For my colleagues in the public health field.

1. DESCRIPTIVE EPIDEMIOLOGY
ASSOCIATION AND CAUSATION
Department of Public Health Dentistry
People’s College of Dental Sciences
and Research Centre.

2. DEFINITION
The study of the distribution and
determinants of disease frequency
in man.

3. EPIDEMIOLOGICAL METHODS
Epidemiology as a science is organized into
three distinct divisions;
1. Descriptive Epidemiology
2. Analytical Epidemiology
3. Experimental Epidemiology

4. 1. DESCRIPTIVE EPIDEMIOLOGY
It is the study which describes the pattern of
occurrence of a disease or a condition relative to other
characteristics of a population.
The various steps involved in a descriptive study are-
1. Defining the population which has to be studied.
2. Defining the disease under study.
3. Describing the disease in terms of time, place and person.
4. Measurement of the disease.
5. Comparing with known indices.
6. Formulating an etiological hypothesis.

5. VARIOUS STEPS IN DESCRIPITVE
EPIDEMIOLOGY:
1.Defining the population : the defined
population can be the whole population in
a geographic area or more often
representative sample taken from it.
- Not only the total number, but also its
composition in terms of age, sex,
occupation, cultural characters etc.
- the community chosen should be
stable, without migration into or out of the
area.

6. 2. Defining the disease under study
The disease is defined in such away that helps
in identifying and measuring the disease in
the defined way with a degree of accuracy.
“Operational Definition” : by which the disease
or condition can be identified and measured
in the defined population with a degree of
accuracy.
If definition is not valid, it can be powerful
source of error in presentation and
comparability of measurements.
Case definition should be must be adhered to
throughout the study.

7. 3. Describing the disease : The occurrence and
distribution of disease is described by time, place, and person
and identifying those characteristics associated with presence or
absence of disease in individuals.
Characteristics examined :
Time Place Person
Year Climate zone Age Birth order
Season Country, region Sex Family
Month Urban, rural Marital status Height
Week Local community Occupation Weight
Day Towns Social status Blood pressure
Hour of onset Cities education habits
duration institutions

8. 4. Measurement of Disease :
• “Disease load”
• The measurement of disease is done in terms of
mortality and morbidity indicators.
• The morbidity can be expressed in terms of two
units-
“incidence” and “prevalence”.
• Descriptive Epidemiology either uses a cross-
sectional or longitudinal study.

9. 4. A. CROSS-SECTIONAL STUDIES :
-are also called as “prevalence studies” as they measures the
prevalence of disease.
- examination at a point in time, so that results can be projected
to the whole population.
-More useful for chronic than short lived diseases.
B. LONGITUDINAL STUDIES :
Observations are repeated in the same population over a long
period of time.
They are extremely useful :
1. For studying the natural history of the disease and its
outcome.
2. For identifying the risk factors associated with the disease
and
3. For calculating the incidence rate of the disease.

10. 5. COMPARING WITH KNOWN INDICES:
-The essence of epidemiology is make
comparisons and ask questions.
-to arrive at clues to disease etiology.
- also helps in defining the groups who are at
increased risk for certain diseases.

11. 6. FORMULATION OF HYPOTHESIS :
-A hypothesis is a supposition, arrived from
observation and reflection.
-Using the techniques of descriptive
epidemiology, it is possible to formulate
hypothesis relating to disease etiology.
-It can be accepted or rejected using the
techniques of analytical epidemiology.

12. An epidemiological hypothesis should specify :
 the population – characteristics of persons to whom
hypothesis applies.
 specific cause being considered.
 expected outcome – the disease.
 the dose - response relationship – the amount needed to lead
to stated incidence of the effect.
 the time – response relationship – the time period that will
elapse between exposure to the cause and observation of the
effect.
Eg : cigarette smoking causes cancer – is incomplete
hypothesis.
Correct : The smoking of 30-40 cigarettes per day causes lung
cancer in 10% of smokers after 20 years of exposure.

13. USES OF DESCRIPTIVE EPIDEMIOLOGY
1.Useful in providing data with regard to the types of
disease problems and their magnitude in community.
(mortality and morbidity rates and ratios)
2.Provides information on the etiology of the disease
and helps in formulating an etiological hypothesis.
3.Provides background data required for the planning,
organizing, and evaluating preventive and curative
services.
4.Leads the path for further research with regard to a
particular disease problem.

14. When the disease is multifactorial,
numerous factors become
implicated in the ‘web of
causation’ and notion of ‘cause’
becomes confused.
The more associations, the more
investigations to disentangle the
web of causation.

15. Association and relationship are interchangeable.
Association : defined as the concurrence of 2
variables more often than would be expected by
chance.
Correlation :
Indicates the degree of association between 2
characteristics.
Range of correlation coefficient : -1.0 to +1.0.
1.0 : means that 2 variables exhibit a perfect linear
relationship.

16. TYPES OF ASSOCIATION
1.Spurious Association
2. Indirect Association
3. Direct (casual) Association
• one-to-one
• Multifactorial

17. SPURIOUS ASSOCIATION
Sometimes an observed association between a
disease and a suspected factor may not be real.
INDIRECT ASSOCIATION
Associations which at first appeared to be casual have
been found on further study to be due to indirect
association.
i.e. association is because of presence of another
factor (aka ‘confounding factor’)
Eg:
This amplifies the fact that
statistical association does not
necessarily mean causation.

18. One-to-one casual relationship
Two variables are stated to be casually related (AB)
if a change in A is followed by a change in B.
This model suggests that when factor A is present,
disease B must result.
Conversely, when disease is present, the factor must
also be present.
Eg: Measles. (Kosh postulates)

19. Multifactorial Causation
Causal relationship is different in considering a non communicable
disease or condition where aetiology is multifactorial.
Each factor can produce the disease independently.
Eg: Lung cancer (smoking, air pollution, exposure to aesbestos) –
discover a common biochemical event at cellular level. So in this
case molecular or cellular factor will then be considered as a
causal factor.
In second model the causal factors act cumulatively to produce
disease. (synergistic effect. )

20. The causal significance of an association is a
matter of judgement which goes beyond any
statement of statistical probability.
No one by itself is self-sufficient for or sine qua-
non for drawing causal inferences from
statistical associations, but each adds to the
quantum of evidence, and put all together
contribute to a probability of the association
being casual.
CONCLUSION