1 Flashcards
what is epizootiology
science of distribution of disease and factors related to health, as well as application of knowledge in disease prevention
division of study according to data collection
interventional (clinical trial) and observational
division of study in relation to time
retrospective, prospective and mixed
surveillance data
classic observation of population and measurement of certain characteristics
research data
comparison of two or more groups
describe cross sectional study
prevalence research - random sample in a certain time
odds ratio (exposed v non exposed)
pro = simple and cheap
con = provides only estimation of prevalence
describe case control study
analyses relation between certain condition (disease) with specific cause
2 groups - animals selected based on health status - diseased and healthy and compare based on previous exposure RETROSPECTIVE study
pro = cheap and fast, good for rare diseases with low incidence, can study many risk factors retrospectively
con = no data given on incidence, heavily depends on sample quality, difficult to find good controls
calculate odds ratio
describe cohort study
compares incidence between groups over certain time period
prospective - condition is not present at start of research -concurrent
retrospective - records on previous exposure to risk factor and traced to present - non-concurrent
observes exposure
pro = monitoring over prolonged period, evaluation of incidence
con = expensive, long studies, rare, difficult follow up and sporadic diseases
can calculate incidence, incidence rate, reactive risk and attributable risk
general considerations for cohort study
cohorts must be free from disease under study
both study and control groups should be equally susceptible to disease under study
both groups should be comparable in respect to all possible variables
diagnostic and eligibility criteria of disease must be defined beforehand
features of cohort study
relative risk will give you causal relationship between disease and exposure
attributable risk measures the change of incidence due to exposure in question
identification of exposures and risk factors for a disease forms basis for prevention
measures of relation
cohort - relative risk, odds ratio
case-control - odds ratio of exposed
cross-section - odds ratio of prevalence
bias
selection, misclassification, confounding
experimental study
studies the impact of certain drugs/procedures on the course of diseases or its onset
experimental study design
similar to cohort
risk factor yes or no = treatment and control
study differences between cohorts
compare treatments and interventions
more comparisons possible 3-4 groups
life cycle of F.magna
liver fluke - miracidum - redia - redia and cercaria - cercaria - metacercária
definition of epidemiology
the study of diseases in population
descriptive epidemiology answers the questions…
what caused the disease, where, when, in which population
analytical epidemiology answers the questions
how and why - hypothesis testing
intrinsic host determinants
species, breed, age, sex
extrinsic determinants
climate, soils, man
what do Kochs postulates describe
causality between a causative organism and subsequent disease
an organism is causal if
it has to be present in every organism
it has to be isolated and grown in pure culture
it has to cause the same disease in other susceptible animals
why are kochs postulates not fully adequate in all cases?
weren’t applicable to non infectious diseases
they ignored interactions between infectious agents, hosts genes and environment in diseases with a multifactorial cause
what came after kochs postulates
evans postulates
surveillance
the systematic ongoing collection, collation and analysis of information related to animal health and the timely dissemination of information so that action can be taken
passive surveillance
collect animal health data and information from disease reporting stakeholders
active surveillance
epidemiological information collected through purposeful and planned interventions
syndromic surveillance
based on observation of main signs of the disease
clinical surveillance
investigate the occurrence of diseases based on observation of clinical signs
targeted surveillance
active surveillance based on occurrence of disease in a given area and/or species
risk based surveillance
active surveillance that focuses on a certain area or livestock population based on perceived level of threat, risk and/or consequences
participatory disease surveillance
active surveillance that uses participatory approaches in search of disease, including input from local livestock producers and others in lifestock value chain
epidemiological unit
group of animals with a defined relationship sharing common likelihood of exposure to a disease
predisposing factors
variety of situations that harbour or promote disease
risk mapping
tool used for identification, assessment, communication and mitigation of a disease in a certain geographical area
zero reporting
periodic standard reports noting that surveillance in any form for a given disease has been carried out and no disease occurrence has been encountered
what is a sample
a smaller but hopefully still representative collection of units from a population used to determine truths about that population
what does sample size depend on
prevalence
probability (random) samples
systematic random sample
stratified random sample
cluster sample
non probability samples
convenience sample
purposive sample
quota
simple random sample
population is 10
sample is 5
each animal
systematic random sample
population 10
sample is 5
take every second animal (10/5)
if fraction of 1-50 animals, we can pick a number eg 7and pick animals 57, 107, 157 etc
stratified random sampling
herd of 20, split in to breeds. take random sample from each breed
benefits of stratified random sampling
reduces variance
increases precision
cluster sampling
split target population in to clusters - eg a sow with her litter
if we want to estimate prevalence of E.coli in 90 piglets, we need to sample 30 piglets so 30/10 - we need to pick 3 clusters randomly
multistage sampling
randomly selected 5 sows
randomly select 6 piglets from 1 cluster
clusters can be
natural - herd, litter
artificial - areas, administrative units
to pick sample size we have to consider
how many animals should be considered to obtain representative results
desired precision
probability or confidence that our results will be acceptable for population
estimation of sample size
n = z2 Pexp (1-Pexp) / d2
n = sample size
Pexp = expected prevalence
d = precision
z = factor that determine confidence levels ( 95% - 1.96)
estimation for small population
nadj = (Nxn) / (N+n)
n = sample size for large population
N = sample size of analysed population
qualitative data
categorical, can’t be counted, measured or easily expressed as numbers eg breed, sex
quantitative data
information that can be expressed in numbers or quantified eg body weight, milk production, body temperature etc
discrete data
type of quantitative
can’t be made more precise eg number of pets (can’t have 1.4 animals)
continuous data
type of quantitative
can be divided and reduced to finer numbers
eg height can be in m, cm, mm etc
qualitative scales
nominal - no quantitative value eg sex, location
ordinal - variable measurement eg satisfaction, degree of pain
continuous scales
interval - order of variables and difference between variables known eg body temp
ratio - order of variable and makes a difference between variables
static measures
proportion and ratio
proportion is
a fraction in which numerator is included in the denominator
dimensionless, from 0-1 and usually a %
relation of 2 groups which are in direct relation
ratio is
a fraction in which numerator is not included in the denominator
can have a dimension
to present one group in relation to another
dynamic measures
rate - related to certain time frame
prevalence calculation
number of sick animals at a particular point in time divided by number of individuals in the population at risk at that point in time
prevalence characteristics
dimensionless
static measure
proportion
value from 0-1
probability measure
incidence definition
number of new cases that occur in a known population over a specified period of time
cumulative incidence calculation
number of animals that become diseased during a particular period divided by number of healthy animals in the population at the beginning of that period
what is cumulative incidence a measure of
average risk or probability that certain event will occur during defined time period
what are censored animals
animals lost from study due to loss and competing causes
CI calculation taking in to account censored animals
number of new cases of disease divided by number at risk at the start of the period minus half of the censored animals
pros and cons of CI
pro - simple to calculate
con - new animal cannot be added, only for first occurrence of disease, not for recurring eg mastitis and can be used in dynamic populations but only for a short period of time
incidence rate
measures the rapidity with which new cases of disease develop over time
IR calculation
number of new cases of disease that occur in a population during a particular period of time divided by the sum, over all animals, of the length of time at risk of developing a disease
IR = I/(number at risk at beginning + at end/2)
pros an cons of IR
pro - can be calculated in case of multiple disease occurrence
con - can’t be interpreted on the individual level and is complicated
morbidity
total number of diseased animals in a certain population over a given period of time divided by total number of animals in population
proportional morbidity
number of cases in a given population over certain time period divided with total number of diseased animals in a population
mortality
number of deaths during certain period of time divided by number of animals at the beginning of study
proportional mortality
total number of deaths of certain specific disease in certain population with respect to all recorded deaths in that population during that specific period of time
case fatality
number of deaths divided by number of diseased animals
survival rate
number of cases minus number of deaths divided by number of cases
crude measure
applied for whole population
specific method
applied for certain specific part of population or subpopulation
what is a diagnostic test
more or less an objective method for the reduction of diagnostic insecurity and to increase the speed of testing
potential problems of diagnostic test
cross reactivity, analytical specificity, non-specific inhibitors, improper timing etc
accuracy
how close are the test results to a real clinical condition (truth - gold standard)
validity
power to detect diseased and non-diseased animals
precision
results of repeated tests
gold standard
best existing test
true positive
sick animals correctly identified as positive
false positive
healthy animals incorrectly identified as positive
true negative
healthy animals correctly identified as negative
false negative
sick animals incorrectly identified as negative
sensitivity
proportion of diseased animals recognised by test as positive ones
100% sensitivity means no false negatives
RULE OUT disease
sensitivity calculation
number of true positives divided by number of true positives plus number of false negatives
false positive rate
proportion of negative animals incorrectly classified as positive
false negative rate
proportion of positive animals incorrectly classified as negative
specificity
proportion of healthy animals recognised by the test as negative
100% specificity = no false positives
RULE IN disease
when sensitivity increases….
specificity decreases and vice versa
predictive values
reflect diagnostic power of test
what do predictive values depend on
sensitivity, specificity and prevalence
positive predictive value
proportion of positive animals that really have the disease
negative predictive value
proportion of negative animals that really dont have the disease
increase in prevalence causes….
increased PPV
decrease in prevalence will cause…
increased NPV
if test is more sensitive then
higher NPV
if test is more specific then
higher PPV
likelihood ratio
assess the value of performing the diagnostic test
higher LR, the better the test to rule IN the disease
smaller LR means
the better the test to rule OUT the disease
parallel testing
application of several tests on the same animal and if one is positive the animal is considered positive
FP more likely to occur but increase NPV and Sn
serial testing
only animals recognised as positive will undergo the second tets
what is a mathematical model
mathematical description of the real world
focuses on specific quantitative features of the scenario and ignores others (simplifies)
epidemic
higher incidence of disease than usual
actively spreading
often localised to a region
epidemic curve shape determined by
incubation period
infectivity
proportion of susceptible animals
potential contact (distance between animals)
endemic
disease/condition present among a population at all times
signs may be present or latent disease
after epidemics
pandemic
epidemics that spread over multiple countries/ continents
eg avian influenza, ASF
reproduction number definition
term that indicates how contagious an infectious disease is
average number of animals that will contract a contagious disease from one sick animal
reproduction number calculation
infection rate divided by removal rate
epidemic can only occur if Ro >1
factors determining reproduction number
infectiousness of pathogen, population density, course of infectiousness (incubation period, latent periods), mode of transmission, mixed population, seasonal variations, genetic variations in population at risk
highest reproduction number in which mode of transmission
airborne and
herd immunity
resistance of a group for attack of disease because of immunity of a large proportion of the members and so less likely of an affected individual to come in to contact with a susceptible individual
prevalence or immunity in a population above which it becomes difficult for the organism to circulate and reach new susceptible animal
herd immunity can be
innate
acquired - had a disease or vaccinated
density models
in case of diseases usually performed for cases where number of infectious agents can be numbered eg parasitic infections
prevalence models
presence or absence of disease in various host cohorts eg age groups, immunity status et c
deterministic models
describing situation with no random variation of input parameters
more suitable for large populations
stochastic methods
enable probability distribution and CI to be associated with outputs
possibility of chances, suitable for small populations
potential application of modelling
to model processes in organism - metabolism, drug kinetics etc
estimation of population dynamics
simulation of spreading diseases
education
animal production - simulation of profitability through reduction of negative factors
SIR model is
susceptible - infectious - recovered
enhancing SIR model
consider additional populations of disease vectors - fleas etc
consider an exposed but not yet infected class - SEIR model
SIR, SIS and double (gendered model) for STDs
consider biased mixing, age differences, multiple types of transmission, geographic spread etc
enhancements often need more compartments
SIR calculation
S+I+R= 1
reed frost model
also considers time and probability that an animal can’t infect another animal
probability defintion
proportion of times an event would occur if an observation was repeated many times
risk definition
probability of an event among those experiencing the event divided by the number who are at risk
odds definiton
probability of an event divided by the probability of the event not happening
association
is present if probability of occurrence of a variable depends upon one or more variable
risk factor
any factor that is related to increased chance of disease/death etc
exposure
means that an animal was exposed (in contact) with specific risk factor
absolute risk
only those who have a condition due to exposure
a/a+b
relative risk
if an association exists, then how strong is it?
what is the ratio of the risk of disease in exposed individuals to the risk of disease in unexposed individuals?
relative risk calculation
risk in exposed divided by risk in unexposed
incidence in exposed divided by incidence among unexposed
interpreting relative risk of a disease
RR >1 = positive association (probably causal)
RR <1 = negative association (possibly protective)
if odds ratio is 1
no association
cohort studies and odds ratio
probability of disease occurrence
case control studies and odds ratio
ratio of exposure to risk factor in group case, compared to group control
what are the odds that a case was exposed?
cross sectional studies and odds ratio
estimate prevalence odds ratio
odds calculation
probability of an event occurring divided by probability of the event not occurring
relationship between OR and RR
OR is a valid measure of association in its own right and is often used as an approximation of the relative risk
OR always further from 1 than RR
the higher the incidence the higher the discrepancy
attributable risk
the amount of proportion of disease incidence (or disease risk) that can be attributed to a specific exposure
what does AR include
baseline incidence and indicates what was the effect of the risk factor in the population
higher AR means
higher effect of the risk factor
attributable fraction
answers the question - which proportion of disease in exposed animals is due to exposure
when is AF difficult to calculate
in case-control studies
preventative fraction asks
how much disease among the non exposed group could be prevented by adding the exposure to the non exposed
population attributable risk
help assess the effect of primary prevention interventions on an entire population
amount of risk that would be eliminated from th population if the exposure were eliminated
types of error
type 1 - alpha - accepting the hypothesis despite the fact Ho is correct
type 2 - beta - acceptign the false Ho
why do we test if there is a difference
to determine what is a probability that differences are accidental or actual - significance
why do we test if there are no differences
how probable is that differences do exist but our test have failed to determine them
