Stats and epi Flashcards
(203 cards)
1
Q
Ratio
A
Differences in rank order plus equal intervals, plus true zero
2
Q
Interval
A
Differences in rank order plus equal intervals
3
Q
parameter
A
the mean value within a whole population
4
Q
statistic
A
the mean value within a sample
5
Q
Regression analysis
A
A type of statistical model that examines the predictive relationship between one or more predictor variables and an outcome variable.
‘Independent variable’ and ‘dependent variable’ are sometimes used as alternative terms, but are best avoided.
aim: 1. To determine how well a pre-selected set of predictor variables predicts values of an outcome variable.2. To identify whichset of predictor variables will best predict values of an outcome variable.3. To predict specific values on the outcome variable from specific values on a set of predictor variables.
6
Q
Standardized regression coefficients
A
A regression coefficient can be standardized so that coefficients of predictors measured on different scales can be compared. It measures the change in Y in SD units for a one-SD increase in X. Useful for comparing predictors within a model, but not for comparing a model across samples.
7
Q
Model fit
R^2
A
Usually measured by R2. This is the proportion of variance in the outcome variable explained by the predictor variable(s) in the model. Adding more predictors to a model will always improve its goodness of fit.
The adjusted R^2 takes into account the number of predictors and the sample size.
R^2 can be tested for significance; this tells you about the significance of the whole model.
8
Q
Interaction OR
A
In regression analysis you can include an effect moderator (e.g. sex) as an interaction term.In a previous example, this could tell you how much larger or smaller the odds ratio for survival would be if the animal were male rather than female.
e. g
1. 47 (OR for females) x 1.06 (OR for interaction term)= 2.53( OR for males)
9
Q
Descriptive epidemiology
A
examine the distribution of disease in a population
observing the basic features of its distribution
10
Q
what are Two broad types of epidemiology
A
. Descriptive epidemiology
. Analytic epidemiology
11
Q
Analytic epidemiology
A
investigate a hypothesis about the cause of disease by studying how exposures relate to disease
12
Q
The epidemiologic triad/triangle
A
an external agent, a susceptible host, and an environment that brings the host and agent together.
13
Q
Exposure
A
risk factor being investigated, and may or may not be the cause
14
Q
Outcome
A
the disease/event/health-related state, we are interested in
15
Q
randomised control trial
A
an investigator assigns exposures
the exposures are randomly allocated
the goal is to invesigate prevention and treatment
Experimental, analytical
Prospective cohort design
Starts at the time of exposure (intervention) – follow-up until outcome occurs
Key features:
Control arm (no exposure)
Random allocation of exposure to intervention groups: similar baseline characteristics; similar distribution of confounders
Blinding of participants (e.g. owners) and clinicians (where possible)
Strong evidence for temporal associations
Can investigate multiple outcomes
Low risk of selection bias and confounding
Blinding: reduce risk of information bias
Not suitable for rare outcomes
Not suitable for harmful exposures
Can take a long time (depending on length of follow-up)
16
Q
non randomised control trial
A
the investigator assigns exposures
the exposures are not randomly allocated
the goal is to investigate prevention and treatment
17
Q
cross sectional (prevelance study)
A
an investigator did not assign exposures
it is a descfriptive study
theres no comparison group
it shows burden and impact
Cross-sectional study design is a type of observational study design.
In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time.
18
Q
ecological study
A
an investigator did not assign exposures it is a descriptive study theres no comparison group it shows burden and impact an observational study defined by the level at which data are analysed, namely at the population or group level, rather than individual level.
19
Q
case report/series
A
an investigator did not assign exposures
it is a descriptive study
thers no comparison group
Careful, detailed description of a single case or series of cases (typically by observant clinician(s))
Analysis: narrative description, simple descriptive statistics (case series)
May be the first clues of new diseases, outbreaks, impact of a condition, unsuspected adverse effects, possible exposures
No comparison group – unable to test hypothesised association between exposure and outcome
could be random finding
Lack epidemiological quantities – not chosen from a representative population sample
Publication bias – journals mostly favour positive outcome findings
Overinterpretation – temptation to generalise when there is no clear justification
When less rigorous methodology for research on rare disorders required
When ethical constraints hinder experimental research
Make it possible to make changes in clinical practise – e.g withdrawal of drug from the
market
20
Q
cohort study
A
the investigator did not assign exposures
it is an observational study
there is a comparison group
its an analytical study
investigates causes and prognosis
direction: exposure> outcome
Careful, detailed description of study population and exposures (risks)
Starts at the time of the exposure – follow-up until outcome occurs
a type of research design that follow groups of people over time
Stronger evidence for temporal associations
Can investigate multiple outcomes
Lower risk of selection bias and information bias
Not suitable for rare outcomes
Can take a long time (depending on length of follow-up)
Risk of information bias due to attrition (loss to follow-up)
21
Q
case control study
A
the investigator did not assign the exposures
there is a comparison group
theres an analytical study
direction: outcome> exposure
Compares cases (diseased animals) and controls (non-diseased animals) with respect to their level of exposure to a suspected risk factor
Starts with the disease (or outcome of interest) and looks back at prior history of exposures
“all the effects are already produced before the investigation begins”
streanghts: Efficient: well-suited to rare diseases
Ideal when long latency between exposure and disease
Relatively quick and inexpensive
Investigate multiple exposures
Limitations
Susceptibility to bias:
Selection bias, information bias
Temporal association difficult to establish
22
Q
cross sectional study
A
an investigator does not assigng an exposure it is an observational study
there is a comparison group it is and analytical study
it minvestigates causes and prognosis
a type of observational study design. In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time.
Relatively quick and inexpensive
Investigate multiple exposures or outcomes
Susceptibility to bias high
Temporal association (nearly always) impossible to establish
23
Q
Two main bias domains
A
Selection bias
Information bias
24
Q
Selection bias
A
The study sample is not a good representation of the population of interest
Selection bias: selection or participation in a study is related to outcome or exposure
25
Information bias
Exposures and outcomes are not measured well, or not in a similar way in all study participants (animals)
Information bias: assessment of exposure varies depending on risk of experiencing the outcome / disease status
misclassification bias- arises when a study participant or is categorised into an incorrect category altering the observed association between study categories and the research outcome of interest.
observer bias- Bias that arises when the process of observing and recording information includes systematic discrepancies from the truth.
recall bias-Recall bias is a systematic error that occurs when participants do not remember previous events or experiences accurately or omit details: the accuracy and volume of memories may be influenced by subsequent events and experiences
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Avoiding chance findings
Generate sufficiently precise estimates of the strength of an association: sample size!
Use robust statistical methods
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parallel testing
the animal is positive if one or more tests are positive
the greatest predictive value is a negative test result
used to rapidly asses individuals
important if there is a penalty for missing the disease
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series testing
animal disease positive if all tests are positive-
| maximises sp and se and ppv- more confident disease is really present
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screening + conformation testing
screening to test every animal (low test cost, high sensitivity) then confirmatory trst on positives that is higher cost and more specific)
used in disease controll programs
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positive predictive value
probability that the animal tested positive is truly positive
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negative predictive value
possibility that the animal tested negative is truly negative
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aggregate testing
sampling and testing groups of animals with the same test
most control programs use this
as prevelance decreases proportion of false positives increase
sensitivity most valued here
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negative herd re-testing
positive animals are removed and negative animals are sampled and retesed again
finds missed infections
used in TB
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sequential testing
used in experimental studies
repeatedly sample and test animals to detect sero-conversion
powerful as does not rely on single result
looking for significant change in test result
labour intensive
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using different tests for different diseases in the same animal
common in small animal- blood paramenters before anasthesia
| used in dairy to produce metabolic herd nutritional status
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simple random sampling
list all the sampling units in the sampling frame and select at random
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systematic sampling
select sampling units at a predefined equal interval e.g. randomly start at no. 17, and then select every 17th animal/herd/flock
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stratified sampling
divide the sampling frame into logical groups (strata) and make random selections from within all strata
39
Cluster sampling
divide the sampling frame into clusters (space or time), and randomly select clusters (one-stage) or also within clusters (two-stage)
40
Bradford-Hill’s “aspects to consider” when trying to infer causality from an association
1. Strength. Very strong associations will generally be harder to explain away by confounding or bias.
2. Consistency. An association that is repeatedly observed by different research teams under different circumstances may be less likely to be produced by confounding or bias.
3. Specificity. A cause leads to a single effect not multiple effects. [Not to be over-emphasised.]
4. Temporality. We should be confident that the exposure preceded the outcome.
5. Biological gradient. Is there a dose-response, such that higher levels of exposure have a greater effect?
6. Plausibility. Is a causal connection biologically plausible [depends on the state of biological knowledge at the time]
7. Coherence. Does a cause-effect interpretation seriously conflict with other established facts about the disease?
8. Experimental evidence. Does removal of the cause prevent (some cases of) the disease? [may not be feasible or ethical]
9. Analogy. Can we draw any parallels?
41
name the two catagorical variables
nominal
| ordinal
42
nominal variables
2 or more catagories
no order
female-male
cat,dog,reabbit
43
name the two numberical variables
discrete
| continuous
44
ordinal variables
```
ranked
e.g disease severity
1= none
2= mild
3= moderate
4= severe
```
45
discrete variables
counts of event
| e.g number o cattle, no. of visits to vet
46
continuous variables
take any value in range
| weight, age, body temp
47
describe mean vs median
the mean- makes more use of data
is distorted by outliers or skewed distribution
good for normal distribution
the median-
makes less use of data
better for skewed data
less easy to analyse
both useful to produce histeogram
48
measures of variablity
with the mean- varience and standard deviation- essentially how different observations are form mean
with median-
range
inerquartile range
49
descriptive statistics for catagorical variable
frequancy distributins and percentages
50
desciptive statistics for numerical variables
mean and std deviation or median and IQR
51
describe 95% confidance intervals
need: point estimate (mean), measure of variability (SD of mean) and sample size
a range of values so defined that there is a specified probability (95%) that the value of a parameter lies within it
small sample size or large variability widens confidance interval because of more uncertanty
52
to measure the assosiation between two nominal varibles we use
chi- squared (X^2) test
eg: assosiation between canned cat food and feline hyperthyroidism
compares the observed count, to count that would be expected if there was no assosiation between variable and outcome
Expected count = (column total * row total) / grand total
X^2= sum of (observed- expected)^2/ expected
from this the p value can be obtained
(will be listed under asymptotic significance (2-sided)
53
odds ratio
a measure of the streangth of an assosiation
the odd of an event is the ratio of the probability of occurence of the event to the probability of non occurance
odds = (P/P-1)
odds ration is the ratio fo odds for group 1 to the odds of group 2
(p1/(1-p1))/(p2/(1-p2)
an odds ratio of one means there is no difference
no difference also means the confidence interval would include 1
An odds ratio of less than 1 implies the odds of the event happening in the exposed group are less than in the non-exposed group
54
how do we test for two independant groups of numerical parametric data
e.g compare mean weight of specific breeds of dogs between deprived and non-deprived areas
unpaired/ 2 sample t-test
55
regession analysis
a way of mathematically sorting out which of those variables does indeed have an impact
examines the predictive relationship between one or more predictor values and and outcome variable
aims to determine how well the predicter variables predicts the outcome variable
which ones best predict the values of the outcome variable
to predicd specific values on the outcome variable from secific values on a set of predictor values
56
multivariable regression
either linear or logistic regression where there is more than one predictor variable included, such as the effect of both food type and age on weight
57
Describe Y=a+bX
```
Y= the predictor value of outcome variable (e.g in comparing quality of life to the level of independence someone has, the ocv is quality of life)
a= the constant (the inercept with the Y value on the graph)
b= the coeffienct for x, the amout that y increases or one unit of x
x= the value of x
```
58
Describe Y=a+b1X1+b2X2
```
Y= the predictor value of outocme variable (e.g in comparing quality of life to the level of independence someone has, the ocv is quality of life)
a= the constant (the inercept with the Y value on the graph
b= the coeffienct for x, the amout that y increases or one unit of x
x= the value of x
```
this is done for both variables
59
standerdised regression coeffiecents
a regression coefficient can be standardised so that the coeffiencts of predictors measured n diffrent scales can be compared
measres the change in Y in SD units for one SD increas in X
60
Model Fit
R^2
the proportion of varience in the outcome variable explained by the predictor variables in the modle
assing mroe predictors improves goodness of fit
ajusted R^2 takes into account the no. of predictors and sample size
R^2 can be tested for significance which tells you the significance of the whole model
61
regression coefficients
value of the coefficient is the relationship between the predictor and the outcome variable when the other predictors are held constant (controlled for)
62
Avian influenza
Highly significant zoonose
diffrerent subtypes more prevelant in certain region than others
severity of the disease in poulrty depends on whether it is HPAI or LPAI
spread by wild birds - high during october through winter and in summer recead as birds migrate
tade offs between managment and welfare as poultry must be housed indoors
free range status of eggs in uk lost- econimic impact
implications for human heath- important source of protien
zoones
63
african swine fever
NOT a zoonose
pig meat however is a massive source of protien and hence threats to it effects human health risk and larg economic loss
contaminated pork products can spread it
matter of time till hits uk
```
losses in pig productin
no vaccine
socioeconomic burder
mortality can be 100%
a threat to and spread by wild pigs (ticks or direct contact)
not zoonotic
resistant in environment
concern for biodiversity
```
64
bovine TB
```
enormous global disease threat
end point of immunosupressed patients
significan TB in uk form infected milk in past
bad for cattle welfare
diagnosis influences spread as it is challenging
big iniciatives to control TB
movemnt of cattle is a risk
wildlife can be resovour
```
chronic bacterial infection
complicated by persistent infection of wild animals
zoonose- very serious
transmitted by direct contact, ingesting of contaminated material
slow course of diseases- can infect others before clincal signs show
estimated to account for up to 10% of human tb cases
improve testing, reduce transmission between animals and humans, improve collaberation
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Rabies
```
threat from animals coming in to the country
quarentine
good vaccines
good tests
sucsessful eradication in UK
```
```
viral disease
effects nervous system
transmitted via saliva- bite
zoonotic
non specific symptoms
incubation weeks to months
fatal
goal of elimination- vaccine very effective
```
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q fever
```
notifiable in uk from the last 12 months
vaccine available
20% of uk dairy herds seropositive
fertility issues
debilitating disease for humans that catch it
unspecific symptoms
fignsed by pcr, serology
can survive in enviroment for long time
bacterial infection
```
67
leptosporosis
```
bacterial infection
" in practice" approach
cattle infertility
-establish herd lepto status
- control and eradicate
- vaccination pros and cons
can beconme endemic in herds and cause low grade chronic repro losses
in niave herds can cause substantil loss
common differetail for repro problems
passed in aborted fluids and urin so can spread to dairy workers
vaccination available
```
different strains in different parts of the world
possibly returning as vaccination drops
good tests available- good survalence
- dont graze with sheep, understanding danger of watercourses, not testing before trading,
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vaccination vs treatment
before lepto vaccine, whole herds were treated with antibiotics
vaccines are now effective- more responsible
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eviromental role in one healt
energy security
food securiy
green energy soloutins with in vet practice
70
Animal health surveillance
a tool to monitor disease trends, to facilitate the control of infection or infestation, to provide data for use in risk analysis, for animal or public health purposes, to substantiate the rationale for sanitary measures and for providing assurances to trading partners.’
71
Data collection for MOSS
Active:
Systemic and regular recording of cases
Population defined by location and/or time
All the population or a sample of?
Depends on objective, expected prevalence, diagnostic tests
Random or targeted sampling
Passive (Scanning):
Relies on notification of disease suspicions and cases – less control
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Active data collection: Population under scrutiny
Accurate information for potentially every individual animal
Labour-intensive – lot of sample collection
Expensive – field work, lab diagnostics, administration
Used for control/eradication programmes
(e.g. brucellosis in N. Ireland – see reference below)
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Active data collection: Random sample
Estimate of disease prevalence / incidence and to describe temporal trends
```
Sample size depends on:
Expected disease prevalence in population
Population size
Required precision of estimate
Sensitivity and specificity of tests
```
Expensive if the disease is rare
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Active data collection: Targeted sample
Focuses on high-risk population in which specific and commonly-known risk factors exist
Appropriate if:
Disease is less common in general population
Specific risk factors are known
Have knowledge of the epidemiology of the disease
Problem:
Undetected cases may occur in other segments of the population
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Passive: Reporting cases
Relies on farmer and vet knowledge and willingness to report and sample – submit to a diagnostic laboratory
```
Limitations:
Availability of diagnostic tools
Inconsistency of data
Under/over-reporting
Lack of central recording
Farmer generally has to pay – an inhibitor to submission – offer incentives?
```
76
MOSS: Overall aims
Need to be able to:
Rapidly and reliably identify outbreaks of infectious disease;
Prove success of an eradication programme, or prove freedom (ongoing or recently achieved) from disease
Work within the budget available – limiting factor
77
EU ‘Animal Health Law’: Regulation (EU) 2016/429
On transmissible animal diseases – consolidates lots of previous EU animal health legislation - in force from 21 April 2021
Article 1:
‘This Regulation lays down rules for the prevention and control of animal diseases which are transmissible to animals or to humans.’
About:
‘the early detection, notification and reporting of diseases, surveillance, eradication programmes and disease-free status (Part II: Articles 18 to 42)’
Implication for UK: to enable ongoing trade with EU as 3rd country, more diseases had to be made notifiable in the UK, based on the listed diseases in Annex 2 of the regulation
15 of these diseases (10 are endemic) were not notifiable or reportable in GB up until then – have been added to domestic legislation
e.g. Included Johne’s disease (paratuberculosis), Q fever, infectious bovine rhinotracheitis (IBR), bovine viral diarrhoea (BVDV), porcine reproductive and respiratory syndrome (PRRS)
Onus on laboratories to report detection to the APHA – usually monthly laboratory reports to be returned, but may require immediate notification (e.g. Q fever – zoonotic)
Article 12: Responsibilities of veterinarians and aquatic animal health professionals
1. Veterinarians shall in the course of their activities which fall within the scope of this Regulation:
(a) take all appropriate measures to prevent the introduction, development and spread of diseases;
(b) take action to ensure the early detection of diseases by carrying out proper diagnosis and differential diagnosis to rule out or confirm a disease;
(c) play an active role in:
(i) raising animal health awareness, and awareness of the interaction between animal health, animal welfare and human health;
(ii) disease prevention;
(iii) the early detection of, and rapid response to, diseases.
(iv) raising awareness of resistance to treatments, including antimicrobial resistance, and its implications;
(d) cooperate with the competent authority, operators, animal professionals and pet keepers in the application of the disease prevention and control measures provided for in this Regulation.
Article 13: Member States’ responsibilities:
‘1. In order to ensure that the competent authority for animal health has the capability to take the necessary and appropriate measures, and to carry out the activities, required by this Regulation, each Member State shall, at the appropriate administrative level, ensure that competent authority has:
(a) qualified personnel, facilities, equipment, financial resources and an effective organisation covering the whole territory of the Member State;
(b) access to laboratories with the qualified personnel, facilities, equipment and financial resources needed to ensure the rapid and accurate diagnosis and differential diagnosis of listed diseases and emerging diseases;
(c) sufficiently trained veterinarians involved in performing the activities referred to in Article 12.
2. Member States shall encourage operators and animal professionals to acquire, maintain and develop the adequate knowledge of animal health provided for in Article 11 through relevant programmes in agricultural or aquaculture sectors or formal education.’
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what are the diffrent levels Prevention/control/biosecurity interventions for infectious agents can occur
Individual- small animal cinic vaccination
Institution- farm, heard health programs, "healthy feet program"
Community/national - removing contamminated food from a production line, "horse meat scandal", abbitours, prevention zones
International/global- bird flu, trade implications, countries being declared free of certain diseases, "transboundry animal diseases", african swine fever
rabies
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risk based intervations
tapeworm treatment for dogs coming back form cetain countries
rabies vaccine for traveling animals
80
disease managemtn strategies
Control
Prevention
Eradication
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disease managemtn strategies- control
all steps to reduce frequency of disease in a population
Sick and healthy
Aim to decrease - communicability and contacts
Isolation, quarantine (in-contacts)
Limiting mixing
Running closed herds, all-in-all-out practices
Slaughter (+/- test)
Treatment of cases
Control measures can be difficult to apply in dynamic populations. Easier in homogenous populations. Early detection is key (flatten the curve!)
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Disease management strategies- Prevention
Exclusion of disease from a population of unaffected animals/people
Again, quarantine of exposed individuals
slaughter
Need to know incubation period; case definition (including clinical signs); mode(s) of transmission; whether zoonotic or not; testing options
Prior testing of individuals (also a type of exclusion).
Need tests to be readily available, affordable and reliable
Need to be CONFIDENT of NEGATIVE test result- fals enegatives result in more spread
Vaccination
Chemoprophylaxis
Genetic engineering and selective breeding
Public awareness and education
Sanitation (hygiene, vector control etc.)
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Disease management strategies- Eradication
Elimination of the agent from a specific geographic region/premises as well as selected host species
Ultimate goal against disease
Stamping out, depopulation/repopulation, mass treatment, mass immunisation
Difficult to accomplish AND maintain
More realistic to think in terms of place, shelter, region, rather than globe
Result is free or ‘officially free’ status
Rinderpest
84
describe the steps of a disease managment program
establish rational (studies ect) > strategic goals and obgectives (erradiction, control ect.) > program planning > impletmentation
reapeat steps 3-4 with monitoring, evaluation and review
85
disease ecology
study of host-pathogen interactions within the context of their environment and evolution.
Explores the relationships between parasitic, bacterial and viral infectious diseases, their animal and human hosts, and their environment.
Many infectious diseases have environmental reservoirs and environmentally-mediated transmission pathways
Global environmental changes is increasingly affecting patterns of infectious disease distribution.
Study of diseases with a population requires understanding of the relationship between organisms (hosts, agents) and their environment.
Natural factors (type of plant) and agricultural practices (pasture management) both exert an effect.
Agricultural changes ( e.g intensification of pig farming in new areas) and ecological destruction are linked to emergence of infectious diseases.
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Basic ecological concepts- Two major factors that determine the occurrence of disease
Distribution - depends on distribution of suitable food
size of animal populations depends on availability of food, mates and species.
rift valley fever is spread by mosquitos and linked to flooding
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Regulation of population size
Balance of nature
Populations grow, reach a certain size, and then stop growing.
Becomes stable and balanced, with the rate of reproduction equalling the death rate.
Two hypotheses formulated to explain the ‘balance of nature
environmental resistance -animal populations had an intrinsic rate of increase but there was some quality of the environment that resisted the increase
competition for the resources of the habitat, the most common of which is food competition therefore is density dependent
Pandemics with high case fatality rates, clearly can have a severe impact on populations
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infectous agents can be divided into two groups according to their generation dynamics
these are
Microparasites – viruses, bacteria and protozoa
Multiply directly when inside the hosts, increasing the level of parasitism
Macroparasites - helminths and arthropods.
Do not increase the level of parasitism
Disease-induced mortality must be high compared with the disease-free rate of growth – population regulation by microparasites
Infections with macroparasites, particularly helminths, could have a widespread regulatory effect on animal populations
Decrease an animal’s growth rate reduce host survival and reproductive capacity
sheep mortality is related to intensity of infection with Fasciola hepatica
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Implications of the distribution and control of populations for disease occurrence
The distribution, the home range of animals, and other behavioural activities of hosts of infectious agents affect disease transmission
Vulpine rabies.
different behaviour patterns affect the survival and spread of rabies virus between animals.
increases in home range also may increase spread of infection – summer winter months
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Landscape epidemiology
Study diseases in relation to the ecosystems in which they are found
Also referred to as Medical ecology, horizontal epidemiology, and medical geography
Investigations frequently qualitative
study of the ecological factors that affect the occurrence, maintenance and, in the case of infectious agents, transmission of disease
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Nidus (landscape epidemiology)
nest or breeding place, where microbes and parasites, are located and multiply
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Nosogenic territory (landscape epidemiology)
an area with ecological, social and environmental conditions that can support a specific disease but the disease is not necessarily present
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Nosoarea (landscape epidemiology)
a nosogenic territory in which a particular disease is present
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Britain is a nosogenic territory for rabies and FMD, but is not a nosoarea for these diseases- what does this mean
Nosogenic territory – an area with ecological, social and environmental conditions that can support a disease
Nosoarea - a nosogenic territory in which a particular disease is present
because the microbes are prevented from entering the country by proper import checks.
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Changes in ecosystems stemming from human activity can modify nosogenic patterns substantially, resulting in the emergence of infectious diseases
Probable factors for the emergence of some infectious diseases:
Ecological changes
(including those due to
economic development and
land use) - Agriculture; dams, changes in water ecosystems; deforestation/reforestation; flood/ drought; famine; climate changes- Rift valley fever
Deliberate introduction- Introduction of natural or engineered agent in a
legal or illegal control programme- rabbit calcivirus
Microbial adaptation and
change-Microbial evolution, response to selection in
environment- antibiotic resistance
Technology and industry-Globalization of food supplies; changes in food
processing and packaging e.g BSE
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Modes of infectious disease transmission
Disease control and prevention depends on disrupting the transmission of pathogens from their source (the infected animal) to new hosts (animal) or locations.
Modes of disease transmission;
direct contact -
indirect contact
droplet particles – can also spread by direct and indirect contact
airborne
vector borne -infections caused by animals and insects
common vehicle - transmission through a contaminated source e.g feed sources
97
Economics in the context of animal health
can provide information that will help the people making animal health decisions to allocate resources effectively.
Pricing of goods can lead to decision making that does not result in general societal well being
Alcohol
Sugar
Milk pricing
Understanding these market drivers for behaviour allows an understanding of how to use the market to guide people
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Resource availability
Socio-economic environment can shape how we respond to a disease
The response can in turn shape the disease spread and it can shape the disease impact
Skilled people- Less skilled labour
Technical people
Management people
```
Capital- Buildings
Infrastructure
Transport
Equipment
Smaller capital items
```
Land
Disease or health problem can affect economies
If health problems are sufficiently problematic there may impact on:
Trade
Movement of people
Limit people’s ability to work and do business
Limit animals production
Death of people and animals
There are diseases that shaped society
Disease has affected economies both by demographic pressure that has changed supply and hence the price of labor and by its effect on the productivity of a particular region or social group
99
Opportunity Cost
Choice involves sacrifice for example:
The more food a person chooses to buy, the less money you will have to spend on other goods
The more money a farmer spends on veterinary advice the less money he has available for other inputs.
The more money spent on veterinary practice signboard the less money laboratory diagnosis equipment
The more money spent by a veterinary faculty the less money for support staff
When a government spends money on disease control it has less available for other projects
In other words, the production or consumption of one thing involves the sacrifice of alternatives.
This sacrifice of alternatives in production (or consumption) of a good is known as its OPPORTUNITY COST
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Rational choices
Knowing the cost of a decision is only part of the problem
Rational decision making also requires information on the benefits
It involves choosing what will give the best value for money, i.e. the greatest benefit relative to cost.
What is our measure for making rational decisions?
Generally the technically oriented professionals will be focused on production levels
But the livestock keepers will be more interested in profitability from their livestock enterprises
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livestock farm productivity=
outputs/ inputs
102
economic optimum
Rather than looking just at the technical relationship, for an economic optimum there is a need to look at the:
Value of the inputs
Total inputs multiplied by input price
Value of the outputs
Total outputs multiplied by output price
The difference between the value of the outputs and the inputs is equal to the profits
An economic optimum is reached where the profit is maximised
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production function- rational level of production
In order to identify an optimum level of production there is a need to have a technical relationship between an input and output – a production function
Livestock production is no different
It depends on various inputs:
Feed
Forage
Animal health care
This technical relationship is very important in terms of defining a rational level of production
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Technical optimum
The maximum amount of production
105
Economic optimum
The maximum profit
106
when are the technical and economical outputs closest
The lower the unit price of an input relative to an output the closer the technical and economic optimums are
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Profit is maximised where
the slope of the total revenue line is equal to the slope of the total cost line
This is equal to the point where there is greatest distance between the two lines
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productivity proxies
Offspring per breeding female
Feed conversion ratio
Key inputs – feed, capital
Key outputs – offspring, liveweight
Critical to identify important inputs and outputs
Prices of key inputs and key outputs dictate profitability and productivity
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optimal control point
Ratio between the value of Losses to the value of Expenditure and Reaction is critical to define the optimal control point
Health Losses are based in changes in biomass of humans and animals
Quantity – human and animal lives saved
Quality – healthier humans and animals
Efficiency of production – great relevance to livestock
The changes in biomass need to be valued
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Gross Margin Analysis
Gross margin analysis is used to assess and compare different enterprises
The basis of the analysis are real data
It does not take into account fixed costs nor the management time given by the owner of the enterprise
It does not take into account a change in an enterprise
Comparisons of gross margins are only valid if the production systems are similar.
Differences between similar systems are a result of different levels of output and prices of inputs and outputs. To make an effective comparison in terms of productivity prices of inputs and outputs and the climatic and soil conditions should be available.
The gross margin is only of value if the calculations of how the final figure are available.
It is a simple representation (model) of an enterprise
It can easily be explained to farmers
The data generated by gross margin analysis are useful for
Other aspects of livestock production, in particular animal health
Development of farm, community and economy models
It allows the identification of the most important prices, inputs and outputs from a livestock enterprise
Provide information that can be used for a monitoring and evaluation systems for the enterprise
Useful for making comparisons between livestock enterprises in different regions and between nations
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Variable Costs
Variable costs are those that vary in the short-term according to the scale of production. If there is no product produced then the variable costs will be zero.
It is easy to identify the variable costs of a livestock enterprise
Generally the variable costs are:
Animal health inputs such as vaccines, drugs
Concentrate feeds
Mineral supplement
Forage costs
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Fixed costs
Fixed costs only vary in the long-term.
If there is no production the fixed costs will still be exist.
In a farm with a mixture of enterprises, the fixed costs will be shared between the enterprises
The fixed costs are things such as:
```
Salaries for permanent staff
Maintenance of machinery and farm infrastructure
Rent
Administration costs
Electricity, water, petrol, diesel
Depreciation
Interest
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Gross Margin =
Output – Variable Costs
| Gross margins do not take into account fixed costs
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It is common to combine a gross margin with an input which is a fixed cost in order to estimate the productivity of an enterprise
The most common measures are:
Gross margin per head of animal
Gross margin per hectare
It is also common to combine the gross margin with the amount of product produced
Gross margin per kg or litre of product produced
Other possible combinations are:
Gross margin per day of labour
Gross margin per US$ of capital investment
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COST STRUCTURE
proportion of the costs contributed by each input
The first step is the identification of the inputs, the amounts used and the price of each input to obtain their cost
Using a spreadsheet it is easy to calculate the proportion of the costs contributed by each input – The COST STRUCTURE
This can be used to
Determine the most important enterprise inputs;
Investigate the impact of a change in price of the important inputs (sensitivity analysis); and
Design a monitoring system for an enterprise
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economic tools for Assessing Animal Health Interventions
Partial Budget Analysis- Short term animal health interventions
Cost Benefit Analysis- Long term animal health interventions – major investments
Financial Feasibility- Practical feasibility of financing an intervention
Decision Tree Analysis- Forces the analyst to assess uncertain and quantify the risks of the intervention
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Partial budget analysis is interested in four basic items:
Additional Costs-
a) New costs
b) Lost revenue
Additional Benefits-
c) Costs saved
d) New revenue
good for Short term animal health interventions
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Cost Benefit Analysis
Same underlying principle as partial budget analysis, the comparison of additional costs with additional benefits
Yet these additional costs and benefits occur in different years (time periods) and need to be converted to present values
It generates three metrics net present value (NPV), benefit cost ratio (BCR) and internal rate of return (IRR) which provide an indication of economic profitability
good for Long term animal health interventions – major investments
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decision tree analysis
Forces the analyst to assess uncertain and quantify the risks of the intervention
Most animal health decisions involve a degree of uncertainty
Whether disease occurs
Whether a vaccine is effective
From an analytical point of view uncertainty needs to be quantified to assess the risks
An ideal framework is a decision tree analysis that combines risk with the estimate of economic profitability of each potential combination
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Grounded theory
Researchers collect rich data on a topic of interest and develop theories inductively. (infering general laws from particular instances)
e.g a team of researchers want to collect data from farmers perception on antibiotic usage in poultry production in order to develop theories inductively
qualitative method
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Ethnography
Researchers immerse themselves in groups or organizations to understand their cultures.
qualitative method
122
Action research
Researchers and participants collaboratively link theory to practice to drive social change.
qualitative method
123
Phenomenological research
Researchers investigate a phenomenon or event by describing and interpreting participants’ lived experiences.
qualitative method
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Narrative research
Researchers examine how stories are told to understand how participants perceive and make sense of their experiences
qualitative method
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interview
collects data on individual personal histories, perspectives and experiences
individual or focus groups
Common quantitative research methods
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observation
collects data on naturally occurring behaviours in the research participants typical context
Common quantitative research methods
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document analysis
reviews documents to identify meaning and gain understanding
Common quantitative research methods
128
Content analysis
Describe and categorise common words, phrases, and ideas in qualitative data.
Qualitative data analysis
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Thematic analysis
Identify and interpret patterns and themes.
| Qualitative data analysis
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Textual analysis
Examine the content, structure, and design of texts.
| Qualitative data analysis
131
Discourse analysis
Study communication and how language is used to achieve effects in specific contexts
Qualitative data analysis
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Evaluating the trustworthiness of qualitative research
Credibility- The research findings are plausible and trustworthy. There is alignment between theory, research question, data collection, analysis and results. Sampling strategy, the depth and volume of data, and the analytical steps taken, are appropriate within that framework
Dependability- The extent to which the research could be replicated in similar conditions. There is sufficient information provided such that another researcher could follow the same procedural steps, albeit possibly reaching different conclusions
Confirmability- There is a clear link or relationship between the data and the findings. The researchers show how they made their findings through detailed descriptions and the use of quotes
Transferability- Findings may be transferred to another setting, context or group. Detailed description of the context in which the research was performed and how this shaped the findings
Reflexivity- A continual process of engaging with and articulating the place of the researcher and the context of the research. Explanations of how reflexivity was embedded and supported in the research process
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MOSS data sources
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general passive surveillance
targeted surveillance
sentinel networks
research projects
animal health services
abattoirs, knackers
quality control programs
secondary data sources
sales yard, livestock markets
import quarantine stations
pre- movement inspections
pre-export feed yards
biological sample banks
routine laboratory submissions
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Diagnostic tests – different roles
New test: test that opens up a completely new test-treatment pathway (for example where, at present, no screening for the target condition is performed)
Triage test: used before the existing test(s), and its results determine which patients will undergo the existing test, e.g. when existing test is more expensive or invasive
Add-on test: is used after an existing test to improve the diagnostic accuracy of the existing testing strategy
Replacement test: aims to replace an existing test, either because it is expected to have higher diagnostic accuracy, is less invasive, less costly, or easier to use than the existing test
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Receiver-Operator Characteristic (ROC) Curve
True positive rate against false positive rate (sens vs 1-spec) for different positivity thresholds
Shows trade-off between sensitivity and specificity
Upper left hand corner ‘ideal’ threshold?
Depends on consequences of test result …
Many test results are measured on a continuous scale, e.g glucose level for diabetes, viral load for infections, body mass index for obesity, ....
Test positivity threshold needed to define presence / absence of disease
Test positivity threshold often decided based on optimal balance (trade-off) of sensitivity and specificity
However, other arguments may be more important, e.g.
minimise number of false negative test results (you want to reduce the risk of missing animals with the disease)
minimise number of false positive test results (you want to reduce the risk of incorrectly classifying a healthy animal as having the disease)
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QUADAS-2
Poor design and conduct of diagnostic studies can lead to bias and over-estimation (or under-estimation) of accuracy of diagnostic tests
Important to assess risk of bias, related to:
Study sample - selection bias
Measurement of the index test(s) – information bias
Reference standard – information bias
Flow and timing of the tests – missing data and loss-to follow-up
Use a standardized tool to assess methodological quality (e.g. QUADAS-2)
Bias domain 1: selection of animals (QUADAS-2)
Was a consecutive or random sample of animals enrolled? Yes/No/Unclear
Was a case-control design avoided? Yes/No/Unclear
Did the study avoid inappropriate exclusions? Yes/No/Unclear
Could the selection of animals have introduced bias?
Risk of bias: LOW/HIGH/UNCLEAR
Bias domain 2: Index test (QUADAS-2 )
Were the index test results interpreted without knowledge of the results of the reference standard? Yes/No/Unclear
If a threshold was used, was it pre-specified? Yes/No/Unclear
Could the conduct or interpretation of the index test have introduced bias?
Risk of bias: LOW/HIGH/UNCLEAR
Bias domain 3: reference standard (QUADAS-2)
Is the reference standard likely to correctly classify the target condition?
Yes/No/Unclear
Were the reference standard results interpreted without knowledge of the results of the index test? Yes/No/Unclear
Could the reference standard, its conduct or interpretation have introduced bias?
Risk of bias: LOW/HIGH/UNCLEAR
Bias domain 4: Flow and timing (QUADAS-2)
Was there an appropriate interval between index test(s) and reference standard? Yes/No/Unclear
Did all animals receive a reference standard? Yes/No/Unclear
Did all animals receive the same reference standard? Yes/No/Unclear
Were all animals included in the analysis? Yes/No/Unclear
Could flow or timing have introduced bias?
Risk of bias: LOW/HIGH/UNCLEAR
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chi squared test calculation
x^2= sum of (observed-expected)^2/ expected
( sum of refferes to doing this calculation for all observed and expected data in the (observed-expected)^"/ expected formula then adding it toghther)
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chi squared test
assosiation between 2 two nominal groups (non parametric and categorical)
e.g gender and disease
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2 sample (unpaired) t test
compare 2 independent groups on numerical value like breed and weight
parametric data
140
mann-whitney u test (wlicoxon ranked sum test)
compare two independent groups on numerical data such as breed and weight. non parametric data
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paired t test
compare 2 paired groups on numerical variables
parametric test
milke yeild pre and post intervention
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wilcoxon matched pair tests
compare 2 paired groups on numerical data. non-parametric
| milk yield pre and post intervention
143
pearsons correlation coefficient
association between 2 numerical statistics
| e.g milk yeild and weight. parametric
144
spearmans correlation coefficient
association between 2 numerical statistics
| e.g milk yeild and weight. non- parametric
145
binary logistics regression
Dichotomous outcome
E.g. disease (yes/no)
Independent effects of breed, age of animal, gender, social class of owner, etc on disease
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linear regression
Numerical outcome
E.g. weight
Independent effects of type of pet, age of pet, age of owner, gender, social class, etc on weight
Used to model a linear (straight-line) relationship between one or more predictors and the outcome variable. Outcome variable must be continuous and the predictors must be either continuous or binary (there is a special way to accommodate multinomial or ordinal predictors).If more then one predictor, we refer to the model as multiple regression, or multivariable (not‘multivariate’) regression.
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difference
is there a difference in rate of skin healing between two skin closure methods? Which diagnostic test for digital flexor tendonitis has greatest specificity?
148
assosiation
Is heart rate correlated with subjective pain intensity? How well do serological data predict respiratory tract infection severity?
149
types of regression
if the interval ratio is and interval/ratio scale: linear regression
ordinal scale: ordinal regression
nominal scale: possion regression or logistic regression- binary or multinomial
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logistic regression
a statistical modelling technique that can be used to analyse the strength and nature of an association between single or multiple variables and a binary outcome- e.g conception to a given insemination
Follows the same principles as linear regression except that the outcome variable is nominal (binary or multinomial).The outcome variable is a logarithmic function that can be anti-logged (exponentiated) to become an odds ratio.
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The value of a regression coefficient and the size of its associated p-value depend in part on the otherpredictors in the model. The value of the coefficient is
the relationship between the predictor and the outcome variable when the other predictors are held constant(i.e. controlled for).
152
components of one health
enviroment
human health
animal health
153
components of sustainable health and welfare
food security
animal health and welfare
antimicrobal resistance
environmental management
154
one health in covid 19
collaberative effort
| provide testing and equipment, ppe
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Herd Lepto Status-Investigation
•Bulk Milk Screening:
ELISA •OD ratio-detects chronic infection•Care with interpretation of infection timescale
* ELISA
* Detects IgG •Takes ~4wks to seroconvert•Persists for 2-3 years (variation!)-chronic infections
MAT
•Detects IgM & IgG1 •2-3 weeks to seroconvert; paired or single dilution result; serovar specific •IgM persists for 6 months-acute infections•Heifer cohort •Post-colostral (and pre-or peri-vaccination) •VN role...
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control strategy for leptospirois
1. Reduce exposure to the pathogens themselves.
•Antibiotic treatment of cattle infected with leptospirosis mayeliminate the carrier stage of this disease. •In addition, controlling exposure to other serovar maintenance hosts and contaminated environments is necessary..unrealistic?
2. Institute an appropriate vaccination program that is designed to reduce the risk of reproductive pathology •Limitations to efficacy-see Europe
Control-Reduce Exposure to Pathogens
•Risk factors•Watercourses•Sheep•Purchased stock•Boundaries
Treatment options:• Before introducing an animal to a clear herd: mixture of streptomycin and dihydrostreptomycin at a combined dose rate of 25mg/kg bodyweight in a single injection has been widely used. Milk withdrawal seven days
•During the acute phase of infection, the combined streptomycin and dihydrostreptomycin antibiotic mixture referred to above is the treatment of choice.
•Although this may not avoid effects on milk production, it may reduce the number of subsequent abortions. •Treatment when abortions are occurring will have little effect. •Other drugs, such as tetracycline and ampicillin, have also been used. There are various dosage regimes
vaccine 1: •Active immunisation of cattle to prevent kidney colonisation and shedding of L.borgpetersenii serovar Hardjo in urine. •12 months duration of immunity (by virulent challenge) but NOT vs L. interrogans challenge...? •Persistently infected 10-16month cattle vaccinated→significant reduction in urinary shedding by 4wks BUT not clearance of renal colonisation..•Skin swellings•
Vaccine 2•To ‘improve herd fertility’•Both strains?•Less likely to become infected or shed..•May reduce conception rates used within 2 weeks either side of service..•Abortions may continue for months after vaccination as incubation upto 12 wks..
Vaccines for Leptospira spp. are not100% effective; therefore, they must be used in conjunction with other control methods (as with most vaccines...)
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food health saftey, one health- E.coli
cleaner cattle and sheep
animals inspected for clenliness at slaghter houses
animals must be cleaned if clenliness level not acceptable
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Prescribing policy- antimicrobial steward ship
does the patient need ABs
prescribing with accordance to local antibiotic policy
prescribing empirically- de-escalate broad spectrum policy when microbe in question is known
colonisation does not equil infection
Foodborne pathogens & commensals in animals
•“resistance” defined in European surveillance (EFSA) by epidemiological cut off values (ECOFFs), notclinical breakpoints
* Human pathogens
* clinical resistance defined by clinical breakpoints (EARS-Net, ECDC)
* but different clinical breakpoints used across Europe
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Surveillance
Animal health surveillance is a tool to monitor disease trends, to facilitate the control of infection or infestation, to provide data for use in risk analysis, for animal or public health purposes, to substantiate the rationale for sanitary measures and for providing assurances to trading partners.’
Endemic and exotic diseases, new diseases, changes in existing disease profiles – data for decision-making support and action
Public health protection – zoonoses, antimicrobial resistance, chemical residues
Animal welfare protection and standards
International trade – freedom from disease certification, legislative requirements
Multiple exotic disease outbreaks in UK in last 20+ years:
- Foot and mouth disease (2001, 2007), avian influenza, Newcastle disease, bluetongue
Antimicrobial resistance (AMR) – a global challenge threatening animal and human health
Residues found in food products of animal origin create food scares, and a collapse in consumer confidence
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Surveillance for Bluetongue in N. Ireland
Active surveillance of cattle – looking for BT virus
All imported animals tested –higher risk - targeted surveillance
Meteorological models to predict Culicoides midge incursions
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survalence Antimicribial resistance
to understand the drivers and burdens of AR form a one health approach
162
Food surveillance: Dioxin/PCB pork contamination
Dioxins/PCBs detected in routine residue testing of pig fat samples in Ireland in November 2008
Polychlorinated biphenyls/dioxins - Toxic, carcinogenic chemicals – coolants in transformers, waste products some industrial processes
Part of the Irish National Residue Monitoring Programme
Traced to farm of origin
Source: contaminated pig feed
Global impact, huge financial cost (> 120 M euro)
Damage to reputation – but fast and transparent response helped to mitigate negative impact
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Disease surveillance systems require:
Defined disease monitoring system
Clear threshold for disease level
Pre-defined direct action
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survalence risk analysis
- Hazard identification
- Risk assessment
- Risk management
- Risk communication
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MOSS: Good laboratory practice (GLP)
System of management controls in place to ensure consistency and reliability of test results – lab. audits and accreditation
We need confidence that a test positive or test negative result is valid
Participation in ‘ring trials’ – national/reference laboratory provides blinded samples for testing and proof of reliability
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MOSS: Implementation and administration
Often administered and implemented by State authorities
Often have a legal framework, esp. if run by the State
Need clear demarcation of who is responsible for what – contractual agreements
Planning and appropriate resourcing – who pays?
Data storage, management and reporting
Effective communication of results and actions – from farm level to global
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Key policy makers, funders and regulators in survalence
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World Animal Health Organisation (OIE)
Food and Agriculture Organisation (FAO)
European Centre for Disease Prevention & Control (ECDC)
Codex Alimentarius Commission
World Trade Organisation (WTO)
European Food Safety Authority (EFSA)
```
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European Food Safety Authority (EFSA)
Established 2002 following series of high-profile EU food safety scares
Based in Parma, Italy
Provide scientific basis for legislation in the EU institutions
Risk assessment and risk communication
Consumer protection and assurance
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descrieb teh higherachy in the uk for national disease survalence
DEFRA
(APHA, VMD, FSA/FSS) >
UKZADI
UK Zoonoses and Animal Diseases and Infections Group >
Farm level - Farm records, animal IDs, movement licences, medicines books, vet visits
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UK zoonoses, animal diseases and infections (UKZADI
Independent committee made up of experts from across the agricultural and public health departments:
Brings together agricultural and human health groups
Co-ordination of public health action at national and local level with regard to:
Existing and emerging zoonotic infections
Trends in antimicrobial resistance
Animal-related chemical risks to the food chain
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farm species sector organisations for survalence
```
Pig Health and Welfare Council
Sheep Health and Welfare (SHAWG)
Cattle Health and Welfare Group (CHAWG)
Equine Infectious Disease Surveillance (EIDS)
Equiflunet’ – Equine influenza reporting
```
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companion animal species sector organisations for survalence
SAVSNET – University of Liverpool
| VetCompass – RVC, London
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Wildlife disease surveillance
Quarterly reports published in GB
HPAI has dominated Winter period in 2021-22 – Europe-wide problem
Remember that what is happening in wildlife may spill over into farmed animals or humans
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Surveillance for disease elimination: canine rabies
Rabies – mostly canine – global problem
Surveillance of vital importance if rabies is to be eliminated
Early detection Early intervention
Allows effectiveness of interventions to be assessed e.g. mass dog vaccination impact
Developing countries (which need it most) often have poor surveillance, due to weak human and animal health infrastructure and lack of expertise
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Veterinary surveillance in abattoirs
Disease – enzootic, epizootic
Welfare
Residues – antimicrobials, hormones etc
Active and passive (TSEs, bTB etc.)
Ante- and post-mortem
Statutory obligations – legal requirements
Vitally important role in notifiable disease surveillance (cf FMD 2001 – first detection in a pig abattoir)
An undervalued and underused resource for MOSS?
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Bovine tuberculosis (bTB) surveillance in abattoirs
Vital part of the bTB control/eradication programme in UK/Ireland
Lesions at Routine Slaughter (LRS) – carcase/organ inspection
Post-mortem examination of skin test reactors
Protection of human health (formalised 1880s)
Efficiency of bTB lesion detection – large variation between abattoirs, even after adjusting for animal-level factors
Variability in meat inspector expertise/aptitude for finding lesions/sampling
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Knackeries and disease monitoring/surveillance
‘Knackeries are aggregation points for sick and dead stock. This makes them potentially efficient places to conduct disease surveillance, providing an opportunity to examine diseased cattle at a central point rather than having to visit a relatively large number of properties. Despite this, there are a limited number of reports in the literature of knackeries being used for disease surveillance.’
Another undervalued and underused resource for MOSS
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Bovine brucellosis in N. Ireland
A historical problem which re-emerged in NI in 1997 (in 1930s, 60% of herds were infected in NI)
NI had been Officially-Brucellosis-Free (OBF) since 1982 after intense efforts in 1960s and 70s
[Great Britain OBF since 1985]
Causes abortion and septic arthritis in cattle
Serious zoonosis
Intensive efforts in surveillance backed by legislation to eradicate the disease
Historically very common – lots of infected older vets
Acquired either by contact with aborting cow and abortion fluids; or drinking unpasteurised milk from infected cows
Signs in humans - Flu-like illness with night sweats and joint pains – ‘undulant fever’, weight loss, miscarriage possible
Control – Eradicate the disease in cattle through test and slaughter, pasteurise milk
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describe how The nature of an ecological zone has influence on animal husbandry affecting morbidity and mortality.
Rangelands – forage availability - infertility
Trekking causing stress – pasteurellossis - stress major component
Brucellosis in rangelands – difficulties in inadequate disposal of aborted foetuses
High pre-weaning and post weaning mortality in sheep and goats – kept extensively on range lands.
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Two hypotheses formulated to explain the ‘balance of nature@
environmental resistance -animal populations had an intrinsic rate of increase but there was some quality of the environment that resisted the increase
competition for the resources of the habitat, the most common of which is food competition therefore is density dependent
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important factors to consider when making disease managment programs
quantify what is important/ research question:
Prevalence or incidence of outbreaks (how many or how often)
Mortality rates (how serious)
Morbidity rates (illness, production loss)
Spatial Location (distribution of disease)
Risk factors for disease
Disease determinants
establish the problem:
Introduce preventative measures
Introduce control measures
```
Consider important factors:
Environmental
Socio-political
Economic
Logistical
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Livestock Farm Production=
outputs
183
Livestock Farm Profit
outputs-inputs
184
production function (technical optimum)
In order to identify an optimum level of production there is a need to have a technical relationship between an input and output – a production function
Livestock production is no different
It depends on various inputs:
Feed
Forage
Animal health care
This technical relationship is very important in terms of defining a rational level of production
185
visible losses
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Dead people & animals
Thin people & animals
People & animals poorly developed
Low returns
Poor quality products
```
lead to health losses and then health impact
impact cused by disease and health problems
186
invisible losses
```
Fertility problems
Change in population structure
Increased labour costs
Delayed sale of animals and products
High prices for livestock and livestock products
```
lead to health losses and then health impact
impact cused by disease and health problems
187
additional costs
```
Medicines
Vaccines
Insecticide
Time
Treatment of products
Public health costs
```
expenditure and reaction and then health impact
impact is caused by human reaction
188
lost revenue
Access to better markets denied
Sub-optimal use of technology
leads to expenditure and reaction and health impact
impact is caused by human reaction
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Change in herd or flock VALUE (calculating output)
There are three important aspects when calculating output from a livestock enterprise
Animals and products that move OUT
Animals and products that move IN
Change in herd or flock VALUE
(+) Value of the herd or flock at the end of the analysis period
(-) Value of the herd or flock at the beginning of the analysis period
e.g
worth US$120
During the year the farmer:
Buys 5 calves at US$50/head; and
All the animals survive
At the end of the year the animals are worth:
Bullocks – US$230/head
Calves – US$120/head
value of claves and bullocks at end of analysis ((230x7 +120x5= 2210.00) - value of calves and bullokcs at start (5x50+7x120= 1090.00)= 1120.00
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the gross margin of a grazing livestock enterprise =
```
enterprise output- variable costs (Concentrates (including homegrown)
Purchased roughages (specific)
Veterinary & medicines
Miscellaneous= gross margin excluding forage variable costs
```
```
gross margin excluding forage variable costs- forage variable costs (Seed (including homegrown)
Fertiliser
Sprays & Chemicals
Contracts (Specific)
Casual Labour
Miscellaneous)= gross margin
```
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the gross margin for nongrazing livestock=
enterpirse output- varaible costs (Concentrates (including homegrown)
Purchased roughages (specific)
Veterinary & medicines
Miscellaneous)
192
sensitivity analysis
Investigate the impact of a change in price of the important inputs
193
Financial Feasibility
Practical feasibility of financing an intervention
194
Sensitivity (Se)
ability of a test to detect diseased animals correctly i.e. the proportion of diseased animals testing positive.
se= tp/tp+fn
195
Specificity (Sp)
ability of a test to give the correct answer if not diseased i.e. proportion of non-diseased animals testing negative.
sp= tn/tn+fp
196
Campylobacteriosis in poultry flocks
This rapid spread of Campylobacter throughout the flock is a result of high levels of shedding and efficient fecal-oral transmission compounded by communal water and feed.
poor hygene
stocking density
poor biosecurity
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risk factors for mastitis
production system, housed, floor type, bedding, floor cleaning frequency, proper milking techniques, milking mastitic cows last, washing of the udder before milking, udder drying
198
Relative risk
a ratio of the probability of an event occurring in the exposed group versus the probability of the event occurring in the non-exposed group. For example, the relative risk of developing lung cancer (event) in smokers (exposed group) versus non-smokers (non-exposed group) would be the probability of developing lung cancer for smokers divided by the probability of developing lung cancer for nonsmokers.
Relative Risk = (Probability of event in exposed group) / (Probability of event in not exposed group)[1]
An example will help clarify this formula.
If we hypothetically find that 17% of smokers develop lung cancer and 1% of non-smokers develop lung cancer, then we can calculate the relative risk of lung cancer in smokers versus non-smokers as:
Relative Risk = 17% / 1% = 17
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confounding
the observed assosiation betwee n an exposure and outcome is the to the influence 0f a thrid variable (a confounder)
200
longitudinal study
esearchers repeatedly examine the same individuals to detect any changes that might occur over a period of time. Longitudinal studies are a type of correlational research in which researchers observe and collect data on a number of variables without trying to influence those variables.
n a study, a group of people are exposed to an environmental toxin but are not treated. Instead, they are
observed over time on a standard set of measures to ascertain the potential effects of the toxin
201
“two-gate” designs
Case control
Starts with information regarding disease status based on reference standard (existing test)
Compares cases (diseased animals) and controls (non-diseased animals) with respect to result of the (new) index test
Efficient: well-suited to rare diseases
Relatively quick and inexpensive
Often based on existing samples with known results regarding disease status
Susceptibility to selection bias:
Controls often healthy (symptom-free), not representative of cattle suspected with BTB
Susceptibility to information bias: interpretation of index test blind to disease status?
Cases
Samples from cattle with BTB based on reference standard (culture / PCR
Controls
Samples from cattle without BTB based on reference standard
202
"one-gate” designs "cross sectional"
Cross-sectional
Careful, detailed description of study population (time and place)
Index tests and reference standard conducted at the same time
Tests conducted at the same time, no change in disease status between tests
Susceptibility to information bias:
Interpretation of index and reference test blind to each other?
203
Cohort (“one-gate”) designs
Careful, detailed description of study population (time and place)
Index test first, reference standard at a later time
Careful, detailed description of study population (time and place)
Index test first, reference standard at a later time
Reflects situation in routine care when index test is used first (e.g. as triage test)
Lower risk of selection bias (no knowledge of disease status)
Lower risk of information bias – index test results assessed before reference standard
Not suitable for rare diseases
Disease status may change between index test and reference standard – interval needs to be short
