Transcript EPIDEMIOLOGY Definition epidemiology is the study of the

Public Health Class
By
Georges Metellus, M.D., M.P.H
American University of Antigua
5th Semester Program Director
Miami Site
Public Health
 Definition:
The science and practice of protecting and improving the
health of a community, as by preventive medicine, health
education, control of communicable diseases, research,
application of sanitary measures, and monitoring of
environmental hazards.
Sciences used in Public Health include Epidemiology and
Vital Statistics, which measure health status and assess
health trends in the population
Epidemiology
Definition:
 Is defined as the study of the distribution of a disease or condition in a
population, and the factors that influence that distribution.
 This definition applies not only to communicable diseases but also to
those which are non communicable and to accidental deaths and
injuries.
Purpose:
 It is used to improve the understanding of disease and has been
particularly effective in helping to clarify etiologic agents, susceptibility
factors, mode of transmission and environmental determinants of
disease
 To analyze the occurrence and distribution of disease according to
characteristics such as age, sex, race, occupation and heredity.
 To help complete the clinical picture and natural history of disease by
group analysis
 To evaluate the need for and effectiveness of health services through
field studies.
Epidemic
An Epidemic occurs when there are significantly more
cases of the same disease than past experience would
have predicted for that place.
 Epidemiological studies are necessary to establish the
cause and effect relationship between disease and
environmental factors. Epidemiological studies may do this
through the establishment of statistical correlations
instead of laboratory experiments which attempt to
replicate field conditions.
 Logic must be used in the interpretation of statistical
correlations to exclude absurd inferences regarding
improbable situation.
Exercise
Question: It was learned during an investigation in
Michigan, that between April 30 and May 16, 1968,
approximately 32 cases of infectious hepatitis had been
reported to the County Health Department in North Trail,
Michigan. Could one conclude that this is a problem of
epidemic proportion? Why?
Answer:
One cannot determine whether or not 32 cases of
jaundice constitute an epidemic unless one knows
how many cases to expect in that place during that
time, In other words, be sure these cases are in
excess of what may be expected. One could also
apply a statistical test.
Disease
Disease in the individual may be considered the outcome of the interaction of
three factors: AGENT, HOST, and ENVIRONMENT.
Scrutiny of the results of such interaction enables one to recognize characteristics
common among the sick and rare among the well.
SPECTRUM of DISEASE: is defined as the sequence of events that occurs in the
human organism from the time of exposure to the etiological agent to death. It is
composed of 2 components:
a) a sub clinical
b) clinical illness
INCUBATION PERIOD: This is the interval between the time of contact and/or
entry of the agent and onset of illness.
CARRIERS: are persons who harbor specific infectious agents without discernible
clinical disease but who can be reservoir or sources of infection.
Exercise
 Question: A male patient was exposed to an infected sex
worker on December 10, 2007. He was tested for HIV on
December 13, 2007. His test result then, was negative. On
the 2nd of April 2008 he tested again and found to be HIV
(+). Should this period between the day of exposure and
the day he became positive, be called “period of
incubation”? Was this person a carrier during this period?
 Answer: This period between the exposure to the
virus to the time HIV become positive is called
“Window Period”. During this time the person may
be infectious (carrier)
Disease (cont’)
 FOMITES: Inanimate objects that have come in contact
with a sick person. Not all fomites are equally dangerous
books, coins). The transmission of disease through fomites
may be considered an indirect-contact transmission.
 ZOONOSIS: diseases transmitted through
animals(Brucellosis, Anthrax, Leptospirosis)
 ARTHROPOD-BORNE diseases (insects and arachnids),
Malaria, yellow fever, dengue, filariasis.
 ORNITHOSIS (Psittacosis): diseases transmitted to man
through direct contact with infected birds, including some
of our domestic fowl-chicken, ducks, and turkeys
Health
 Health is a state of complete physical, mental,
and social well-being and not merely the
absence of disease or infirmity.
 According to WHO any impairment of
physiological and mental functioning or physical
and mental growth and development would be
considered to be ill-health or disease.
 HERD IMMUNITY: decreases the probability that
an individual will develop a particular disease
when exposed to an infectious agent.
Health Outcome and Clinical Events
 Dissatisfaction: emotional and mental states such as
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agitation, sadness, or anger.
Discomfort: uncomfortable symptoms such as pain,
nausea, vertigo, vomiting, fatigue.
Disease: a combination of symptoms, physical signs and
laboratory test results.
Disability: the functional status of patients in terms of
ability to live independently and go about their daily lives
at home, work or recreation.
Death: A universal health outcome, the timeliness of the
event being the issue.
Environment
 Environmental health refers to characteristics of
environmental conditions which affect the quality of
health. This is that aspect of public health that is
concerned with those forms of life, substances, forces,
and conditions in the surroundings of man that may
exert an influence on human health and well-being.
Environmental Factors Sources of Diseases
Agents of disease may be:
 Physical (mechanical, thermal, radiant…)
 Chemical (carbon monoxide, fluoride food poisoning)
 Biological (bacteria, viruses, protozoa, fungi, insects)
 Sociological and Psychological
Water supply as source of disease:
Water is required for many other purposes:
 Human consumption
 Agricultural purpose
 Recreational purpose
 In the disposal of human and industrial wastes
 For fire fighting
Food: Source of Diseases
Food Poisoning
1. Toxic Food poisoning
 Bacterial toxin (Staphylococcal;
Botulism)
 Chemical Food Poisoning (Insecticides, cyanide in
silver polish, sodium fluoride and arsenate used in
insecticides)
 Poisonous plants (Mussel poisoning)
Food: Source of Diseases
2. Bacterial Food Poisoning
Salmonella
Streptococcus Faecalis
Clostridium Welchii
Bacillus cereus
Shigella
E. Coli
AIR POLLUTION
Definition:
 The presence in the atmosphere of one or more air
contaminants or combinations thereof in such quantities
and of such duration that they are or may tend to be
injurious to human, plant, or animal life
Sources of pollution:
 Industry has for many years discharged its waste materials
into the air…(oil refineries)
 Homes, public buildings, trains, buses, automobiles: All
contribute to the general contamination of the air.
 Ionizing radiation (genetic effect)
Pollutants include gases, fumes, vapors, aerosols and
particles
International Classification of Diseases
(ICD)
The international classification of Diseases
(ICD) was developed for the classification of
morbidity and mortality information for statistical
purposes. For comparisons to be made in data
reported from one country with that of another, it
has been necessary to establish a standardized
classification system. This system has been revised
at least 10 times by the WHO
Host
Many factors influence the susceptibility of the
host to injury by an agent:
 Customs and habits
 The front line defense that includes the skin,
hair and nails.
 Physiologic defense mechanisms
 Age, sex, race
 Genetics
 Immunity
 Socioeconomic and educational background
Epidemiology cont’
 Epidemiology draws on:
Biology
Sociology
Mathematics
Statistics
Anthropology
Psychology
Economics and Policy
Biostatistics
Definition:
Statistics is a branch of mathematics that consists
of a set of analytical techniques that we apply to
data to help us make judgments and decisions in
problems involving uncertainty. When those
techniques are applied to biological variables to
determine the etiology of diseases and their
distribution in populations, we call it
“Biostatistics”
Categories of statistics
 Descriptive statistics deal with the enumeration,
organization, and graphical representation of data.
 Inferential statistics are concerned with reaching
conclusions from incomplete information, that is,
generalizing from the specific. Inferential statistics use
information obtained from a sample to say something
about the entire population.
Types of Epidemiological Studies
 Prospective Studies: (Cohort; Longitudinal
Studies)
 Subjects are selected based on their exposure
status, and they are generally healthy at the
beginning of the study. The cohort is followed
through time to assess their later disease or
outcome status. An example would be watching a
group of smokers versus nonsmokers through time
and measuring incidence of eventual lung cancer.
Exercise
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The association between low birth weight and maternal
smoking during pregnancy can be studied by obtaining
smoking histories from women at the time of their prenatal
visit and then subsequently correlating birth weight with
smoking histories.
(A) clinical trial
(B) cross-sectional
(C) cohort (prospective)
(D) case-control (retrospective)
(E) None of the above
Answer to previous problem
 (C) This study is a cohort (prospective) study
because the subjects (pregnant women) were
categorized on the basis of exposure or lack of
exposure to a risk factor (smoking during
pregnancy), and then followed to determine if the
outcome(low-birth-weight babies) resulted. The
term of cohort refers to the group of subjects who
are followed forward in time to see which ones
develop the outcome.
Retrospective Studies(Case control studies)
 Case control studies select subjects based on their
disease status. The study population is comprised
of individuals that are disease positive while the
controls are disease negative. The case control
study then looks back through time at potential
exposures these populations may have
encountered. The statistic generated to measure
association is the odds ratio. If the odds ratio is >
than1 then the conclusion is: “those with the
disease are more likely to have the exposure.”
Exercise
 Problem: A study is designed to determine the relationship between
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emotional stress and ulcers. To do this, the researchers used hospital
records of patients diagnosed with peptic ulcer disease and patients
diagnosed with other disorders over a period from July 1988-July 1998.
The amount of emotional stress each patient was exposed to was
determined from these records. This study is best described as a
(A) cohort study
(B) cross-sectional study
(C) Case-control study
(D) Historical cohort study
(E) Clinical treatment trial
Answer to previous exercise
(C). Case-control studies begin with
the identification of subjects who have
a specific disorder (ulcer patients) and
subjects who do not have that disorder
(controls). Information on the prior
exposure of cases and controls to risk
factors is then obtained. In this casecontrol, the investigators used cases
(ulcer patients), and controls(patients
with other disorders), and looked into
their histories (hospital records), to
determine the occurrence of the risk
factor (emotional stress) in each
group.
Case Series
 Describe the experience of a single patient or a group
of patients with a similar diagnosis. Good for
extremely rare diseases. They are purely descriptive
and cannot be used to make inferences about the
general population of patients with that disease. Case
series may suggest the need for a retrospective studies.
Important concepts in Epi. Studies
Hypothesis:
 A statement of belief used in the evaluation of population values
Null hypothesis (Ho):
 Ho states that there is no association between the exposure and
outcome of interest. If the null hypothesis is rejected, we are left with
no choice but to accept that there is an association.
P value: (probability of association)
 If the probability (p) of an association is less than a pre-established
level (usually 0.05), then the investigator concludes that the association
is too unlikely to result from chance (i.e. the association is statistically
significant) . If an association is statistically significant, and if bias and
confounders are not viable explanations for the association, then the
association may reflect a causal relationship between exposure and
outcome.
Example
 In a study relating patient characteristics to serum creatine
levels in patients recovering from myocardial infarction,
investigators tested the null hypothesis that serum creatine
levels are equal in men and women. They found that the
mean serum creatine levels are 1.13mg/dL in men and 0.92
mg/dL in women (p <0.05). Because p is less than 0.05, the
investigators rejected the null hypothesis and concluded
that serum creatine levels in men are significantly different
from those in women.
Populations and Samples
POPULATION:
 A statistical population could be defined as the largest
collection of entities for which we have an interest at a
particular time. A population may consist of animals,
people, machine, plants, or cells.
 There are 2 different kinds of populations:
A. Quantitative: when the characteristic being studied
can be expressed numerically, such as a person’s age,
income, or daily expenditure on food or a car’s cost, the red
blood cells…, then the population is quantitative.
B. Qualitative: when the characteristics being studied is
non numerical, such as a person’s sex, marital status,
favorite food, or occupation or a person’s color, then the
population is qualitative.
Population and Samples Cont’
 VARIABLE: A particular observation of a
quantitative characteristic is a number called
variable.
 POPULATION PROPORTION: In a population
the proportion of observations that possess a
certain characteristic or fall within a particular
category is called population proportion.
SAMPLE:
A sample is a portion of a population. There are
many kinds of sample that can be selected from a
population.
Sampling
 The primary reason for selecting a sample from a
population is to draw inferences about the population it
represents.
 The way the sample is selected determines whether we may
draw appropriate inferences about a population.
 TYPES of SAMPLING
A) Random Sampling: ensures that each individual in the
population has an equal chance of being selected
B) Systematic Sampling: (every nth case)
C) Stratified sampling: (we whish the sample
proportionately to represent the various strata (subgroups)
of the population
D) Cluster Sampling: ( people in a city block)
Sampling error
Sampling error is the difference between the sample and the
population characteristic we seek to estimate.
There are several factors related to sampling that contribute to false
result in epidemiological studies:
 Selection bias: occurs when observations are made on a group of
patients that has been assembled incorrectly.
 Measurement bias: when the methods of measurement are
consistently dissimilar among groups of patients.
 Confounding bias: occurs when two factors or processes are
interrelated or “travel together”, and it is incorrectly concluded that one
of factors is the causal agent.
 Recall bias: Individuals with a particular exposure or adverse health
outcome are likely to remember their experiences differently from
those who are not similarly affected.
Exercise
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To determine the proportion of cesarean sections among
obstetrical deliveries in Baltimore, a random sample of
histories was obtained from two obstetric services: Johns
Hopkins Hospital and University Hospital. The rate of
cesarean sections for the sample was 20%. Later more
complete information revealed that it was not indicative of
the general experience throughout the city. Most hospitals
in the city were found to have rates ranging from 10% to
12%.
 Questions: 1) What constitutes the “target population” for
this study? 2) Why would you regard the sample as biased,
even though a random selection of histories was obtained?
Exercise cont’
 Answers to above questions related to random biases:
 1) All obstetric cases in Baltimore
 2) The sample was restricted by the hospitals used in
the study. These are the two teaching hospitals in the
city and therefore would be expected to handle an
unusually large proportion of difficult cases
Central Tendency
Central tendency expresses characteristics of frequency
distribution:
 MEAN: (or average) is the sum of all data values divided by
the number of data values.
Properties: uniqueness, simplicity, every value in a set of
data enters into the computation of the mean, it is affected
by each value.
 MEDIAN: is the middle data value, below which, and above
which, half of all data values occur.
Properties: uniqueness, simplicity, and it is not as
drastically affected by extreme values as is the mean
 MODE: is the most frequently occurring data value. The
mode may use for describing qualitative data. (modal
diagnosis)
Exercise on Central Tendency Measurement
 In nine families surveyed, the numbers of children per
family were 4, 6, 2, 2, 4, 3, 2, 1, 7. The mean, median, and
mode numbers of children per family are:
(A) 3.4, 2, 3
(B) 3, 3, 4, 2
(C) 3, 3, 2
(D) 2, 3, 5, 3
(E) None of the above
Previous Exercise: Explanation
 The answer is (E)
 The correct values for mean, median, and mode are 3.4, 3,
and 2. The mean is the average: the sum of the observations
divided by he number of observations. In this case, the
mean is 31/9=3.4. The median is the middle observation in
a series of ordered observations, i.e., the 50th percentile. In
this case when the observations are ordered1,2,2,2,3,4,4,6,7- the median is 3. The mode is the
observation that occurs with greatset frequency; in this
case it is 2, which occurs three times.
Measures of Dispersion
The Range.
 The range is the difference between the smallest and the
largest value in a set of observations. (R= XL – XS)
The Variance.
 The measure of dispersion relative to the scatter of the
values about their mean. In computing the variance, we
subtract the mean from each of the values, square the
differences and add them up. this sum of the squared
deviations of the values from their mean is divided by the
sample size, minus 1.
Standard Deviation.
 Is the square root of the variance
Coefficient of Variation
 Expresses the standard deviation as a percentage of the
mean
Frequency Distribution
Frequency distributions represent the frequency of
occurrence of all values of a variable in a data set
 Different frequency distributions have different shapes.
 In a symmetrical distribution, one side of the distribution is
the mirror image of the other.
 In a skewed distribution, the peak of the distribution is
closer to one side. The mean and the median are not equal.
 If the mean is greater than the median, the distribution is
skewed to the right (positive)
 If the mean is less than the median, the distribution is
skewed to the left (negative)
Normal distribution
 Also known as Gaussian or bell-shaped distribution.
 A normal distribution is a theoretical model that has been
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found to fit many naturally occurring phenomena.
The normal distribution curve has a bell- shaped
appearance, symmetric about the mean.
In a normal distribution, the mean, the median and the
mode are equal.
All normal curves have an area equal to 1.0
In a normal distribution, approximately 68% of data values
fall within +/- one SD of the mean, approximately 95% of
data values fall within +/- two SDs of the mean, and 99.7%
of data values fall within +/- 3 Sods.
Skewed Distribution
 Positive Skewed: is asymmetry with an excess of high
values (tail on right: mean > median>mode)
 Negative Skewed: is asymmetry with an excess of low
values (tail on left, mean < median<mode).
 These skewed curves are not normal distribution
Organizing and Displaying of Data
Frequency table:
 The most convenient way of summarizing data is by mean
of “frequency table”
 1St step is to list all observations from the smallest to the
largest.
 The next step is to divide this observations into equal and
non overlapping called “class intervals” the number of
intervals depends on the number of observations but in
general should range from 5 to 15.
 Frequency tables should include an appropriate descriptive
title, specify the units of measurement, and cite the source
of data.
Relative Frequency
Relative frequency
 Represents the relative percentage to the total
cases of any class interval. It is obtained by
dividing the number of cases in the class interval
by the total number of cases and multiplying by
100.
 The use of relative frequency is helpful in making
comparison between two set of data that have a
different number of observations, like 63
nonsmokers and 37 smokers
Graphing Data
Graphs are designed to help the user obtain an intuitive
feeling for the data at a glance. So it is essential that each
graph be self-explanatory.
 Histogram: is nothing more than a pictorial
representation of the frequency table. It consists of an
abscissa which depicts the class intervals and a
perpendicular ordinate which depicts the frequency of
observations. A vertical bar is constructed above each class
interval equal in height to its class frequency.
 Frequency polygon: is constructed by plotting the
individual values at the mid-point of their respective class
interval (of the Histogram. Never show the Histogram)
Graph (cont’)
Arithmetic Line Graph:
 It is obtained by plotting frequencies of occurrence
and the independent variable. Variation arises
because of differences of occurrences. From this
process a line is drawn outlining trends,
similarities and differences in data, identification
of patterns.
 A slope of the line indicates either an increase or a
decrease in the frequency of cases.
 A broken line indicates variations in the values
assigned to the independent variable.
Graph (cont’)
Maps:
 Maps are the graphic representation of data using
location and geographic coordinates.
Pie Charts
 Pie charts represent the different percentage of
categories of variables by proportionally sized pieces of
pie
Some important concepts
Rate:
 Is a common term used to describe a variety of measures of
the frequency of a disease in relationship to the size of a
population. This is a special form of proportion that
includes a specification of time.
 Rates help us formulate hypotheses
 Rates allow valid comparisons within or among population
 Rates are proven to be quite useful when analyzing the
impact, the history, and the trends of an epidemic.
Incidence and Prevalence
 Incidence and prevalence are two major
measurements of disease.
 INCIDENCE: the number of new cases of a
disease in a population over a period of time.
 INCIDENCE RATE =
# of new cases over a period of time x 1000
Population at risk of developing disease
Exercise
Problem: A town in the western United States has a population of 1,200.
In 2004, 200 residents of the town are diagnosed with a disease. In
2005, 100 residents of the town are discovered to have the same disease.
The disease is lifelong and chronic but not fatal.
The incidence rate of this disease in 2005 among this town’s
population is:
(A) 100/1,200
(B) 200/1,200
(C) 300/1,200
(D) 100/1,000
(E) 300/1,000
 The answer is (D). The incidence rate of the
disease in 2005 is 100/1000, the number diagnosed
with the illness divided by the number of people at
risk for the illness. Because the 200 people who got
the disease in 2004 are no longer at risk for getting
the illness in 2005.
Prevalence
 Prevalence measures the number of people in a
population who have the disease at a given point in
time.
 Prevalence Rate=
Total # of cases at a given time X 1000
Total population
Exercise on Prevalence
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Using the same town in the western United States used to study the
incidence of a disease, what would be the prevalence rate of this disease
among the town’s population?
(A) 100/1,200
(B) 200/1,200
(C) 300/1,200
(D) 100/1,000
(E) 300/1,000
 The answer is (C). The prevalence rate of this
disease in 2005 is 300/1200. This figure
represented the people who were diagnosed in
2005 (100) plus the people who were diagnosed in
2004 and still have the disease (200) divided by the
total population (1,200)
Exercise
 In a visual examination survey conducted in
Framingham, Massachusetts among individuals 52 to
85 years of age, 310 of the 2477 persons examined had
cataract at the time of the survey. The prevalence of
cataract in that age group was therefore 310/2477 X 100
or 12.5 percent
Vital Statistics Rates
Crude death rate=
All death during a calendar year X1,000
population at midyear
Age-specific death rate=
# of deaths in age group on 7/1
X1000
Population of same age group
Vital Statistics (cont’)
Cause Specific death rate=
# of death from a specific cause X100,000
Population on July 1
Infant mortality rate=
# of death of person, age 0 to 1 year x1,000
# live births in that year
Vital Statistics (cont’)
Crude birth rate=
# of live births in a calendar year X1000
Population on July 1of that year
Case fatality rate=
# of deaths to a disease / time X100
# of cases of the disease/ time
Probability
The Probability of an event is the quantitative
expression of the likelihood of its occurrence
 We cannot know in advance of a toss whether a
penny will fall heads or tails, nor can we predict
what number will occur when a pair of dices are
rolled. The fact that the outcome cannot be
predicted is due to the element of chance or
randomness. We can only consider the probability
of an outcome and is calculated by using the
formula: P(A) = a/n (a= number of times that the
event does occur; n=number of times that the
event can occur)
Illustration
 In a food poisoning epidemic, there were 99 cases of
illness among the 158 people who attended a banquet.
The probability of illness for a person selected at
random is therefore:
 Pr (illness)= 99/158 = 0.63 or 63%
Measures of risk
Relative and attributable risk are two measures of
association between exposure to a particular factor
and risk of a certain outcome.
 Relative Risk: compares the disease risk in the
exposed population to the disease risk in the
unexposed population. It is calculated by dividing:
Incidence rate among exposed
Incidence rate among no exposed
Attributable risk
 Attributable Risk=
Incidence rate among exposed – incidence rate
among no exposed. It is defined as the amount you
would expect the incidence to decrease if a risk
factor were removed (or the number of cases
attributable to one risk factor.
Risk estimates are probability statements, and it
must be remembered that (1) all those exposed to
the factor do not develop the disease, they merely
have an increase probability of doing so; and (2)
some who have not been exposed to the factor will
develop the disease.
Odds Ratio
 Used only for retrospective studies (case-control.
 The Odds ratio compares disease in exposed, and
nondisease in unexposed population /with disease
in unexposed and nondisease in exposed
population to determine whether there is a
difference between the two. There should be more
disease in exposed than unexposed populations.
Indicators of the Value of diagnostic tests
 Sensitivity: Is the ability of a test to detect truly
infected individual.
 Specificity: is the ability of a test to identify all
non-infected individuals correctly.
 Positive predictive value (PPV): probability of
having a condition, given a positive test. The
number of true positives is divided by the number
of people with a positive test. (An overly sensitive
test that gives more false positives has a lower PPV.
Sensitivity and Specificity
 Sensitivity: can be measured By:
 Person with the disease by screening test X 100
total # of persons with the disease
 Specificity: can be measured by:
Person w/o the disease tested neg X 100
# of person without the disease
Indicators of the value of diagnostic tests
(Con’t)
 Negative Predictive Value (NPV)
Probability of not having a condition, given a negative
test. The true number of true negatives is divided by
the number of people with a negative test. (The higher
the prevalence, the lower the NPV)
Correlation analysis/Correlation Coefficient
(r)
 Correlation indicates magnitude of association,
(not causation) between two variables (i.e. Y and
X).
 The best way of describing the relationship
between Y and X is by a graph called a
scattergram.
 To construct a scattergram, the level of Y is plotted
against the Level of X for each subject.
 The scattergram is very useful for gaining a visual
impression of the relationship but a more
quantitative description is often needed.
Correlation Coefficient (r)
 Correlation coefficient (r) is an index of the extent
to which two variables are associated.
 It can take values between +1.0 and -1.0 depending
on the strength of the association and whether a
positive change in X produces a positive or
negative change in Y.
 A correlation coefficient of “0” indicates the two
variables are not related.
Disease Surveillance
 Definition: Disease surveillance is the systematic
collection, organization, and analysis of the morbidity
and mortality data related to a pathological condition.
Surveillance Steps
 Collection of cases
 Organization of information from cases collected
 Analysis of the organized data
 Dissemination of information
Collection of information
Collection of information is done through
reported cases.
 Identify reporting sources
 Establish liaison with reporting sources
(Physicians, Hospital, other institutions dealing
with patients)
 Access records to generate case reports when
necessary.
 Review and file case reports on a timely basis
 Maitain and complete an accurate surveillance
database.
Surveillance Tools
 A case definition needs to be established.
 Case report form.
 Guarantied confidentiality
 Adequate resources
 Computer program
Major sources of information about
patients
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Hospital &hospital-based physicians
Physician in non-hospital practice.
Public and private clinics.
Record systems:
Death certificates
Tumor registries
Laboratory records
Hemophilia registries
Hospital discharge & abstract summaries
Pharmacy Records
Birth certificate
 TB registries
 Laboratories
 Medical Examiner’s office
Purposes of disease surveillance
 To detect changes in health practices
 To identify research needs & to facilitate epidemiological &
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laboratory research.
To facilitate planning
To provide the necessary information to the Department of
Health for possible follow-up cases & notification of
partner or family members when necessary.
To justify funds for prevention & patient care.
To understand the natural history of the disease and its
magnitude.
To evaluate control strategies
To monitor changes in the behavior of the etiological agent.
Identify risks
Data Analysis and interpretation
 Data from surveillance must be analysed carefully
and interpreted prudently.
 The data need to be organized (in tables, charts,
graphs, maps…) to reflect the basic
epidemiological parameters of TIME, PLACE,
PERSON.
 Differentiate between diagnosed cases and
reported cases.
Data analysis and interpretation cont’
 Proceed from the simplest to the most complex data.
 Examine each condition separately, by numbers and crude
trends. How many cases were reported each year? How
many cases were reported in each age group, sex, race, each
year?
 What are the most reported risks? The most affected
group?
 Examine specific variable such as RATIOS, PROPORTION,
RATES of cases by population or sub-population.
 After looking at each variable separately, one should
examine the relationship among these variables, allowing
for comparison among population or sub-population at
risk.
Dissemination of Surveillance Data
 Establish the message
The message should reflect the basic purpose of the surveillance
system. Information should include: routine data report, routine
analyses of the data, notification of changes in the course of the
disease.
 Define the audience:
Population at risk of exposure or disease.
Public health practitioners
Health care providers.
Policy makers.
The press
The general public
 Develop formats (maps, graphs, diagrams…)
 Evaluation of the effect
How to best use surveillance data in a day
to day HERR
 To identify those who are affected (population, age
groups, race/ethnic groups, etc)
 What are the exposures or behaviors that place
individuals at risk for diseases
 Where are the diseases occurring, Where are the
events that place individuals at risk occurring
 What are the trends?
 To prioritize HERR activities, shape messages
according to risk behaviors.
End of the Epidemiology Class
Thank
You!
Have a nice day!