Public Health Class

By Georges Metellus, M.D., M.P.H

American University of Antigua 5 th 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 2 nd 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 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

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 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 case control, 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

 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 50 th percentile. In this case when the observations are ordered 1,2,2,2,3,4,4,6,7- 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 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



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” 1 St 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) (B) (C) (D) (E) 100/1,200 200/1,200 300/1,200 100/1,000 300/1,000

 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

     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 population at midyear

Age-specific death rate=

# of deaths in age group on 7/1 Population of same age group X1,000 X1000

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 # of cases of the disease/ time X100

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

       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 & 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!