Transcript Opportunities in Swine Medicine and Epidemiology

Epidemiology Overview and Concepts
Robert W. Wills, DVM, PhD
Diplomate ACVPM (Epidemiology)
Pathobiology and Population Medicine
College of Veterinary Medicine
Mississippi State University
Definition
 Epidemiology
 Greek
 Epi – about or upon
 Demos – populace or people of districts
 Logos – word
 Study of that which is upon the people
 The study of disease in populations
 Study of the frequency, distribution, and
determinants of health and disease in
populations
 Analogous to pathogenesis of disease in individuals
 Epidemiology is a fundamental science for medicine in
populations.
Martin et al., 1987
Epidemiological Approaches
 Ecological Epidemiology
 Medical Epidemiology
 Understanding how disease
agents are transmitted and are
maintained in environment
 Life cycle or natural history
of disease
 Foundation for disease
eradication programs
Agent
Environment
Model
of Disease
Host
Epidemiological Approaches
 Etiologic Epidemiology
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Determining the cause of disease
“Medical detection” epidemiology
“Shoe leather” epidemiology
Outbreak investigation
Epidemiological Approaches
 Clinical Epidemiology
 Answers questions asked
in practice of veterinary
and human medicine
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Normality/Abnormality
Diagnosis
Frequency
Risk/Prevention
Prognosis
Treatment
Cause
Epidemiological Approaches
 Quantitative Epidemiology
 Mathematically describe diseases and associated
factors
 Explore potential “cause and effect” associations
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Epidemiological Approaches
 Preventive Medicine
 Design optimal management, control or
preventive strategies
 Use all available epidemiological approaches
to accomplish this
 Cost-effectiveness or cost-benefit is important
component
Application of Epidemiology
 It integrates well with basic science
 by testing the application of experimental
models in the real world
 by discovering relationships between
outcomes and risk factors which may generate
hypotheses for mechanisms of disease.
Ecology of Disease
Environment
Model
of Disease
Agent
Host
Ecology of Disease
Environment
Model
of Disease
Agent
Host
Ecology of Disease
Environment
Model
of Disease
Agent
Host
Model of Disease
 Agent
 A factor whose presence is required for the
occurrence of a disease.
Model of Disease
 Host
 Animal that supports the replication or
development of an agent or is affected by
an agent under natural conditions.
Host Determinants
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Genotype
Age
Sex
Species and Breed
 Nutritional status
 Immune status
 Size and
Conformation
 Coat Color
Model of Disease
 Environment
 Physical surroundings and management
factors that affect hosts and agents
Environmental Determinants
 Location
 Climate
 Macroclimate
 Microclimate
 Management
 Housing
 Diet
 Husbandry – density, pig-flow, etc.
Model of Disease
 Changes in relationships result in different
outcomes
 Agent overcomes host
 Host overcomes agent
 Agent and host maintained in equilibrium
Measuring and Expressing Occurrence
of Disease
 Epidemic
 An increase in the number of subjects affected
by a disease over the EXPECTED rate of
occurrence
 Epizootic
 Term used to express an epidemic in a
population of animals
Measuring and Expressing Occurrence
of Disease
 Pandemic
 An epidemic that occurs over a large
geographical area or the world
Measuring and Expressing Occurrence
of Disease
 Outbreak
 Localized epidemic
Measuring and Expressing Occurrence
of Disease
 Endemic
 Occurrence of disease at a constant or
expected level
Measuring and Expressing Occurrence
of Disease
 Sporadic
 Pattern of disease in which the disease occurs
rarely and without regularity
Frequency of Clinical Events
 Mathematically describing occurrence of
events such as disease and death
 Rates
 Ratios
 Proportions
Frequency of Clinical Events
 Prevalence – Proportion of animals within
a population that have a condition of
interest at a given point in time
Number of Cases
Prevalence =
Total Number of Animals
Frequency of Clinical Events
 Incidence Rate – Proportion of animals that
develop a condition of interest over a
specific period of time
No. of new cases over a time period
Incidence Rate =
Average population at risk during
time period (e.g. animal-months)
Frequency of Clinical Events
 Prevalence represents the risk of being a
case, whereas incidence represents the risk
of becoming a case (Smith, 1995)
Frequency of Clinical Events
 Morbidity rate –
 As measure of prevalence
 Proportion of animals that are affected with disease
at a point in time
 As measure of incidence
 Number of new cases of disease that occur in the
average population at risk during a specified time
period
Frequency of Clinical Events
 Mortality rate – Number of animals that
die during a period of time
Frequency of Clinical Events
 Attack rate
 Special kind of incidence rate
 Numerator is the number of new cases
 Denominator is the number of individuals
exposed at the START of an outbreak
 Of the individuals exposed to an agent, how
many acquired the disease
Frequency of Clinical Events
 Factors Affecting Incidence and Prevalence
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Temporal Sequences
Disease Duration
Case Definition
Dangling Numerators
Population at Risk
Crude vs Adjusted Rates
Real vs Apparent Prevalence
Factors Affecting
Incidence and Prevalence
 Real vs Apparent Prevalence
 No test is 100% accurate
 Tests give us apparent prevalence not the
true prevalence
 Need to know the sensitivity and specificity
of the test to calculate true prevalence
Test Outcomes
DISEASE
Present
Absent
a
b
Positive
TEST
Negative
a+b
True
Positive
False
Positive
c
d
False
Negative
True
Negative
c+d
a+c
b+d
n
True Prevalence =
a+c
n
DISEASE
Present
Absent
TEST
RESULT
Positive
a
b
a+b
Negative
c
d
c+d
a+c
b+d
n
a+b
n
Apparent Prevalence =
DISEASE
Present
Absent
TEST
RESULT
Positive
a
b
a+b
Negative
c
d
c+d
a+c
b+d
n
Accuracy
 How close is a test result to the truth
 Proportion of all tests, both positive and
negative, that are correct
Accuracy =
a+d
n
DISEASE
Present
Absent
a
b
Positive
TEST
Negative
a+b
True
Positive
False
Positive
c
d
False
Negative
True
Negative
c+d
a+c
b+d
n
What is Truth?
 Gold Standard
 The test that is used to
determine if a disease is
truly present or not
What is Truth?
 Gold Standard
 The test that is used to
determine if a disease is
truly present or not
 Other tests are compared to it to determine
their accuracy
Test (Diagnostic) Sensitivity
 Ability to correctly detect diseased
animals
 Not the same as analytical
sensitivity which denotes the
detection limits of a test
 100-200 KNOWN diseased animals needed
to establish diagnostic sensitivity
Sensitivity =
a
a+c
DISEASE
Present
Absent
a
b
Positive
TEST
Negative
a+b
True
Positive
False
Positive
c
d
False
Negative
True
Negative
c+d
a+c
b+d
n
False Negative Rate
 Likelihood of a negative result when
patient actually has disease
Sensitivity
False
Negatives
False Negative Rate
 Likelihood of a negative result when
patient actually has disease
Sensitivity
False
Negatives
False Negative Rate
 Likelihood of a negative result when
patient actually has disease
False
Negatives
Sensitivity
 False negative rate increases with decreased
sensitivity
Reasons for
False Negative Reactions
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Natural or induced tolerance
Improper timing
Improper selection of test
Analytically insensitive tests
Non-specific inhibitors e.g. anticomplementary
serum; tissue culture toxic substances
 Antibiotic induced immunoglobulin suppression
 Incomplete or blocking antibody
Test (Diagnostic) Specificity
 Ability to correctly detect
non-diseased animals
 Not just analytical specificity
 ability to measure the correct substance
 2000 KNOWN non-diseased animals
needed to establish
Specificity =
d
b+d
DISEASE
Present
Absent
a
b
Positive
TEST
Negative
a+b
True
Positive
False
Positive
c
d
False
Negative
True
Negative
c+d
a+c
b+d
n
False Positive Rate
 Likelihood of a positive result when patient
does not have the disease
Specificity
False
Positives
False Positive Rate
 Likelihood of a positive result when patient
does not have the disease
Specificity
False
Positives
False Positive Rate
 Likelihood of a positive result when patient
does not have the disease
False
Positives
Specificity
 False positive rate increases with decreased
specificity
Reasons for
False Positive Reactions
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Cross-reaction
Non-specific inhibitors
Non-specific agglutinins
Contamination
Relationship of
Sensitivity and Specificity
40
35
Relative Frequency
30
25
20
Non
diseased
Diseased
15
10
5
0
Titer
40
35
Critical Titer
Relative Frequency
30
25
20
Non
diseased
Diseased
15
10
5
0
Titer
40
35
Critical Titer
Relative Frequency
30
25
20
15
Non
diseased
Diseased
False Negatives
10
5
0
Titer
40
35
Critical Titer
Relative Frequency
30
25
20
15
Non
diseased
False Negatives
Diseased
False Positives
10
5
0
Titer
Increased Sensitivity – Decreased Specificity
40
35
Critical Titer
Relative Frequency
30
25
20
15
Non
diseased
False Negatives
Diseased
False Positives
10
5
0
Titer
Decreased Sensitivity – Increased Specificity
40
Critical Titer
35
Relative Frequency
30
25
20
15
Non
diseased
False Negatives
Diseased
False Positives
10
5
0
Titer
Predictive Value of a Positive Test
 Probability that an animal which is
positive, according to the test, is actually
positive
 Dependent upon:
 Sensitivity
 Specificity
 Prevalence
a
Predictive Value(+) = a+b
DISEASE
Present
Absent
a
b
Positive
TEST
Negative
a+b
True
Positive
False
Positive
c
d
False
Negative
True
Negative
c+d
a+c
b+d
n
Effect of prevalence on positive predictive value
when sensitivity and specificity of a test equal 95%
Prevalence (%)
50.0
5.0
2.0
1.0
0.1
Positive Predictive Value (%)
95.0
50.0
27.9
16.1
1.9
Predictive Value of a Negative Test
 Probability that an animal which is
negative according to the test is actually
negative
 Dependent upon:
 Sensitivity
 Specificity
 Prevalence
d
Predictive Value(-) = c+d
DISEASE
Present
Absent
a
b
Positive
TEST
Negative
a+b
True
Positive
False
Positive
c
d
False
Negative
True
Negative
c+d
a+c
b+d
n
Effect of prevalence on negative predictive value
when sensitivity and specificity of a test equal 95%
Prevalence (%)
Negative Predictive Value (%)
1
100
10
99
50
95
75
86
90
68
Establishing Cause of Disease
 Koch’s Postulates (1882)
 Organism must be present in every case of the
disease
 Organism must be isolated and grown in pure
culture
 Organism must, when inoculated into a
susceptible animal, cause the specific disease
 Organism must then be recovered from the
animal and identified
Establishing Cause of Disease
 Limitations of Koch’s Postulates
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Multiple etiologic factors
Multiple effects of a singe cause
Asymptomatic carriers
Non agent factors such as age
Immunologic processes as cause of disease
Host-agent, host-environment interactions
Noninfectious causes of disease
Establishing Cause of Disease
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Temporal relationship between cause and effect
Strength of association
Dose-response relationship
Biological plausibility
Consistency of multiple studies
Rule out other possible causes
Reversible associations
Measures of Association
 Relative Risk
 Quantifies the association of a factor with a
disease by comparing the incidence rate in a
population with the factor to the incidence rate
in a population without the factor
 It gives an estimate of the strength of
association between a factor and a disease
 A relative risk of 1 indicates there is no
increased risk
a/(a+b)
c/(c+d)
Relative Risk =
DISEASE
Present
Absent
Present
a
b
a+b
Absent
c
d
c+d
a+c
b+d
FACTOR
Measures of Association
 Odds Ratio
 Measure of the association of a risk factor with disease
by comparing the odds of having a disease in a
population with the factor to the odds of having a
disease in a population without the factor
 Can be used in case control studies where the size of
the population at risk, and therefore incidence, is not
known
 Good estimate of relative risk if disease is relatively
infrequent
Odds Ration =
a/b
c/d
ad
=
bc
DISEASE
Present
Absent
Present
a
b
a+b
Absent
c
d
c+d
a+c
b+d
FACTOR
Measures of Association
 Attributable Risk
 Additional incidence of disease attributable to
the risk factor itself
 Calculated by subtracting the incidence of
disease in a population not exposed to a factor
from the incidence of disease in a population
exposed to the factor
 Provides a measure of the magnitude of the
effect of a factor
Attributable Risk = a/(a+b) - c/(c+d)
DISEASE
Present
Absent
Present
a
b
a+b
Absent
c
d
c+d
a+c
b+d
FACTOR
Statistical Significance
 A strong association between a factor and a
clinical event does not prove causality
 Confounding with an unknown factor
 Insufficient sample size
Summary
 When studying disease, many factors and
relationships must be considered.
 Typically, as researchers, we separate out
certain components and look at them
independently.
 Simulation modeling has the potential to
incorporate as many factors as we can
recognize and develop a more holistic view