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Causal Inference in Epidemiology

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Title: Causal Inference in Epidemiology


1
 Causal Inference in Epidemiology
  • Ahmed Mandil, MBChB, DrPH
  • Prof of Epidemiology
  • High Institute of Public Health
  • University of Alexandria

2
Headlines
  • Levels of causality
  • Definitions
  • Koch's postulates (1877)
  • Hill's criteria (1965)
  • Susser's criteria (1988, 1991)

3
Relating
  • Exposures causes, risk factors, independent
    variables to
  • Outcomes effects, diseases, injuries,
    disabilities, deaths, dependent variables
  • Statistical association versus biological
    causation cause-effect relationship

4
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

5
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

6
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)

7
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)

8
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)

9
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

10
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

11
Deterministic causality (I)
12
Deterministic causality (II)
13
Deterministic causality (III)
14
Deterministic causality (IV)
15
Deterministic causality (V)
16
Deterministic causality (VI)
17
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).

18
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)

19
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.

20
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

21
Consistency (I)
22
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)

23
Strength of Association
24
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

25
Dose-response relationship (I)
26
Dose-response relationship (II)
27
Time-order (temporality, directionality)
28
Time order
29
Specificity of Outcome
30
Specificity of Exposure
31
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)

32
Biological Coherence (I)
33
Biological Coherence (II)
34
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).

35
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.

36
Comparison of Causal Criteria
37
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.

38
  • Thank you for your kind attention
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