association and causation
TRANSCRIPT
Association and Causation
Ravi M R
PG Student
Dept. of Community Medicine
JSS Medical College
Moderator- Dr Prakash B
Proffesor
JSS Medical College
ASSOCIATION AND CAUSATION
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.
Descriptive studies
• It endeavours to suggest an etiological hypothesis.
Analytical and experimental studies
• Test the hypotheses derived from descriptive studies.
ASSOCIATION
If two attributes say A and B are found to co-exit more often than an ordinary chance.
It is useful to consider the concept of correlation.
Correlation indicates the degree of association between two variables.
• Correlation co-efficent ranges from -1.0 to + 1.0.
• Value of 1.0 means that the two variables exhibit a perfect linear relationship.
• Causation implies correlation, but correlation doesnot imply causation
CAUSATION
• If one of these attributes say A is the suspected cause and the other say B is a disease then we have a reason to suspect that A has caused B
Environmental exposure or host characterisctics
Disease or other health outcome
association
A
B
APPROACHES FOR STUDYING DISEASE ETIOLOGY
• Conceptually, a two-step process is followed in carrying out studies and evaluating evidence.
• 1. We 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
• Ecologic Studies
• The first approach in determining whether an association exists might be to conduct studies of group characteristics, called ecologic studies
• Eg.relationship between breast cancer incidence and average dietary fat consumption in each country
Ecologic fallacy
• 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). In ecologic studies, we only have data on groups
Types Of Association
1. Spurious association
2. Indirect association
3. Direct association One-to-one causal association Multifactorial causation
Spurious Association
This is an association which appears due to improper comparison.
Observed association between a disease and suspected factor may not be real.
E.g.; 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.
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).
E.g.: Neonatal mortality (A) was found to be associated with maternal age above 30 years (B) and with birth order 4 and above (c).
It was also shown that the attribute B and C were associated with each other.
Direct Association
The association between the two attributes is not through the third attributes.
When the study reveals it is not a spurious association.
When the disease is present, the factor must also be present.
Direct Association Is Classified Into Two Types
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 or cause may lead to more than one outcome.
Multifactorial causation:
Alternative causal factors each acting independently.
E.g. In lung cancer more than one factor (e.g. air pollution, smoking, heredity) can produce the disease independently.
Measures of association
Ratio measures
measures of association in which relative differences between groups being compared
Difference measures
Difference measures are measures of association in which absolute differences between groups being compared .
Absolute differences• Main goal is often an absolute reduction in the risk of an
undesirable outcome.
• 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• Can be assessed for discrete outcomes.
• To assess causal associations
Type Example Usual application
Absolute difference AR (Attributable Risk) Primary prevention impact: search for causes.
PAR(Population Attributable risk)
Primary prevention impact
Efficacy Impact of intervention on recurrences, case fatality etc.
Mean differences(continuous outcome)
Search for determinants
Relative difference Relative risk/rate Search for causes
Relative odds(ODDS ratio)
Search for causes
Incidence among exposed
Relative risk = ________________
Incidence among unexposed
It is direct measure of the strength of association.
RR = 1 No associationRR > 1 Positive association
(possibly causal)RR < 1 Negative association
(possibly protective)
• Odd’s ratio(Relative odds)
Cases(with disease) Controls(without disease)
H/O of expos a b
No H/O expos c d
product of the two cells that support the hypothesis of an
association
product of the two cells that negate the hypothesis of an
association:
ad bc:
ATTRIBUTABLE RISK (AR)
• How much of the disease that occurs can be attributed to a certain exposure?
• It is often 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
• When causality has not been firmly established then the AR is termed as excess fraction
POPULATION ATTRIBUTABLE RISK• What proportion of the disease incidence in a total population
can be attributed to a specific exposure?• To know the PAR , we need to know • incidence in total population =a• incidence in unexposed group(background risk)=b• PAR= a-b ÷ a
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
TYPES OF CAUSAL RELATIONSHIPS
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 (the factor is necessary), and in the presence of that factor, the disease always develops (the factor is sufficient)
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.
• For example, carcinogenesis is considered to be a multistage process involving both initiation and promotion. For cancer to result, 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: II. 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 alone
• Thus, either radiation exposure 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
Types of causal relationships: III. 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
EVIDENCE FOR A CAUSAL RELATIONSHIP
• In 1840, Henle proposed postulates for causation that were expanded by Koch in the 1880s.[6] 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 animals).
• 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 infectious diseases
• 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
• the U.S. Surgeon General appointed an expert committee to review the evidence. The committee developed a set of guidelines,[7] which have been revised over the years.
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
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
If the disease develops in a period of time too soon after exposure, the causal relationship is called into question
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 viz; calculation of relative risk, attributable risk etc.
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 (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
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 (should probably be eliminated)
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.
DERIVING CAUSAL INFERENCES: EXAMPLE
• Until the 1980s, the major causes of peptic ulcer disease were considered to be stress and lifestyle factors, including smoking
• In 1984, Australian physicians Drs. Barry J. Marshall and J. Robin Warren reported that they had observed small curved bacteria colonizing the lower part of the stomach in patients with gastritis and peptic ulcers
• After several attempts, Marshall succeeded in cultivating a hitherto unknown bacterial species (later named Helicobacter pylori) from several of these biopsies
• Together they found that the organism was present in almost all patients with gastric inflammation or peptic ulcer
• Many of these patients had biopsies performed which showed evidence of inflammation present in the gastric mucosa close to where the bacteria were seen
• Based on these results, they proposed that Helicobacter pylori is involved in the etiology of these diseases. It was subsequently shown that the ulcer was often not cured until Helicobacter pylori had been eliminated.
Assessment of the Evidence Suggesting Helicobacter pylori as a Causative Agent of Duodenal Ulcers
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-year period.
2. Strength of the relationship.
• Helicobacter pylori is found in at least 90% of patients with duodenal ulcer.
In at least one population reported to lack duodenal ulcers, a northern Australian aboriginal tribe that is isolated from other people, it has never been found.
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 ulcer
4. Replication of the findings.
• Many of the observations regarding Helicobacter pylori have been replicated repeatedly
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.
• Although originally it was difficult to envision a bacterium that infects the stomach antrum causing ulcers in the duodenum, it 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, compared with a 60% to 80% relapse rate often found in patients with duodenal ulcers treated with histamine receptor antagonists.
8. Specificity of the association.
• Prevalence of Helicobacter pylori in patients with duodenal ulcers in 90% to 100%. However, it is found in some patients with gastric ulcer and even in asymptomatic individuals.
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 years.
• 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 conditions.
Modified Guidelines for Evaluating the Evidence of a Causal Relationship. (In each category, studies are
listed in descending priority order.) 1990
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 developed.
b. Biological plausibility: A biologically plausible mechanism should be able to explain why such a relationship would be expected to occur.
c. Consistency:
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 indeed the cause of a disease, usually (but not invariably) 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 observed effects.
b. Strength of the association:
The strength of the association is usually measured by the extent to which the relative risk or odds depart from unity, either above 1 (in the case of disease-causing exposures) or below 1 (in the case of preventive interventions).
c. Cessation effects:
If an intervention has a beneficial effect, then the benefit should cease when it is removed from a population (unless carryover effect is operant).
• The lesson has reviewed some of the considerations necessary in making causal inferences relevant to public health
• Results from epidemiological studies are often used as inputs for policy and judicial decisions
eg., studies on link between smoking and lung cancer have led to labelling of cigarette packs.
• It is thus important for public health and policy makers to understand the fundamentals of causal inference.
Conclusion
• 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
• [1] Park K. Textbook of Preventive and Social Medicine. 21st ed. Jabalpur(India): Banarsidas Bhanot Publishers; 2011
• [2]Gordis, Leon Epidemiology / Leon Gordis.—4th ed. Philadelphia: Saunders Elsevier; 2009
• [3]Roger Detels et al. Oxford Text Book of Public Health. 5th ed. New york(U.S.A): Oxford University Press; 2011
• [4]K R Sundaram. Medical statistics principles & methods. 1st ed. New Delhi(India): BI Publications Pvt Ltd; 2010
REFERENCES
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