Causal Inference in Epidemiology

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 Causal Inference in Epidemiology

• Ahmed Mandil, MBChB, DrPH
• Prof of Epidemiology
• High Institute of Public Health
• University of Alexandria

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Headlines

• Levels of causality
• Definitions
• Koch's postulates (1877)
• Hill's criteria (1965)
• Susser's criteria (1988, 1991)

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Relating

• Exposures causes, risk factors, independent
  variables to
• Outcomes effects, diseases, injuries,
  disabilities, deaths, dependent variables
• Statistical association versus biological
  causation cause-effect relationship

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Levels / Types of causality

• Molecular / Physiological
• Personal / Social
• Deterministic / probabilistic
• What aspect of environment (broadly defined) if
  removed / reduced / controlled would reduce
  outcome / burden of disease

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Definitions (I)

• Deduction reasoned argument proceeding from the
  general to the particular.
• Induction any method of logical analysis that
  proceeds from the particular to the general.
  Conceptually bright ideas, breakthroughs and
  ordinary statistical inference belong to the
  realm of induction.
• Induction period the period required for a
  specific cause to produce the disease
  (health-related outcome). Usually longer with
  NCDs

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Definitions (II)

• Association (relationship) statistical
  dependence between two or more events,
  characteristics or other variables. Positive
  association implies a direct relationship, while
  negative association implies an inverse one. The
  presence of a statistical association alone does
  not necessarily imply a causal relationship.
• Causality (causation / cause-effect
  relationship) relating causes to the effects
  they produce.
• Cause an event, condition, characteristic (or a
  combination) which plays an important role /
  regular / predicable change in occurrence of the
  outcome (e.g. smoking and lung cancer)
• Causes may be genetic and / or environmental
  (e.g. many NCDs including diabetes, cancers,
  COPD, etc)

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Definitions (III)

• Deterministic causality cause closely related to
  effect, as in necessary / sufficient causes
• Necessary cause must always PRECEDE the effect.
  This effect need not be the sole result of the
  one cause
• Sufficient cause inevitably initiates or
  produces an effect, includes component causes
• Any given cause may be necessary, sufficient,
  both, neither (examples)

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Definitions (IV)

• Component causes together they constitute a
  sufficient cause for the outcome in question. In
  CDs, this may include the biological agent as
  well as environmental conditions (e.g. TB,
  measles, ARF/RHD). In NCDs, this may include a
  whole range of genetic, environmental as well as
  personal / psychosocial / behavioral
  characteristics (e.g. diabetes, cancers, IHD)

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Definitions (V)

• Probabilistic Causality in epidemiology, most
  associations are rather weak (e.g. relationship
  between high serum cholesterol and IHD), which is
  neither necessary nor sufficient
• Multiple causes result in what is known as web
  of causationor chain of causation
• which is very common for noncommunicable /
  chronic diseases

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Effect Measures / Impact Fractions

• Effect measures (e.g. odds ratio, risk ratio) and
  impact fractions (e.g. population attributable
  risk) are closely related to the strength of
  association
• The higher effect measures (away from unity) and
  population attributable risk (closer to 100 )
  the more the exposure is predictive of the
  outcome in question
• E.g. PAR of 100 means that a factor is
  necessary

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Deterministic causality (I)
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Deterministic causality (II)
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Deterministic causality (III)
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Deterministic causality (IV)
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Deterministic causality (V)
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Deterministic causality (VI)
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Definitions (IV)

• Predisposing factors factors that prepare,
  sensitize, condition or otherwise create a
  situation (such as level of immunity or
  state of susceptibility) so that the host tends
  to react in a specific fashion to a disease
  agent, personal interaction, environmental
  stimulus or specific incentive. Examples age,
  sex, marital status, family size, education, etc.
  (necessary, rarely sufficient).
• Precipitating factors those associated with the
  definitive onset of a disease, illness, accident,
  behavioral response, or course of action.
  Examples exposure to specific disease, amount or
  level of an infectious agent, drug, physical
  trauma, personal interaction, occupational
  stimulus, etc. (usually necessary).

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Weighing Evidence

• At individual level clinical judgment (which
  management scheme)
• At population level epidemiological judgment
  (which intervention)
• When weighing evidence from epidemiological
  studies, we use causal criteria (usually
  applied to a group of articles, to deal with
  confounding) e.g. Hills / Sussers criteria,
  which were preceded by Kochs postulates (on
  infectious diseases)

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Henle-Koch's postulates (1877,1882)

• Koch stated that four postulates should be met
  before a causal relationship can be accepted
  between a particular bacterial parasite (or
  disease agent) and the disease in question.
  These are
• 1. The agent must be shown to be present in
  every case of the disease by isolation in pure
  culture.
• 2. The agent must not be found in cases of other
  disease.
• 3. Once isolated, the agent must be capable of
  reproducing the disease in experimental animals.
• 4. The agent must be recovered from the
  experimental disease produced.

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Hill's Criteria (1897 - 1991)

• The first complete statement of the
  epidemiologic criteria of a causality is
  attributed to Austin Hill (1897 - 1991). They
  are
• Consistency (on replication)
• Strength (of association)
• Specificity
• Dose response relationship
• Temporal relationship (directionality)
• Biological plausibility (evidence)
• Coherence
• Experiment

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Consistency (I)
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Consistency (II)

• Meta-analysis is an good method for testing
  consistency. It summarizes odds ratios from
  various studies, excludes bias
• Consistency could either mean
• Exact replication (as in lab sciences, impossible
  in epidemiological studies)
• Replication under similar circumstances (possible)

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Strength of Association
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Expressions of Strength of Association

• Quantitatively
• Effect measure (OR, RR) away from unity (the
  higher, the stronger the association)
• P-value (at 95 confidence level) less than 0.05
  (the smaller, the stronger the association)
• Qualitatively
• Accept alternative hypothesis an association
  between the studied exposure and outcome exists
• Reject null hypothesis no association exists

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Dose-response relationship (I)
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Dose-response relationship (II)
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Time-order (temporality, directionality)
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Time order
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Specificity of Outcome
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Specificity of Exposure
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Coherence

• Theoretical compatible with pre-existing theory
• Factual compatible with pre-existing knowledge
• Biological compatible with current biological
  knowledge from other species or other levels of
  organization
• Statistical compatible with a reasonable
  statistical model (e.g. dose-response)

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Biological Coherence (I)
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Biological Coherence (II)
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Susser's criteria (I)

• Mervyn Susser (1988) used similar criteria to
  judge causal relationships.
• In agreement with previous authors, he mentioned
  that two criteria have to be present for any
  association that has a claim to be causal i.e.
  time order (X precedes Y) and direction (X leads
  to Y).

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Sussers Criteria (II)

• Rejection of a hypothesis can accomplished with
  confidence by only three criteria time order,
  consistency, factual incompatibility or
  incoherence.
• Acceptance or affirmation can be achieved by only
  four, namely strength, consistency, predictive
  performance, and statistical coherence in the
  form of regular exposure/effect relation.

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Comparison of Causal Criteria
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References

• Porta M. A dictionary of epidemiology. New
  York, Oxford Oxford University Press, 2008.
• Rothman KJ (editor). Causal inference. Chestnut
  Hill Epidemiology Resources Inc., 1988.
• Hill AB. The environment and disease Association
  or causation. Proceedings of the Royal Society
  of Medicine 1965 58 295-300.
• Susser MW. What is a cause and how do we know one
  ? A grammar for pragmatic epidemiology.
  American Journal of Epidemiology 1991 133
  635- 648.
• Paneth N. Causal inference. Michigan State
  University.
• Rothman J, Greenland S. Modern epidemiology.
  Second edition. Lippincott - Raven Publishers,
  1998.

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• Thank you for your kind attention