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Causal Inference in Epidemiology. Ahmed Mandil, MBChB, DrPH Prof of Epidemiology High Institute of Public Health University of Alexandria. Headlines. Levels of causality Definitions Koch's postulates (1877) Hill's criteria (1965) Susser's criteria (1988, 1991). Relating.

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causal inference in epidemiology

Causal Inference in Epidemiology

Ahmed Mandil, MBChB, DrPH

Prof of Epidemiology

High Institute of Public Health

University of Alexandria

  • Levels of causality
  • Definitions
  • Koch's postulates (1877)
  • Hill's criteria (1965)
  • Susser's criteria (1988, 1991)
  • Exposures: causes, risk factors, independent variables to
  • Outcomes: effects, diseases, injuries, disabilities, deaths, dependent variables
  • Statistical association versus biological causation: cause-effect relationship
levels types of causality
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
definitions i
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
definitions ii
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)
definitions iii
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)

definitions iv
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)
definitions v
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 causation”or “chain of causation”

which is very common for noncommunicable / chronic diseases

effect measures impact fractions
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”
definitions iv1
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).
weighing evidence
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. Hill’s / Susser’s criteria, which were preceded by Koch’s postulates (on infectious diseases)
henle koch s postulates 1877 1882
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.

hill s criteria 1897 1991
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
consistency ii
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)
expressions of strength of association
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
  • 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)
susser s criteria i
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).
susser s criteria ii
Susser’s 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.
  • 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.