Robert W. Wills, DVM, PhD Diplomate ACVPM (Epidemiology) Pathobiology and Population Medicine College 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 Agent Host 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

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

n a+c True Prevalence =

Apparent Prevalence = n a+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

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  KNOWN diseased animals needed to establish diagnostic sensitivity

Sensitivity = a+c a

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 Negatives Sensitivity

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

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 Positives Specificity

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

Non diseased Diseased Relationship of Sensitivity and Specificity

Critical Titer Non diseased Diseased

Critical Titer Non diseased Diseased False Negatives

Critical Titer False Positives Non diseased Diseased False Negatives

Critical Titer False Positives Increased Sensitivity – Decreased Specificity Diseased Non diseased False Negatives

Critical Titer False Positives False Negatives Decreased Sensitivity – Increased Specificity Non diseased 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(+) = a+b a

Effect of prevalence on positive predictive value when sensitivity and specificity of a test equal 95%

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+d d

Effect of prevalence on negative predictive value when sensitivity and specificity of a test equal 95%

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)

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

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) -

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