1. health and fitness
2. disease

Descriptive
Epidemiology
Dr. KANUPRIYA CHATURVEDI
How we view the world…..

Pessimist: The glass is
half empty.

Optimist: The glass is
half full.

Epidemiologist: As
compared to what?
Epidemiology is...
Epidemiology is...

"The worst taught course in Medical
school."
 Medical
Student
Epidemiology is...

"The science of making the obvious
obscure."
 Clinical
Professor
Epidemiology is...

"The science of long division....
I'=[(480)(log2)(10E6)]/[(9.1)(0.955po)+0.45
n]"
 Statistician
Definition of Epidemiology*
"The STUDY of the DISTRIBUTION and
DETERMINANTS of HEALTHRELATED STATES in specified
POPULATIONS, and the application of this
study to CONTROL of health problems."
*Last, J.M. 1988. A Dictionary of Epidemiology, 2nd ed.
Epidemiology: Definition
Dynamic study of the
Determinants
Occurrence
Distribution
Control
Pattern
Of health and disease in a population
Epidemiology
EPI
DEMO
Upon,on,befall
LOGOS
People,population,man
The study of anything that happens to
people
“That which befalls man”
the Study of
Definition of Epidemiology

A quantitative basic science, built on a working
knowledge of probability, statistics and sound
research methods.

A method of causal reasoning, based on
developing and testing biologically plausible
hypothesis pertaining to occurrence and
prevention of morbidity and mortality.

A tool for public health action to promote and
protect the public's health based on science, causal
reasoning, and a dose of practical common sense.
Epidemiology is a Quantitative
Discipline

Measures of frequency


Counts and rates
Measures of association
Relative risk
 Odds ratio


Statistical inference
P-value
 Confidence limits

Clinician
Epidemiologist
Patient’s
diagnostician
 Community’s
diagnostician
Investigations
 Investigations
Diagnosis
 Predict trend
Therapy
 Control
Cure
 Prevention
Epidemiology
Describes
health events
cause and risk factors of disease
clinical pattern of disease

 Identify
syndromes
Identify control and/or preventive measures
So, Epidemiology

Is the basic science of public health

Provides insight regarding the nature, causes,
and extent of health and disease

Provides information needed to plan and target
resources appropriately
Kinds of Epidemiology
 Descriptive
Study of the occurrence and
distribution of disease
 Analytic
Further studies to determine the
validity of a hypothesis concerning
the occurrence of disease.
 Experimental
Deliberate manipulation of the
cause is predictably followed
by an alteration in the effect
not due to chance
Overview of epidemiologic design
strategies

Descriptive


Populations{Correlational studies}
Individual




Case report
Case series
Cross sectional studies
Analytic studies

Observational



Case control
Cohort
 Retrospective
 Prospective
Interventional/Experimental



Randomized controlled trial
Field trial
Clinical trial
Descriptive vs. Analytic Epidemiology
Descriptive

Used when little is
known about the
disease

Analytic

Used when insight about
various aspects of disease is
available
Rely on preexisting
data

Rely on development of new
data

Who, where, when

Why

Illustrates potential
associations

Evaluates the causality of
associations
Both are important!
Descriptive Studies

Relatively inexpensive and less time-consuming
than analytic studies, they describe,

Patterns of disease occurrence, in terms of,




Who gets sick and/or who does not
Where rates are highest and lowest
Temporal patterns of disease
Data provided are useful for,


Public health administrators (for allocation of resources)
Epidemiologists (first step in risk factor determination)
Descriptive Epidemiology

Correlational studies

Case reports

Case series

Cross sectional studies
Correlational Studies (Ecological Studies)



Uses measures that represent characteristics of
entire populations
It describes outcomes in relation to age, time,
utilization of services, or exposures
ADVANTAGES


We can generate hypotheses for case-control studies and
environmental studies
We can target high-risk populations, time-periods, or
geographic regions for future studies
Correlational Studies

LIMITATIONS
 Because data are for groups, we cannot link disease and
exposure in individual

We cannot control for potential confounders

Data represent average exposures rather than individual
exposures, so we cannot determine a dose-response
relationship

Caution must be taken to avoid drawing inappropriate
conclusions, or ecological fallacy
Patterns of disease Occurrence :
Correlation of Population statistics

Ecologic ( correlation ) studies

Used as first step in determining association
plot : disease (population) burden [ Y axis ]
vs. prevalence of “risk factor” [ X axis ]
e.g. smoking vs. lung cancer

-- correlation coefficient : r ; + 1 to -1

Quantifies linear relationship between exposure & disease
Case Reports (case series)


Report of a single individual or a group of
individuals with the same diagnosis
Advantages


We can aggregate cases from disparate sources to generate
hypotheses and describe new syndromes
Example: hepatitis, AIDS
Limitations
We cannot test for statistical association because there is no
relevant comparison group
 Based on individual exposure {may simply be coincidental}

Case report/Case series(contd.)

Important interface between clinical medicine &
epidemiology

Most common type of studies published in
medical journals{1/3rd of all}

e.g. Frisbee finger , break dancing neck

AIDS ~ b/w oct1980-may81, 5 cases of P.carinii
pneumonia were diagnosed among previously healthy
young homosexual males in L.A.
Cross-Sectional Studies (prevalence studies)

Measures disease and exposure simultaneously in a
well-defined population

Advantages



They cut across the general population, not simply those
seeking medical care
Good for identifying prevalence of common outcomes, such
as arthritis, blood pressure or allergies
Limitations



Cannot determine whether exposure preceded disease
It considers prevalent rather than incident cases, results
will be influenced by survival factors
Remember: P = I x D
Cross-Sectional Studies

Can be used as a type of analytic study for testing
hypothesis, when;

Current values of exposure variables are unalterable over
time

Represents value present at initiation of disease


E.g. eye colour or blood group
If risk factor is subject to alterations by disease, only
hypothesis formulation can be done
The epidemiologic approach:
Steps to public health action
DESCRIPTIVE





What (case
definition)
Who (person)
Where (place)
When (time)
How many
(measures)
ANALYTIC


Why (Causes)
How (Causes)
MEASURES
 Counts
 Times
 Rates
 Risks/Odds
 Prevalence
METHODS
 Design
 Conduct
 Analysis
 Interpretation
ALTERNATIVE
EXPLANATIONS
 Chance
 Bias
 Confounding
INFERENCES
 Epidemiologic
 Causal
ACTION
 Behavioural
 Clinical
 Community
 Environmental
Key questions



Why now?
Why here?
Why in this group?
Descriptive Epidemiology





Study of the occurrence and distribution of
disease
Terms:
Time
Place
Person
What are the three categories of
descriptive epidemiologic clues?
□ Person: Who is getting sick?
 □ Place:
Where is the sickness occurring?
 □ Time:
When is the sickness occurring?


PPT = person, place, time
Time




Secular
Periodic
Seasonal
Epidemic
Secular Trend
The long-time trend of disease
occurrence
Tetanus – by year, USA, 1955-2000
During 2000, a total of 35 cases of tetanus were reported. The percentage of cases among persons aged 25-59 years
Has increased in the last decade. Note: A tetanus vaccine was first available in 1933.
900
800
700
600
500
400
300
200
100
0
1955
1960
1965
1970
1975
1980
Year
1985
1990
1995
2000
Possible Reasons for Changes in
Trends

Artifactual
 Errors
in numerator due to
Changes
in the recognition of disease
Changes in the rules and procedures for
classification of causes of death
Changes in the classification code of causes of
death
Changes in accuracy of reporting age at death
Errors in the denominator due to error in the
enumeration of the population
Possible Reasons for Changes in
Trends (cont.)

Real



Changes in age distribution of the population
Changes in survivorship
Changes in incidence of disease resulting
from


Genetic factors
Environmental factors
Other phrases

Cyclic trends ~ recurrent alterations in
occurrence , interval or frequency of disease
 Secular cyclicity
 Levels
of immunizations
 Build up of susceptibles
 e.g.
 Short
Hep A-7 yr cycle,Measles-2yr cycle
term cyclicity
 Chickenpox,salmonella(yearly
basis)
Periodic Trend
Temporal interruption of the general
trend of secular variation
Whooping Cough - Four-monthly
admissions, 1954-1973
Seasonal
A
cyclic variation in disease frequency
by time of year & season.
Seasonal
fluctuations in,
Environmental factors
Occupational activities
Recreational activities
Seasonal Trend
Pneumonia-Influenza Deaths – By year,
1934-1980
Epidemic
An increase in incidence above the expected
in a defined geographic area within a
defined time period
Endemic, Epidemic and Pandemic



Endemic - The habitual presence (or usual occurrence) of a
disease within a given geographic area
Epidemic - The occurrence of an infectious disease clearly in
excess of normal expectancy, and generated
from a common or propagated source
Pandemic - A worldwide epidemic affecting an exceptionally
high proportion of the global population
Number
of Cases
of
Disease
Time
Time clustering
Time Place Cluster/disease cluster
A
group of cases occur close together
& have a well aligned distribution
pattern {in terms of time and place}

Cluster analysis-used for rare or special disease
events.
Time/Place clustering analysis using the
Poisson model
{Poisson spatial/nearest neighbor distribution}

Poisson probability distribution is an inferential statistics probability
measure.

Describes objects/events as they are distributed geographically.

Geographical area divided into a series of equal square areas.

Randomization i.e. each case has equal probability of falling into each
square.

If clustering occurs, probability of cause-effect relationship goes up &
vice versa.
Place
Geographic Area

Diagnosis is Made

Contact occurred
between agent
and host

Source became
infected
Example
Action Level
Home – Patient ill
Investigation
Restaurant – Food
Eaten
Control
Farm – Eggs Infected
Prevention
Person
Age
Sex
Occupation
Immunization status
Underlying disease
Medication
Nutritional status
Socioeconomic factors
Crowding
Hobbies
Pets
Travel
Personal Habits
Stress
Family unit
School
Genetics
Religion
Descriptive epidemiology :
Patterns of Disease Occurrence

distribution of disease in populations
numerator ( “event” count ) / denominator ( group “at
risk” )

by “person” : age , race / ethnicity , gender ,
occupation , education , marital status , genetic
marker , sexual preference

by “place” : residence (urban vs. rural) , worksite ,
social event

by “time” : week , month , year ; sporadic , seasonal
, trends
--- incubation period ; latency
Sources of information





Census data
Vital statistical records
Employment health examinations
Clinical records from hospitals
National figures on food consumption ,
medications, health events etc
Epidemiologic ( scientific ) Approach

1. Identify a PROBLEM :
clinical suspicion ; case series ; review of medical literature

2. Formulate a HYPOTHESIS ( asking the right question ) ;
good hypotheses are: Specific, Measurable, and Plausible

3. TEST that HYPOTHESIS ( assumptions vs. type of data )

4. always Question the VALIDITY of the result(s) :
Chance ; Bias ; and Causality
Epidemiologic Study: threats to Validity

Chance : role of random error in outcome measure(s)
( p - value ; power of the study and the confidence interval )
--- largely determined by sample size

Bias : role of systematic error in outcome measure(s)

Selection bias - subjects not representative

Information bias - error(s) in subject data / classification

Confounding - 3rd variable (causal) assoc. w/ both X and Y
What is a hypothesis?

An educated guess

an unproven idea

based on observation or reasoning, that can be
proven or disproven through investigation.
What goes into a hypothesis?

Characteristics of the disease



The illness
Established modes of transmission
Distribution



In time
By place
By person
Hypothesis formulation

4 methods {derived from 5 canons of inductive
reasoning by John Stuart Mill}
 Method
of difference
 Method of agreement
 Method of concomitant variation
 Method of analogy
Measures

Morbidity: Refers to the presence of disease in a
population

Mortality: Refers to the occurrence of death in a
population
Methods for Measuring
How do we determine disease frequency for a
population?

Rate = Frequency of defined events in specified
population for given time period

Rates allow comparisons between two or more
populations of different sizes or of a population
over time
Compute Disease Rate
Number of persons at risk = 5,595,211
Number of persons with disease = 17,382
Rate = 17,382 persons with heart disease
5,595,211 persons
= .003107 heart disease / resident / year
Rates
Rates are usually expressed as integers and
decimals for populations at risk during specified
periods to make comparisons easier.
.003107 heart disease / resident / year x 100,000
= 310.7 heart disease / 100,000 residents / year
Prevalence vs. Incidence

Prevalence is the number of existing cases of
disease in the population during a defined
period.

Incidence is the number of new cases of
disease that develop in the population during a
defined period.
Incidence

Incidence rate is a measure of the
probability of the event among persons at
risk.
Incidence Rates

Population denominator:
IR = # new cases during time period X K
specified population at risk
Example (Incidence Rate)
During a six-month time period, a total of 53 nosocomial
infections were recorded by an infection control nurse
at a community hospital. During this time, there were
832 patients with a total of 1,290 patient days. What is
the rate of nosocomial infections per 100 patient days?
Mortality Rates

A special type of incidence rate

Number of deaths occurring in a specified
population in a given time period
Use of Mortality rates

Mortality rates are used to estimate disease
frequency when…

incidence data are not available,

case-fatality rates are high,

goal is to reduce mortality among screened or
targeted populations
Mortality Rates: Examples

Crude mortality: death rate in an entire
population


Rates can also be calculated for sub-groups within
the population
Cause-specific mortality: rate at which deaths
occur for a specific cause
Mortality Rates: Examples

Case-fatality: Rate at which deaths occur from a
disease among those with the disease

Maternal mortality: Ratio of death from
childbearing for a given time period per number
of live births during same time period
Mortality Rates: Examples

Infant mortality: Rate of death for children less
than 1 year per number of live births

Neonatal mortality: Rate of death for children
less than 28 days of age per number of live
births
Prevalence

Prevalence: Existing cases in a specified
population during a specified time period (both
new and ongoing cases)

Prevalence is a measure of burden of disease or
health problem in a population
Prevalence
Prevalence: The number of existing cases in the
population during a given time period.
PR
=
# existing cases during time period
population at same point in time
Prevalence rates are often expressed as a percentage.
Factors Influencing Prevalence
Increased by:


Longer duration of the
disease
Prolongation of life of
patients without cure

Increase in new cases
(increase in incidence)

In-migration of cases

Out-migration of
healthy people
In-migration of
susceptible people




Improved diagnostic
facilities
(better reporting)
Decreased by:
Shorter duration of
disease
High case-fatality
rate from disease
Decrease in new
cases (decrease in
incidence)
In-migration of
healthy people
Out-migration of
cases
 Improved cure rate
of cases
Basic Measures of Association

Relative risk& odds ratio

We often need to know the relationship between
an outcome and certain factors (e.g., age, sex,
race, smoking status, etc.)

Used to guide planning and intervention
strategies
2 x 2 contingency table for Calculation of
Measures of Association
Outcome
Exposure
Present
Absent
TOTAL
Present
a
b
a+b
Absent
c
d
c+d
TOTAL
a+c
b+d
a+b+c+d
Note: “Exposure” is a broad term that represents any
factor that may be related to an outcome.
Relative Risk



Ratio of the incidence rates between two groups
Can only be calculated from prospective studies
(cohort studies)
Interpretation



RR > 1: Increased risk of outcome among “exposed”
group
RR < 1: Decreased risk, or protective effects, among
“exposed” group
RR = 1: No association between exposure and
outcome
Calculation of Relative Risk
incidence rate among exposed
RR =
incidence rate among non-exposed
Calculation of Relative Risk
Outcome
Exposure
Present
Absent
TOTAL
Present
a
b
a+b
Absent
c
d
c+d
TOTAL
a+c
b+d
a+b+c+d
Relative Risk =
a




 a  b
c




 c  d 
Relative Risk Case Study
Smoking and low birth weight
Birth Weight
Smoking status
<2500 g
>2500 g
TOTAL
Smoker
120
240
360
Non-smoker
60
580
640
TOTAL
180
820
1000
Answers to Relative Risk Case Study

1. Incidence of LBW among
smokers
120

x1,000  3333
.
360

2. Incidence of LBW among
non-smokers
60

x1,000  938
.
640

3. Relative risk for having a
LBW baby among smokers
versus non-smokers
333.3

 3.6
93.8
Understanding Probability and Odds

Probability: Chance or risk of an event occurring (a
proportion)

Probability= no. of times an event occurs
no. of times an event can occur

Odds: ratio of the probability of an event occurring to
the probability of an event not occurring

Odds = P/(1-P)
Calculation of Odds Ratio
Outcome
Exposure
Present
Absent
TOTAL
Present
a
b
a+b
Absent
c
d
c+d
TOTAL
a+c
b+d
a+b+c+d
Odds Ratio =
ad
bc
Odds Ratio

The odds ratio (OR) is a ratio of two odds.
The OR can be calculated for all three study
designs
 Cross-sectional
 Case-control
 Cohort.

Various approaches to Odds ratio

Cross product/odds ratio


Prevalence odds ratio


cross sectional studies
Exposure odds ratio( odds of exposure in diseased vs. nondiseased)


2 x 2 contingency table (ad/bc)
In rare cases or exotic diseases
Disease odds/Rate odds ratio(odds of getting a disease if exposed
or unexposed)
 Cohort

& cross sectional
Risk odds ratio
 Cross
sectional ,cohort & case control
Odds Ratio

For cohort & cross sectional studies: OR is a
ratio of the odds of the outcome in exposed
persons to the odds of the outcome in nonexposed persons.

For case-control studies: OR is a ratio of the
odds of exposure in cases to the odds of
exposure in controls.

Provides an estimate of the relative risk when
the outcome is rare
Interpretation of Odds Ratio

OR > 1: Increased odds of exposure among those
with outcome

OR < 1: Decreased odds, or protective effects,
among those with outcome

OR = 1: No association between exposure and
outcome
Keeping the Terms Straight

“Risk ratio” = “relative risk”

“Relative odds” = “odds ratio”

Remember – the key is recognizing the terms
“risk” and “odds”
Appropriateness of Measures

Remember that the relative risk can only be
calculated in prospective studies

Odds ratio can be calculated for any design
Cohort / prospective
 Case-control
 Cross-sectional

Inference

The relative risk and odds ratio provide the
magnitude of difference between some factor
and an outcome

How do we know if the magnitude is statistically
significant?
Confidence Intervals

A confidence interval is a range of values that is
likely (e.g., 95%) to contain the true value in the
underlying population
The 10 Steps of Outbreak Investigation










Prepare for field work
Establish the existence of an outbreak
Verify the diagnosis
Define & identify cases
Perform descriptive epidemiology
Develop hypotheses
Perform analytic epidemiology
Refine hypotheses & conduct additional studies
Implement control & prevention measures
Communicate findings
Objectives of Descriptive Epidemiology

To evaluate trends in health and disease and allow
comparisons among countries and subgroups within
countries

To provide a basis for planning, provision and
evaluation of services

To identify problems to be studied by analytic methods
and to test hypotheses related to those problems