This document provides an overview of epidemiology including its definition, history, aims, tools of measurement, and study designs. Some key points: - Epidemiology is the study of disease distribution and determinants in populations and its application to disease control and prevention. It aims to describe disease problems, identify risk factors, and provide data to plan and evaluate health services. - Important historical figures include Hippocrates, Galen, Sydenham, and John Snow, considered the "Father of Epidemiology" for his cholera studies. Measurement tools include rates, ratios, and proportions to compare disease frequency. - Major epidemiological study designs are observational (descriptive, analytical) and experimental (

1. EPIDEMIOLOGY
Presented by- Dr. Parikshit S Kadam
( JR-1)

2. Content
• Introduction
• Definition
• History
• Aims of epidemiology
• Tools of measurements
• Epidemiological studies
• Bias

3. • Association and causation
• Conclusion
• References

4. Introduction
• Epidemiology is the basic science of Preventive and
Social Medicine
• Epidemiology is scientific discipline of public health
to study diseases in the community to acquire
knowledge for health care of the society

5. • Epi – upon
demos- people
logos – study or science

6. Definition
• “ The branch of medical science which deals with
treatment of epidemics”
Parkin ( 1873)
• “ The study of the distribution and determinants of
disease frequency in man”
MacMohan ( 1960)

7. • “The study of the distribution and determinants of
health-related states or events in specified populations,
and the application of this study to the prevention and
control of health problems”
John M. Last (1988)

8. History
• First known epidemiologist was Hippocrates ( 460-375
B.C)
• “ No disease”, he said, “is sent by evils or demons, but
is the result of natural causes”
• Claudius Galen (130-200 A.D) a Greek wrote that “
reason alone discovers some things; experience alone
discovers some things; but to find others, requires both
reason and experience”

9. • In the 17th century, Thomas Sydenham (1624-1689) a
London physician, stressed the importance of careful
observations
• He wrote the history of disease and was called the “
Father of English medicine, or the English
Hippocrates”

10. • John Snow (1813-1858) is considered as the “ Father
of Epidemiology”
• Develop interest in the epidemic of cholera in London,
in August 1854
• Similar study was done by William Budd (1811-1880)
on typhoid fever

11. • First dental field study was reported in Britain 1803 by
Sir John Lincour
• Collected details of the health habits and dental state
of 96 old men all aged over 80 years
• Edwin Saunders, a young dentist studies the eruption
of teeth between ages 9 and 13
• In 1837 addressed his findings to parliament in a
report entitled, “ The teeth a test of age”

12. Aims Epidemiology
• To describe the size and distribution of disease
problems in human populations
• To identify etiological factors (risk factors) in the
pathogenesis of disease
• To provide data essential to the planning,
implementation and evaluation of health services for
the prevention, control and treatment of disease and
for setting priorities among those services

13. Principles Of Epidemiology
1. Exact observation
2. Correct interpretation
3. Rationale explanation
4. Scientific construction

14. Tools In Epidemilogy
• Numerator – Number of events in a population during
specified time
• Denominator
1. Total population
- Mid-year population
- Population at risk
2. Total events
10
100

15. • Basic tools are
1. Rate
2. Ratio
3. Proportion

16. • Comparison between 2 same things
• Numerator is the part of denominator
RATE
• Comparison between 2 different things
• Numerator is not part of denominator
RATIO
• Numerator is the part of denominator
PROPORTION

17. Rate
• It is the frequency of a disease or characteristics
expressed per unit size of the population or group in
which it is observed
• Rate = ×10n
Number of events in a specific period
Population at risk of experiencing the
events or disease

18. • 2000 × 1000
1. Crude rate
2. Specific rate
3. Standardized rate
5000

19. Ratio
• “It expresses a relation in size between two random
quantities, in this numerator is not a component of the
denominator”
• x : y or x
y

20. Proportion
• A proportion is a ratio which expresses the relation in
magnitude of a part of the whole
• Numerator always a part of denominator

21. Measurements in Epidemiology
• Measurement of MORTALITY
• Measurement of MOBIDITY
• Measurement of DISABILITY
• Measurement of NATALITY
• Measurement of presence, absence or distribution of
the characteristics or attributes of the disease

22. • Measurements of medical needs, health care facilities,
utilization of health care services and other health
related events
• Measurement of presence, absence or distribution of
the environment and other factors suspected of causing
the disease
• Measurement of demographic variables

23. Measurement of MORTALITY
• Mortality is an integral part of demography
• Many countries have routine systems for collecting
mortality data. Traditionally and universally, most
epidemiological studies begin with mortality data

24. Problems
The
incomplete
reporting of
deaths
Lack of
accuracy
Lack of
uniformity
Choosing a
single cause
of death

25. Uses
• In indicating priorities for health action
• Allocation of resources, In designing intervention
programmes, And in the assessment and monitoring of
public health programmes

26. Mortality rates
1. Crude death rate :- “ the number of deaths per 1000
people in a population in a given year ”
Crude death rate =
No. of death during the year in a population
Mid-year population

27. 2 . Specific death rate:- A specific death rates measures
the number of deaths among people in a category per
1000 people in that category in a given year
Specific death rate = No. of deaths due to cause
Mid-year population

28. 3. Age-specific death rate:- Death rate specific to a given
age group
4. Case fatality rate :- Represents the killing power of a
disease
5. Proportional mortality rate :- The number of deaths
due to a particular cause in a specific age groups per
100 or 1000 total deaths

29. 6. Standardized rates:- The overall rates adjusted for the
affects of differences in population composition

30. Factors affecting mortality rate
• Birth rate
• Density of population
• Geographical
• Season
• Epidemic experience
• Secular variation

31. MEASUREMENT OF MORBIDITY
‘ Morbidity’ as “any departure, subjective or objective,
from a state of physiological well being ’’.
Linenfeid A M.,Linenfeid D E. Foundation of epidemiology 1990

32. Uses
Describe the
extent and
nature of the
disease
Provide more
comprehensive
and more
accurate and
clinically
relevant
information
Serve as
starting point
for etiological
studies
Needed for
monitoring
and evaluation
of disease
control
activities

33. Incidence
• The number of new cases occurring in a defined
population during a specified period of time
• It refers to
1. Only to new cases
2. During a given period
3. In a specified population or population at risk

34. • Incidence rate = ×1000
Number of new cases of a specific disease
during given time period
The population at risk
Episode
Cumulative

35. • when the population is
exposed to risk for a
limited period of time
such as in epidemic
Attack
rate
• The number of exposed
persons developing the
disease within the range
of incubation period,
following exposure to
the primary case
Secondary
attack
rate

36. Uses
Gives clues to
research into the
etiology and
pathogenesis of
disease
Study of
distribution of
disease
Helps in taking
action to
control disease
Useful in
evaluating the
efficacy of
preventive and
therapeutic
measures

37. Prevalence
• The total number of all current cases (old+new)
existing at a given point in time, or over a period of
time in given population
Point
prevalence
Period
prevalence

38. • Uses of prevalence rate is
 Useful in estimating the magnitude of disease or
health problems of community
 Helpful in identifying the potential high – risk
population
 Useful in administrative and planning purposes like
assessing manpower needs in health services, delivery
of health services etc.

39. Longer duration of the disease
Prolongation of life of patient
Increase in new cases
In-migration cases
Out-migration of healthy patients
Improved diagnostic facilities
Shorter duration of the disease
High case-fatality rate from disease
Decrease in new cases
In-migration of healthy people
Out-migration of cases
Improved cure rate of cases

40. Incidence Prevalence
Numerator Number of new cases of disease
during specific period of time
Number of existing cases of
disease at a given point of time
Denominator Population at risk Population at risk
Focus Whether the event is a new case
Time of onset of the disease
Presence or absence of a disease
Time period is arbitrary
Uses Express the risk of becoming ill
To study acute disease
Useful for studies of causation
Estimate the probability of a
population being ill at the
period of time being studied
Useful for study of chronic
disease and implication of
health services

41. RELATION BETWEEN
PREVALENCE & INCIDENCE
• Prevalence depends upon two factors, the incidence &
duration of illness
• Prevalence = Incidence × Mean duration
P = I × D

42. Epidemiological
study
Observational
Group
Descriptive or
analytical
Ecological
study
Individual
Descriptive Analytical
Experimental
Individual
Randomised
control trial
Group
Field trial
Cross-sectional Cohort study Case control

43. DESCRIPTIVE EPIDEMIOLOGY
• A simple description of the health status of a
community
• Based on routinely available data or data obtained in
special surveys
• Is often the first step in an epidemiological
investigation

44. Formulating an etiological hypothesis
Comparing with known indices
Measurement of the disease
Describing the disease in terms of
Time Place Person
Defining the disease under study
Defining the population to be studies

45. 1. Defining population to be studied
• It is a ‘Population study’ not of an individual
• Defining population by total number and composition
(age, sex, occupation etc. )
• Defined population- can ‘whole population’or ‘a
representative sample’
• It provides ‘denominator’ for calculating rates and
frequency

46. 2. Defining disease under study
• Operation Definition - of disease is essential for
measuring the disease in defined population
• ‘Case definition’ should be adhered throughout the
study

47. 3. Describing disease
Time Year, Month, Week, Season, Duration
Place Country, Region, Climatic zone, Urban/rural,
Community, Cities, Towns
Person Age, Sex, Marital status, Occupation,
Education, Socioeconomic status

48. Time
distribution
Short-term
Common
source
Single
exposure or
point source
Continuous or
multiple
exposure
Propagated
epidemics
Person to
person
Arthropod
vectors
Animal
reservoir
Periodic
Seasonal Cyclic trends
Long-term

49. Place distribution
• Variation in frequency of different disease manifestation from
place to place has long been organized
• The distribution of disease according to places can be classified
as
• International variation
• National variation
• Rural – Urban variation
• Local variation/ distributions

50. INTERNATIONAL VARIATION
• Examine mortality and morbidity in relation to socioeconomic
factors, dietary differences and difference in culture and
behaviour
• Ex : Oral cancer has highest incidence in countries like India
, Bangladesh, Srilanka & Pakistan .
• Breast cancer shows highest prevalence in the countries of the
western world like Netherlands , England and Wales
• Britain has highest rates for lung cancer

51. NATIONAL VARIATION
• Variation in disease occurs in the same country
• Ex : In India endemic goiter, malaria, fluorosis, leprosy with
some parts of country more affected & other parts less
affected or not affected at all
RURAL – URBAN VARIATION
• It is a well established fact that health and disease are not
equally distributed in urban and rural population
• Chronic bronchitis, lung cancer, CVS disease - common in
urban areas
• Skin and zoonotic infections - in rural areas

52. LOCAL DISTRIBUTER
• The preparations of maps showing the distribution of cases
of a disease within the local community is a long
established epidemiologic procedures
• ‘Spot maps’ or ‘shaded maps’ are used to study the
variations in disease frequency
• Eg. If map show clustering of cases it may suggest common
source of infection

53. Migration study
• It is of two types;
• Comparing the rate of occurrence of disease and the death rate
for migrants with those if their same group who have stayed at
home
• Comparing the migrants with the local population of the host
country provides valuable information on the genetically
different groups living in a similar environment
• Anderson M. An introduction to epidemiology. 2nd edition

54. • PERSON DISTRIBUTION
• In descriptive studies, disease is further characterized by
defining the person who developed disease
1. Age :
• It is variable and must always be considered in epidemiologic
study
• Certain disease are more prevalent in specific age group
• Measles – children
• Cancer – middle age
• Atherosclerosis – old age

55. 2 . SEX
• Chronic conditions are more common in females like
thyrotoxicosis, diabetes mellitus, obesity, arthritis etc
• In Males, diseases include peptic ulcer, respiratory
cancer, lung cancer etc

56. 3. Ethnic groups / Ethnicity
• Can be identified in terms of race , religion , place of birth or
combination of three
• Examples :Dental caries-western countries, Periodontal
disease - in Blacks
4. Occupation
- Alter the habit pattern of employee like sleep, alcohol,
smoking
- for identification of risk associated with exposure to
chemicals, physical , biologic agents peculiar to certain
occupations

57. 5. Socioeconomic status
• It has different meanings for different persons , income ,
living conditions, occupation, education and social prestige
• The individuals belonging to the upper social classes exhibit
a longer life expectancy and better health and nutritional
state than the individuals belonging to lower social classes

58. 6. Marital status
• Marital is a descriptive variable that appears on
medical, dental and civil records almost as
regularly as age and sex
• The married persons have lower mortality rates
from nearly all causes of death , than the single
,widowed or divorced person

59. 4. Measurement of disease
• To obtain the clear picture of ‘disease load’ in the
population
• In terms of Mortality, Morbidity and Disability
• Morbidity has two aspects –
- Incidence – Longitudinal Studies
- Prevalence - Cross-sectional studies

60. 5. Comparing with known indices
• Basic epidemiological approach –
1. making comparisons
2. Asking questions
• Making comparison with known indices in population
• By making comparisons - clues about
- Disease etiology
- High risk population

61. 6. Formulation of etiological hypothesis
• A hypothesis is supposition arrived at observation or
reflection
• Hypothesis should specify –
1. Population
2. Specific cause – risk factors/exposures
3. Outcome – disease/disability
4. Dose-response relationship
5. Time response relationship
Smoking 40-50 beedis per day, will result in leukoplakia among 4% of beedi smokers
after 10 years

62. Uses of Descriptive Epidemiology
1. Provide data of magnitude of problem- disease load
2. Provide clues for etiology
3. Provide background data for planning, organizing and
evaluating the preventive and curative services
4. Contributes to research

63. Analytical Studies
• Analyzing relationships between health status and
other variables
• The objective is testing the hypothesis
• Subject of interest is individual, but inference applied
to population
• TYPES
1. Case-control studies(Case reference studies)
2. Cohort studies (Follow-up studies)

64. Case-control studies
• It is first approach to testing causal hypothesis,
• Especially for rare disease
• Three features:-
1. Both exposure and outcome (disease) has occurred
2. Study proceeds backwards from effect to cause
3. It uses a control group to support or refuse a inference

65. Population
Cases
Control
Exposed
Non-exposed
Exposed
Non-exposed
Time
Direction of inquiry

66. • Basic steps in Case-control study:-
1. Selection of cases and controls
2. Matching
3. Measurement of exposure
4. Analysis and interpretation

67. 1. Selection of cases and controls
• CASES –
- Case definition – (Diagnostic criteria and Eligibility criteria)
- Source of Cases – (Hospital or General population)

68. • CONTROLS
- Free from the disease under study
- Similar to the cases in all other aspects
• Sources:-
Hospital, Relative, Neighbourhood, General population

69. 2. Matching
• Matching is process by we selecting controls in a manner
that they are similar to cases in all variables
• Matching is essential for comparability and for
elimination of confounding bias

70. • A Confounding factor is a factor which associated with
both exposure and disease and unequally distributed in
study and control groups
• Matching procedure –
- Group matching (Strata matching)
- Pair matching

71. 3. Measurement of exposure
• Information of exposure of risk factor should be obtain in
same manner for both cases and controls
• Information obtain by:-
- Questionnaire
- Interviews
- Hospital records
- Employment records

72. 4. Analysis and interpretation
1. Exposure rates:- Estimation of rates of exposure of
suspected factor among cases & controls
2. Odds Ratio:- Estimation of disease risk associated
with exposure among cases & controls

73. 1. Exposure rates
CASES (Lung
Cancer)
CONTROLS
(Without
Lung Cancer)
TOTAL
SMOKERS 33 (a) 55 (b) 88 (a+b)
NON-
SMOKERS
2 (c) 27 (d) 29 (c+d)
TOTAL 35 (a+c) 82 (b+d) N= a+b+c+d

74. 2. Odds Ratio
• It is estimation of risk of disease associated with exposure
• It measures strength of association of risk factor and
outcome(disease)
• Odds Ratio = 33 x 27 / 55 x 2 = 8.1
• Smokers have risk of developing lung cancer 8.1 times
higher than non-smoker
ODDS RATIO = AD/BC

75. Cohort Studies
• Also known as prospective study, longitudinal study,
incidence study, forward looking study
• Cohort is group of people with similar characteristics
• Begin with a group of people who are free of disease.
• Whole cohort is followed up to see the effect of
exposure

76. Population
Non-exposed
Exposed
Time
Direction of inquiry
People
without the
disease
Disease
No disease
Disease
No disease

77. • Types of Cohort Studies:-
1. Prospective cohort studies
2. Retrospective cohort studies
3. Combination of retrospective and prospective cohort
studies

78. Elements of Cohort studies
1. Selection of study subjects
2. Obtaining data on exposure
3. Selection of comparison group
4. Follow-up
5. Analysis

79. 1. Selection of study subjects
• General population or
• Special group (Doctors, Teachers, Lawyers)
• Cohort should be selected from the group with
special exposure under study

80. 2. Obtaining data on exposure
a. Cohort members- questionnaire, interview
b. Review of records
c. Medical Examination or tests
d. Environmental surveys

81. • Categorized according to exposure –
1. Whether exposed or not exposed to special causal
factor
2. Degree of exposure

82. 3. Selection of comparison group
• Subjects are categorized in group
according to degree of exposure &
mortality and morbidity compared
Internal comparison
• When degree of exposure not known
• Control group with similar in other
variable
External
comparison
• Comparison with the general population as
exposed group
Comparison with
general population

83. 4. Follow-up
• Regular follow-up of all participants
• Measurement of variable depends upon outcome
• Procedure:-
1. Periodical medical examination
2. Review of hospital records
3. Routine surveillance and death records
4. Mailed questionnaire and phone calls

84. 5. Analysis
• Data are analyzed in terms of :–
a. Incidence rates-
Among exposed and non-exposed
b. Estimation of risk.:-
1. Relative Risk
2. Attributable Risk

85. Incidence rates
SMOKING DEVELOP
ED LUNG
CANCER
NOT
DEVELOP
ED LUNG
CANCER
TOTAL
YES 70 (a) 6930(b) 7000 (a+b)
NO 3(c) 2997 (d) 3000 (c+d)

86. Relative Risk
• Relative risk is the ratio of the incidence of disease
among exposed and incidence among non-exposed
• It is direct measure of strength of the association
between suspected cause and effect

87. Attributable Risk
• AR is the difference in incidence rates of disease among
exposed and non-exposed group
• AR= I.R. among exposed - I.R. among non-exposed
I Incidence among exposed x100
• AR is the proportion of disease due to particular risk factor
exposure
• That means- amount of disease eliminated if the suspected
risk factor is removed

88. Population Attributable Risk
• Population A. R. = I.R. in total population – I.R. among non-
exposed
I.R. in total population X 100
• Population Attributable Risk is useful concept as it give the
magnitude of disease that can be reduced from the
population if the suspected risk factor is eliminated or
modified

89. Exposure and outcome
Exposure Outcome Remarks Direction
Prospective
cohort study
Occurred Followed-
up
Start with
exposure
Forward
looking
Retrospective
cohort study
Occurred Occurred Start with
exposure
Forward
looking
Mixed cohort
study
Occurred Occurred Start with
exposure
Forward
looking
Case control
study
Occurred Occurred Start with
outcome
Backward
looking
Cross-sectional
study
Occurred Occurred Both exposure
and outcome
assesed
Neither forward
nor backward
looking

90. Case control Cohort
Starts with the disease, proceeds from
effect to cause
Starts with the people exposed to the risk
factor, proceeds from cause to effect
It is first approach to test a hypothesis Reserved for testing precisely formulated
hypothesis
Involves fewer subjects Involves large number of subjects
Yields results quickly Results are delayed due to long follow up
periods
Suitable for studying rare diseases unsuitable
Gives relative risk Gives relative risk and attributed risk
Inexpensive Expensive

91. Characteristics Cross-sectional Case-control Cohort study
Time One time point Retrospective Prospective
Other names Prevalence study Case reference study Longitudinal study
Forward looking study
Follow up study
Incidence study
Incidence No No Allows
Prevalence Allows No No
Casuality No Yes Yes
Role of disease Measures disease Begin with disease End with disease
Assesses Association of risk factors and
disease
Many risk factors for single
disease
Single risk factor affecting
many diseases
Data anlaysis Chi-square to assess
association
Odds ratio to estimate risk Provide direct estimate of
relative risk
Advantages Used to calculate prevalence
Faster
Quick and inexpensive
Useful to study rare diseases
Require few subjects
Easy to conduct
Incidence can be calculated
Provides direct estimate of
relative risk
Disadvantages Unusable for acute diseases Recall bias and selection bias
are present
Miss the undiagnosed or
asymptomatic cases
Expensive
Time consuming
Involves large number of
subjects

92. EXPERIMENTAL EPIDEMIOLOGY
• Interventional or experimental study involves attempting to
change a variable in subjects under study
• The effects of an intervention are measured by comparing
the outcome in the experimental group with that in a
control group

93. Objectives of Experimental Studies
1. To provide ‘scientific proof’ for etiology of disease and
risk factor which may allow modification of occurrence of
disease
2. To provide a method of measurement for effectiveness and
efficiency of therapeutic / preventive measure for disease
3. To provide method to measurement for the efficiency
health services for prevention, control and treatment of
disease

94. Types of Experimental Studies
1. Randomized Control Trials
2. Field Trials & Community Trials

95. Randomized Control Trials
• RCT is a planned experiment designed to asses the efficacy
of an intervention in human beings by comparing the effect
of intervention in a study group to a control group
• The allocation of subjects to study or control is determined
purely by chance (randomization)
• For new programme or new therapy RCT is best method of
evaluation

96. Basic Steps in RCT
1. Drawing-up a protocol
2. Selecting reference and experimental population
3. Randomization
4. Manipulation or Intervention
5. Follow-up
6. Assessment of outcome

97. The Protocol
• Study conducted under strict protocol
• Protocol specifies :- aim, objectives, criteria for selection of
study and control group, sample size, intervention applied,
standardization and schedule and responsibilities

98. Reference and Experimental
population
• Reference population (Target Population)
• Is the population in which the results of the study is
applicable
• A reference population may be – Human being, country,
specific age, sex, occupation etc.

99. • Experimental Population (Study Population)
• It is derived from the target population
• Three criteria:-
1. They must be representative of RP
2. Qualified for the study
3. Ready to give informed consents

100. Randomization
• It is statistical procedure to allocate participants in groups –
Study group and Control group
• Randomization gives equal chance to participants to be
allocated in Study or Control group
• Randomization is an attempt to eliminate ‘bias’ and allow
‘comparability’

101. • Randomization eliminates ‘Selection Bias’
• Matching is for only those variable which are known
• Randomization is best done by the table of random numbers
• In Analytical study there is no randomization, we already
study the difference of risk factor. So only option is Matching

102. Manipulation or Intervention
• Manipulation by application of therapy or reduction or
withdrawal of suspected causal factor in Study and
control group
• This manipulation creates independent variable whose
effect is measured in final outcome

103. Follow-up
• Follow-up of both study and control group in standard
manner in definite time period
• Duration of trial depends on the changes expected in
duration since study started
• Some loss of subjects due to migration, death is k/as
Attrition

104. Assessment
• Final step is assessment of outcome in terms of
positive and negative results
• The incidence of positive and negative results are
compared in both group- Study group and Control
group
• Results are tested for statistical significance (p value)

105. Study designs
• Concurrent parallel study design
• Cross-over type of study design

106. Field trials
• Field trials, in contrast to clinical trials, involve people who
are healthy but presumed to be at risk
• Data collection takes place “in the field,” usually among
non-institutionalized people in the general population
• Since the subjects are disease-free and the purpose is to
prevent diseases

107. Community Trials
• In this form of experiment, the treatment groups are
communities rather than individuals
• This is particularly appropriate for diseases that are
influenced by social conditions, and for which
prevention efforts target group behaviour

108. Clinical trials
• Phase I – Human pharmacology and safety
• Phase II – Therapeutic exploration and dose ranging
• Phase III – Therapeutic conformation
• Phase IV – Post marketing surveillance

110. Potential errors in epidemiological
studies
• Bias may arise from the errors of assessment of
outcome due to human element
• Apprehension
• Attention (Hawthorne effect)
• Berksonian bias
• Recall bias
• Neyman bias

111. Bias in cohort studies Selection bias
Information bias
Confounding bias
Post hoc bias
Bias in control studies Berkesonian bias
Recall bias
Telescoic bias
Interviewer’s bias
Bias due to confounding
Prevalence incidence bias
Bias in RCT Subject variation
Observer bias
Investigator bias
Others Hawthorne bias
Pygmalion effect

112. Blinding
• Single blinding
• Double blinding
• Triple blinding

113. Association and causation
1. Causal association
• Direct causal association
• Indirect causal association
2. Non-causal association

114. Conclusion
• Epidemiological research can be particularly useful in
promoting public health because it provides evidence
to enable the public health practitioners to identify
priorities and explore the risk factors

115. References
• Soben Peter - Essentials Of Preventive And
Community Dentirsty-sixth Edition
• Park - A Textbook Of Prventive And Social
Medicine - Nineteenth Edition
• Review of preventive and social medicine by vivek
jain

116. THANK YOU

117. BIOSTATISTICS
Presented by- Dr. Parikshit Kadam
( JR-1)

118. CONTENTS
• Introduction
• Categories of research
• Scientific methods
• Definition
• Uses of biostatistics
• Common statistical terms
• Sources and collection of Data
• Presentation of Data
• Analysis and interpretation

119. • Statistical averages
• Measures of Dispersion
• Sampling and sampling methods
• Sampling errors
• Tests of significance
• Correlation and regression
• Conclusion
• References

120. • The word statistics comes from the italian word “statista”
meaning “statesman” or the german word “statistik” which
means a political state.
• John Graunt(1620-1674) – Father of health statistics

121. Statistics is the science of compiling,
classifying and tabulating numerical data
and expressing the results in a
mathematical or graph form
Biostatistics is that branch of statistics
concerned with mathematical facts and
data related to biological events

122. • We, medical and dental students during period of our
study, learn best methods of diagnosis and therapy
• After graduation, we go through research papers
presented at conferences and in current journals to
know new methods of therapy, improvement in
diagnosis and surgical techniques
• It must be admitted that essence of papers contributed
to medical journals is largely statistical

123. • Training in statistics has been recognized as
“indispensible” for students of medical science
• For eg. if we want to establish cause and effect
relationship, we need statistics
• If we want to measure state of health and also burden
of disease in community, we need statistics

124. • Statistics are widely used in epidemiology, clinical
trial of drug vaccine, program planning, community
medicine, health management, health information
system etc.
• The knowledge of medical statistics enables one to
develop a self- confidence & this will enable us to
become a good clinician, good medical research
worker, knowledgeable in statistical thinking

125. • Everything in medicine, be it research, diagnosis or
treatment depends on counting or measurement
• According to Lord Kelvin, when you can measure
what you are speaking about and express it in
numbers, you know something about it but when you
can not measure, when you can not express it in
numbers, your knowledge is of meagre and
unsatisfactory kind

126. • In Public Health or Community Health, it is called
Health Statistics
• In Medicine, it is called Medical Statistics. In this we
study the defect, injury, disease, efficacy of drug,
serum and line of treatment, etc.
• In population related study it is called Vital Statistics.
e.g. study of vital events like births, marriages and
deaths

127. 127
Research
Basic
v/s
Applied
Observational
v/s
Experimental
Qualitative
v/s
Quantitative
Conceptual
v/s
Empirical

128. BASIC V/S APPLIED
• Basic research is usually considered to involve a
search for knowledge without a defined goal of
utility or specific purpose
• Applied research is problem-oriented, and is directed
towards the solution of an existing problem

129. OBSERVATIONAL V/S
EXPERIMENTAL
• Observational – to observe things
happening without interfering with it
• Experimental – manipulating some
aspect of environment and observing
its effects

130. QUALITATIVE V/S
QUANTITATIVE
• Qualitative research – deals with subjective
aspects
• Quantitative research – based on
measurement of quantity or amount
(objective aspects )

131. CONCEPTUAL V/S EMPIRICAL
• Conceptual – related to some abstract idea
or theory
• Empirical – experience or observation alone
are the tools of research (data based
research )

132. PPRIMARY & SECONDARY
RESEARCH
• PRIMARY RESEARCH- first hand
reports of facts or findings
• SECONDARY RESEARCH- from
primary research
132

133. SCIENTIFIC METHOD
Problem
formulation
Hypothesis
formulation
Data collection
Analysis and
interpretation
Writing a
report

134. DEFINITIONS
• American Heritage Dictionary defines statistics as:
"The mathematics of the collection, organization, and
interpretation of numerical data, especially the analysis
of population characteristics by inference from
sampling”
• The Merriam-Webster’s Collegiate Dictionary
definition is: "A branch of mathematics dealing with
the collection, analysis, interpretation, and presentation
of masses of numerical data"

135. • A Simple but Concise definition by Croxton and
Cowden: “Statistics is defined as the Collection,
Presentation, Analysis and Interpretation of numerical
data”
• “Statistics defined as the science of
 Collection
 Organisation
 Presentation
 Analysis and interpretation of numerical data”

136. Test difference is
real or chance
Study the correlation
Evaluate the efficacy
of vaccines, sera
Measure mortality
and morbidity
Evaluate
achievements of
public health
programs
Help promote health
legislation and create
administrative
standards for oral
health

137. Common statistical terms
• Variable:- A characteristic that takes on different
values in different persons, places/ things
• Constant:- Quantities that do not vary such as π =
3.141, e = 2.718. These do not require statistical study.
In Biostatistics, mean, standard deviation, standard
error, correlation coefficient and proportion of a
particular population are considered constant
• Observation:- An event and its measurement. for eg..
BP and its measurement

138. • Observational unit:- The “sources” that gives
observation for eg. Object, person etc. in medical
statistics:- terms like individuals, subjects etc are used
more often
• Data :- A set of values recorded on one or more
observational units
• Population:- It is an entire group of people or study
elements persons, things or measurements for which
we have an interest at particular time
• Sampling unit:- Each member of a population
• Sample:- It may be defined as a part of a population

139. • STATISTIC/ DATUM:- Measured/ counted fact or piece of
information such as height of person, birth weight of baby
• STATISTICS/ DATA:- Plural of the same such as height of
2 persons, birth weight of 5 babies, plaque score of 3 person
• BIOSTATISTICS:- Term used when tools of statistics are
applied to the data that is derived from biological sciences
such as medicine

140. • Demographic data comprises of population size, geographic
distribution, ethnic groups, socio economic factors and their
trends over time. Such data are obtained from census/
surveys, experiments, hospital records and other public
service reports and are important determinants for oral health
care programs

141. DATA
Qualitative
Data
Nominal Ordinal
Quantitative
Data
Discrete Continuous

142. COLLECTION
OF DATA
PRIMARY
SOURCE
SECONDARY
SOURCE

143. Questionnaire
method
Oral health
examination
Direct
personal
interviews

144. • Main sources for collection of medical statistics:
1. Experiments
2. Surveys
3. Records
• Experiments and surveys are applied to generate data
needed for specific purposes
• While Records provide ready- made data for routine
and continuous information

145. Methods of collection of data
• Method of direct observation:- Clinical signs and symptoms
and prognosis are collected by direct observation
• Method of house to house visit:- Vital statistics and morbidity
statistics are usually collected by visiting house to house
• Method of mailed questionnaire:- This method is followed in
community where literacy status of people is very high

146. Tabulation
Master table
Simple table
Frequency distribution
table
Chartsanddiagrams
Bar chart
Pie diagram
Line diagram
Histogram frequency
polygon
Cartogram
Pictogram
Scatter diagram

147. Tabulation
• As simple as possible
• Data must be according to size or importance,
chronologically or alphabetically
• Should be self-explanatory
• Each row and column labelled concisely and clearly
• Title should be clear, concise and to the point and it
should be separated from body of the table by lines or
spaces

148. Simple Table
States Population 1st march 2011
Andhra pradesh 8,46,65,533
Madhya pradesh 7,25,97,565
Uttar pradesh 19,95,81,477
Karnataka 7,14,83,435
Rajasthan 18,23,45,998
kerela 6,43,35,772

149. Frequency distribution table
• The following figures are the ages of patients admitted to a hospital
with poliomyelitis.. 8, 24, 18, 5, 6, 12, 14, 3, 23, 9, 18, 16, 1, 2, 3, 5, 11,
13, 15, 9, 11, 11, 7, 106, 9, 5, 16, 20, 4, 3, 3, 3, 10, 3, 2, 1, 6, 9, 3, 7, 14,
8, 1, 4, 6, 4, 15, 22, 2, 1, 4, 6, 4, 15, 22, 2, 1, 4, 7, 1, 12, 3, 23, 4, 19, 6,
2, 2, 4, 14, 2, 2, 21, 3, 2, 1, 7, 19
Age Number of patients
0-4 35
5-9 18
10-14 11
15-19 8
20-24 6

150. Attractive to eyes
Give a bird’s eye view of entire data
Lasting impression
Facilitate comparison of data

151. Title self
explanatory
Simple and
consistent
Values on x-axis
and frequency
on y-axis
Few lines drawn
in graphs
Scale of
presentation
mentioned
Scale of
division
proportional

152. Quantitative
data
• Histogram
• Frequency polygon
• Frequency curve
• Line chart or graph
• Cumulative frequency
diagram
• Scatter diagram
Qualitative
data
• Bar diagram
• Pie or sector diagram
• Pictogram
• Map diagram

154. Frequency polygon

155. Frequency curve

156. Line Diagram
10
25
60
85
0
10
20
30
40
50
60
70
80
90
0 1 2 3 4 5
Patients with
periodontitis

157. Biostatistics 157
Cumulative Frequency Diagram
25
35
40
45
55
70
90
0
10
20
30
40
50
60
70
80
90
100
0 to
10
yrs
10 to
20
yrs
20 to
30
yrs
30 to
40
yrs
40 to
50
yrs
50 to
60
yrs
60 to
70
yrs
Prevalence of Dental
Caries ( in percent)

158. Biostatistics 158
Scatter or Dot diagram
0
2
4
6
8
10
12
14
0 5 10 15
Carious lesion
Sugar Exposure

159. Bar chart
• Length of bars drawn vertical or horizontal is
proportional to frequency of variable
• Suitable scale is chosen
• Bars usually equally spaced

160. Biostatistics 160
Bar chart
• They are of three types
- Simple bar chart
- Multiple bar chart
• Two or more variables are grouped together
- Component bar chart
• Bars are divided into two parts
• Each part representing certain item and
proportional to magnitude of that item

161. Biostatistics 161
Simple bar chart
0
50
100
150
200
250
300
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
Number of CD
Patients

162. Biostatistics 162
Multiple bar chart
250
320
45
180
370
80
220
280
95
290
390
40
0
50
100
150
200
250
300
350
400
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
CD Patients
RPD Patients
FPD Patients

163. Biostatistics 163
Component bar chart
1500
1850
1400
2100
300
450
200
500
0
500
1000
1500
2000
2500
3000
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
Patients to prostho
Patients to other
Departments

164. Biostatistics 164
Pie chart
200, 31%
150, 24%
180, 29%
70, 11%
30, 5%
PROSTHO
CONSO
PERIO
ORTHO
PEDO

167. Probability
distribution(P)
Variables(V)
Population (U)

168. • Is collection of units of observation that are of interest
and is the target of investigation

169. • It is a state, condition, concept or event whose value is free
to vary within the population
• A variable is an attribute that describes a person, place or
thing
Qualitative (categorical) Quantitative (numerical)

170. Variables
Categorical
Nominal
Categories are
mutually
exclusive and
unordered
e.g –
gender,blood
group
Ordinal
Categories are
mutually
exclusive but
ordered
e.g- stage of
disease, pain
score(mild/mod
erate severe)
Numerical
Discrete
Integer, values
or counts
e.g – no. of
teeth with
caries
Continuous
Any value in a
range of values-
e.g weight in
kgs, height in
cms

171. Variables
Independent
variables
Dependent
variables
Confounding
or
intervening
variables
Background
variables

172. • P- value is defined as the probability under the assumption of
null hypothesis of obtaining a result equal to or more extreme
than that was actually observed
Binominal distribution
• Two parameters
• Occurs when a fixed number
of subjects
• Characteristic is dichotomous
in nature
• P or 1-p
Normal distribution
• Mathematical curve
represented by two quantities
m and s

173. Should be easy to understand and compute
Should be based on each and every item in
series
Should not be affected by extreme
observations
Sampling stability
PROPERTIES

174. MEAN
• Simplest
measure
MEDIAN
• Middle value
MODE
• Occurs with
greatest
frequency
Mode = 3 Median – 2 Mean

175. • For eg.. the income of 7 people per day in rupees are as
follows. 5, 5, 5, 7, 10, 20, 102= (total 154)
• Mean = 154/7 = 22
• Median= 7
• Median, therefore, is a better indicator of central tendency
when more of the lowest or the highest observations are
wide apart
• Mode is rarely used as series can have no modes, 1 mode
or multiple modes

176. Measures of Dispersion
• Widely known measures of dispersion are
a. The Range
b. The Mean or Average Deviation
c. The Standard Deviation
• Range : Simplest difference between highest and lowest
figures for eg.. Diastolic BP – 83, 75, 81, 79, 71, 90, 75, 95,
77, 94 so, the range is expressed as 71 to 95 or by actual
difference of 24

177. Mean deviation
• It is the summation of difference or deviations from the
mean in any distribution ignoring the + or – sign
• Denoted by MD
MD = € ( x – x )
n
X = observation
X = mean
n = no of observation

178. Standard deviation
• Also called root mean square deviation
• It is an improvement over mean deviation used most
commonly in statistical analysis
• Denoted by SD or s for sample and σ for a population
• Denoted by the formula
SD = € ( x – x )2
n or n-1

179. Coefficient of variation
• It is used to compare attributes having two different
units of measurement e.g. height and weight
• Denoted by CV
CV = SD X 100
Mean
and is expressed as percentage

180. Bell shaped
Perfectly symmetrical
Total area of curve is one,
mean is zero and standard
deviation one
All three measures of central
tendency coincide

182. A sample is a part of a
population called the
universe, reference or parent
population.
Sampling is the process or
technique of selecting a
sample of appropriate
characteristics and adequate
size
Sample frame Sample unit

183. ADVANTAGES
Reduces cost, time and number
Thorough investigation
Provide adequate and in-depth
coverage of sample

184. Efficiency
Representativeness
Measuribility
Size
Coverage
Goal orientation
Feasibility
Economy and cost
efficiency

185. Purposive selection Random selection

186. Non probability sampling
Quota sampling
Purposive sampling
Convenience sampling
Probability sampling
Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling

187. QUOTA SAMPLING
General composition is decided in advance

188. PURPOSIVE SAMPLING
 Non representative subset of some larger population
 Constructed to serve a very specific need or purpose

189. CONVENIENCE SAMPLING
 Is a matter of taking what you get
 It is an accidental sample

190. SIMPLE RANDOM SAMPLING
 Each and every unit in a population has an equal chance of
being included in the sample
 Selection of unit is by chance
Lottery method
Table of
random
numbers

191. SYSTEMATIC SAMPLING
 Selecting one unit at random and then additional units
at evenly spaced interval

192. STRATIFIED SAMPLING
 The population is first divided into subgroups or strata
according to certain common characteristics.
Stratified
random
Stratified
systematic

193. CLUSTER SAMPLING
 Used when the population forms natural groups or
clusters
 Villages, wards blocks or children of a school
If cluster contains
similar persons,
findings cannot be
generalized to the
parent population
Administratively
simple, less
expensive than
random sampling

194. Multiphase
• Part of information from
whole sample and a part
from the sub-sample
Multistage
• First stage is to select the
groups or clusters
• Then subsamples are
taken in many
subsequent stages as
necessary to obtain the
desired sample size

195. Sampling
error
Non sampling
error
Coverage error
Observational error
Processing error

196. • Deals with techniques to know how far the difference
between the estimates of different samples is due to
sampling variation or not
• Rejecting a null
hypothesis
when its true
Type 1 error
• Accepting a
null hypothesis
when its false
Type 2 error
• Probability of
rejecting a null
hypothesis
when it is false
Power

197. Parametric tests Non-parametric tests
t-test- paired/unpaired Mann Whitney Mc nemar’s
ANOVA Test of significance b/w
means
Wilcoxon’s signed rank test
Pearson’s Correlation Coefficient Mc nemar’s
Z-test Chi- square test
Spearman’s Rank Correlation
Freidman

198. Student’s t- Test
 Designed by W.S Gossett whose pen name was
student
Sample randomly selected
Quantitative data
Follow normal distribution
Sample less than 30

199. ANOVA Test
• Used for comparing more than two samples mean
drawn from corresponding normal populations
• One way ANOVA
• Two way ANOVA

200. Z Test
• The sample or the samples must be randomly selected
• The data must be quantitative
• The variable is assumed to follow normal distribution
in the population
• The sample size must be larger than 30

201. Chi-square Test
• Developed by Karl Pearson
• Data measured - terms of attributes/qualities- intended to
test if difference is due to sampling variation
• Involves calculation of a quantity
• 3 important applications:
1. Proportion
2. Association
3. Goodness of fit

202. Relationship
between two sets
of variable
Denoted by r
From -1 to +1
CORRELATION
Statistical method
for studying the
relationship
between a single
dependent variable
and one or more
independent
variables
REGRESSION

203. • Perfect positive correlation: The correlation co-efficient(r) =
+1 i.e. both variables rise or fall in the same proportion.
• Perfect negative correlation: The correlation co-efficient(r)
= -1 i.e. variables are inversely proportional to each other,
when one rises, the other falls in the same proportions.
• Moderately positive correlation: Value lie between 0< r< 1
• Moderately negative correlation: Value lies between -1< r<
0
• Absolutely no correlation: r = 0, indicating that no linear
relationship exits between the 2 variables

204. Conclusion
• The knowledge of medical statistics enables one to
develop a self- confidence & this will enable us to
become a good clinician, good medical research
worker, knowledgeable in statistical thinking

205. References
• Soben Peter - Essentials Of Preventive And
Community Dentirsty-sixth Edition
• Park - A Textbook Of Prventive And Social
Medicine - Nineteenth Edition
• Review of preventive and social medicine by vivek
jain
• Methods in Biostatistics- 7th edition by BK Mahajan

206. THANK YOU