Post on 19-Dec-2015
Robert W. Wills, DVM, PhDDiplomate ACVPM (Epidemiology)
Pathobiology and Population MedicineCollege of Veterinary Medicine
Mississippi State University
Epidemiology Overview and Concepts
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
Environment
AgentHost
Model of Disease
Epidemiological Approaches
Etiologic Epidemiology 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 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
Agent
Model of Disease
Host
Ecology of Disease
Model of Disease
Environment
Agent Host
Ecology of Disease
Model of Disease
Environment
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
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
Prevalence =Number of Cases
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
Incidence Rate =No. of new cases over a time period
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 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
Positive a
True Positive
b False
Positive a+b
TEST
Negative c
False Negative
d True
Negative c+d
a+c b+d n
DISEASE
Present Absent
Positive a b a+b TEST RESULT
Negative c d c+d
a+c b+d n
na+c
True Prevalence =
Apparent Prevalence =
DISEASE
Present Absent
Positive a b a+b TEST RESULT
Negative c d c+d
a+c b+d n
na+b
Accuracy
How close is a test result to the truth Proportion of all tests, both positive and
negative, that are correct
Accuracy = n
a+d
DISEASE
Present Absent
Positive a
True Positive
b False
Positive a+b
TEST
Negative c
False Negative
d 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+c
a
DISEASE
Present Absent
Positive a
True Positive
b False
Positive a+b
TEST
Negative c
False Negative
d True
Negative c+d
a+c b+d n
False Negative Rate
Likelihood of a negative result when patient actually has disease
False Negatives
Sensitivity
False Negative Rate
Likelihood of a negative result when patient actually has disease
False NegativesSensitivity
False Negative Rate
False negative rate increases with decreased sensitivity
Sensitivity
False Negatives
Likelihood of a negative result when patient actually has disease
Reasons for False Negative Reactions
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 = b+d
d
DISEASE
Present Absent
Positive a
True Positive
b False
Positive a+b
TEST
Negative c
False Negative
d True
Negative c+d
a+c b+d n
False Positive Rate
Likelihood of a positive result when patient does not have the disease
False Positives
Specificity
False Positive Rate
Likelihood of a positive result when patient does not have the disease
False PositivesSpecificity
False Positive Rate
False positive rate increases with decreased specificity
Specificity
False Positives
Likelihood of a positive result when patient does not have the disease
Reasons for False Positive Reactions
Cross-reaction Non-specific inhibitors Non-specific agglutinins Contamination
0
5
10
15
20
25
30
35
40
Titer
Rel
ativ
e F
req
uen
cy
Nondiseased
Diseased
Relationship of Sensitivity and Specificity
0
5
10
15
20
25
30
35
40
Titer
Rel
ativ
e F
req
uen
cy
Critical Titer
Nondiseased
Diseased
0
5
10
15
20
25
30
35
40
Titer
Rel
ativ
e F
req
uen
cy
Critical Titer
Nondiseased
Diseased
False Negatives
0
5
10
15
20
25
30
35
40
Titer
Rel
ativ
e F
req
uen
cy
Critical Titer
False Positives
Nondiseased
Diseased
False Negatives
0
5
10
15
20
25
30
35
40
Titer
Rel
ativ
e F
req
uen
cy
Critical Titer
False Positives
Increased Sensitivity – Decreased Specificity
DiseasedNondiseased
False Negatives
0
5
10
15
20
25
30
35
40
Titer
Rel
ativ
e F
req
uen
cy
Critical Titer
False PositivesFalse Negatives
Decreased Sensitivity – Increased Specificity
Nondiseased
Diseased
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
Predictive Value(+) =
DISEASE
Present Absent
Positive a
True Positive
b False
Positive a+b
TEST
Negative c
False Negative
d True
Negative c+d
a+c b+d n
a+ba
Effect of prevalence on positive predictive value when sensitivity and specificity of a test equal 95%
Prevalence (%) Positive Predictive Value (%) 50.0 95.0 5.0 50.0 2.0 27.9 1.0 16.1 0.1 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
Predictive Value(-) = c+dd
DISEASE
Present Absent
Positive a
True Positive
b False
Positive a+b
TEST
Negative c
False Negative
d 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 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
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
Relative Risk = c/(c+d)a/(a+b)
DISEASE
Present Absent
Present a b a+b FACTOR
Absent c d c+d
a+c b+d
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 = =c/d
a/b
bc
ad
DISEASE
Present Absent
Present a b a+b FACTOR
Absent c d c+d
a+c b+d
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 = c/(c+d)a/(a+b) -
DISEASE
Present Absent
Present a b a+b FACTOR
Absent c d c+d
a+c b+d
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