WIN EPISCOPE 2.0: improved epidemiological software for veterinary medicine.
ABSTRACT Recent changes in veterinary medicine have required quantitative epidemiological techniques for designing field surveys, identifying risk factors for multifactorial diseases, and assessing diagnostic tests. Several relevant techniques are brought together in the package of veterinary epidemiological computer software, WIN EPISCOPE 2.0, described in this paper. It is based on Microsoft Windows and includes modules for the design and analysis of field surveys, control campaigns and observational studies, and a simple mathematical model. It provides comprehensive 'Help' screens and should therefore be useful not only in field investigations but also for teaching veterinary epidemiology.
- Prapas Patchanee, Pakpoom Tadee, Orapun Arjkumpa, David Love, Karoon Chanachai, Thomas Alter, Soawapak Hinjoy, Prasit Tharavichitkul[Show abstract] [Hide abstract]
ABSTRACT: This study aimed to determine the prevalence of LA-MRSA in pigs, workers and the environment in Northern Thailand and to investigate phenotypic characteristics of LA-MRSA isolates. One hundred and four pig farms were randomly selected from the total of 21,152 pig farms in Chiang Mai and Lamphun provinces in 2012. In each farm, nasal and skin swab samples were collected from pigs and workers. As well, environmental samples (pig stable floor, faucet and feeder) were collected using cotton swabs. MRSA was identified by conventional methods, confirmed by multiplex PCR and typing as the sequence type by MLST. The herd prevalence of MRSA was 9.61% (10 of 104 farms). The prevalence of MRSA in pigs, workers and the farm environment was 0.68% (2 of 292 samples), 2.53% (7 of 276 samples) and 1.28% (4 of 312 samples), respectively. Thirteen MRSA isolates were identified as SCCmecIV-ST9 from seven workers, four isolates from environmental samples and two isolates from pigs. Antibiotic susceptibility test was demonstrated 100% resistant to clindamycin, oxytetracycline, tetracycline and 100 % susceptible to cloxacillin and vancomycin. Moreover all of isolates showed multidrug resistant phenotype. This survey provided the first evidence of interrelationships for LA-MRSA among pigs, workers and the farm environment in Thailand.Journal of Veterinary Science 06/2014; · 1.14 Impact Factor - SourceAvailable from: saber.ula.veAlfredo Sánchez-Villalobos, Regino Villarroel-Neri, Ana Oviedo-Bustos, Gilberto Sandrea, Julio Boscán-Ocando, Roymi Pinto-Patiño, Francisco Pirela-Larrazábal, Luís Becerra-Ramírez, Edgar López[Show abstract] [Hide abstract]
ABSTRACT: Bovine brucellosis (Brucella abortus) behavior in Machiques of Perijá County was evaluated for 5 years (2003-2007). The sur- vey was undertaken through Ring Test and indirect ELISA (iELISA) performed on milk samples collected 3 times per year from farms located in Machiques de Perijá County. Moreover, serum samples from the adult bovine population of those farms were analyzed to detect reactors to Rose Bengal Test, which were subsequently confirmed by competitive ELISA (cELISA). Brucellosis prevalence was determined at a county slaughter-Revista científica de veterinaria 08/2009; 19(4):325-333. · 0.20 Impact Factor - SourceAvailable from: Domenico OtrantoEmanuele Brianti, Gabriella Gaglio, Ettore Napoli, Luigi Falsone, Chiara Prudente, Fabrizio Solari Basano, Maria S Latrofa, Viviana D Tarallo, Filipe Dantas-Torres, Gioia Capelli, Dorothee Stanneck, Salvatore Giannetto, Domenico Otranto[Show abstract] [Hide abstract]
ABSTRACT: The efficacy of a slow-release insecticidal and repellent collar containing 10% imidacloprid and 4.5% flumethrin (Seresto(R), Bayer Animal Health) in preventing Leishmania infantum infection was evaluated in a large population of dogs living in a hyper-endemic area of Sicily (Italy).Parasites & Vectors 07/2014; 7(1):327. · 3.25 Impact Factor
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PAPERS & ARTICLES
tions (Figs 3a and 4b) are presumed to have been caused by
the microtome knife in cutting the calcified tissue. The cal-
cified inclusions within neurons (Figs 4a,b) were interpreted
as calcified aggregates of storage bodies. These novel inclu-
sions, the intense secondary calcification and the occurrence
of calcium oxalate uroliths and urinary tract pathology war-
rant further investigation.
References
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48, 144- 148
JOLLY, R. D. & WALKLEY, S. U. (1997) Lysosomal storage diseases of animals:
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LUNA, L. G. (1993) Histopathologic Methods anid Color Atlas of Special Stains
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NEUFELD, E. F. & MUENZER, J. (1989) The mucopolysaccharidoses. In The
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W. S. Sly, D. Valle. New York, NMcGraw Hill. pp 1565-1587
NISHIO, S., ABE, Y., WAKASUKI, A., IWATA, H., OCHI, K., TAKEUCHI, M.
& MATSUMOTO, A. (1985) Matrix glycosaminoglvcanis in uriniary stones.
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WALKLEY, S. U. (11998) Cellular pathology of lysosomal storage diseases. Braint
Patholol0g8, 175- 183
WIN EPISCOPE 2.0: improved epidemiological
software for veterinary medicine
Veterinary Record (2001)
148, 567-572
M. Thrusfield, BVMS,
MSc, DTVM, CBiol, FIBiol,
MRCVS, Department of
Veterinary Clinical
Studies, University of
Edinburgh, Royal (Dick)
School ofVeterinary
Studies, Easter Bush
Veterinary Centre, Easter
Bush, Roslin, Midlothian
EH25 9RG
C. Ortega, DMV, PhD,
I. de Blas, DMV,
Department of Animal
Pathology, Faculty of
Veterinary Medicine,
University of Zaragoza,
C/Miguel Servet 177,
50013 Zaragoza, Spain
J. P. Noordhuizen, DVM,
PhD, Utrecht University,
Faculty ofVeterinary
Medicine, Department of
Ruminant Health Care,
PO Box 80151, 3508 TD
Utrecht, The Netherlands
K. Frankena, MSc, PhD,
Wageningen University,
Wageningen Institute of
Animal Sciences,
Quantitative Veterinary
Epidemiology Group,
PO Box 338, 6700 AH
Wageningen,
The Netherlands
M. THRUSFIELD, C. ORTEGA, 1. DE BLAS, J. P. NOORDHUIZEN, K. FRANKENA
Recent changes in veterinary medicine have required quantitative epidemiological techniques for designing
field surveys, identifying risk factors for multifactorial diseases, and assessing diagnostic tests. Several
relevant techniques are brought together in the package of veterinary epidemiological computer software,
WIN EPISCOPE 2.0, described in this paper. It is based on Microsoft Windows and includes modules for the
design and analysis of field surveys, control campaigns and observational studies, and a simple
mathematical model. It provides comprehensive 'Help' screens and should therefore be useful not only in
field investigations but also for teaching veterinary epidemiology.
RECENT decades have witnessed changes in the practice of
veterinary medicine (Thrusfield 1998). In developed coun-
tries, the successful control of the major infectious diseases
and the resultant intensification of livestock enterprises has
produced a shift in interest towards complex, frequently non-
infectious, diseases, and increasing emphasis is being placed
on the health ofherds rather than individual animals (Brand
and others 1996). In contrast, in developing countries, the
control of infectious diseases is still the major problem and
progress needs to be made both in the measurement of dis-
ease frequencies, for example of trypanosomiasis in Africa
and Asia, and in the implementation of control and/or erad-
ication campaigns, for example for rinderpest (IAEA 1991).
These changes have required the application of quantita-
tive epidemiological procedures (Noordhuizen 1996), notably
the use of rigorous sampling theory when conducting field
surveys, and observational studies to identify the risk factors
associated with multifactorial diseases in both farm practice
and companion animal practice. Moreover, diagnostic tests
applied either to individual animals, or to animal populations
in eradication campaigns, can only be interpreted correctly
when their validity and reliability has been assessed. Despite
a long history in veterinary medicine, many diagnostic tests,
particularly serological tests, have not been so assessed.
The methods and statistical theory that underpin these
quantitative procedures have been described by Martin and
others (1987), Thrusfield (1997), and Noordhuizen and oth-
ers ( 1997), and several computer programs have been designed
to facilitate the necessary computations by veterinarians and
physicians. These include suites ofprograms, such as EPI INFO
(Dean and others 1995), PEPI (Gahlinger and Abramson 1995),
EPISCOPE (Frankena and others 1990) and EPIZOO (Kouba
1997), and many programs for specific, individual analyses
(The Epidemiology Monitor). However, these programs have
limitations, especially for users who lack a grounding in ana-
lytical techniques. The package WIN EPISCOPE 2.0 has therefore
been designed to combine the procedures that are commonly
used in the design and analysis ofepidemiological studies into
an easy-to-understand form based on Microsoft Windows.
The package provides the main computational procedures
used in the design and analysis ofsimple field surveys, in con-
trol campaigns and observational studies, and in the assess-
ment ofdiagnostic tests, and includes an introduction to basic
mathematical modelling ofinfectious diseases. For each com-
putation, comprehensive'Help' menus are provided which
describe the techniques and list, with references, the formu-
lae that are used. The package is an improved and expanded
version of EPISCOPE for MS DOS (Frankena and others 1990)
and of its first Microsoft Windows release, WIN EPISCOPE 1.0
(Ortega and others 1996).
MATERIALS AND METHODS
Basic structure of the program
WIN EPISCOPE 2.0 has been programmed with Borland Delphi
1.0, and can be run on IBM-compatible personal computers
with theMSWindows 3.1 operating system (or higher).
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567
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PAPERS & ARTICLES
ityl I -0 U RICS]a
w6'
Main module
Submodule
Files
Tests
Tests
Tests
Tests
Tests
Samples
Samples
Samples
Samples
Samples
Samples
Samples
Samples
Samples
Analysis
Analysis
Analysis
Analysis
Analysis
Analysis
Analysis
Analysis
Analysis
Models
Help
Agreement
Evaluation
Advanced evaluation
Cut-off value*
Multiple tests*
Estimate a mean
Estimate difference between means
Estimate a percentage
Estimate difference between percentages
Detection of disease
Threshold value*
Unmatched case-control
Matched case-control
Cohort*
Cross-sectional*
Stratified cross-sectional*
Case-control
Stratified case-control
Matched case-control
Cohort (cumulative incidence)
Cohort (incidence rate)
Stratified cohort (cumulative incidence)
Stratified cohort (incidence rate)
Reed-Frost
*Not available in earlier versions of EPISCOPE
Six modules can be accessed via the main menu bar, by
using either the computer's arrow keys or its 'mouse'. and
these give access to 'submodules' (Table 1). For example, the
module 'Tests' makes available the following submodules:
'Agreement, 'Evaluation', 'Advanced evaluation', 'Cut-off value'
and'Multiple tests' When a submodule is selected, a window
appears with two parts: 'Data entry' (either at the top or to
the left), and 'Results' (either at bottom or to the right).
Two graphic buttons enable the user to 'Calculate' or
'Close', and tabular results can be converted to graphs by
using a simple graphic button.
Source texts
The definitions of the statistical terms, parameters and for-
mulae used by the submodules have been obtained from
several standard epidemiological and statistical texts
(Snedecor and Cochran 1980, Fleiss 1981, Schlesselman
1982, Rothman 1986, Martin and others 1987, Levy and
Lemeshow 1991, Lwanga and Lemeshow 1991, Armitage
and Berry 1994, Noordhuizen and others 1997, Thrusfield
1997).
Data entry
Each submodule includes a field for inputting data, either
directly or by'pasting' from other packages. Other parame-
ters may also be changed. For example, the precision of sam-
ple estimates may be altered by selecting different levels of
confidence, and the statistical power of a study may be var-
ied. The program has built-in checks to ensure that imper-
missible data are not entered, for example, values outside the
permitted ranges or non-numerical characters.
File module
The first module,'Files', allows the working language (cur-
rently either English or Spanish) to be selected and the printer
settings to be defined. It also makes it possible to 'paste' data
from other applications, and to print the screen's current
display when a submodule is in use.
Tests module
This module has five submodules for the assessment ofdiag-
nostic tests:
Agreement Compares two diagnostic tests, and computes
the 'kappa' statistic as a measure of the agreement beyond
chance between them, and its specified confidence interval.
When applied to two or more runs of the same test, it is a
measure ofthe test's repeatability (reliability).
Evaluation Computes diagnostic sensitivity and specificity
(the primary estimators of the validity of a test), true and
apparent prevalence, and positive and negative predictive
values with their confidence intervals.
Youden's J statistic (a summary measure of validity) and its
confidence interval.
It also computes
Advanced evaluation Explores the relationships between
sensitivity, specificity, and true or apparent prevalence by
holding two of the three parameters constant while the third
increases in increments of 10 per cent. The results can be
displayed graphically.
Cut-off value Applies to tests that are based on continuous
variables, for example, antibody titres. The numbers of
animals of known disease status that are scored positive and
negative for different values of the variable are entered in a
data table. The sensitivity, specificity and predictive values
are displayed, with their confidence intervals, for different
cut-off values
of the
variable. A
Characteristic
(ROC)
curve and
interval can also be displayed.
Receiver Operating
associated
confidence
Multiple tests Computes the sensitivity, specificity and
predictive values of a test strategy when two independent
tests are applied under either a parallel test interpretation
(when an animal is classified as affected if positive to either
test) or a serial interpretation (when an animal is classified
as affected only if positive to both tests). Independent tests
assess different indicators of disease. For example, a test for
an antibody would be independent of a test for a pathogen
but two tests that each identify an antibody would not be
independent. Consideration of dependency
(Smith 1995) and is beyond the scope of this package.
is complex
Samples module
This module computes sample sizes for animal health surveys
and observational studies and has nine submodules:
Estimate a mean Determines the size of a single sample
required to estimate a population mean with a given level of
confidence and absolute precision (absolute error). The size
of the population from which the sample will be drawn may
be specified. An estimate of the standard deviation
required.
is
Estimate difference between means Calculates the
sample size required to detect a difference between the
means of two populations with
confidence and power. The expected means of the two
populations and an estimate of the common standard
deviation are required. The sample sizes are computed for
independent and related samples, and for one-tailed and
two-tailed hypotheses.
a
specified
level of
Estimate a percentage Determines the sample size required
to estimate a percentage, for example a prevalence, with a
specified absolute precision (absolute error) at a given level of
confidence. Alternatively, the precision can be calculated for a
specified sample size. The results of both calculations can be
presented graphically for various percentages.
Estimate difference between percentages Displays the
sample size required to estimate a specified difference
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568
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PAPERS & ARTICLES
between two percentages with a specified level of confidence
and power for one-tailed and two-tailed hypotheses.
Detection of disease Determines the sample size required, at
a specified level of confidence, to identify at least one affected
animal when a minimum percentage of animals can be
assumed to be affected, for example in eradication campaigns.
Alternatively, either the maximum number ofanimals affected
in a population can be calculated when a sample of specified
size identifies no cases, or the probability ofidentifying at least
one positive animal in a sample of a given size can be
calculated when a specified percentage of the population is
assumed to be affected. The results of these three calculations
can be presented graphically for ranges ofthe specified values.
Threshold value Calculates the number of animals that
need to be sampled from a group (for example, a farm)
selected from a population (for example, the farms in a
region), to determine whether the group has a prevalence
above or below a specified value in the population, and to
identify a level above or below this value, at which specified
action should be taken, with a user-defined level of
confidence and statistical power.
Unmatched case-control and Matched case-control
Calculates the number ofcases and controls to be selected in
a case-control study to detect a disease/factor relationship,
expressed in terms of a minimum odds ratio, with a defined
level of confidence and power. The matched option is
selected when the cases and controls are matched with
respect to potential confounders, for example, age, breed or
sex; the sample size can be determined for both one-tailed
and two-tailed hypotheses.
Cohort Calculates the numbers of animals exposed and not
exposed to a factor in a cohort study that are needed to
detect a disease/factor relationship, expressed in terms of a
minimum relative risk, with a defined level of confidence
and power.
Analysis module
The fourth module is concerned with the analysis ofthe three
main types of observational study; cross-sectional, case-
control and cohort. It estimates the main measures of asso-
ciation between disease and risk factors: the prevalence ratio,
odds ratio and relative risk, and some secondary measures.
The module has nine submodules:
Cross-sectional Calculates the prevalence ratio, odds ratio,
attributable risk, attributable proportion and attributable
proportion in the exposed group.
It also calculates approximate logarithmic and test-based
95 per cent confidence intervals for the prevalence ratio and
odds ratio, with an indication of their validity.
Stratified cross-sectional Allows up to 10 strata to be
defined for a potential confounder, and computes the crude
and adjusted prevalence ratio and odds ratio, and their
interval estimates. A Breslow-Day test for homogeneity of
the prevalence ratio and odds ratio is applied to assist the
investigator in distinguishing between confounding factors
and interactions.
Case-control Estimates the odds ratio and its selected
approximate
logarithmic
and
intervals, and the attributable proportion and attributable
proportion in the exposed group.
test-based
confidence
Stratified case-control Makes the same calculations as
'Stratified cross-sectional' but for the odds ratio only.
Matched case-control Allows up to five controls per case
to be matched, and calculates the same parameters as'Case-
control'.
Cohort (cumulative incidence) Estimates the relative risk
and odds ratio, and their selected approximate logarithmic
and test-based confidence
incidence-derived data. The attributable risk, attributable
proportion and attributable proportion in the exposed
group are also calculated.
intervals, using cumulative-
Cohort (incidence rate) Estimates the relative risk and its
selected approximate logarithmic and test-based confidence
intervals by using true incidence rates, that is with 'animal-
time' denominators. The attributable
proportion and attributable proportion in the exposed
group are also calculated.
risk, attributable
Stratified cohort (cumulative incidence) Makes the same
calculations
cumulative incidence-derived relative risk replacing the
prevalence ratio.
as 'Stratified cross-sectional' but with the
Stratified cohort (incidence rate) Uses incidence rate-
derived data, rather than cumulative incidence values.
Models module
The final analytical module simulates the progress of a sim-
ple epidemic of infectious disease by using the Reed-Frost
model. The model requires the number of cases, the number
ofimmune animals and the number ofsusceptible animals at
the beginning of the epidemic, and the probability of infec-
tion (contact rate). The results can be displayed either as a
table listing the number of animals in each of the three cate-
gories over time, or graphically.
Help module
The last module,'Help', provides an index of the different
parts of the program. Each submodule also contains its own
'Help'. detailing the purpose of the submodule, the data
required for the calculations, the formulae used in the com-
putations, and guidance on the interpretation of the results.
Further reading is also sometimes listed.
The 'Help' of some submodules includes examples of the
use of the program, selected either from the users' manual
available with the earlier version of EPISCOPE (Frankena and
others 1990) or from a clinical veterinary epidemiology man-
ual (Ortega and others 1995).
Downloading the package from the Internet
WIN EPISCOPE 2.0 is available free from the websites of the
Veterinary Faculty of the University of Zaragoza (Spain) at
http://infecepi.unizar.es/pages/ratio/soft_uk.htm, Wageningen
Agricultural University (The Netherlands) at http://www.zod.
wau.nl/genr/epi.html, and the Royal Dick School of
Veterinary Studies, University of Edinburgh at http://www.
clive.ed.ac.uk/winepiscope. Each site provides instructions for
downloading the program.
RESULTS
Examples of some results generated by WIN EPISCOPE from
data derived from published studies are given below.
Example 1: Diagnostic test cut-off values-
diagnosis of Neospora caninum infection in dairy
cattle (Davison and others 1999)
Neospora caninum is now recognised as a major infectious
cause ofbovine abortion in many countries. In the UK a com-
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(a
FIG 2: Sample output
from Tests:Multiple
tests
FIG 1: Sample output from Tests: Cut-off value. (a) ROC curve,
(b) Results at a given cut-off
mercial antibody-detection ELISA (MAST Diagnostics) is used
to diagnose the infection. The results of this test are a con-
tinuous variable, per cent positivity (pp), and so various cut-
offvalues can be used to define an animal's status. The results
can be assessed by drawing a ROC curve describing the rela-
tionship between pairs of true-positive rates (sensitivity) on
the vertical axis, and false-positive rates (100-specificity) per
cent on the horizontal axis, for a range ofcut-offvalues. Tests
with ROC curves furthest into the top left-hand corner of the
graph are better tests, and, for a given test, the'best' cut-off
in terms of minimising the proportion of animals that are
misclassified, is the point on the ROC closest to the top left-
hand corner.
Fig Ia shows the ROC curve for the ELISA forN caninum for
cut-off values up to a maximum recorded value of 107 pp,
based on an investigation of 114 infected cattle and 199 unin-
fected cattle. This curve suggests that the test is good. However,
the cut-off values of 20, 25 and 30 PP are all close to the top
left-hand corner, and the most appropriate value can be
selected by determining the sensitivity and specificity for each
ofthem by altering the cut-offvalue; in this way the 20 PP cut-
off value is shown to maximise the sensitivity and specificity
(Fig lb). In practice, the selection ofthe most appropriate cut-
off value also depends on several other factors, including the
prevalence and the impact, for example, the cost of false-pos-
itive and false-negative results (Smith 1995).
Example 2: Multiple testing strategies-diagnosis
of hepatobiliary disorders in cats (based on Smith
1995, after Center and others 1986)
Feline hepatobiliary disorders are difficult to diagnose clini-
cally in their early stages, but high serum activities of alka-
line phosphatase (ALP) and y-glutamyl transferase (GGT), can
provide evidence of hepatic disease. However, an assessment
of 69 affected cats and 20 healthy animals showed that their
individual diagnostic sensitivities (50 per cent and 86 per
cent, respectively) are not high. Thus, the application ofeither
test alone would fail to diagnose the condition in 50 per cent
and 14 per cent of cases, respectively. Entry of their specifici-
ties (ALP 93 per cent, GGT 67 per cent) and sensitivities reveals
that the proportion of affected cats that would be diagnosed
with the condition, that is, the sensitivity of the test, would
increase to 93 per cent if both enzymes were measured (Fig
2), and that at the same time the negative predictive value (the
proportion of test-negative animals that are unaffected)
would increase. Both enzymes should therefore be measured
to rule out the disease.
Example 3: Detection of disease - Pan African
rinderpest campaign (IAEA 1991)
During the stage of serosurveillance in the Pan African rinder-
pest campaign, it was necessary to sample herds to determine
whether unvaccinated animals had seroconverted, that is, had
been exposed to natural infection. On the basis of previous
knowledge, an endemic seroprevalence of at least 5 per cent
was considered to be plausible in herds exposed to natural
infection. In one case, the campaigners needed to know how
many ofa herd of80 cattle had to be sampled to be 95 per cent
confident ofdetecting at least one seropositive animal, that is,
one ofthe four animals assumed to have been infected ifnat-
ural exposure had occurred. Forty-two animals were required
(Fig 3). The graph to the right of the figure shows the num-
bers ofanimals that would have been required ifdifferent lev-
els of endemic seroprevalence had been assumed.
Example 4: Sample size in unmatched case-
control studies - breed disposition to canine
pyometra (Niskanen and Thrusfield 1998)
The investigators wished to identify the breed disposition to
canine pyometra in the breeds of dog that constituted 1 per
cent or more ofthe dog population ofFinland. The minimum
breed-specific risk to be detected was an approximate two-
FIG 3: Sample output
from Samples:
Detection of disease
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PAPERS & ARTICLES
FIG 4: Sample output
from Samples:
Unmatched case-
control studies
fold increase, relative to the risk in the total dog population,
with the level of confidence set at 95 per cent and a statisti-
cal power of 80 per cent. Clinical records of approximately
1100 cases were available.
Exploration of various values of the input parameters
indicated that approximately 10 controls would be required
for each case, that is, approximately 11,000 controls with a
two-tailed hypothesis (Fig 4).
Example 5: Sample size in cohort studies - the
relationship between sexual status and acquired
urinary incontinence in bitches (Thrusfield and
Holt 1998)
The possibility that the neutering ofbitches increases the risk
of acquired urinary incontinence was investigated in a five-
year cohort study. The investigators wished to detect a rela-
tive risk of at least 2, with the level ofconfidence and statistical
power set at 95 per cent and 80 per cent, respectively. The
examination of a clinic's records suggested that the cumula-
tive incidence of the condition in entire bitches during the
period ofthe study was likely to be about 1 per cent. The entry
ofthese parameters in the relevant submodule indicated that
approximately 2300 animals were required in each cohort
(neutered and entire) (Fig 5).
Example 6: Reed-Frost model-control of Johne's
disease (Collins and Morgan 1991 a)
Johne's disease (paratuberculosis) can be controlled at herd
level by a test-and-cull programme. The economic justifica-
FIG 6: Sample output from Models: Reed-Frost model
FIG 5: Sample output from Samples: Cohort studies
tion of a control programme depends on several factors,
including the prevalence of the infection, which can be mod-
elled over time by using the Reed-Frost model. This model
can investigate the effect ofchanging some ofthe input para-
meters, for example, the number of cattle initially infected
and the probability of calves becoming infected, to determine
to which ofthem the progress of infection in the herd is most
'sensitive'.
Fig 6 shows the progression of the infection in successive
years in a herd consisting of 30 infectious cows and 100 sus-
ceptible calves, with a contact rate (the probability that an
infected cow will infect a calf) of 0-025 (2-5 per cent). Cattle
do not become immune to paratuberculosis and so the
'immune' class in the model represents affected animals. The
results indicate that there is a peak of cases within the first
year, but that four ofthe calves remain uninfected after three
years. Further simulations indicate that the spread of paratu-
berculosis in infected herds is most sensitive to the contact
rate, supporting the recommendations that Johne's disease
should be controlled by minimising the contacts between
cows and calves.
The modellers refined the simulations to include, for
example, the risk ofpurchasing Mycobacterium paratubercu-
losis-infected cattle as replacements. The combination of the
results of the Reed-Frost model with the analysis of all other
relevant factors indicated that a test-and-cull control pro-
gramme should be profitable when the herd prevalence before
testing was more than 5 per cent (Collins and Morgan 199lb).
DISCUSSION
This new version of WIN EPISCOPE includes several submod-
ules which were not available in the earlier versions (Table 1)
and as a result it provides a more comprehensive package for
the design and analysis of epidemiological investigations.
Furthermore, data from other epidemiological packages such
as EPI INFO (Dean and others 1995), and databases and spread-
sheets such as MS ACCESS and MS EXCEL can be input into the
program.
The design of the program as a MSWindows application,
and the structure of the modules' screens, with menu-driven
data entry and results on the same screen, makes it easy to use.
The graphical presentation of some of the results, the avail-
ability of comprehensive'Help' pages detailing the formulae
that are used, the data that are required, and the interpreta-
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PAPERS & ARTICLES
tion that should be applied to the results, makes the program
valuable for teaching veterinary epidemiology, as more quan-
titative epidemiology is introduced into veterinary curricula
(WHO 1991).
WIN EPISCOPE 2.0 is not suitable for advanced epidemio-
logical studies, for example those requiring multivariate
analyses, which require not only more sophisticated statisti-
cal packages but also considerable statistical knowledge.
However, it should satisfy the main requirements for many
epidemiological studies and for the formal teaching of epi-
demiology at basic and intermediate levels.
The authors are preparing a manual of exercises to com-
plement the package, which will be included on the websites
from which it can be downloaded.
ACKNOWLEDGEMENTS
The authors thank the Government of Arag6n, Spain, for
supporting the development of WIN EPISCOPE 2.0 (project
CONSI+D). The programme is a part of the activities of the EPI-
DECON and RATIO networks. Dr Helen Davison, formerly of
the Centre for Tropical Veterinary Medicine, University of
Edinburgh, provided the raw data for the ROC curve.
I.................................................I
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_ ABSTRACT
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