This document discusses variables, study variables, response and explanatory variables, and different types of associations that can exist between variables. It defines statistical and non-statistical associations. It discusses causal relationships between variables and how confounding variables can influence associations. Different causal models are described including sufficient and necessary causes. Guidelines for establishing causal relationships are provided, including Hill's criteria of strength of association, consistency, temporal relationship, biological gradient, and compatibility with existing knowledge.

1. PROF. DR. MANSUR-UD-DIN AHMAD
Associations(Chap3)

2. Variables
 A variable is any observable event that can
vary:
 Examples:
 Weight
 Age of an animal
 Number of cases of disease

3. Study variable
 A study variable is any variable that is being
considered in an investigation

4. Response and explanatory
variables
 A response variable is one that is affected by
another (explanatory) variable
 Example:
 Effects of dry cat food on the occurrence of
urolithiasis, cat food is the explanatory variable
and urolithiasis is the response variable

5. Types of association
 Association is the degree of dependence or
independence between two variables.
 Main types of association
1. Non-statistical association;
2. Statistical association

6. causal network

7. Types of association between disease and
hypothesized causal factors.
 (1) Statistically unassociated
 (2) Statistically associated
Causally
associated
Non·causally
associated

8.  Causally associated
Indirectly
associated
Directly
associated

9. Non-statistical association
 A non-statistical association between a
disease and a hypothesized causal factor is an
association that arises by chance
 Frequency of joint occurrence of the disease
and factor is no greater than would be
expected by chance

10. Example
 Mycoplasma felis has been isolated from the
eyes of some cats with conjunctivitis.
 This represents an association between the
Mycoplasma and conjunctivitis in these cats

11.  Surveys have shown that M. felis also can be
recovered from the conjunctivae of 80% of
apparently normal
 Analysis:
 Association between conjunctivitis and
the presence of M. felis arose by chance

12. Statistical association
 Variables are positively statistically
associated when they occur together more
frequently than would be expected by chance
 Negatively statistically associated when they
occur together less frequently than would be
expected by chance.

13. Path diagrams
 Indicating the paradigm
 (a) an example
 (b) of causal and non-causal statistical
associations.
 A = Cause of disease (explanatory variable); B
and C = manifestations of disease
 (response variables) causal association;
non-causal association.

14. B
 A
C
Infection with
haemonchus contortus
Abomasal mucosal
hyperplasia
Anaemia

15. Infection of cattle with Haemonchus
contortus
 If infection of cattle with Haemonchus contortus
were being investigated, then the following
positive statistical associations could be found:
 Between the presence of the parasite and
Abomasal mucosal hyperplasia;
 Between the presence of the parasite and
anaemia;
 BetweenAbomasal mucosal hyperplasia and
anaemia.

16.  The first two associations are causal and the
third non-causal
 Abomasal mucosal hyperplasia and infection
with H. contortus are risk factors for anaemia,
that is, their presence increases the risk of
anaemia

17. Confounding
 Confounding (Latin: confundere = to mix
together) is the effect of an extraneous
variable that can wholly or partly account for
an apparent association between variables

18. Confounder
 A variable that confounds is called a
confounding variable or confounder
 A confounding variable is distributed non-
randomly (i.e., positively or negatively
correlated with the explanatory and response
variables that are being studied)

19.  A confounding variable generally must:
 Be a risk factor for the disease that is
being studied;
 Be associated with the explanatory
variable, but not be a consequence of
exposure to it.

20. The association between coffee
drinking and pancreatic cancer

21. Schematic representation of the
issue of potential confounding

22. Causal models
 The associations and interactions between
direct and indirect causes can be viewed in
two ways, producing two causal 'models

23. Causal model 1
 The relationship of causes to their effects
allows classification of causes into two types:
 Sufficient
 Necessary

24. Sufficient cause
 A cause is sufficient if it inevitably produces an
effect (assuming that nothing happens that
interrupts the development of the effect, such as
death or prophylaxis).
 A sufficient cause virtually always comprises a
range of component causes; disease therefore is
multifactorial.

25. Example
 Distemper virus-cause of distemper, although
the sufficient cause actually involves
 Exposure to the virus
 Lack of immunity
 Other components.

26.  It is not necessary to identify all components
of a sufficient cause to prevent disease
because removal of one component may
render the cause insufficient

27. necessary cause
 If a cause is a component of every sufficient
cause, then it is necessary
 A necessary cause must always be present to
produce an effect

28. Example
 Cause that is necessary but not sufficient
is infection with Actinobacillus ligneresi,
which must occur before actinobacillosis
('wooden tongue') can develop

29. Component causes therefore include factors
that have been classified as
 Predisposing factors:
 Which increase the level of susceptibility
in the host (e.g., age and immune status)
 Enabling factors:
 Which facilitate manifestation of a
disease (e.g., housing and nutrition)

30.  Precipitating factors:
 which are associated with the definitive onset of
disease (e.g., many toxic and infectious agents)
 Reinforcing factors:
 Which tend to aggravate the presence of a disease
(e.g., repeated exposure to an infectious agent in
the absence of an immune response).

31. example
 Pneumonia is a disease that has sufficient
causes, none of which has a necessary
component.
 Pneumonia may have been produced in one case
by heat stress where a dry, dusty environment
allowed microscopic particulate matter to reach
the alveoli.
 Cold stress could produce a clinically similar
result

32. Formulating a causal
hypothesis
 First step in any epidemiological investigation
of cause is descriptive.
 A description of time, place, and population is
useful initially.

33. Time
 Associations with year, season, month, day,
or even hour in the case of food poisoning
investigations, should be considered.
 Information on climatic influences,
incubation periods and sources of infection

34. example
 An outbreak of Salmonellosis in a group of
cattle may be associated with the
introduction of infected cattle feed

35. Place
 The geographical distribution of a disease
may indicate an association with local
geological, management or ecological factors
 Epidemiological maps are a valuable aid to
identifying geographical associations

36. Population
 The type of animal that is affected often is of
considerable importance.
 Hereford cattle are more susceptible to
squamous cell carcinoma of the eye than
other breeds, suggesting that the cause may
be partly genetic

37.  An epidemiological investigation is similar to
any detective novel that unfolds a list of
'suspects' (possible causal factors), some of
which may be non-statistically associated
with a disease, and some statistically
associated with the disease, either causally or
non-causally.

38. Principles for establishing
cause: Hill's criteria
 The British medical statistician, Austin Bradford
Hill, proposed several criteria for establishing a
causal association including
 The time sequence of the events
 The strength of the association
 Biological gradient
 Consistency
 Compatibility with existing knowledge

39. Time sequence
 Cause must precede effect
 Unless bacterial infections were present
before the mares became infertile (incorrect
to infer the bacterial infections)

40. Strength of association
 If a factor is causal, then there will be a strong
positive statistical association between the
factor and the disease.

41. Biological gradient
 If a dose-response relationship can be found
between a factor and a disease, the
plausibility of a factor being causal is
increased.
 This is the basis of reasoning by the method
of concomitant variation

42. Examples
 Frequency of milking in relation to
Leptospirosis
 Smoking in relation to lung cancer

43. Consistency
 If an association exists in a number of
different circumstances, then a causal
relationship is probable.
 This is the basis of reasoning by the method
of agreement.
 An example is bovine hyperkeratosis

44. Example; Bovine hyperkeratosis
 The disease was called 'X disease' because initially the
cause was unknown. It occurred in different
circumstances:
 in cattle that were fed sliced bread;
 in calves that had been licking lubricating oil;
 in cattle that were in contact with wood preservative.
 The bread slicing machine was lubricated with a similar oil to that
which had been licked by the calves.The lubricating oil and the
wood preservative both contained chlorinated naphthalene.This
chemical was common to the different circumstances and
subsequently was shown to cause hyperkeratosis

45. Compatibility with existing
knowledge
 It is more reasonable to infer that a factor
causes a disease if a plausible biological
mechanism has been identified than if such a
mechanism is not known

46. Example
 Smoking can be suggested as a likely cause of
lung cancer because other chemical and
environmental pollutants are known to have
a carcinogenic effect on laboratory animals

47. THANKS