Epidemiology Made Easy

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Epidemiology Made Easy

• Presented By
• Catherine Tapp, MPH
• August 23, 2005

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Overview of Topics

• General Introduction to Epidemiology
• Dynamics of Disease Transmission
• Measuring the Occurrence of Disease
• Surveillance Overview
• Validity and Reliability
• Statistics
• Epidemiologic Study Designs
• Estimating Risk
• Evaluation
• Chronic Disease Data Sources
• NAACCR Educational CD Overview

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What is Public Health?

• An organized community effort to prevent disease
  and promote health (Institute of Medicine, 1988)
• Goals are to reduce the burden of disease,
  disability and premature death in a population.
• A group of activities
• Composed of many different disciplines i.e.)
  health education, MCH, biostatistics, lab
  science, family planning, nutrition, health
  policy development, veterinary health,
  EPIDEMIOLOGY, etc.

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What is Epidemiology?

• Derived from the Greek words epi (upon) demos
  (people) logy (study of)
• Epidemiology is the dynamic study of the
  distribution, determinants, occurrence and
  control of diseases, health, and injuries in
  human populations.
• The core science of public health.
• Distribution - Descriptive Epidemiology
• Determinants - Analytic Epidemiology

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What is Studied in Epi.?

• Morbidity events and factors related to or
  caused by disease or disability
• Mortality events and factors related to death

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Other Definitions of Epi.

• The relationship of disease or health to the
  population at risk
• The determination, analysis, and interpretation
  of rates
• The study of the patterns of disease occurrence
• Identifying risk factors

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Descriptive Epidemiology

• Examining the distribution of disease in a
  population and observing the basic features of
  its distribution in terms of person, place and
  time.

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Time When?

• Changing or stable?
• pertusis (whooping cough)
• Seasonal variation?
• influenza
• Clustered (epidemic) or evenly distributed
  (endemic)?
• cancer

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Place Where?

• Geographically restricted or widespread
  (pandemic)?
• Relation to water or food supply?
• Multiple clusters or one?

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Person Whom?

• Age
• Gender
• Ethnicity
• Race
• Socio-economic status
• Behavior
• Genetics

• Occupation
• Religion
• Stress
• Personal habits
• Marital status
• School
• Travel

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Analytic Epidemiology

• Focus on causation of disease by testing a
  specific hypothesis about the relationship of a
  disease to a cause (risk factor).

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Risk Factors/Determinants

• The presence of certain risk factors may be
  associated with increased probability for disease
  development.
• What are some examples?

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Underlying Assumption

• Disease or illness does NOT occur RANDOMLY in a
  population.
• Everyone does not have
• equal risk because of characteristics that
  predispose us to or protects us against a variety
  of diseases.

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Objectives of Epidemiology

• To identify the etiology or cause of a disease
  and the risk factors (characteristics that
  increase an individuals risk for a disease).
• Ultimate goal is to intervene to reduce morbidity
  and mortality
• To determine the extent of disease found in the
  community.
• Help planning programs, obtain resources, etc
• To study the natural history and prognosis of
  disease.
• Define the baseline of a disease for comparisons
  post intervention

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Objectives Continued

• To evaluate both existing and new preventive and
  therapeutic measures and modes of health care
  delivery.
• HMOs better outcomes? Worse?
• PSAs and survival status in prostate cancer
  patients
• To provide the foundation for developing public
  policy and regulatory decisions relating to
  environmental problems.
• Radon in the homes
• Occupational risk in workers and required
  regulations

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Changing Patterns of Health

• A major role of epidemiology is to provide clues
  to changes in population health that take place
  over time.
• The goal is to plan for resources for research,
  intervention and services.

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Life Expectancy
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Life Expectancy

• In the U.S., life expectancy has increased
  dramatically over the past century.
• 1900 avg. life expectancy at birth was 47.3
  years
• 1992 avg. life expectancy was 75.8 years
• Mortality reductions due to improved nutrition,
  pasteurization of milk, immunization, smaller
  family size, reduced risk of fatal infections,
  prenatal carenow it is due to antibiotics,
  better anesthesia and post-op care etc.
• Gender and race differences

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Epidemiology Prevention

• A major goal of epidemiology is to identify
  high-risk subgroups in the population. Why?
• Identify risk factors that may be modifiable.
• Direct preventive efforts at these groups
• Screening program for early detection of disease
  i.e.) breast cancer, prostate cancer

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Prevention

• Primary Prevention actions taken to prevent the
  development of a disease in a person who is well
  and does not have the disease in question.
• Examples immunization, exercise, removal of an
  exposure such as smoking or an environmental
  agent
• Active (seatbelt usage) or passive (fluoridation
  of public water supplies)
• Our ultimate goal in public health

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Prevention

• Secondary Prevention the identification of
  people who have already developed a disease, at
  an early stage in the diseases natural history
  through early detection and early intervention.
• Examples breast cancer screening, occult blood
  in stool for colon cancer
• If disease is identified early then intervention
  measures will be more effectiveless costlyless
  invasive

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Prevention

• Tertiary activities designed to reduce the
  limitation of disability from disease and to
  restore function.
• Examples physical therapy for stroke victims,
  cardiac rehab for heart attack victims, halfway
  houses for recovering alcoholics

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Prevention

• Population-based approach a preventive measure
  is widely applied to the population a public
  health approach
• Relatively inexpensive and noninvasive
• Examples dietary advice, advice against smoking
• High risk group approach usually requires a
  clinical action to identify the high-risk group
  to be targeted
• Expensive, more invasive and inconvenient
• Examples screening for cholesterol in children
  from high risk families

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Epidemiology Clinical Practice

• Go hand-in-hand
• The practice of medicine is dependent on
  population data
• Diagnosis population-based process
• Prognosis the prediction of disease
  population-based
• Selection of therapy population-based
  randomized clinical trials
• Based on how groups of people respond to certain
  therapies

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Epidemiology Clinical Practice - Metaphor

• Imagine a torrential flood contributed to by
  a failure in a levee system. This flood is
  washing away citizens in record numbers. In such
  circumstances, it is the physicians task to
  offer life-jackets to citizens one at a time.
  The epidemiologist, on the other hand, attempts
  to find the flaw in the levee system to prevent
  further flooding.
• Fixing the flaw in the levee is a matter of
  public health.

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The Epidemiologic Approach

• How does the epidemiologist proceed to identify
  the cause of a disease? Via a multi-step
  process.
• First determine if an association exists
  between a factor (e.g., sun exposure) or a
  characteristic of a person (e.g., moles, fair
  skin) and the development of disease (e.g., skin
  cancer).
• Second is this association a causal one? (not
  all associations are causal)

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Usual Sequence of Epi. Method
Theory or Observation
Review Existing Information
Define/Refine Hypothesis
Preventive Action
Descriptive Studies
Analytic Studies
Collect Analyze Data
Formulate Conclusions
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From Observation to Preventive Action

• Shoe Leather Epi.
• Edward Jenner and smallpox vaccine
• John Snow (the Father of Epidemiology) and
  London cholera epidemic
• Smoking and lung cancer

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Jenner Smallpox

• Late 18th century worldwide epidemic with 400,000
  deaths a year.
• Those who survived smallpox were then immune
• Jenner observed dairy maids developed cowpox and
  did not contract smallpox during outbreaks
• Hypothesis - cowpox was protective
• Jenner knew nothing about viruses or etiologyhe
  operated purely on observational data that became
  the basis for a preventive intervention.

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Snow Cholera

• Cholera was a major problem in England in the mid
  19th century
• Snow hypothesized that cholera was transmitted
  through contaminated water
• Broad Street Pump 600 deaths in one week
• Water was supplied via water supply companies
  with intakes from the polluted part of the river
• Lambeth water company moved intake upstream
• Mortality should then be lower in people getting
  water from L.

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Take Home Message

• Although important to understand the biology and
  pathogenesis of disease, it is not always needed
  to take preventive action.

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In Summary

• Epidemiology is the study of the distribution and
  determinants of disease in populations.
• Public health uses epidemiologic study findings
  to prevent and control health problems in human
  populations.
• Major causes of mortality have changed radically
  over the current century.
• Epidemiology is an invaluable tool in the control
  of disease and the human suffering associated
  with it.

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Dynamics of Disease Transmission
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The Dynamics of Disease Transmission

• Human disease results from an interaction of the
  HOST, the AGENT and the ENVIRONMENT.
• Communicable diseases are most commonly used to
  describe the underlying principles of disease
  transmission.
• Disease causation is usually described in terms
  of two models epidemiologic triad/triangle
  web of causation

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Epi. Triad of Disease
HOST
VECTOR
AGENT
ENVIRONMENT
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Host Characteristics

• Include personal characteristics and behaviors,
  genetic predispositions, immunologic and other
  susceptibility-related factors that increase or
  decrease the likelihood of disease.
• Examples age, sex, race, religion, customs,
  occupation, genetics, marital status, family
  background, previous diseases, immune status

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Agent Characteristics

• Biological, physical, or chemical factors whose
  presence, absence, or relative amount (too much
  or too little) are necessary for the disease to
  occur.
• Examples bacteria, viruses, fungi, poison,
  alcohol, smoke, drugs, trauma, radiation, fire

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Environmental Characteristics

• External conditions, other than the agent, that
  contribute to the disease process.
• Can be physical, biological, or social in nature.
• Examples temperature, humidity, altitude,
  crowding, housing, neighborhood, water, milk,
  food, radiation, air pollution, noise

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Web of Causation

• This model de-emphasizes agent and stresses the
  multiplicity of interactions between the host and
  environment.
• Multiple actions and reactions occur between
  promoters and inhibitors of disease.
• Example diabetes, cancer

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Multiplicity of Factors Underlying Adult-onset
Diabetes
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Modes of Transmission

• Direct
• Person to person
• Indirect
• Common vehicle single, multiple, continuous
  exposures
• contaminated air, food, or water supply
• Vector
• Mosquito (West Nile Virus), deer tick (Lymes
  Disease
• Airborne
• dust, droplet nuclei

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Stages of Disease

• Important to recognize the broad spectrum of
  disease severity
• Iceberg concept the tip is only visible much
  like the clinical appearance of diseasebulk of
  the problem may be hidden from view.
• Example tuberculosis cases are not always
  clinically visible other examples??

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Iceberg Concept of Infectious Diseases
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Dog Bite Example
451,000 Medically Treated
3.73 Million Nonmedically treated
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Stages of Disease

• Clinical Disease signs and symptoms
• Nonclinical (inapparent) Disease can include
• Preclinical disease not clinically apparent but
  destined to progress to clinical disease
• Subclinical disease not clinically apparent and
  disease will not develop diagnosed by antibody
  response or culture
• Persistent (chronic) disease infection persists
  for years or for life
• Latent disease no active multiplication of the
  agent

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Stages of Disease Prevention
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Carrier Status

• Individual has the organism but no antibody
  response or no evidence of clinical illness
• Can infect others
• Carrier status may be of limited duration or last
  for months or years
• Example Typhoid Mary carried Salmonella typhi,
  died in 1938, worked as a cook in NYC, caused 10
  typhoid fever outbreaks 51 cases and 3 deaths

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Reservoirs

• Living organisms or inanimate matter (such as
  soil) in which an infectious agent normally lives
  and multiplies.
• Essential for infectious agent to maintain and
  perpetuate itself.
• Examples humans, animals and environmental
  sources

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The emics

• Endemic habitual presence or usual occurrence
  of disease in a geographic area. (e.g.) chicken
  pox in OK
• Epidemic occurrence in a community or region of
  a group of illnesses of similar nature, clearly
  in excess of normal expectancy, and derived from
  a common or propagated source. (e.g.) obesity in
  U.S.
• How do we know if there is an excess? Anything
  above normal baseline. (On-going surveillance)
• How much to expect? Really dont know no clear
  cut example
• Pandemic a worldwide epidemic (e.g.) certain
  flu viruses

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Endemic vs. Epidemic
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Excess of Deaths in London
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Disease Outbreaks

• Common-vehicle exposure (e.g.) outbreak in a
  group of people who ate a certain food
• Single exposure (e.g.) food served only once
• Multiple exposures (e.g) food served more than
  once and people eat more than once
• Periodic or continuous (e.g.) a water supply is
  contaminated with sewage b/c of leaky pipes

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Single-exposure, common-vehicle outbreak

• Such outbreaks are explosive a sudden and rapid
  increase in the of cases of a disease
• The cases are limited to those who share the
  common exposure.
• Cases rarely occur in persons who acquire the
  disease from a primary case.

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Herd Immunity

• Resistance of a group to an attack by a disease
  to which large proportions of the group are
  immune.
• If a large percent of the population is immune,
  the entire population is likely to be protected,
  not just those who are immune.
• Why does it occur? Disease spreads from one
  person to another in any community and once a
  certain amount of immune people is reached, the
  likelihood is small that an infected person will
  encounter a susceptible person.

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Herd Immunity Continued

• Why is herd immunity so important? It is not
  necessary to carry out 100 immunization in a
  population to be highly effectivepart of the
  population will be protected due to herd
  immunity.
• Certain conditions must be met
• Transmission must occur within one host species
• Assumes random mixing of the population
• Percentage of population necessary for immunity
  varies with each disease (e.g.) est. that 94 of
  the population requires immunity before the chain
  of transmission of measles is interrupted.

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Incubation Period

• Interval from receipt of infection to the time of
  onset of clinical illness.
• Person usually feels well
• Person may be infectious during this period
• Different diseases have varying incubation
  periods.
• In noninfectious diseases the incubation period
  is referred to as the latent period.
• Variability may be due to differences in host
  susceptibility, pathogenicity or the agent, or
  dose of exposure

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Latent Periods of Bladder Tumors
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Epidemic Curve

• Distribution of times of onset of disease
• In a single exposure, common-vehicle epidemic,
  the epidemic curve represents distribution of
  incubation periods.
• If the infection took place at one point in time,
  the interval from that point to the onset of each
  case is the incubation period in that person.

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Graphical Representation of an Outbreak
Classic epi. curve
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Outbreak Investigations

• Three critical variables in investigating an
  outbreak or epidemic are
• When did the exposure take place?
• When did the disease begin?
• What was the incubation period for the disease?

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Outline of an Epidemic Investigation

• Preliminary Analysis
• Verify the diagnosis
• Verify the existence of an epidemic
• Learn about the disease using existing
  information
• Describe the epidemic with respect to time, place
  and person (cases numerator)
• What population is at risk? (denominator)
• Formulate and test hypotheses

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Outline of an Epidemic Investigation Continued

• Further Investigation and Analysis
• Search for additional cases
• Collect additional data
• Analyze the data (case-control studies)
• Make a decision about the hypotheses considered
• Intervention and follow-up

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Outline of an Epidemic Investigation Continued

• Report of the Investigation
• Discussion of factors leading to the epidemic
• Evaluation of measures used for control of
  present epidemic
• Recommendations for future prevention of outbreaks

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Measuring the Occurrence of DiseasePart I
Measures of Morbidity
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Measuring the Occurrence of Disease

• Ones knowledge of science begins when he can
  measure what he is speaking about and express it
  in numbers.
• - Kelvin

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Development of Disease in an Individual
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Transmission of Disease

• Measure the frequency of disease occurrence
• Measure the frequency of deaths
• How are these frequencies measured?
• Rates are used to express the extent of morbidity
  and mortality from a disease how fast the
  disease is occurring in a population
• Proportions describe what fraction of the
  population is affected

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Measures of Morbidity

• Incidence the number of new cases of a disease
  that occur during a specified period of time in a
  population at risk for developing the disease.
• Prevalence number of affected (diseased)
  persons present in the population at a specified
  time divided by the number of persons in the
  population at that time.

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INCIDENCE
Incidence per _________ of new cases in a
specified time of persons at risk for
developing the disease during that time period
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INCIDENCE

• NEW cases transition from non-disease to
  disease (the numerator)
• Measure of events
• Measure of risk
• Looked at in any population group
• Examples particular age-group, males, females,
  occupational group, exposed group of people, etc.

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INCIDENCE

• Denominator - of people who are at risk for
  developing the disease.
• Those individuals included in the denominator
  must have the potential to become part of the
  group that is counted in the numerator.
• Incidence of uterine cancer only in women
• A period of time must be specified for incidence
  to be a measure of risk.
• Arbitrary 1 week, month, year(s)
• Must be clearly specified.

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PREVALENCE
Prevalance per ______ of cases of disease in
pop at specified time of persons in the pop at
the specified time
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PREVALENCE

• Numerator existing cases (old and new) with
  differing durations of disease
• NOT a measure of risk but a measure of the
  disease burden on the community
• A slice through the population at a point in
  time to determine who has disease and who does
  not.
• Does not determine when the disease developed

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PREVALENCE

• Point Prevalence prevalence of disease at a
  point in time
• Do you currently have asthma?
• Period Prevalence prevalence of disease at a
  specified period of time (e.g.) a single calendar
  year
• Have you had asthma during the last 2 years?

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Incidence Prevalence
Point prevalence Numerator depends on when survey
is done.
5 cases (numerators) of a disease. What is the
numerator for incidence in 2000?
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Relationship Between Incidence and Prevalence
New Cases
Incidence (inflow)
Prevalence (water level)
Old Cases
Recovery or death
A continual addition of new cases is increasing
the prevalence, while death and/or cure is
decreasing the prevalence
Former Cases
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PREVALENCE

• A dynamic situation
• A continual addition of new cases increases the
  prevalence
• Death and/or cure decreases the prevalence
• Can be seen as a paradox a new measure is
  introduced that enhances survival or detects
  disease in more people thus increases prevalence
  (not always bad if death is prevented) Example
  insulin diabetes
• Valuable for planning health services

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Comparison of Incidence Prevalence
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Problems with Incidence and Prevalence
Measurements
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Problems with Numerators

• Defining who has the disease
• Differing sets of diagnostic criteria
• Prevalence estimates are affected by the set of
  criteria that is used
• Who should be included in the numerator?
• How are cases found?
• Use available data
• Conduct a study that is designed to gather data
• Can involve interviewsmany errors can arise
  (refer to Gordis table 3-4)

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Problems with Hospital Data

• Data from medical records are very important for
  epidemiologic studies.
• Hospital admissions are selective
• Medical records are not designed for research but
  for patient care
• Can be incomplete, illegible, missing data
• Problem defining denominators b/c no defined
  catchment areas therefore difficult to calculate
  rates

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Problems with Denominators

• Selective undercounting of certain population
  groups
• Example young males in ethnic minority groups
• Everyone in the denominator must have the
  potential to enter the numerator not that
  simple
• Example hysterectomy and uterine cancer rates

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Corrected rates are higher. Why? Women who had
hysterectomies are removed from the
denominatorthis decreases and the rate gets
larger. Trend over time is not significantly
changed.
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Measuring the Occurrence of DiseasePart II
Measures of Mortality
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Mortality Rates

• Rates are used to address the risk of dying
• Same rules as morbidity apply n/d, time factor
• Several types of mortality rates
• Annual mortality rate from all causes
• Age-specific mortality rate
• Disease-specific or cause-specific mortality rate
• Simultaneous restrictions ex.) age and cause of
  death
• when a restriction is placed on a rate it is
  called a specific rate

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Specific Rates

• Stratifies populations into more homogeneous
  groups (strata) based on the demographic
  characteristic thought to be related to the
  outcome of interest.
• Examples
• Age-specific, sex-specific, race-specific

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Age-Specific Mortality Rate

• Provide a broader view of mortality for subgroups
  stratified by age
• Numerator and denominator are limited to a
  specific age group
• Comparable across populations

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Crude Rates

• Summary statistics that ignore the heterogeneity
  of the population under investigation

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Crude Rates

• Advantages
• Actual summary rates
• Easy calculation for international comparisons
• Disadvantages
• Difficult to interpret and differences in crude
  rates because populations usually vary in
  composition (e.g. age)

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Years of Potential Life Lost (YPLL)

• A mortality index to gauge the loss of productive
  years in a person who dies.
• Assumes that death in the same person at a
  younger age results in a greater loss of future
  productivity years than death in older age.
• Helps prioritize resources to have maximum impact.

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Why look at mortality?

• It is an index of severity of a problem from both
  clinical and public health standpoints.
• It can be an index of risk of disease, especially
  if the disease is quickly fatal but not if the
  disease is mild and not fatal. (e.g., pancreatic
  ca thus a good surrogate for incidence)

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Problems with Mortality Data

• Accuracy and trends over time may be influenced
  by several factors
• Changes in coding of underlying cause of death on
  death certificates e.g., change to a newly
  revised ICD
• Changes in definition of diseases e.g., AIDS
• Countries and regions vary greatly in the quality
  of data on their death certificates
• Whenever a time trend of increased or decreased
  mortality we must first askIs this real?

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Reported causes of death in early 1900s

• Died suddenly without the aid of a physician
• A mother died in infancy
• Deceased had never been fatally sick
• Died suddenly, nothing serious
• Went to bed feeling well, but work up dead

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Comparing Mortality in Different Populations

• Important use of morality data is to compare two
  or more populations or one population over time.
• Many characteristics that can affect mortality
  (age, gender, race) may differ in populations
  thus making comparisons of rates problematic.
• Methods developed for comparing mortality in
  differing populations that hold constant (adjust
  for) certain characteristics like age or others.
• Rate Standardization

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Age Adjustment Methodologies

• Allows comparisons of rates between populations
  that differ by age, a common variable that can
  influence the rate
• Direct Method
• Indirect Method (Standard Mortality Ratios)

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Adjusted Rates

• Advantages
• Summary statements
• Differences in group composition removed,
  allows unbiased comparison
• Disadvantages
• Fictional rates
• Absolute magnitude is dependent on the standard
  population that is chosen
• Trends in subgroups can be masked

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Direct Age Adjustment

• Requires age-specific rates in the population
• The age for each case
• The population at risk for each age group in the
  pop.
• Requires an age structure of a standard pop.
• Requires a standard population, to which the
  estimated age-specific rates can be applied
• Can affect the magnitude of the age-adjusted
  rates
• 1940 vs. 1970 vs. 2000 U.S. Standard Population

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Interpreting Observed Changes in Mortality

• An increase or decrease in mortality could be
  artifactual or real
• If artifactual, could be a result of problems
  with the numerator or denominator (Table 3-22 pg.
  56)
• If real, what are some possible explanations?
  (Table 3-23)

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Overview of Surveillance
A state of continuing watchfulness the
systematic collection, analysis and dissemination
of data on adverse health outcomes occurring in a
defined area. -Centers for Disease Control
and Prevention
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Purpose of Public Health Surveillance

• Assess public health status
• Define public health priorities
• Evaluate programs
• Stimulate research

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Why Surveillance Data is Important

• Detect and control disease outbreaks
• Determine disease etiology and natural history
• Monitor disease trends
• Detect changes in health practice and health
  behaviors

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Why Surveillance Data is Important Continued

• Evaluate the effectiveness of intervention
  programs, policies and activities
• Detect need for changes in provision of health
  care services
• Determine appropriate and efficient allocation of
  resources and personnel, and development of
  appropriate policies

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Cancer Surveillance

• The foundation for a national, comprehensive
  strategy to reduce illness and death from cancer.

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Cancer Surveillance

• Guide planning evaluation of cancer control
  programs and interventions
• Are prevention measures making a difference?
• Determine cancer patterns in various populations
• Identify geographic and/or ethnic variations
• Monitor cancer trends over time
• Advance clinical, epidemiological, and health
  services research
• Help prioritize health resource allocation

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Good surveillance does not necessarily ensure
the making of right decisions, but it reduces the
chances of the wrong ones. - A. Langmuir MD,
MPH former Director of Epidemiology for CDC
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• Since cancer is the second leading cause of
  death in Arkansas, it is essential that specific
  information concerning this group of diseases be
  collected, analyzed and reported.

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Heart Disease and Cancer Mortality Arkansas,
1979 - 1998
Age-adjusted to 1940 U.S. population
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Cancer Registry

• Cornerstone of cancer surveillance
• Best tool to measure the nations progress
  against cancer

The unique role of the central cancer registry
is to be the eyes through which cancer control
problems can be seen. - Thomas C. Tucker
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National Cancer Registries

• CDC NPCR (National Program of Cancer
  Registries)
• Cancer Registries Amendment Act in 1992
• Every state plus certain territories has a cancer
  registry in place
• SEER (Surveillance Epidemiology End Results)
  NCI
• Gathers in-depth data on cancer cases in specific
  locations
• NAACCR (North American Association of Central
  Cancer Registries)
• Standard setting organization certification

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Data Utilization

• Data Linkages
• Mortality
• BreastCare data
• Occupational cohorts
• Site specific fact sheets
• Prostate Cancer Foundation
• Media inquiries
• Presentations
• General concern and curiosity
• Cluster Investigations

• Grants
• Community profiles
• Komen
• Cancer coalition
• Cancer Plan
• Research
• Cancer cluster investigations
• Interventions
• Data Requests
• GIS

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Data Resources

• NAACCR www.naaccr.org
• SEER http//seer.cancer.gov/
• ACS www.cancer.org
• NCI www.cancer.gov
• CDC/NPCR www.cdc.gov/cancer
• Various Publications
• CINA, USCS, Facts Figures, etc
• ACCRs Homepage - www.healthyarkansas.com/arkcan
  cer/arkcancer.html
• ACCR On-line Cancer Data Query System
  http//cancer-rates.info

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Validity Reliability of Diagnostic Screening
Tests
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Validity Reliability

• Important to identify who has the disease and who
  does nota challenge in both public health and
  clinical settings.
• Quality of screening and diagnostic tests is
  critical
• Main question to ask of all tests is, How good
  is the test in separating populations of people
  with and without the disease in question?

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Screening

• Screening is the identification of unrecognized
  disease by application of rapid tests to separate
  well persons who probably have the disease from
  those who probably do not have the disease.
• A screening test is not intended to be
  diagnostic.
• Persons with positive results should be referred
  for diagnosis and treatment.

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Screening

• Main concern is with asymptomatic healthy
  individuals.
• Theoretically, if a disease has not yet reached
  the threshold of clinical visibility (still at an
  early stage), the chances are that cure is good.
• The screening method should be reliable and cost
  effective.
• Treatment should be possible and facilities
  should be made available to those who require
  treatment.

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Screening vs. Diagnostic

• Screening tests aim to detect unknown disease in
  an otherwise well-appearing person it is the
  search for disease that has not been determined
  to be, or is suspected of being, present.
• Test examples temperature, Pap smear, mammogram,
  FOBT, PSA
• Diagnostic tests aim to test persons who have a
  symptom or other evidence of potential disease.
• Test examples chest x-ray, biopsy, blood/urine
  test, colonoscopy

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Quality of Screening Tests

• Depends on
• Validity ability of the test to distinguish
  between who has a disease and who does not
• A perfect test would be perfectly valid
• Reliability repeatability of a test
• A perfectly reproducible method of disease
  ascertainment would produce the same results
  every time it was used in the same individual.

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Validity

• Components (expressed as percentages)
• Sensitivity the ability of the test to identify
  correctly those who HAVE the disease the search
  for diseased persons
• Specificity the ability of the test to identify
  correctly those who DO NOT HAVE the disease the
  search for well persons
• sensitivity and specificity quantify a tests
  accuracy in the presence of known disease status
• Note When calculating sensitivity or
  specificity, another more definitive test (gold
  standard) is used to know who really has or does
  not have the disease, e.g.) FOBT then colonoscopy
  w/ biopsy (the gold standard will determine true
  presence of ca)

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2 X 2 Table
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Disease Screening
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Disease Screening
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Screening Measures

• Sensitivity A / AC or TP / TP FN
• Specificity D / BD or TN / TN FP
• Prevalence AC / N

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Screening

• The ideal screening test would identify all
  tested subjects as True Positives or True
  Negatives.
• Ideally, we want a test to have 100 sensitivity
  and 100 specificity.
• The rise in sensitivity is usually compensated
  for by a drop in specificity and vice versa.

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False Positives

• The complement for specificity is the false
  positive value people who are well but are
  classified as having the disease being screened
  for. (1 - specificity FP)
• These cases are usually brought for further
  investigationthis can cause problems.
• Burden on health care system/costly
• Needless anxiety
• Difficulty of removing label of disease
• Problems in employment

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False Negatives

• The complement for sensitivity is the false
  negative value people actually with the disease
  who are determined to be well.
• (1 - sensitivity FN)
• Erroneously missed diseased
• If disease is serious and early intervention is
  successful (e.g., cancer), this could be fatal.
• Importance of false negative results depends on
• Nature and severity of disease being screened for
• Effectiveness of available measures
• Whether early effectiveness is greater

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Reliability (Repeatability) of Tests

• Can the results of a test be replicated?
• If a test is valid but NOT reliable, results are
  meaningless
• Factors that contribute to variation between test
  results
• Intra-subject variation variation within
  individual subjects
• E.g.) blood pressure variation in an individual
• Inter-observer variation variation between
  those reading test results

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Inter-observer Variation

• Two observers may not always arrive at the same
  results
• The extent or magnitude of disagreement is
  importantif disagreement is large the results
  are less meaningful or less reliable
• Variation between observers can be quantified by
  calculating a percent agreement

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Kappa Statistic (k)

• Expect agreement about certain subjects by two
  observers solely as a function of chance
• Answers the question To what extent do the
  results agree beyond what we would expect by
  chance alone?
• Kappa is a chance-corrected measure of
  repeatability
• Kappa 1 Complete agreement
• Kappa gt 0.75 Excellent agreement
• Kappa 0.40 0.75 Intermediate to good agreement
• Kappa lt 0.40 Poor agreement

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Kappa Statistic (k)
Kappa ( observed agreement) ( agreement
expected by chance alone) 100 - ( agreement
expected by chance alone)
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Relation Between Validity Reliability

• Graphically, a narrow curve indicates that the
  results are quite reliable (repeatable)if far
  from the true value then they are not valid.
• A broad curve indicates low reliability but valid
  if clustered around the true value.
• Goal is to achieve results that are both valid
  and reliable.

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Relation Between Validity Reliability
Valid but not reliable
Both valid and reliable
Reliable but invalid
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Statistics The Basics
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Definitions

• Statistics a branch of applied mathematics that
  utilizes procedures for condensing, describing,
  analyzing and interpreting sets of information.
• Biostatistics a subset of statistics used to
  handle health-relevant information.

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Types of Statistics

• Descriptive statistics methods of producing
  quantitative summaries of information
• Measures of central tendency
• Measures of dispersion
• Inferential statistics methods of making
  generalizations about a larger group based on
  information about a subset (sample) of that group

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Populations Samples

• Before the statistical test to use is determined
  we must know if the information represents a
  population or a sample
• A population is an aggregate of cases, things,
  people, etc.
• A sample is a subset which should be
  representative of a population if selected
  randomly (i.e., each subject should have the same
  chance for selection as every other subject)

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Classification of Data

• Qualitative - non-numeric or categorical (what
  type?)
• Examples gender, race/ethnicity
• Quantitative numeric or discrete, continuous
  (how much?)
• Examples age, temperature, blood pressure

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Classification of Data

• Discrete having a fixed number of values
• Ordinal (ordered), nominal (unordered)
• Examples marital status, blood type, number of
  children in a family, number of attacks of
  asthma/day
• Continuous having an infinite number of values
  in theory can take any value within a given range
• Examples height, weight, temperature, heart rate

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Hint

• Qualitative (categorical) data are discrete
• Quantitative (numerical) data may be discrete or
  continuous

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Qualitative Data Nominal

• Data which fall into mutually exclusive
  categories (discrete) for which there is no
  natural order
• Examples race/ethnicity, gender, blood group,
  marital status, ICD-10 codes, dichotomous data
  (binomial data) such as HIV or HIV- yes or no
  absent or present

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Qualitative Data Ordinal

• Data which fall into mutually exclusive
  categories (discrete data) which have a rank or
  graded order.
• Examples grades, socioeconomic status, stage of
  disease, tumor size, low medium - high

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Quantitative Data Interval

• Data which are measured by standard units
• The scale measures not only that one data point
  is different than another, but by how much
• Examples
• number of days since onset of illness (discrete)
• Temperature in Fahrenheit or Celsius (continuous)

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Descriptive Statistics

• Get a feel for the data.
• Assess the quality of the data
• Type of variables
• Summary statistics
• Distribution
• Pictorial representation

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Descriptive Statistics

• Used as a first step to look at health-related
  outcomes
• Examine numbers of cases to identify an increase
  (epidemic)
• Examine patterns of cases to see who gets sick
  (demographic variables) and where and when they
  get sick (space/time variables)

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Descriptive Statistics

• Measures of central tendency
• Mean
• Median
• Mode
• Measures of dispersion
• Variance
• Standard deviation (square root of the variance)
  1sd, 2sd, 3sd
• Percentiles 25th, 50th, 75th, 95th
• Range largest value-smallest value

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Mean

• Most commonly used measure of central tendency
• Arithmetic average
• The mean is affected by extreme values/sensitive
  to outliers

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Mean - Example
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Median

• The value which divides a ranked set into two
  equal parts
• Order the data
• If n is even, take the mean of the two middle
  observations
• If n is odd, the median is the middle observation

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Median - Example
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Mode

• The number which occurs the most frequently in a
  set.
• Example 1, 2, 2, 2, 3, 4, 5, 5, 6, 7, 8 Mode
  2

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Example
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Variance Standard Deviation

• Measures of dispersion (or scatter) of the values
  around the mean
• If the numbers are near the mean, variance is
  small
• If numbers are far from the mean, the variance is
  large
• The standard deviation is an estimate of the
  variability of observationsit is a summary of
  how widely dispersed the values are around the
  mean.

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Variance Standard Deviation
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Standard Deviation
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Percentiles
A set of divisions that produce exactly 100 equal
parts in a series of continuous values, such as
childrens heights or weights.
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Range

• The difference between the largest and smallest
  values in a distribution
• Example 1, 2, 2, 2, 3, 4, 5, 6, 7, 8
• Range 8-1 7

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Rates

• The frequency of defined events in a specified
  population for a given time period.
• A rate is a proportion.
• A proportion is a ratio in which the numerator is
  included in the denominator.
• Usually expressed as fractions, decimals or
  percentages.

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Frequency Diagram
169
Histogram
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Histogram vs. Bar Chart

• There is a difference between the two
• A histogram shows the distribution of a
  continuous variable thus, there should not be any
  gaps between the bars.
• A bar chart shows the distribution of a discrete
  variable or a categorical one and will have
  spaces between the bars.

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Histogram Example

Lead Concentration
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COHORT STUDIES
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Epidemiologic Studies

• Identify new diseases
• Identify populations at risk for a disease
• Identify possible causative agents of disease
• Identify factors or behaviors that increase risk
  of a disease

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Study Designs

• Means to assess possible causes by gathering and
  analyzing evidence.
• The key to any epidemiologic study is in the
  definition of what constitutes a case and what
  constitutes an exposure.

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Types of Study Designs

• Analytic Studies (to test hypotheses)
• Experimental studies
• Randomized clinical trials
• Observational studies
• Cohort studies (prospective study)
• Case-control studies
• Cross-sectional studies

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Cohort Study Design

• Investigator starts with a group of individuals
  apparently free of disease
• The cohort is divided on the basis of exposure
• Those exposed to the possible risk factor
• Those not exposed to the risk factor
• The cohort is followed through time to determine
  the outcome of interest
• Measure/compare the incidence of disease or rate
  of death from disease in the two groups
• If there is a positive association, the incidence
  rates in exposed group will be greater.

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TIME
The Present
Begin With
Disease
Exposure
No Disease -
Disease
No Exposure -
No Disease -
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Incidence rates of disease Exposed a / ab
Not Exposed c / cd
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Types of Cohort Studies

• Concurrent Cohort Study (prospective or
  longitudinal)
• Retrospective Cohort Study (historical cohort or
  non-concurrent prospective study)
• Both designs are identicalcomparing exposed and
  non-exposed populations
• The only difference is calendar time.

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Concurrent Cohort Study

• Investigator identifies original study population
  at the beginning of the study.
• The individuals are followed prospectively
  through time until disease develops or does not
  develop.
• Disadvantages
• Requires long follow-up time (years)
• Expensive
• Age of investigator

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Retrospective Cohort Study

• The cohort is defined from historical data and
  followed up for disease up to the present time
• Can telescope the frame of calendar time for the
  study and obtain results sooner
• Disadvantage
• Quality depends on the historical data that are
  available both to define exposure and to
  identify the outcome.

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Types of Cohort Studies

• In a concurrent cohort study design, exposure and
  non-exposure status are ascertained as they occur
  in the study groups are followed-up for several
  years into the future and incidence is measured.
• In a retrospective cohort study design, exposure
  is ascertained from past records, and outcome
  (development or no development of disease) is
  ascertained from existing records at the
  beginning of the study.

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Assessment of Exposure

• Techniques used to measure exposure include
  questionnaires (age, smoking habits), laboratory
  tests (cholesterol, hemoglobin), physical
  measurements (height, weight, blood pressure) and
  various special procedures (EKG, x-rays)
• Quantifying exposures includes information such
  as date of onset, frequency of exposure and
  duration and intensity of exposure

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Assessment of Exposure

• Changes in exposure status frequently occur.
• Example smokers quit smoking and people switch
  occupations
• This information must be incorporated into the
  analysis and interpretation.

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Sources of Error in Epi. Studies

• Bias systematic preferences built into the
  study design
• Confounding occurs when a variable is included
  in the study design that is related to both the
  outcome of interest and the exposurecan lead to
  false conclusions.
• Example gambling and lung cancer drinking
  coffee and lung cancer

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Potential Biases

• Bias in the assessment of the outcome
• Person deciding on whether disease developed or
  not may be aware of exposure status of a subject
  and may be potentially biased (blinding may be
  helpful)
• Information bias
• Incomparable quality of information between
  exposed and non-exposed subjects

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Potential Biases

• Biases from non-response and loss to follow-up
• Non-participation and non-response can lead to
  bias
• Loss to follow-up can introduce bias. If people
  with the disease are lost, interpretation of
  results are difficult.
• Analytic bias
• Preconceived notions of investigators may
  unintentionally introduce bias
• Epidemiologists have to work with dirty data.
  The trick is to do it with a clean mind.

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Advantages of a Cohort Study

• Can assess multiple outcomes (effects) of a
  single exposure
• It is suitable for the study of rare exposures
• Can demonstrate a temporal relationship between
  exposure and disease
• If prospective, minimizes bias in the
  ascertainment of exposure
• Allows direct measurement of incidence of disease
  in the exposed and non-exposed population

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Disadvantages of a Cohort Study

• Is inefficient for the evaluation of a rare
  disease, unless a large sample size is obtained
• Disease process may already be underway and not
  known at the onset of the study
• If prospective, can be very expensive and time
  consuming
• If retrospective, requires the availability of
  existing records
• Validity of the result can be affected by loss to
  follow-up and tracking study subjects

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To Review

• The objective of the cohort study is to test a
  hypothesis regarding the causation of disease.
• The group of persons to be studied (cohort) are
  defined in terms of characteristics manifest
  prior to appearance of the disease being
  investigated.
• The defined study groups are observed over a
  period of time to determine and compare the
  frequency of disease among them.

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Exercise

• How would you design a cohort study of the
  association between preterm delivery and
  cigarette smoking?

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Results

• An exposed and a non-exposed group would first be
  identified e.g.) women presenting to a local
  county health department for prenatal care would
  be classified by smoking status smokers and
  non-smokers.
• These women would be followed to determine
  whether or not preterm delivery occurred.
• The rates of the preterm delivery would be
  compared among the smokers and non-smokers.

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CASE-CONTROL STUDIES
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Types of Study Designs

• Analytic Studies (to test hypotheses)
• Experimental studies
• Randomized clinical trials
• Observational studies
• Cohort studies (prospective study)
• Case-control studies
• Cross-sectional studies

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CASE CONTROL STUDIES

• Definition comparison of exposure frequencies
  between persons with a specified illness or
  injury (CASES) and other persons (CONTROLS).
• The hallmark of this study design is that it
  begins with people with the disease (cases) and
  compares them to people without the disease
  (controls).

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TIME
The Present
Begin With
Exposure
Disease
No Exposure -
Exposure
No Disease -
No Exposure -
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Proportions Exposed Cases Exposed a / ac
Controls Exposed b / bd
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SELECTION OF CASES
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Diagnostic Criteria

• Establish a set of objective diagnostic criteria
  for case selection
• Best to use standardized, expert criteria for
  disease diagnosis e.g.) ICD code

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Criteria for Eligibility

• Establish a set of inclusion and exclusion
  criteria for cases
• Some reasons may exist to exclude cases
• Existence of a chronic disease other than the
  disease under study
• Mental problems that might preclude exposure
  ascertainment

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Incident vs. Prevalent Cases

• Usually best to use incident (newly diagnosed)
  cases
• The inclusion of prevalent cases may introduce
  bias and other problems with determining that
  exposure preceded disease.

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Sources of Cases

• Representativeness of cases is very important!
• Hospital-based cases are the most commonly used
  (depends on the disease being studied)
• Random sample of the general population
• Highly representative
• Very time intensive and labor intensive
• Cases can be identified from other sources such
  as cancer registries, ambulatory care facilities,
  medical insurance companies, retirement groups,
  etc

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SELECTION OF CONTROLS

• Challenge
• If a case-control study is conducted and more
  exposure is found in the cases than in the
  controls, then we would like to be able to
  conclude that there is an association between the
  exposure and the disease in question. The way
  the controls are selected is a major determinant
  of whether such a conclusion is valid.

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Controls

• Controls are intended to provide the frequency of
  exposure (risk factor) among people without the
  disease in the population from which cases were
  identified.
• Criteria for eligibility inclusion and
  exclusion criteria should be the same for both
  cases and controls

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Types of Controls

• Hospital-based Controls
• Individuals seeking medical care at the same
  hospital as the cases for a condition believed to
  be unrelated to the disease being studied
• The hospital population may be very different
  from the general population, thus less
  generalizability of results
• Neighborhood Controls
• Usually of similar socioeconomic status
• Similar environment

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Types of Controls Cont.

• General Population Controls
• Controls selected from a random sample of the
  general population e.g.) via random digit dialing
• Provides highly representative controls
• Costly method, refusal rates, lack of phone
  coverage
• Other Controls
• Friends, relatives, spouses, colleagues,
  co-workers