DR. PRIYANKA SHARMA
III YEAR M.D.S
DEPARTMENT OF PUBLIC HEALTH DENTISTRY
JSS DENTAL COLLEGE & HOSPITAL
APPROACHES FOR STUDYING DISEASE ETIOLOGY
WHAT IS ASSOCIATION
TYPES OF ASSOCIATION
WHAT IS CAUSE
GENERAL MODELS OF CAUSATION
TYPES OF CAUSAL RELATIONSHIP
CRITERIA FOR A CAUSAL RELATIONSHIP
GUIDELINES FOR JUDGING WHETHER THE ASSOCIATION IS CAUSAL
EVIDENCE FOR A CAUSAL RELATIONSHIP
DERIVING CAUSAL INFERENCES: EXAMPLE
MODIFIED GUIDELINES FOR EVALUATING THE EVIDENCE OF A CAUSAL RELATIONSHIP
MEASURES OF ASSOCIATION
In The Magic Years, Fraiberg (1959) characterized every toddler as a
scientist, busily fulfilling an earnest mission to develop a logical
structure for the strange objects and events that make up the world
that he or she inhabits.
Each person develops and tests an inventory of causal explanations
that brings meaning to the events that are perceived and ultimately
leads to increasing power to control those events.
The fruit of such scientific labours is a working knowledge of the
essential system of causal relations that enables each of us to
navigate our complex world.
In epidemiological studies, ascertainment of cause-effect relationships
is one of the central and most difficult tasks of all scientific activities.
Epidemiological principles stand on two basic assumptions:
Human disease does not occur at random.
The disease and its cause as well as preventive factors can be
identified by a thorough investigation of population.
Hence, identification of causal relationship between a disease and
suspected risk factors forms part of epidemiological research.
Strength of evidence of studies
Systematic review or meta-analysis of RCTs
Randomized, controlled trials (RCTs)
Non-randomized / uncontrolled experimental studies
Expert opinions, anecdotal reports
Approach for studying
Conceptually, a two-step process is followed in carrying out studies and
1. Determine whether there is an association between an exposure or
characteristic and the risk of a disease. To do so, we use:
a. Studies of group characteristics: ecologic studies
b. Studies of individual characteristics: case-control and cohort studies
2. If an association is demonstrated, we determine whether the observed
association is likely to be a causal one or not.
The first approach in determining whether an association exists might
be to conduct studies of group characteristics, called ecologic
ECOLOGICAL FALLACY : Eg.relationship between breast cancer
incidence and average dietary fat consumption in each country
ECOLOGICAL INFERENCE FALLACY: Eg.areas with high concentrations
of farm animals are also the areas with lowest concentrations of
It’s a fallacy to then assume that a child who has asthma must not live
near any farm animals
So? Do You Have Enough Info
To Inform The Patient?
Recognizing the limitations discussed above of ecologic studies that
use only group data, we turn next to studies of individual
characteristics: case-control and cohort studies.
In case-control or cohort studies, for each subject we have information
on both exposure (whether or not and, often, how much exposure
occurred) and disease outcome (whether or not the person
developed the disease in question).
Historical Theories of
• “Supernatural causes”& Karma
• Theory of humors (humor means fluid)
• The miasmatic theory of disease
• Theory of contagion
• Germ theory
• Koch’s postulates
EVIDENCE FOR A CAUSAL
In 1840, Henle proposed postulates for causation that were expanded by
Koch in the 1880s.The postulates for causation were as follows:
1. The organism is always found with the disease.
2. The organism is not found with any other disease.
3. The organism, isolated from one who has the disease, and cultured
through several generations, produces the disease (in experimental
Koch added that “Even when an infectious disease cannot be transmitted to
animals, the ‘regular’ and ‘exclusive’ presence of the organism
[postulates 1 and 2] proves a causal relationship.”
These postulates, though not perfect, proved very useful for
However, as apparently noninfectious diseases assumed
increasing importance toward the middle of the 20th century,
The issue arose as to what would represent strong evidence of
causation in diseases that were generally not of infectious origin.
Syn: Correlation, Covariation, Statistical dependence, Relationship
Defined as occurrence of two variables more often than would be
expected by chance.
An association is present if probability of occurrence of a variable
depends upon one or more variable.
(A dictionary of Epidemiology by John M. Last)
If two attributes say A and B are found to co-exit more often than an
It is useful to consider the concept of correlation.
Correlation indicates the degree of association between two variables
Causal association: when cause and effect relation is seen.
Pyramid Of Associations
Raj Bhopal : Cause and effect: the epidemiological approach
Positive: Occurrence of higher value of a predictor variable is
associated with occurrence of higher value of another dependent
variable. Ex- education and suicide.
Negative: Occurrence of higher value of a predictor variable is
associated with lower value of another dependent variable.
Ex - Female literacy and IMR
Causal: Independent variable must cause change in dependent
Definite condition of causal associations are time and direction
Ex – salt intake and hypertension
Non-causal: Non-directional association between two variables.
Ex – alcohol use and smoking
(Spurious= not real, artificial, fortuitous, false, non-causal associations due to
chance, bias or confounding)
Observed association between a disease and suspected factor may not
This is due to selection bias
Eg: Increased water intake and crime rate in summer.
The ringing of alarm clocks and rising of the sun.
Cock’s crow causes sun to rise.
Ex : Neonatal mortality was observed to be more in the newborns born in
a hospital than those born at home. This is likely to lead to a conclusion
that home delivery is better for the health of newborn.
However, this conclusion was not drawn in the study because the
proportion of “high risk” deliveries was found to be higher in the
hospital than in home.
It is a statistical association between a characteristic of interest and
a disease due to the presence of another factor i.e. common
factor (confounding variable).
So the association is due to the presence of another factor which is
common to both, known as CONFOUNDING factor.
1.Rahul is a friend with Suma, and Suma is Shoba’s friend, so Shoba
is Rahul ’s friend too but indirectly. The common friend is Suma.
2. Altitude and endemic goiter confounding factor is iodine
3. Glucose and CHD ,confounding factor is cigarette smoking(it
increase the of cups of coffee and amount of sugar u consume)
The association between the two attributes is not through the third
When the disease is present, the factor must also be present.
Direct (Causal) association:
1. One –to- one causal association
2. Multifactorial causation
Sufficient & necessary cause
Web of causation (Interaction)
One-to-one Casual Relationship
The variables are stated to be casual related (AB) if a change in A is
followed by a change in B.
When the disease is present, the factor must also be present.
A single factor (cause) may lead to more than one outcome.
But its not always that simple , as some causes can cause more than 1
disease like streptococci
Multiple factor leads to the disease.
Common in non-communicable diseases
Alternative causal factors each acting independently.
Ex: In lung cancer more than one factor (e.g. air pollution, smoking,
heredity) can produce the disease independently.
Either the causes are acting
Independently OR Cumulatively
Air pollution Reaction at cellular level Lung cancer
Exposure to asbestos
Air pollution Reaction at cellular level Lung cancer
Exposure to asbestos
WHAT IS CAUSE
The word cause is the one in general usage in connection with matters
considered in this study, and it is capable of conveying the notion of a
significant, effectual relationship between an agent and an
associated disorder or disease in the host.”
1964 Surgeon General Report
General Models of Causation
The most widely applied models are:
– The epidemiological triad (triangle),
– The web
– The wheel and
– The sufficient cause and component causes models
(Rothman’s component causes model)
Wheel of Causation
Sufficient & Necessary Cause
NECESSARY cause - causal factor whose presence is required for the
occurrence of the effect. If disease does not develop without the
factor being present, then we term the causative factor “necessary”.
Ex: Agent in Malaria: Plasmodium falciparum parasite is necessary factor-
SUFFICIENT cause - “minimum set of conditions, factors or events
needed to produce a given outcome. Usually there’s no sufficient
The factors or conditions that form a sufficient cause are called
Necessary causes + Component causes = Sufficient cause
Rothman’s Component Causes and
Causal Pies Model
• Rothman's model has emphasised that the causes of disease comprise
a collection of factors.
• These factors represent pieces of a pie, the whole pie (combinations of
factors) are the sufficient causes for a disease.
• It shows that a disease may have more that one sufficient cause, with
each sufficient cause being composed of several factors
• The factors represented by the pieces of the pie in this model are called
• Each single component cause is rarely a sufficient cause by itself, But may
be necessary cause.
• Control of the disease could be achieved by removing one of the
components in each "pie" and if there were a factor common to all "pies“
(necessary cause) the disease would be eliminated by removing that
Known components (causes) – A, B,
Unknown component (cause) - U
N – Necessary cause
Known components causes
Unknown component cause = Sufficient cause
42Causes of tuberculosis
If a relationship is causal, four types of causal relationships are possible:
(1) Necessary And Sufficient
(2) Necessary, But Not Sufficient
(3) Sufficient, But Not Necessary
(4) Neither Sufficient Nor Necessary
Necessary and Sufficient
A factor is both necessary and sufficient for producing the disease.
Without that factor, the disease never develops and in the
presence of that factor, the disease always develops
Types of causal relationships I:
Each factor is both necessary and sufficient
FACTOR A DISEASE
Necessary, But Not Sufficient
Each factor is necessary, but not, in itself, sufficient to cause the disease .
Thus, multiple factors are required, often in a specific temporal sequence.
Ex: Carcinogenesis is considered to be a multistage process involving both
initiation and promotion. A promoter must act after an initiator has acted.
Action of an initiator or a promoter alone will not produce a cancer
Types of causal relationships:
Each factor is necessary, but not sufficient
Sufficient But Not Necessary
The factor alone can produce the disease, but so can other factors that are acting
Either radiation or benzene exposure can each produce leukemia without the
presence of the other.
Even in this situation, however, cancer does not develop in everyone who has
experienced radiation or benzene exposure, so although both factors are not
needed, other cofactors probably are. Thus, the criterion of sufficient is rarely met by
a single factor.
Each factor is sufficient, but not necessary
Neither Sufficient Nor Necessary
A factor by itself, is neither sufficient nor necessary to produce disease
This is a more complex model, which probably most accurately represents
the causal relationships that operate in most chronic diseases.
Types of causal relationships: IV.
Each factor is neither sufficient nor necessary
When we can say that this association is
likely to be causation??
We have certain criteria that should be present:
– Temporal association
– Strength of association
– Specificity of association
– Consistency of association
– Biological plausibility
– Coherence of association
Guidelines for Judging Whether an
Association Is Causal (Leon Gordis)
1. Temporal relationship
2. Strength of the association
3. Dose-response relationship
4. Replication of the findings
5. Biologic plausibility
6. Consideration of alternate explanations
7. Cessation of exposure
8. Consistency with other knowledge
9. Specificity of the association
The causal attribute must precede the disease or unfavorable outcome.
Exposure to the factor must have occurred before the disease
Length of interval between exposure and disease very important .
Its more obvious in acute disease more than in chronic disease
Temporal relationship (Relationship with
• Cause must precede the effect.
Drinking contaminated water occurrence of diarrhea
However in 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.
Strength Of The Association
Relationship between cause and outcome could be strong or
With increasing level of exposure to the risk factor an increase in
incidence of the disease is found.
Strong associations are more likely to be causal than weak.
Weaker associations are more likely to be explained by
But weaker association does not rule out causation.
• Strength of association can be estimated by relative risk, attributable
• Relative risks/Odds ratio greater than 2 can be considered strong
( The Biological gradient )
As the dose of exposure increases, the risk of disease also increases
If a dose-response relationship is present, it is strong evidence for a
However, the absence of a dose-response relationship does not
necessarily rule out a causal relationship.
In some cases in which a threshold may exist, no disease may develop
up to a certain level of exposure (a threshold); above this level, disease
Death rates from lung cancer (per 1000) by
number of cigarettes smoked, British male
doctors, 1951 –1961
Biologic Plausibility Of The Association
The association must be consistent with the other knowledge (viz
mechanism of action, evidence from animal experiments etc).
Sometimes the lack of plausibility may simply be due to the lack of
sufficient knowledge about the pathogenesis of a disease.
It is too often not based on logic or data but only on prior beliefs.
It is difficult to demonstrate where the confounder itself exhibits a
biological gradient in relation to the outcome.
Consideration of Alternate Explanations
Interprets an observed association in regard to whether a
relationship is causal or is the result of confounding.
In judging whether a reported association is causal, the extent to
which the investigators have taken other possible explanations
into account and the extent to which they have ruled out such
explanations are important considerations.
Cessation of Exposure
If a factor is a cause of a disease, we would expect
the risk of the disease to decline when exposure to
the factor is reduced or eliminated
Consistency Of The Association
Consistency is the occurrence of the association at some other time
and place repeatedly unless there is a clear reason to expect
If a relationship is causal, the findings should be consistent with other
data. Lack of consistency however does not rule out a causal
Repeated observation of an association in different populations
under different circumstances.
Specificity Of The Association
The weakest of the criteria. (should probably be eliminated)
Specific exposure is associated with only one disease.
Specificity implies a one to one relationship between the cause and effect.
It’s the most difficult to occur for 2 reasons:
Single cause or factor can give rise to more than 1 disease
Most diseases are due to multiple factors.
Ex: Smoking is associated with many diseases.
• Not everyone who smokes develops cancer
• Not every one who develop cancer has smoke
Analogy (Similarity, reasoning from
• Provides a source of more elaborate hypotheses about the associations
• Absence of such analogies only reflects lack of imagination or
experience , not falsity of the hypothesis.
Ex: Known effect of drug Thalidomide & Rubella in pregnancy
• Accepting slighter but similar evidence with another drug or another
Coherence of the association and
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.
Deriving causal inferences: example
Assessment of the Evidence Suggesting Helicobacter pylori Ulcers as a
Causative Agent of Duodenal
1. Temporal relationship.
• Helicobacter pylori is clearly linked to chronic gastritis. About 11% of
chronic gastritis patients will go on to have duodenal ulcers over a 10-
2. Strength of the relationship.
• Helicobacter pylori is found in at least 90% of patients with duodenal
3. Dose-response relationship.
• Density of Helicobacter pylori per square millimeter of gastric mucosa is
higher in patients with duodenal ulcer than in patients without duodenal
4. Replication of the findings.(consistency)
• Many of the observations regarding Helicobacter pylori have been
5. Consideration of alternate explanations.
• Data suggest that smoking can increase the risk of duodenal ulcer in
Helicobacter pylori-infected patients but is not a risk factor in patients in
whom Helicobacter pylori has been eradicated
6. Biologic plausibility.
• Originally it was difficult to envision a bacterium that infects the stomach
antrum causing ulcers in the duodenum, but is now recognized that
Helicobacter pylori has binding sites on antral cells and can follow these
cells into the duodenum.
• Helicobacter pylori also induces mediators of inflammation.
• Helicobacter pylori-infected mucosa is weakened and is susceptible to the
damaging effects of acid.
7. Cessation of exposure.
• Eradication of Helicobacter pylori heals duodenal ulcers at the same rate
as histamine receptor antagonists.
• Long-term ulcer recurrence rates were zero after Helicobacter pylori was
eradicated using triple-antimicrobial therapy,.
8. Specificity of the association.
• Prevalence of Helicobacter pylori in patients with duodenal ulcers is
90% to 100%.
9. Consistency with other knowledge.
• Prevalence of Helicobacter pylori infection is the same in men as in
women. The incidence of duodenal ulcer, which in earlier years was
believed to be higher in men than in women, has been equal in recent
• The prevalence of ulcer disease is believed to have peaked in the
latter part of the 19th century, and the prevalence of Helicobacter
pylori may have been much higher at that time because of poor living
Modified Guidelines for Evaluating the Evidence
of a Causal Relationship. (In each category,
studies are listed in descending priority order.)
1. Major criteria
a. Temporal relationship: An intervention can be considered evidence of
a reduction in risk of disease or abnormality only if the intervention was
applied before the time the disease or abnormality would have
b. Biological plausibility: A biologically plausible mechanism should be
able to explain why such a relationship would be expected to occur.
Single studies are rarely definitive. Study findings that are replicated in
different populations and by different investigators carry more weight
than those that are not. If the findings of studies are inconsistent, the
inconsistency must be explained.
d. Alternative explanations (confounding):
The extent to which alternative explanations have been explored is
an important criterion in judging causality
2. Other considerations
a. Dose-response relationship:
If a factor is the cause of a disease, usually the greater the exposure to
the factor, the greater the risk of the disease. Such a dose-response
relationship may not always be seen because many important biologic
relationships are dichotomous, and reach a threshold level for
b. Strength of the association:
Usually measured by the extent to which the relative risk or
odds depart from unity.
c. Cessation effects:
If an intervention has a beneficial effect, then the benefit should cease
when it is removed from a population.
Modern concepts in causation
• Counterfactual Model
• Causal diagram
Counterfactual model (Potential outcome
When we are interested to measure effect of a particular cause, we
measure effect in a population who are exposed.
• 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.
• Confounding is complex phenomenon.
• 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)
• Measures of association /strength of association
• Testing hypothesis of association
• Controlling confounders
Measures of association
Measures of association in which relative differences between groups
Difference measures are measures of association in which absolute
differences between groups being compared .
Absolute differences:(difference measures )
Main goal is often an absolute reduction in the risk of an undesirable
When outcome of interest is continuous, the assessment of mean
absolute differences between exposed and unexposed individuals may
be an appropriate method for the determination of association.
Relative differences: ( ratio measures)
Can be assessed for discrete outcomes.
To assess causal associations
If an association exist, then how strong is it?
What is the ratio of the risk of disease in exposed individuals to the risk of
disease in unexposed individual?
Incidence among exposed
Relative risk =
Incidence among unexposed
It is direct measure of the strength of association.
Relative risk of developing the disease is expressed as the ratio of
the risk(incidence) in exposed individuals (q+) to that in
exposed = a+b
unexposed = c+d
Incidence among exposed
Relative risk =
Incidence among unexposed
RR = q+/q- =
86Odds ratio in a cohort study
• Odds that an exposed person
develop disease = a/b
• Odds that an unexposed person
develop disease = c/d
Odds ratio = (a/b ) / (c/d) = ad/bc
Exposed a b
Unexposed c d
What are the odds that the disease will develop in an exposed person?
Relationship between OR and RR
OR is a valid measure of association in its own right and it is
often used as an approximation of the relative risk’.
Use of OR as an estimate of the relative risk biases it in a
direction opposite to the null hypothesis, i.e. it tends to
exaggerate the magnitude of the association.
ATTRIBUTABLE RISK (AR)
AR is defined as the amount of proportion of disease incidence (or
disease risk) that can be attributed to a specific exposure.
Based on the absolute difference between two risk estimates.
Used to imply a cause-effect relationship and should be interpreted
as a true etiologic fraction only when there is a reasonable certainty
of a causal connection between exposure and outcome.
AR in exposed individuals
• It is merely a difference between the risk estimates of different
exposure levels and a reference exposure level.
• If q+ = risk in exposed individual.
q- = risk in unexposed individual.
• ARexp = q+ - q-
• It measures the excess risk for a given exposure category
associated with the exposure
Percent AR exposure
When AR is expressed as a percentage
The percentage of the total risk in the exposed attributable to the
POPULATION ATTRIBUTABLE RISK
What proportion of the disease incidence can be attributed
to a specific exposure in a total population .
To know the PAR , we need to know incidence in total
incidence in unexposed group(background risk)=b
PAR= a-b ÷ a
Various correlation tests
• Pearsson’s product-moment correlation
• Spearmans rank order correlation
• Kendall correlation
• Point biserial correlation
• Tetrachoric correlation
• Phi correlation
93Types of correlation
Based on linearity of correlation
Based on direction of correlation
As X increases ,Y also increases,
ex: As height increases, so does weight.
As X increases ,Y decreases.
ex: As time of watching TV increases , grade scores decreases.
Based on degree of correlation
It can also be used in measuring association.
They are the measure of the mean changes to be expected in the
dependent variable for a unit change in the value of the
When more than 1 independent variable is associated with the
dependent variable, multiple regression analysis will indicate how
much of the variation observed in the dependent variable can be
accounted for, by one or a combination of independent variables.
PROBLEMS IN ESTABLISHING
The existence of correlation/ association does not necessarily imply
Concept of single cause concept of multiple causation
Koch’s postulates cannot be used for non-infectious diseases.
The period between exposure to a factor and appearance of
clinical diseases is long in non-infectious diseases.
Specificity established in one disease does not apply on others.
Confounders associated with disease tend to distort relationship
with the suspected factors.
Systematics errors/ bias can produce spurious association.
No statistical method can differentiate between causal and non-
Because of these many uncertainties, the terms : Causal
inference, causal possibility, or likelihood are preferred to causal
This helps in formulating policy rather than waiting for the
unequivocal proof ( Unattainable in several disease conditions)
Results from epidemiological studies are often used as inputs for policy and
It is thus important for public health and policy makers to understand the
fundamentals of causal inference.
Association does not imply causation.
Apart from outbreak investigations, no single study is capable of establishing
a causal relation or fully informing either individual or policy decisions.
Those decisions should be based on a carefull consideration of the entire
relevant scientific and policy literature
 Park K. Textbook of Preventive and Social Medicine. 23rd
Gordis, Leon Epidemiology / Leon Gordis.—5th ed.
Roger Detels et al. Oxford Text Book of Public Health. 5th
ed. New york(U.S.A): Oxford University Press; 201
WHO research methodology. Second edition.
AFMC WHO – Text book of Public Health and Community
Medicine – Rajvir Balwar – 1st edition
Soben peters – Text book of Community Dentistry – 5th edi
Raj Bhopal : Cause and effect: the epidemiological
approach : Google book source