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Deriving Biological Inferences From Epidemiologic Studies

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Title: Deriving Biological Inferences From Epidemiologic Studies


1
Deriving Biological Inferences From Epidemiologic
Studies
2
  • Dr. Mostafa Arafa
  • Associate Prof. of Family and
    Community medicine
  • Faculty of Medicine Medical Sciences
  • King Khaled University, S.A.
  • mostafaarafa_at_hotmail.com

3
Learning Objectives
  • To learn the inferences about a diseases
    etiology that can be derived from different
    epidemiologic studies.
  • To learn the reasoning by which
    epidemiologists select the most plausible
    inference.

4
  • The first step in the epidemiologic analysis is
    the demonstration of a statistical relationship
    between a disease and a biological
    characteristic. The second step is to ascertain
    the meaning of that relationship.

5
  • Statistical associations can be explained as
  • 1- Artificial
  • 2- Due to association of interrelated
  • but non-causal variables
  • 3- Due to uncontrolled confounding
  • 4- Causal or etiological

6
  • Artificial Association
  • Artificial association can result from biased
    methods of selecting cases and controls. It may
    also arise from biased methods of recording or
    obtaining information by interview. Errors in
    conduction or design of the study also may
    introduce spurious association.

7
  • Non-causal association
  • An association between many variables can be
    observed and still be non-causal, because many
    variables can occur together without being a part
    of causal chain. Examination of interrelated
    associations is useful as they may suggest ways
    to reduce exposure to causal variables.

8
  • Confounding
  • If any factor either increasing or decreasing
    the risk of a disease besides the exposure under
    study is unequally distributed in the groups that
    are compared with regards to the disease, this
    will give rise to difference in diseases
    frequency in the compared groups. Such
    distortion, termed confounding and variables are
    called confounder variables.

9
E Etiological Factor
D Diseases
CF Confounding factor
10
Cigarette smoking
Lung cancer
Alcohols
11
  • If any study did not control adequately
    control for potential confounders, the inferences
    drawn from the results may not be well founded.
    Studies in which there was inadequate control of
    all known confounders, the results of which may
    be explained by an unequal distribution of
    extraneous variables in the study groups and not
    by the effect of exposure on disease.

12
Methods used for controlling of confounders
  • During the design of the study
  • Restriction to a specific group
  • Matching
  • During analysis
  • Stratification multivariate analysis

13
  • Causal association
  • The logicians definition of cause is that a
    factor which must be necessary and sufficient for
    the occurrence of a disease.
  • The concept necessary and sufficient implies
    there must be a one-to-one relationship between
    the factor and the disease.

14
  • Example of the sufficient cause for development
    of a disease
  • A1
  • A2
  • A3 B
    C
  • A4
  • A5

Disease
Cellular reaction
15
  • Example for necessary cause for development of
    a disease
  • A1 A2 A3 B C

Cellular reaction
Disease
16
Assessing Causality
  • The following concepts are used in making a
    causal inference
  • Strength of association
  • Consistency of observed association
  • Specificity of the association
  • Temporal sequence of events
  • Dose-response relationship
  • Biological plausibility of association
  • Experimental evidence

17
  • Strength of association
  • It is measured by Relative Risk, and Odds
    ratio. A strong association between exposure and
    outcome gives support to causal hypothesis. When
    a weak association is present, other information
    is needed to support causality.

18
  • Consistency of observed association
  • Confirmation by repeated findings of an
    association in different studies, in different
    population, and in different settings strengths
    the inference of causal association. It is
    equivalent to replication of results in
    laboratory experiments.

19
  • Specificity of the association
  • It has been postulated that one exposure
    should cause one disease and no other exposures
    should cause the disease. This has its roots in
    bacteriological models where one organism is
    associated with one disease. This could not be
    applied in chronic diseases as one exposure could
    lead to many adverse outcomes e.g. smoking and
    cancers, CVD, Birth-outcome.

20
  • Temporal sequence of events
  • It is very obvious that an exposure must
    precede the disease to cause an effect. An
    example for that is the prenatal exposure and
    malformation. In case control studies the problem
    of temporality is quite obvious, however a cohort
    design can resolve the issues of temporality.

21
  • Dose response relationships
  • The risk of development of a disease should be
    related to the degree of exposure of the causal
    factor e.g. duration of estrogen use and the risk
    of endometrial cancer, dosage of smoking and lung
    cancer staging.

22
  • Biological plausibility
  • A causal hypothesis must be viewed in the
    light of its biological plausibility. Statistical
    significant relationship should be understood in
    the view of biological significance. A good
    example is cigarette smoking and lung cancer
    relationship which was initially biologically
    implausible by some, but carcinogens in
    cigarettes were identified, which lent biological
    plausibility to the observed association.

23
  • Experimental evidence
  • Randomized clinical trials is a well run trial
    that may confirm a causal relationship between an
    exposure and outcome. However ethical issues may
    prevent the conduction of such trials.

24
Summary
  • Epidemiologic inferences lead to action, to
    changes in clinical practice, public policy,
    legislation, health education or new health
    directions. Epidemiologic studies can provide
    very strong support for hypotheses of either a
    causal or indirect association. Inferences from
    such studies must take into account all relevant
    biological information. Epidemiologic and other
    evidence can accumulate to point where a causal
    hypothesis becomes highly probable.
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