ASSOCIATION AND CAUSATION

Presented by : Dr. Vini Mehta MDS 1st Year

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Contents

• Introduction• Defining Association• Types of Association• Additional Criteria for Judging Causality• Establishing a Casual Inference• Problems in Establishing Causality• Conclusion• References

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Introduction

Epidemiological principles stand on two basic assumptions

Human disease does not occur at random

The disease and its causal as well as preventive factors can be identified by a thorough investigation of population

• Identification of causal relationship between a disease and suspected risk factors forms part of epidemiological research.

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Association(Correlation, Covariation, Statistical dependence, Relationship)• Defined as occurrence of two variables more often than

would be expected by chance• If two attributes say A and B are found to co-exit more often

than an ordinary chance• Correlation indicates the degree of association between two

variables

• Causal association: when cause and effect relation is seen

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• Association does not necessarily imply a causal relationship.

• Association can be broadly grouped under three headings:

• a. Spurious association• b. Indirect association• c. Direct (causal) association

• one to one causal association• multifactorial causation

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a. Spurious Association• (Spurious= not real, artificial, false, non-causal

associations due to chance, bias or confounding)• Observed association between a disease and suspected

factor may not be real• This is due to selection bias

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 6

a. Indirect association:

• 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.

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Example of an indirect association is 1) Altitude and endemic goiter• Endemic goiter is generally found in high

altitudes, showing thereby an association between altitude and endemic goiter.• Current knowledge- endemic goiter is not due

to altitude but due to environmental deficiency of iodine.

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a. Direct (causal) association: One to one causal relationship:• The association between the two attributes is not

through the third attributes.• When the disease is present, the factor must also

be present.

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• Direct (Causal) association:

1.One –to- one causal association

2.Multifactorial causation

Sufficient & necessary cause

Web of causation (Interaction) 10

Koch’s postulates-

The germ theory of disease insisted that the cause must be

a. necessary andb. sufficient for the occurrence of the

disease..

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One –to- one causal association• The variables are stated to be causal related 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.

• Hemolytic Streptococci Streptococcal

tonsillitisScarlet fever

Erysipelas12

Multifactorial causation:

• 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.

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Model of multifactorial causation

• Factor 1

• Factor 2

• Factor 3

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REACTION AT

CELLULAR

LEVEL

Disease

• Model of multifactorial causation showing synergism

• In this model , the causal factors act cumulatively to produce disease. This is probably the correct model for many diseases. It is possible that each of the several factors act independently , but when an individual is exposed to 2 or more factors, there may be a synergistic effect.

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Factor 1

Factor 2

Factor 3

REACTION AT

CELLULAR

LEVEL

Disease+

+

ADDITIONAL CRITERIA FOR JUDGING CAUSALITY

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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

Temporal association

• The causal attribute must precede the disease or unfavorable outcome.

• Exposure to the factor must have occurred before the disease developed.

• Length of interval between exposure and disease very important

Strength Of The Association

• Relationship between cause and outcome could be strong or weak.

• With increasing level of exposure to the risk factor an increase in incidence of the disease is found.

• There are statistical methods to quantify the strength of association ( calculation of relative risk, attributable risk )

Dose-Response Relationship

• 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 causal relationship.

• 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 may develop

Replication of the Findings

• If the relationship is causal, we would expect to find it consistently in different studies and in different populations

• Replication of findings is particularly important in epidemiology.

• If an association is observed, we would also expect it to be seen consistently within subgroups of the population and in different populations, unless there is a clear reason to expect different results.

Biologic Plausibility Of The Association

• The association must be consistent with the other knowledge ( 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

Consideration of Alternate Explanations

• We have discussed the problem in interpreting 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.

• If a relationship is causal, the findings should be consistent with other data.

• If lung cancer incidence increased as cigarette use was on the decline, we would have to be able to explain how this was consistent with a causal relationship.

• If there is no consistency it will weaken a causal interpretation.

• The causal association between smoking and lung cancer due to its consistency.

Specificity Of The Association

• The weakest of the criteria

• Specific exposure is associated with only one disease.

• This is used by tobacco companies to argue that smoking is not causal in lung cancer. Smoking is associated with many diseases.

• Specificity implies a one to one relationship between the cause and effect.

Establishing a Causal Inference

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Problems in Establishing Causality

• The existence of correlation/ association does not necessarily imply causation.

• 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.

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Conclusion

• Results from epidemiological studies are often used as inputs for policy and judicial decisions.

• It is thus important for public health and policy makers to understand the fundamentals of causal inference.

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References

1. Health Research Methodology- A guide for training in Research methods – Second edition-World Health Organization, Pg 125-140

2. Park’s Textbook of Preventive and Social Medicine-20th edition,Pg 83-87

3. Epidemiology-Leon Gordis, W.B.Saunders Company1996, Pg 167-182

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