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05/01/2023
1
Association & Causation A BASIC CONCEPT IN EPIDEMIOLOGYDR SHYAM ASHTEKAR, SMBT MED COLLEGE DEC 2016
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2Is Nota-bandi cause of Q deaths?
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3A chain of Events
Demonetization of
high value notes
No funds
at home
Long queues
Heart attack
Man died
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4Contributory causes
Demonetization
Long queue
Heart attack
Old age
Man died
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5On 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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6The 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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7What 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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9The 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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10Factors…Risk factors (variables)
Predisposing
Enabling/disabling Precipitatin
g Reinforcing
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11Confounding -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
Diabetes
Type2
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12
About Association & CausationIMPORTANT CONCEPTS
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13Why 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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14Association & 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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15Types of Association
Association
Causal
Direct Indirect Interaction
Non-causal
ChanceBias/
Confounding
Ecological
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16Spurious Association
Spurious (not true) association
Not real, only apparentExample1: 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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17Direct 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.
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18Indirect 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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19Interaction 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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20Conditional 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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21Necessary 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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22Necessary 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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23One 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.
alcoholism
Liver cirrhosis
neuritis
Gastritis
dementia
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24Multiple –multifactorial-causes ..
In most non-communicable diseases ,multiple factors have a varying role to play..cancers, ischemic heart disease, aging etc
IHDBMI
Stress
Hypertension
Smoking
??
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25Multifactorial causation-Often true of NCDs
Ageing
Obesity
High Calorie diets
Insulin Resistance
Lack of exercise
Genetic traits
Diabetes Type 2
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26Multiple 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/
emissions
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27Multi-factorial cause—Epidemiological Triangle
Disease Agent factors
Host/Group factors
Time
Environmental
factors
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28Summary of Causal Models
Caus
al m
odel
s 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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30Problems 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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31Steps 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)
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32Evidence 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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33Hill’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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34Temporal 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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35Temporal relationship cont.
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36Strength 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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37Dose 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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38Dose response relationship cont.
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39Replication 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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40Biologic 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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41Cessation 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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42Consistency with other knowledge
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43Specificity 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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44Basic 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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45Recap-keywords
VariablesIndependent or exposure variableDependent or outcome variablePre-disposing factorsContributing factorsEnabling factorsPrecipitating factorsRisk factorsConfounding variables
Association & CausationAssociation, CausationDirect Causation, Indirect causationMultifactorial causationEpidemiological triadInteraction of factors, SynergisticConditional causationConfounding variablesSpurious relationNecessary Cause, Sufficient cause
Proving CausationTake care of BIAS/ERRORSHills Modified criteriaStrength of Association (Relative Risk/Odds ratio)Temporality Specificity ConsistencyStudy designEvidence Experimental proofDose-Response relationCoherenceAgreement, difference, analogy, residual