Epidemiology Made Easy - PowerPoint PPT Presentation

Loading...

PPT – Epidemiology Made Easy PowerPoint presentation | free to view - id: 35760-ZjI0N



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Epidemiology Made Easy

Description:

How does the epidemiologist proceed to identify the cause of a disease? ... Depends on duration of disease (long duration will increase the prevalence) ... – PowerPoint PPT presentation

Number of Views:9604
Avg rating:5.0/5.0
Slides: 336
Provided by: catheri93
Category:
Tags: chicken | cook | does | easy | epidemiology | how | long | made | need | to

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Epidemiology Made Easy


1
Epidemiology Made Easy
  • Presented By
  • Catherine Tapp, MPH
  • August 23, 2005

2
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

3
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.

4
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

5
What is Studied in Epi.?
  • Morbidity events and factors related to or
    caused by disease or disability
  • Mortality events and factors related to death

6
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

7
(No Transcript)
8
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.

9
(No Transcript)
10
Time When?
  • Changing or stable?
  • pertusis (whooping cough)
  • Seasonal variation?
  • influenza
  • Clustered (epidemic) or evenly distributed
    (endemic)?
  • cancer

11
Place Where?
  • Geographically restricted or widespread
    (pandemic)?
  • Relation to water or food supply?
  • Multiple clusters or one?

12
Person Whom?
  • Age
  • Gender
  • Ethnicity
  • Race
  • Socio-economic status
  • Behavior
  • Genetics
  • Occupation
  • Religion
  • Stress
  • Personal habits
  • Marital status
  • School
  • Travel

13
Analytic Epidemiology
  • Focus on causation of disease by testing a
    specific hypothesis about the relationship of a
    disease to a cause (risk factor).

14
Risk Factors/Determinants
  • The presence of certain risk factors may be
    associated with increased probability for disease
    development.
  • What are some examples?

15
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.

16
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

17
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

18
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.

19
(No Transcript)
20
Life Expectancy
21
(No Transcript)
22
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 care…now it is due to antibiotics,
    better anesthesia and post-op care etc.
  • Gender and race differences

23
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

24
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

25
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 effective…less costly…less
    invasive…

26
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

27
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

28
(No Transcript)
29
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

30
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.

31
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)

32
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
33
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

34
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 etiology…he
    operated purely on observational data that became
    the basis for a preventive intervention.

35
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.

36
(No Transcript)
37
(No Transcript)
38
Take Home Message…
  • Although important to understand the biology and
    pathogenesis of disease, it is not always needed
    to take preventive action.

39
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.

40
Dynamics of Disease Transmission
41
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

42
Epi. Triad of Disease
HOST
VECTOR
AGENT
ENVIRONMENT
43
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

44
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

45
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

46
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

47
Multiplicity of Factors Underlying Adult-onset
Diabetes
48
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

49
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 disease…bulk of
    the problem may be hidden from view.
  • Example tuberculosis cases are not always
    clinically visible other examples??

50
Iceberg Concept of Infectious Diseases
51
Dog Bite Example
451,000 Medically Treated
3.73 Million Nonmedically treated
52
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

53
Stages of Disease Prevention
54
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

55
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

56
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

57
Endemic vs. Epidemic
58
Excess of Deaths in London
59
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

60
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.

61
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.

62
Herd Immunity Continued
  • Why is herd immunity so important? It is not
    necessary to carry out 100 immunization in a
    population to be highly effective…part 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.

63
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

64
Latent Periods of Bladder Tumors
65
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.

66
Graphical Representation of an Outbreak
Classic epi. curve
67
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?

68
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

69
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

70
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

71
Measuring the Occurrence of Disease Part I
Measures of Morbidity
72
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

73
Development of Disease in an Individual
74
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

75
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.

76
INCIDENCE
Incidence per _________ of new cases in a
specified time of persons at risk for
developing the disease during that time period
77
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.

78
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.

79
PREVALENCE
Prevalance per ______ of cases of disease in
pop at specified time of persons in the pop at
the specified time
80
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

81
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?

82
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?
83
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
84
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

85
Comparison of Incidence Prevalence
86
Problems with Incidence and Prevalence
Measurements
87
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 interviews…many errors can arise
    (refer to Gordis table 3-4)

88
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

89
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

90
Corrected rates are higher. Why? Women who had
hysterectomies are removed from the
denominator…this decreases and the rate gets
larger. Trend over time is not significantly
changed.
91
Measuring the Occurrence of Disease Part II
Measures of Mortality
92
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

93
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

94
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

95
Crude Rates
  • Summary statistics that ignore the heterogeneity
    of the population under investigation

96
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)

97
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.

98
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)

99
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 ask…Is this real?

100
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

101
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

102
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)

103
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

104
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

105
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)

106
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
107
Purpose of Public Health Surveillance
  • Assess public health status
  • Define public health priorities
  • Evaluate programs
  • Stimulate research

108
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

109
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

110
Cancer Surveillance
  • The foundation for a national, comprehensive
    strategy to reduce illness and death from cancer.

111
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

112
(No Transcript)
113
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
114
  • 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.

115
Heart Disease and Cancer Mortality Arkansas,
1979 - 1998
Age-adjusted to 1940 U.S. population
116
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
117
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

118
(No Transcript)
119
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

120
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

121
Validity Reliability of Diagnostic Screening
Tests
122
Validity Reliability
  • Important to identify who has the disease and who
    does not…a 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?

123
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.

124
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.

125
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

126
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.

127
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)

128
2 X 2 Table
129
Disease Screening
130
Disease Screening
131
Screening Measures
  • Sensitivity A / AC or TP / TP FN
  • Specificity D / BD or TN / TN FP
  • Prevalence AC / N

132
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.

133
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
    investigation…this can cause problems.
  • Burden on health care system/costly
  • Needless anxiety
  • Difficulty of removing label of disease
  • Problems in employment

134
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

135
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

136
Inter-observer Variation
  • Two observers may not always arrive at the same
    results
  • The extent or magnitude of disagreement is
    important…if disagreement is large the results
    are less meaningful or less reliable
  • Variation between observers can be quantified by
    calculating a percent agreement

137
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

138
Kappa Statistic (k)
Kappa ( observed agreement) ( agreement
expected by chance alone) 100 - ( agreement
expected by chance alone)
139
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.

140
Relation Between Validity Reliability
Valid but not reliable
Both valid and reliable
Reliable but invalid
141
Statistics The Basics
142
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.

143
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

144
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)

145
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

146
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

147
Hint
  • Qualitative (categorical) data are discrete
  • Quantitative (numerical) data may be discrete or
    continuous

148
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

149
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

150
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)

151
Descriptive Statistics
  • Get a feel for the data.
  • Assess the quality of the data
  • Type of variables
  • Summary statistics
  • Distribution
  • Pictorial representation

152
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)

153
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

154
Mean
  • Most commonly used measure of central tendency
  • Arithmetic average
  • The mean is affected by extreme values/sensitive
    to outliers

155
Mean - Example
156
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

157
Median - Example
158
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

159
(No Transcript)
160
Example
161
(No Transcript)
162
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 observations…it is a summary of
    how widely dispersed the values are around the
    mean.

163
Variance Standard Deviation
164
Standard Deviation
165
Percentiles
A set of divisions that produce exactly 100 equal
parts in a series of continuous values, such as
childrens heights or weights.
166
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

167
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.

168
Frequency Diagram
169
Histogram
170
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.

171
Histogram Example

Lead Concentration
172
(No Transcript)
173
(No Transcript)
174
COHORT STUDIES
175
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

176
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.

177
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

178
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.

179
TIME
The Present
Begin With
Disease
Exposure
No Disease -
Disease
No Exposure -
No Disease -
180
Incidence rates of disease Exposed a / ab
Not Exposed c / cd
181
(No Transcript)
182
(No Transcript)
183
Types of Cohort Studies
  • Concurrent Cohort Study (prospective or
    longitudinal)
  • Retrospective Cohort Study (historical cohort or
    non-concurrent prospective study)
  • Both designs are identical…comparing exposed and
    non-exposed populations
  • The only difference is calendar time.

184
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

185
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.

186
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.

187
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

188
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.

189
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 exposure…can lead to
    false conclusions.
  • Example gambling and lung cancer drinking
    coffee and lung cancer

190
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

191
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.

192
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

193
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

194
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.

195
Exercise
  • How would you design a cohort study of the
    association between preterm delivery and
    cigarette smoking?

196
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.

197
CASE-CONTROL STUDIES
198
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

199
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).

200
TIME
The Present
Begin With
Exposure
Disease
No Exposure -
Exposure
No Disease -
No Exposure -
201
Proportions Exposed Cases Exposed a / ac
Controls Exposed b / bd
202
SELECTION OF CASES
203
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

204
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

205
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.

206
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…

207
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.

208
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

209
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

210
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
About PowerShow.com