Public Health Class

1
Public Health Class

• By
• Georges Metellus, M.D., M.P.H
• American University of Antigua
• 5th Semester Program Director
• Miami Site

2
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

3
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.

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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.

5
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?

6

• 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.

7
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.

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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?

9

• 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)

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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

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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.

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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.

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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.

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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

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Food Source of Diseases

• Food Poisoning
• 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)

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Food Source of Diseases

• 2. Bacterial Food Poisoning
• Salmonella
• Streptococcus Faecalis
• Clostridium Welchii
• Bacillus cereus
• Shigella
• E. Coli

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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

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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

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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

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Epidemiology cont

• Epidemiology draws on
• Biology
• Sociology
• Mathematics
• Statistics
• Anthropology
• Psychology
• Economics and Policy

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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

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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.

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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.

24
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

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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.

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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 gt than1 then the conclusion is
  those with the disease are more likely to have
  the exposure.

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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

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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.
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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.

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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.
  

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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 lt0.05). 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.

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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 persons age, income, or daily expenditure
  on food or a cars cost, the red blood cells,
  then the population is quantitative.
• B. Qualitative when the characteristics
  being studied is non numerical, such as a
  persons sex, marital status, favorite food, or
  occupation or a persons color, then the
  population is qualitative.

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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.

34
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)

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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.

36
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?

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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

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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)

39
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

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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 31/93.4.
  The median is the middle observation in a series
  of ordered observations, i.e., the 50th
  percentile. In this case when the observations
  are 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.

41
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

42
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)

43
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.

44
Skewed Distribution

• Positive Skewed is asymmetry with an excess of
  high values (tail on right mean gt mediangtmode)
• Negative Skewed is asymmetry with an excess of
  low values (tail on left, mean lt medianltmode).
• These skewed curves are not normal distribution

45
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.

46
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

47
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)

48
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.

49
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

50
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.

51
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

52
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 towns population is
• 100/1,200
• 200/1,200
• 300/1,200
• 100/1,000
• 300/1,000

53

• 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.

54
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

55
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 towns population?
• (A) 100/1,200
• (B) 200/1,200
• (C) 300/1,200
• (D) 100/1,000
• (E) 300/1,000

56

• 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)

57
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

58
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

59
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

60
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

61
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 nnumber of times that
  the event can occur)

62
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

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

64
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.

65
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.

66
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.

67
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

68
Indicators of the value of diagnostic tests
(Cont)

• 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)

69
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.

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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.

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Disease Surveillance

• Definition Disease surveillance is the
  systematic collection, organization, and analysis
  of the morbidity and mortality data related to a
  pathological condition.

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Surveillance Steps

• Collection of cases
• Organization of information from cases collected
• Analysis of the organized data
• Dissemination of information

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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.

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Surveillance Tools

• A case definition needs to be established.
• Case report form.
• Guarantied confidentiality
• Adequate resources
• Computer program

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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 Examiners office

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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

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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.

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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.

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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

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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.

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End of the Epidemiology Class

• Thank
• You!
• Have a nice day!