This is a ppt for 4th semester MBBS students: topic Association and causation (community Medicine).

1. Association & Causation
A BASIC CONCEPT IN EPIDEMIOLOGY
DR SHYAM ASHTEKAR, SMBT MED COLLEGE
DEC 2016
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2. 12/21/2016
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Is Nota-bandi
cause of Q deaths?

3. A chain of Events
Demonetiz
ation of
high value
notes
No funds
at home
Long
queues
Heart
attack
Man
died
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4. Contributory
causes
Demonetizatio
n
Long queue
Heart attack
Old age
Man
died
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5. On deaths ‘due to’ demonetization!
 The deaths were due to heart disease, old age
 Long queues, stress and waiting.
 Could be due to cold of night or hot weather of
afternoon.
 Because nobody helped the dying
 It was demonetization that killed.
 Could be all of these factors.
 They could have died even at home.. So no link to
demonetization
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6. The questions
 Did it happen by chance/error?
 Is their a bias in saying event A caused event B
 Is there a true relation between A as cause to
event B?
 Is the relation of A to B strong enough?
 Are their confounding/confusing variables
involved?
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7. What we shall learn in this?
About
‘variables’
Proving
causation in
Epidemiology
Association
to causation
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8. It is all about
Variables/Factors/Events
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9. The relation of variables!
Independent, dependent, and
confounding variables
 We have fundamentally two
variables to measure/monitor—(a)
the exposure/INDEPENDENT
variable-often on X axis and (b)
the dependent or the OUTCOME
variable-usually Y axis
 But there are OTHER variables that
can influence the independent and
dependent variables. These are
called CONFOUNDING variables
Relation between BMI (X axis) and MAC (Y
axis): correlation (0.9) close to 1
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10. Factors…Risk factors (variables)
Predisposing
Enabling/
disabling Precipitating Reinforcing
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11. Confounding -factors that confuse/mix
up/hide
 Influences both cause and effect
differentially
 For instance, increasing AGE is
associated with type2 Diabetes. But
BMI is a confounding factor. BMI
increases with age and BMI also
independently predisposes to
diabetes.
 So you have to account for BMI in
this relation –hidden factor in both
cause and effect
 Confounding means a hidden factor,
a factor that is mixed up etc.
BMI
Aging
Diabete
s Type2
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12. About Association &
Causation
IMPORTANT CONCEPTS
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13. Why is Association & Causation important?
 To decide if a factor A causes disease B or not!
 Is the link true or only facile?
 Is it true or by chance?
 If we know the cause(s) we can cure/treat
/prevent/minimize the illness. (in a patient or the society)
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14. Association & Causation
Association
 Relation between two or more
variables
 Generally found in snapshot
(cross-sectional) studies
 Things found together!
 Relationships can be positive or
negative
 Correlation! (factors moving
together– like poverty and under
nutrition)
Causation
 A variable (s) lead to another
variable that is dependent/
outcome/ event/disease
 So it suggests Etiology of
disease
 We need analytical studies to
find out/prove cause(s)
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15. Types of Association
Association
Causal
Direct Indirect
Interactio
n
Non-
causal
Chance
Bias/Conf
ounding
Ecologica
l
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16. Spurious Association
Spurious (not true) association
Not real, only apparent
Example1: Incomes and alcohol
consumption are strongly
associated (Is it true?)
Exapmle2: Fire and Fire
Brigade may be found together
in a snapshot--but Fire brigade
is not the cause of FIRE.

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17. Direct Causation
 Independent variable A leads to
dependent variable B, without
help of any other factor. This is
rare in life.
 Cyanide poisoning and death is
an example.
 This happens more with
infectious diseases that are
highly virulent and there is no
immunity-like smallpox, anthrax,
rabies. 12/21/2016
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18. Indirect causation
 Some factor leads to another
factors/event and through that the
disease event.
Streptococcal
sore throat
Rheumatic
fever
Rheumatic
carditis/valve
damage
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19. Interaction of causative factors-
Synergy-both factors work
together- IHD
BMI Smoking
Protective (negative) effect of a
factor--IHD
Physical
work
Aging
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20. Conditional factors
 Sometimes/Often another factor is
necessary for a causative factor to
lead to disease.
Viral Fever
in child
Aspirin
Rey’s
syndrome
(rapidly
progressing
encephalitis
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21. Necessary AND sufficient cause
 Cyanide poison alone can
cause death..no other factor
is necessary!
 Another is rabies infection
leading death!
 Without that factor the
diseases never develops, and
in its presence the disease
always develops
Death
Cyanide
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22. Necessary but not sufficient
Common Situation
 The causative variable factor is
always necessary but often not
enough to cause disease by
itself
 It needs other variable/
factor(s) to cause the disease
 This is more common in health
and medicine
Example
TB
disease
Malnutrition
TB infection
??
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23. One cause , many effects
 Some causes/factors can
cause multiple effects.
Common examples are
malnutrition, smoking,
alcoholism etc
 Diabetes can cause multiple
organ damage-heart,
kidneys, eyes, nerves etc
 So it is wiser to curb these
factors to maximize health
gains. 12/21/2016
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alcoholis
m
Liver
cirrhosis
neuritis
Gastritis
dementia

24. Multiple –multifactorial-causes ..
 In most non-
communicable
diseases
,multiple
factors have a
varying role to
play..cancers,
ischemic heart
disease, aging
etc
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IHDBMI
Stress
Hypertension
Smoking
??

25. Multifactorial
causation-Often true
of NCDs
Ageing
Obesity
High Calorie
diets
Insulin Resistance
Lack of
exercise
Genetic traits
Diabetes
Type 2
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26. Multiple variables in causation
 Often the relationships are not linear-or chain
like
 They can be a complex web of causative factors
 An example is the Pollution hazard of Delhi in
Nov2016 has following factors: winter, diwali
crackers, vehicular emissions, coal-power
plants, burning of rice-stubs in UP, Haryana and
Punjab, winds flowing into Delhi from east-west-
north-south etc, construction activity, dust
raised because of stopping of rains, etc.
Stub-
burning
Winds/
emissi
ons
Winte
r
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27. Multi-factorial cause—
Epidemiological Triangle
Disease
Agent
factors
Host/Group
factors
Time
Environme
ntal factors
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28. Summary of Causal Models
Causalmodels
1 Causal
Direct (A causes B) HIV causes AIDS
One cause-multiple effects (
A causes B,C,D)
Smoking causes
cancer, IHD, Bronchial
disease etc
Multiple causes (A, B, C
together cause D)
Hypertension caused by
age, BMI, smoking etc
2 Effect
Modification
Synergistic (B helps A to
cause C)
Obesity hastens knee
arthritis with age
Negative/Protective (B protects
from effect C to cause D)
Exercise can protect
against effects of
ageing on IHD
3 Conditional causation (A can
cause B only in presence C)
Rh-ve mother will have
abortions only if father
is Rh+ve
4 Indirect causal (A causes B
only through C)
Ageing causes
hypertension through
BMI
5.Confounding association (factor B
influences both A and C)
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29. Proving
association/
causation
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30. Problems of proving causal relation
 Correlation may not be equal to CAUSATION-it could be coincidence!
 There could be multiple causes of an effect/event
 Factors operating in Communicable and Non-communicable diseases are
different
 May be a time lag between cause and effect– eg occupational chemical
exposures. (or Silicosis)
 Bias in study design--selecting wrong sample!
 Confounders--factors that influence cause and effect/underlying factors
 There is no statistical method to prove cause from association, we suggest
only probability and strength of association.
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31. Steps for Establishing Causality between-
exposure and outcome variables
 Look for chance variation (probability-take
enough and proper sample)
 Rule out bias-tilt/obliqueness in sample taking,
observation,
 Follow correct methods of measurements,
comparing
 Look & account for confounding variables
 Look for Hill’s criteria, confirmatory criteria
(specific) 12/21/2016
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32. Evidence for a causal relationship-Now not followed
due to limitations
 Infectious diseases: Henle assumptions 1840 – which was expanded by Koch
in 1880s:
 The organism is always found with the disease
 The organism is not found with any other disease
 The organism, isolated from one who has the disease, and cultured through several
generations, produces the disease (in experimental animals)
 NCDs, no organism to detect and culture --- causal relationship more complex
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33. Hill’s Modified
Criteria of
causation
Temporal precedence (must happen before the disease)
Strength of association (Higher Risk)
Specificity (event A should lead to event B)
Consistent (should be found again & again)
Coherence (must fit in existing knowledge/observations)
Dose response relationship (more exposure-more
disease)
Strength of study design
Biological plausibility (biologically possible)
Should be proven by experiment (??)-eg in animals!
Existing Evidence!
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34. Temporal relationship
 Exposure to the factor must occur before the disease
developed
 It is easy to establish a temporal relationship in a
prospective cohort study than case control and
retrospective cohort
 Length of the interval between the exposure and disease
(asbestos in lung cancer)
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35. Temporal relationship cont.
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36. Strength of association
 Strength of association is
measured by Relative Risk
or Odds Ratio/attributable
risk or risk difference
 The stronger the association,
the more likely the relation is
causal
Exposed
to silica
dust
Non
exposed
to silica
dust
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37. Dose response relationship
 As the dose of exposure increase, the
risk of disease also increases
 If a dose response relationship is
present, it is strong evidence for a
causal relationship
 In some cases a threshold may exist
 Sometimes it could be a J shaped
relation
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38. Dose response relationship cont.
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39. Replication of findings
 If the relationship is causal,
we would expect to find it
consistently in different
studies and in different
population
 It is expected to be present in
subgroups of the population
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40. Biologic plausibility
 Coherence with the current body of biologic knowledge
 Sometimes, epidemiological observation preceded biologic
knowledge
 E.g. Gregg’s observation on Rubella and congenital cataracts preceded any
knowledge of teratogenic viruses
 If epidemiological findings are not consistent with the existing
knowledge – interpreting the meaning of observed association
might be difficult
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41. Cessation of exposure
 If a factor is a
cause of a
diseases, the risk
of the disease to
decline when
exposure to the
factor is reduced
or eliminated
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42. Consistency with other knowledge
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43. Specificity of the association
 An association is specific when a certain exposure is
associated with only one disease
 This is the weakest point of the Hills criteria –
 Smoking is linked with lung, pancreatic & bladder cancers;
hearth disease, emphysema …
 Specificity of an association provides additional support for a
causal inference
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44. Basic methods of arriving at ‘The Cause’
 Agreement ..common factor points to ‘cause’ (e.g in food poisoning episode,
the food item common to meals of all affected is most suspect cause)
 Difference: In similar situations, the ‘only difference’ points to probable cause
of a disease. (Polished rice vs unpolished rice caused beriberi in the first group,
not the other)
 Analogy: parallel example to help suggest a cause (Kyasnur Forest Disease
cause found by analogy to Yellow fever)
 Concomitant variation (seasonal changes in diseases)-more allergies in
flowering seasons
 Residual or elimination method.
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45. Recap-keywords
Variables
Independent or exposure variable
Dependent or outcome variable
Pre-disposing factors
Contributing factors
Enabling factors
Precipitating factors
Risk factors
Confounding variables
Association &
Causation
Association, Causation
Direct Causation, Indirect
causation
Multifactorial causation
Epidemiological triad
Interaction of factors, Synergistic
Conditional causation
Confounding variables
Spurious relation
Necessary Cause, Sufficient
cause
Proving Causation
Take care of BIAS/ERRORS
Hills Modified criteria
Strength of Association (Relative Risk/Odds
ratio)
Temporality
Specificity
Consistency
Study design
Evidence
Experimental proof
Dose-Response relation
Coherence
Agreement, difference, analogy, residual
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