1. Dr. Amna Rehana Siddiqui Department of Family and Community Medicine
Causal Inference
Dr. Amna Rehana Siddiqui
Department of Family and Community Medicine

2. Objectives: Explain basic models of disease causation.
To understand concepts related to scientific inference for cause effect relation
To understand the applicability of causal criteria as applied to epidemiological studies

3. Approach to etiology
To see whether a certain substance is an agent / microorganism; a controlled laboratory experiment can be done by
Exposing animals to organism
Setting the exposure dose
Monitoring environmental conditions
Selecting genetic factors
Minimum loss to follow up
Species differ in response

4. Observations in Human populations
Cannot randomize human beings for harmful substances
Depend on nonrandomized observations
Important populations – occupational cohorts
Natural experiments
Residents of Hiroshima and Nagasaki
Residents of Bhopal

5. Stages of disease and Levels of prevention
Primary prevention
Secondary prevention
(Screening)
Tertiary prevention
Susceptibility
Pre-symptomatic
Clinical
Disability or Recovery

6. Development of Disease Combination of events
A harmful agent
A susceptible host
An appropriate environment

7. General Models of Causation
In epidemiology, there are several models of disease causation that help understand disease process.
The most widely applied models are:
The epidemiological triad (triangle),
the wheel, and
the web. And
The sufficient cause and component causes models (Rothman’s component causes model)

8. The Epidemiologic Triad
HOST
AGENT
ENVIRONMENT
The best known, but most dated model of communicable disease, is the Epidemiologic Triad. This model comprises a susceptible host (the person at risk for the disease), a disease agent (the proximate cause), and an environmental context for the interaction between host and agent.

9. Agent factors
Infectious agents: agent might be microorganism—virus, bacterium, parasite, or other microbes. e.g. polio, measles, malaria, tuberculosis Generally, these agents must be present for disease to occur.
Nutritive: excesses or deficiencies (Cholesterol, vitamins, proteins)
Chemical agents: (carbon monoxide, drugs, medications)
Physical agents (Ionizing radiation,…

10. Host factors
Host factors are intrinsic factors that influence an individual’s exposure, susceptibility, or response to a causative agent.
Host factors that affect a person’s risk of exposure to an agent:
e.g. Age, race, sex, socioeconomic status, and behaviors (smoking, drug abuse, lifestyle, sexual practices and eating habits)
Host factors which affect susceptibility &response to an agent:
Age, genetic composition, nutritional and immunologic status, anatomic structure, presence of disease or medications, and psychological makeup.

11. Environmental factors
Environmental factors are extrinsic factors which affect the agent and the opportunity for exposure.
Environmental factors include:
physical factors such as geology, climate,..
biologic factors such as insects that transmit an agent; and
socioeconomic factors such as crowding, sanitation, and the availability of health services.

12. Malaria
Agent
Vector
Environment
Host

13. The epidemiologic triad Model
Host:
Intrinsic factors, genetic, physiologic factors, psychological factors, immunity
Health or
Illness ?
Agent:
Amount, infectivity, pathogenicity, virulence, chemical composition, cell reproduction
Environment:
Physical, biological, social

14. Web of Causation There is no single cause
Causes of disease are interacting
Illustrates the interconnectedness of possible causes

15. The Web of causation Developed to de-emphasis agent Chain of causation
Complexity of origin is web
Multiple factors promote or inhibit
Emphasizes multiple interactions between host and environment

16. Web of Causation Disease social organization phenotype behaviour
microbes
Disease
genes
environment
Unknown factors
workplace

17. Web of Causation - CHD Disease stress genetic susceptibility
medications
genetic susceptibility
smoking
lipids
Disease
gender
physical activity
Unknown factors
inflammation
blood pressure
RS Bhopal

18. Example of a Web of Causation
Overcrowding
Malnutrition
Exposure to Mycobacterium
Susceptible Host
Infection
Tuberculosis
Tissue Invasion and Reaction
Vaccination
Genetic

19. The Wheel of Causation
The Wheel of Causation de-emphasizes the agent as the sole cause of disease,
It emphasizes the interplay of physical, biological and social environments. It also brings genetics into the mix.

20. Biological Environment
The Wheel of Causation
Social Environment
Biological Environment
Host (human)
Genetic Core
Physical Environment

21. Association Vs. Causation
Association refers to the statistical dependence between two variables
The presence of an association…in no way implies that the observed relationship is one of cause and effect

22. Types of causes Sufficient causes: Necessary cause:
a set of conditions without any one of which the disease would not have occurred
not usually a single factor, often several
Necessary cause:
must be present for disease to occur, disease never develops in the absence of that factor.
a component cause that is a member of every sufficient cause

23. The sufficient cause and component causes model Rothman’s component causes model

24. Necessary and sufficient causes
A necessary cause is a 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”.
Sufficient cause is a “minimum set of conditions, factors or events needed to produce a given outcome.
The factors or conditions that form a sufficient cause are called component causes.

25. Example
The tubercle bacillus is required to cause tuberculosis but, alone, does not always cause it,
so tubercle bacillus is a necessary, not a sufficient, cause.

26. 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.

27. Rothman’s Component Causes and Causal Pies
The factors represented by the pieces of the pie in this model are called component causes.
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 alone.

28. Exercise
Some of the risk factors for heart disease are smoking, hypertension, obesity, diabetes, high cholesterol, inactivity, stress, and type A personality.
- Are these risk factors necessary causes, sufficient causes, or component causes?

29. Causal pies representing all sufficient causes of a particular disease

30. Types of Associations
Real: probability depends upon the occurrence of one or more other events, characteristics, or other variables
Spurious: Non causal associations depend on bias, chance, failure to control for extraneous variables (confounding)

31. Percentage of pregnancies (n=50,267) with infant weighing < 2500 g by maternal cigarette smoking category (peri-natal mort study Comm Vol 1, 1967
% less than
2500 g

32. Percentage of LBW infants by smoking status of their mothers (Yerushalmay J, Am J Obs & Gynecol)
Non Smoker Non Smoker Smoker Smoker
All pregnancies Future All Preg Future
Smoker Ex smoker

33. “Is there an association between an exposure and a disease?”
IF SO….
Is the association likely to be due to chance?
Is the association likely to be due to bias?
Is the association likely to be due to confounding?
Is the association real/causal?

34. Establishing the cause of disease
Association?
Chance?
Bias ?
Confounding?
Causal?
present
absent
likely

35. Association Vs. Causation
Association refers to the statistical dependence between two variables
The presence of an association…in no way implies that the observed relationship is one of cause and effect

36. Epidemiological criteria (guidelines) for causality
An association rarely reflects a causal relationship but it may.
Criteria for causality provide a way of reaching judgements on the likelihood of an association being causal.

37. Hill’s Criteria for Causal Relation
Strength of association
Consistency of findings
Specificity of association
Temporal sequence
Biological gradient (dose-response)
Biological plausibility
Coherence with established facts
Experimental evidence

38. Strength of association association
Does exposure to the cause change disease incidence?
The strength of the association is measured by the relative risk.
The stronger the association, the higher the likelihood of a causal relationship.
Strong associations are less likely to be caused by chance or bias

39. Consistency of findings
Consistency refers to the repeated observation of an association in different populations under different circumstances.
Causality is more likely when the association is repeated by other investigations conducted by different persons in different places, circumstances and time-frames, and using different research designs.

40. Specificity of association
It means that an exposure leads to a single or characteristic effect, or affects people with a specific susceptibility
easier to support causation when associations are specific, but
this may not always be the case
as many exposures cause multiple diseases
This is more feasible in infectious diseases than in non-infectious diseases, which can result from different risk agents.

41. Temporal sequence (temporality)
Did the cause precede the effect?
Temporality refers to the necessity that the cause must precede the disease in time.
This is the only absolutely essential criterion. 
It is easier to establish temporality in experimental and cohort studies than in case-control and cross-sectional studies.

42. Biological gradient
Does the disease incidence vary with the level of exposure? (dose-response relationship)
Changes in exposure are related to a trend in relative risk
A dose-response relationship (if present) can increase the likelihood of a causal association.

43. Biological gradient (Dose Response)

44. Age standardized death rates due to bronchogenic carcinoma by current amount of smoking
Dose-response relationship

45. Biological plausibility
Is there a logical mechanism by which the supposed cause can induce the effect?
Findings should not disagree with established understanding of biological processes.

46. Coherence
Coherence implies that a cause-and-effect interpretation for an association
does not conflict with what is known of the natural history and biology of the disease

47. Experimental evidence
It refers to evidence from laboratory experiments on animal or to evidence from human experiments
Causal understanding can be greatly advanced by laboratory and experimental observations.

48. Judging the causal basis of the association
No single study is sufficient for causal inference
It is always necessary to consider multiple alternate explanations before making conclusions about the causal relationship between any two items under investigation. 
Causal inference is not a simple process
consider weight of evidence
requires judgment and interpretation

49. Figure 5.12 The scales of causal judgement
Weigh up weaknesses in data and alternative explanations
Weigh up quality of science and results of applying causal frameworks
Causality is based on judgement and open to change with new evidence
Researchers are expert witnesses not judge and jury
Acceptability of hypothesis is an important factor

50. Pyramid of Associations
Causal
Non-causal
Confounded
Spurious / artefact
Chance
RS Bhopal

51. Evaluating Evidence of Causal relationship
Major Criteria
Temporal relationship
Biologic plausibility
Consistency of Results
Alternative explanations
Other criteria
Strength of association
Dose-response relationship
Cessation of effects