in absence of ban?
1 – Factor present/ Ds present
0 – Factor absent /Ds absent
Disease
1
1
Exp
0
0
R11
R10
R01
Risk
difference
Risk ratio
R00
R11-R01 R10-R00
R11/R00
R01/R00
Risk
difference
Risk ratio
R11-R10
R01-R00
R11/R10
R01/R00
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)
Causal Diagram
X
Y
U
Z
Collider
EXAMPLE
ASPIRIN
PLATELET
AGGREGATION
?
CHD
Backdoor path
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
References :
•Hill AB. The environment and disease: association or causation? Proc R Soc Med
1965;58:295-300.
•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. 2010 Feb; 39(1):97-106.
•Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. New Delhi:
Wolter kluwar (India) pvt; 2009.