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www.elsevier.com/locate/vetmic
Veterinary Microbiology 110 (2005) 17–26
Spatial and temporal patterns of pig herds diagnosed with
Postweaning Multisystemic Wasting Syndrome (PMWS)
during the first two years of its occurrence in Denmark
Hakan Vigre a,*, Poul Bækbo b, Sven Erik Jorsal c, Vivi Bille-Hansen c,Anne-Grete Hassing b, Claes Enøe d, Anette Bøtner e
a The Danish Institute for Food and Veterinary Research, Department of Epidemiology and Risk Assessment,
Mørkhøj Bygade 19, DK-2860 Søborg, Denmarkb The National Committee for Pig Production, Danish Bacon & Meat Council, Vinkelvej 11,
DK-8620 Kjellerup, Denmarkc The Danish Institute for Food and Veterinary Research, Department of Veterinary Diagnostics and Research,
Bulowsvej 27, DK-1790 Copenhagen V, Denmarkd The national Committee for Pig Production, Danish Bacon and Meat Council, Axeltorv 3,
DK-1609 Copenhagen V, Denmarke The Danish Institute for Food and Veterinary Research, Department of Virology,
Lindholm, DK-4771 Kalvehave, Denmark
Received 31 January 2005; received in revised form 21 June 2005; accepted 11 July 2005
Abstract
The clinical syndrome Postweaning Multisystemic Wasting Syndrome (PMWS) in pigs has emerged globally during the last
decade. In October 2001, the first pig herd diagnosed with PMWS was reported in Denmark, and since then the number of herds
diagnosed with PMWS has increased markedly.
The etiology of PMWS is not well understood, but increased knowledge of the causal factors is prerequisite for applying
preventive interventions. In this study we described the temporal (time of diagnosis), spatial (location of herds) and spatio-
temporal pattern of Danish pig herds diagnosed with PMWS during the first two years after the first herd was diagnosed, and we
tested for spatial and spatio-temporal clustering using scan statistics.
The study population consisted of pig herds that during the study period (October 2001–September 2003) performed
diagnostic submissions to the two major veterinary diagnostic laboratories in Denmark (6724 herds). Of these, 277 herds were
diagnosed with PMWS. Two statistically significant spatial clusters of herds diagnosed with PMWS were identified. These
clusters included 11% and 8% of the study herds, respectively. Within these two clusters the relative risk for a herd to be
diagnosed with PMWS was twice as high as expected. One statistically significant spatio-temporal cluster was identified
between February and May 2002.
* Corresponding author. Tel.: +45 72347323.
E-mail address: hvi@dfvf.dk (H. Vigre).
0378-1135/$ – see front matter # 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.vetmic.2005.07.001
H. Vigre et al. / Veterinary Microbiology 110 (2005) 17–2618
We discuss different hypotheses that could explain why pig herds diagnosed with PMWS were clustered both spatially and
spatio-temporally, and conclude that the results support the hypothesis that PMWS is caused by introduction of a new,
unidentified, pathogen into the Danish pig production.
# 2005 Elsevier B.V. All rights reserved.
Keywords: PMWS; Denmark; Spatial; Temporal; Clustering; SaTScan
1. Introduction
Postweaning multisystemic wasting syndrome
(PMWS) is a recently described clinical condition
characterised by unthriftiness, wasting and increased
mortality among pigs in the post-weaned age group.
Within affected herds, also coughing, dyspnoea and
diarrhoea are frequent observed clinical signs. PMWS
was first identified in Canada in 1991 (Clark, 1997).
Since then, the distribution of PMWS has grown and
the syndrome is recognized worldwide. In Denmark,
both the veterinary practitioners and the diagnostic
laboratories have been aware of the clinical symptoms
and laboratory findings associated with PMWS since
the beginning of 2000 (Hassing et al., 2002), and in
October 2001, the first herd with PMWS was
diagnosed. Subsequently, all herds diagnosed with
PMWS have been registered at the Danish Institute of
Food and Veterinary Research, and between 2001 and
2003 the number of herds diagnosed with PMWS
increased markedly.
Infection with porcine circovirus type-2 (PCV2) is
necessary for development of PMWS (Kennedy et al.,
2000; Krakowka et al., 2000; Ladekjær-Mikkelsen
et al., 2002). Infection with PCV2 is distributed
worldwide and subclinical infection probably occurs
in almost all pig herds (Pogranichniy et al., 2002;
Boisseson et al., 2004; Sibila et al., 2004). This
indicates that also other pathogens or factors other
than infection with PCV2 are necessary for develop-
ment of PMWS.
However, knowledge of the epidemiology of
PMWS and factors triggering the clinical appearance
of the syndrome is limited. Previous studies in animal
diseases have shown that spatio-temporal investiga-
tion of disease patterns can generate and support
hypotheses with respect to etiology and transmission
(outbreak of acute respiratory disease at cattle herds –
Nordstrom et al., 2000; leptospirosis among dogs –
Ward, 2002). In this paper we describe the temporal
(time of diagnosis), spatial (location of herds) and
spatio-temporal (interaction between space and time)
pattern of Danish pig herds diagnosed with PMWS
during the first two years after the first herd was
diagnosed. We also tested for spatial and spatio-
temporal clustering using scan statistics. Subse-
quently, we discuss three possible hypotheses that
can explain the pattern of the occurrence of the
disease.
2. Materials and methods
2.1. Study area (Denmark) and target population
(the Danish pig population)
The area of Denmark is 43,090 km2 and consists of
the peninsula Jutland, the major islands Funen,
Sealand, Lolland, Falster and Bornholm plus a number
of smaller islands. The Danish pig production is
characterised by relatively intensive production
systems and produces over 21 million slaughter pigs
annually (Danish Bacon and Meat Council, 2003). In
October 2001, 21,348 pig farms were registered in the
Danish Central Husbandry Register (CHR). (How-
ever, the CHR has a lag phase in recording closing
down pig herds, and according to Danmarks Statistik
(2003) in 2001 there were 12,936 pig herds in
Denmark.) The pig herd density in each of the 276
municipalities in Denmark (number of pig herds
registered in CHR located in the municipality divided
by the area (km2) of the municipality) is presented in
Fig. 1.
2.2. Study design
We used a retrospective longitudinal study to
describe the temporal, spatial and spatio-temporal
pattern of herds diagnosed with PMWS in Denmark
and to analyse for presence of spatial and spatio-
H. Vigre et al. / Veterinary Microbiology 110 (2005) 17–26 19
Fig. 1. Geographical map of Denmark and neighbouring countries, including information on the pig herd density (number of pig herds registered
in CHR per km2) in the 276 municipalities in Denmark.
temporal clusters of herds diagnosed with PMWS. The
study period started at the beginning of October 2001,
where the first herd with PMWS was diagnosed and
ended at the end of September 2003.
Theoretically, all pig herds in Denmark should have
been included in this study. However, not all pig herds
do have equal probability of submitting diagnostic
samples for laboratory examinations. Therefore, the
study population – study herds – consisted of the 6724
pig herds that between the beginning of October 2001
and the end of September 2003 performed one or
several diagnostic submissions either to the laboratory
run by the Danish Institute of Food and Veterinary
Research (DFVF) located at Sealand or to the
laboratory in Kjellerup, Jutland run by the Danish
Bacon and Meat Council. These two laboratories
process the majority (approximately 99%) of all
diagnostic samples submitted from Danish pig herds.
2.3. Definition of the diagnose PMWS
In this study the diagnosis of PMWS was made at
herd level. A herd was diagnosed with PMWS when
multinucleated cells and/or intracytoplasmatic inclu-
sions (histopathology) plus PCV2 (immunostaining of
cryostat sections) were detected in lymphoid organs of
pigs submitted for laboratory examination – either
with the purpose of specific examination for PMWS,
or with a disease description and post mortem findings
indicative of PMWS. The aggregation of diagnostic
information was done for all samples from pigs that
were submitted to the laboratories. Histopathology
and immunohistochemistry was performed at DFVF
according to previously described methods (Ladekjær-
Mikkelsen et al., 2002).
2.4. Geographical location of herds
The geographical locations of the pig herds were
obtained by merging the addresses of the herds to
geographical coordinates (National Survey and
Cadastre, 2004). The coordinates of the addresses
of 68 herds (of 6724 study herds) could not be
retrieved and these herds were excluded from the
statistical analysis. None of the 68 herds had a history
of PMWS.
H. Vigre et al. / Veterinary Microbiology 110 (2005) 17–2620
2.5. Collection of data concerning herd
characteristics
Data of herd type and herd size was extracted from
the Danish Central Husbandry Register (CHR). The
Danish SPF (specific pathogen free) Company
provided data concerning health status.
2.6. Descriptive spatial and temporal analysis
The laboratory data and the geographical informa-
tion were organised in one record per submission per
herd (the data contain one or several records per herd).
Each record was classified as a case (submission
fulfilling our criteria of PMWS) or a control
(submission not fulfilling our criteria of PMWS).
The case and control time, respectively, was defined as
the date of receiving the material for diagnostic
examination at the laboratories. Note that a herd could
be classified as control herd at one or multiple
submissions, and then in a later submission as a case
herd. After a herd was classified as a case herd, it was
censored from the study. Case and control time was
used in computation of month-specific proportion of
herds diagnosed with PMWS. Case time was also used
in the analysis of spatio-temporal clustering.
Data were organised, edited, manipulated and
summarised using the software SAS1, version 8
(Statistical Analysis System, 1999). From SAS, data
were exported as dBASE file for mapping purposes.
Using ArcView 8.3 GIS software (ESRI, 2002,
Redlands, CA) we produced maps displaying the
spatio-temporal distribution of herds diagnosed with
PMWS.
2.7. Analysing spatial and spatio-temporal
clustering
To investigate if the incidence of herds diagnosed
with PMWS were particularly high in some geogra-
phical areas during the study period (spatial cluster-
ing) and in some geographical areas during some
specific time-periods of the study period (spatio-
temporal clustering) we used scan statistics (Kulldorff,
1997). In the analysis we assumed that the herds
diagnosed with PMWS is a representative and an
unbiased spatial and temporal sample of all herds with
onset of PMWS in Denmark during the study period.
The data were organized into a case-file, a control-
file and a geographical coordinate-file and used as
input data in the spatio-temporal scanning software
SaTScan version 4.0 (Kulldorff, 2003). In this study
we used two different models to analyse the data – a
spatial Bernoulli model for case and control (non-
case) data and a space–time permutation model
utilising only case data.
In the spatial Bernoulli model, by scanning for
areas with significantly high proportions of cases we
investigated the data for spatial clusters, ignoring the
time of the diagnosis. The null-hypothesis in the
spatial Bernoulli model is that the number of cases in
any given area is binomially distributed with a
common prevalence parameter. By using scan statistic
we took the uneven density of pig farms in Denmark
into account when identifying spatial clusters of the
disease – a potential cause of confounding.
The spatial analysis in SaTScan imposes a circular
window on the map. In our analysis, the window was
in turn centered on the coordinates of each study herd
(both case and control). For each location, the radius
of the window varies in size from 0 to 50% of the total
population at risk.
To identify statistically significant spatial clusters
the likelihood ratio test was used. The likelihood
function was computed for each specific window, and
the one with the maximum likelihood constitute the
most likely cluster. The likelihood functions used in
this study and the likelihood ratio test are described
fully by Kulldorff (2003). Briefly, in the Bernoulli
model the value of the likelihood function for a
window increases by an increased difference between
proportion of cases within the window and outside the
window. The statistical significance of the identified
clusters was evaluated by comparing the values of the
likelihood function of identified clusters with the
maximum likelihoods in 999 random replicates of the
data set. The generation of random replicates was done
usingMonte Carlo sampling under the null-hypothesis
that the number of cases in any given area was
binomially distributed with a proportion equal to (total
number of cases/total number of study herds). The P-
value for each cluster identified in the real data was
obtained by comparing the likelihood value with the
maximum likelihoods from the random data sets. If the
likelihood value for a cluster identified in the real data
was in the most extreme 5% of all maximum
H. Vigre et al. / Veterinary Microbiology 110 (2005) 17–26 21
Table 1
Number and distribution of Danish pig herds diagnosed with PMWS (October 2001–September 2003) and distribution of all Danish pig herds,
respectively, according to herd type and health status
Pig herds diagnosed with PMWS All Danish pig herds
No. of herds Distribution (%) Distribution (%)
Herd typea
Breeding and multiplying herd 3 1.1 1.6
Conventional production herd 263 94.9 92.3
Outdoor/organic herd 11 4.0 6.1
Health statusb
SPFc 30 10.8 8.7
MSd 85 30.7 21.2
Conventionale 162 58.5 70.1
a Herd type recorded in the Danish Central Husbandry Register.b Health status recorded in the Danish SPF Company.c Declared free from Enzootic pneumonia, Pleuropneumonia and Atrophic rhinitis.d Declared free from Pleuropneumonia and Atrophic rhinitis.e No disease declaration.
likelihoods, then the identified cluster was statistically
significant (Kulldorff, 2003).
In the spatial Bernoulli model, the relative risk
(RR) to be diagnosed with PMWS for a herd within an
identified cluster was calculated as the ratio of the
number of cases observed in the cluster and the
number of cases expected, assuming that the location
of herds diagnosed with PMWS was randomly
distributed.
Given the increase in incidence over the study
period, performing a space–time scan statistic to test
for spatio-temporal clusters would pick up the
increasing trend by assigning clusters during the
end of the study. Instead, to adjust the scan statistic for
the temporal trend we performed a space–time
permutation model (Kulldorff, 2003). In a space–time
permutation model, the number of observed cases in a
cluster is compared to what would have been expected
if there was no spatio-temporal interaction. Therefore,
we get a spatio-temporal cluster in a geographical area
if, during a specific time period, that area has a high
proportion of excess cases compared to surrounding
areas (the likelihood function of a spatio-temporal
window increases by an increased proportion of
excess cases in a specific area compared to surround-
ing area within a specific time-period). The temporal
dimension in our analysis was expressed with duration
of one month as the basic unit of analysis to reduce
computing time. The maximum spatial and the
maximum temporal cluster size were set to 50% of
the total population at risk and total study period,
respectively.
The P-value for each spatio-temporal cluster
identified in the real data by the space–time
permutation model was obtained following a similar
procedure as described for spatial clusters.
3. Results
Among the 6724 study herds, 277 herds were
diagnosed with PMWS (4% of the study herds)
according to the criteria outlined in this paper. The
most frequent number of pigs, from which diagnostic
samples were submitted for examination of PMWS
was five, but ranged between one and eight. The
distribution of the 277 pig herds diagnosed with
PMWS according to herd type and health status,
respectively, was comparable to the overall health
status of all Danish pig herds (Table 1). Among the
herds diagnosed with PMWS, 15.5% were specialised
in rearing growers and 12.6% reared only finishing
pigs. The number of weaners at farms diagnosed with
PMWS (Q1 = 600, median = 900, Q3 = 1500) was
slightly higher than the number of weaners in other
farms (Q1 = 500, median = 800, Q3 = 1300). The
month-specific proportion of herds diagnosed with
PMWS among the herds submitting samples to the
laboratories the month of concern is presented in
Fig. 2.
H. Vigre et al. / Veterinary Microbiology 110 (2005) 17–2622
Fig. 2. The month-specific percent of pig herds diagnosed with
PMWS among herds submitting samples for laboratory examination
the month of concern. The herds submitting samples include pig
herds submitting to the laboratories involved in this study.
The spatio-temporal pattern of the herds diagnosed
with PMWS is presented in Fig. 3. Around one year
passed from the first reported case until PMWS was
diagnosed in most parts of Denmark. However, no
cases were reported at Lolland, Falster or Bornholm
during the study period.
Two significant spatial clusters (P-value = 0.001,
0.031) of herds diagnosed with PMWS were identified
(Fig. 4). The spatial cluster located in south Jutland
has a radius of 49 km and included 732 study herds
(11% of the study population). Out of these herds 75
were diagnosed with PMWS during the study period.
The spatial cluster located in northwest Jutland has a
radius of 31 km and included 541 study herds (8% of
the study population). Out of these herds, 48 were
diagnosed with PMWS during the study period.
Within these two clusters the relative risk for a herd to
be diagnosed with PMWS was twice as high as
expected (RR: 2.5 and 2.1, respectively).
In the space–time permutation model one sig-
nificant spatio-temporal cluster was identified (P-
value = 0.001) in northwest Jutland between February
and May 2002 (Fig. 4). Within this geographical area
during this period there were 11 herds diagnosed with
PMWS, whereas under the hypothesis of no space–
time interaction only one case was expected. The
cluster was located within one of the significant spatial
clusters identified in the spatial analysis. The
characteristics concerning herd type, herd size and
health status of these 11 herds were not different
compared to other herds diagnosed with PWMS or to
the overall characteristics of Danish pig herds.
4. Discussion
This study describes and analyses the spatial and
temporal patterns of herds diagnosed with PMWS
during the first two years (October 2001–September
2003) of its occurrence in the Danish pig production.
However, PMWS has probably occurred in Denmark
before 2001. One year before, two herds were
clinically suspected for PMWS, but the histopatho-
logical criteria as previously outlined in this paper
were not completely fulfilled in pigs examined from
those herds (Hassing et al., 2002). Recently, at DFVF
we detected histopathological changes consistent with
PMWS as well as PCV2 in formalin fixed tissue
samples from a pig from 1989. The samples came
from a pig herd affected with wasting, pneumonia and
diarrhoea. So, PMWS probably may have occurred
long before 2001, but during 2001–2003 there seemed
to be a significant increase in the occurrence of PMWS
in some part of the country.
We used scan statistic to identify spatial and spatio-
temporal clusters of herds diagnosed with PMWS.
Generally, with scan statistics it is possible to pinpoint
the general location and duration of a cluster. However,
the exact boundaries of the clusters remain unclear.
The most important results in this study were:
(i) D
uring the first two years the incidence of herdsdiagnosed with PMWS increased markedly and
unpredictably.
(ii) I
n the spatial Bernoulli model, two geographicalclusters were identified. This indicated that
during the first two years with PMWS in the
Danish pig production the risk for herds to be
diagnosed with PMWS was significantly higher
in these two geographical areas compared to the
remaining part of the study population.
(iii) I
n the space–time permutation model, one spatio-temporal cluster was identified. In herds within
this area, the relative risk of being diagnosed with
PMWS during the first half of 2002 compared to
other areas was significantly higher than during
other time-periods.
H. Vigre et al. / Veterinary Microbiology 110 (2005) 17–26 23
Fig. 3. Map of Denmark showing the location of herds that was diagnosed with PMWS within four different periods of the study period.
Below, we discuss three hypotheses that might
explain these results.
4.1. Hypothesis 1: introduction of a new pathogen
The identification of a significant spatio-temporal
cluster early in the apparent disease outbreak
supports the hypothesis that PMWS is caused by a
new pathogen that has been introduced to one or
several pig herds in Denmark. After a local outbreak,
the pathogen has been spread to most parts of
Denmark during the first two years after introduc-
tion. The hypothesis is further supported by the
result obtained in the spatial model – the two
identified geographical clusters are located in the
geographical areas with the highest pig herd
densities in Denmark. If the transmission of a
potential new pathogen is partly airborne, then the
H. Vigre et al. / Veterinary Microbiology 110 (2005) 17–2624
Fig. 4. The map of Denmark with the results from the SaTScan
analysis showing (i) the geographical location of the statistically
significant spatial clusters (during the whole study period) as black
circles, and (ii) the geographical location of the statistically sig-
nificant spatio-temporal cluster as a grey circle. Herds diagnosed
with PMWS during the study period are shown on the map as black
dots.
infectious pressure – and subsequently the risk of
onset with PMWS – will be higher in geographical
areas with high density of pig herds compared to
other geographical areas. Because most veterinary
practitioners work in specific geographical areas
transmission of a new pathogen by the veterinarian
might also result in spatial and spatio-temporal
clustering of the syndrome. However, since most
pig herds in Denmark perform sanitary routines at the
level of SPF herds, the risk of transmitting infectious
agents passively from herd to herd by personnel is
expected to be very small. The geographically
independent increased incidence of onset of PMWS
throughout Denmark can be ascribed to transmission
of a pathogen by movement of living pigs between
herds.
Hypothesis 1 is also supported by the result in a
previous study investigating the impact of potential
risk factors upon the occurrence of PMWS (Cook
et al., 2001). In that study the risk of PMWS was
significantly higher in pig herds in close proximity
(within 3 km) to other affected herds. Also larger
herds and those that purchased greater numbers of
replacement breeding stock were more likely to report
PMWS.
Even though the results strongly support the
hypothesis that the spread of PMWS is related to
introduction of a new pathogen, there is so far no
reported microbiological evidence confirming this.
Instead, the molecular characterisation of PCV2
strains from PMWS-affected and non-affected pig
herds has not identified any molecular marker of
virulence in PCV2 that can explain the sudden
outbreak and spread throughout the world (Pogra-
nichniy et al., 2002; Boisseson et al., 2004). These
studies converge on the conclusion that the PMWS
epidemics do not originate in the appearance of a new
PCV2 variant, but is caused by strains with endemic
occurrence in the pig industry. Also, in an experi-
mental study, pigs inoculated with PCV2 strains from
non PMWS-affected herds developed clinical signs of
PMWS (Allan et al., 2002). Therefore Allan et al.
(2004) speculated whether it is possible that changes
in the global pig industry have resulted in up-
regulating a common sub-clinical PCV2 infection to
PMWS. This leads us to the second hypothesis.
4.2. Hypothesis 2: change in management
Even if events of diseases may be found to cluster in
space and/or time, the clustering is not necessarily a
result of a contagious process involving local transmis-
sion (Carpenter, 2001). Another hypothesis that might
explain the apparent outbreak of PMWS is that a
progressive change of management factors in pig herds
throughout Denmark have resulted in occurrence of a
new clinical syndrome – PMWS – whereas the causal
agent – PCV2 – has been present in the pig population
for several years. This hypothesis is supported by
observations from the Swedish pig production (Wallg-
ren et al., 2004) that revealed no evident links (trade or
close distance) between affected herds, but when
PMWS was diagnosed there were always significant
management problems on the farms.
However, it is difficult to explain from this hy-
pothesis the presence of significant spatial and spatio-
temporal clusters of herds diagnosedwith PMWS in the
western part of Denmark, and that PMWS was almost
absent in the south-eastern part of Denmark during the
first two years. We found that the distribution of pig
herds diagnosed with PMWS according to herd type
and health status was similar to the overall distribution
of Danish pig herds. Further, wewould have expected a
H. Vigre et al. / Veterinary Microbiology 110 (2005) 17–26 25
more even geographical distribution of the disease
syndrome if it was related solely to management
factors.
Nevertheless, significant effect of different manage-
ment factors on PMWShas been reported byRose et al.
(2003). They found that large pens in weaning facilities
increased the odds for PMWS, whereas long empty
periods in farrowing and weaning facilities decreased
the odds for PMWS. These results are in accordance
with Danish experience from the field concerning
treatment of clinical PMWS. This indicates that the
degree of problems with PMWS depends on manage-
ment factors,which againmight be in accordancewith a
disease syndrome with multifactorial causes.
4.3. Hypothesis 3: spatial and temporal variation
in sensitivity of the diagnostic procedures
In the data analysis and the discussion whether
PMWS is caused by a new pathogen or change in
management, we assume that the herds diagnosed with
PMWS at the laboratories represented a random
spatial and temporal sample of all pig herds that
utilised the laboratories during the study period.
However, the spatial and temporal pattern of herds
diagnosed with PMWS might be a result of variation
in sensitivity in the diagnostic procedure.
In Denmark, most veterinary practitioners work in
specific geographical areas, and variation in how
intensively veterinary practitioners utilize the
laboratories, might bias the result of the spatial
analysis. By using only herds from which diagnostic
samples were submitted to the laboratories during
the study period we partly adjusted for that bias.
However, because our study was carried out by
exclusively using herds that had submitted diag-
nostic samples to the two laboratories there is a
potential selection bias because herds with other
health problems than PMWS or herds that utilize the
laboratories might not be evenly distributed in
Denmark. In areas with a general low frequency of
submitting diagnostic samples there might be a
relatively higher proportion (spatial clustering) of
herds diagnosed with PMWS compared to areas
were the submission frequency is higher. To evaluate
the importance of this potential bias, we reanalysed
the Bernoulli model using all herds registered in
CHR as control. Two spatial clusters were identified in
this analysis, and these clusters were geographi-
cally distributed almost equal to the two clusters
identified in the Bernoulli model using the herds
from which diagnostic samples were submitted as
controls. This similarity indicates that the size of the
potential bias is limited.
However, due to the inconvenience related to the
PMWS diagnosis for some herds, there tend to be
some reluctance to submit material for laboratory
examination. A geographical related difference in
attitude to submitting material for laboratory exam-
inations of PMWS could not be completely excluded.
The temporal pattern of an increased incidence of
herds diagnosed with PMWS may be a direct result of
lack of sensitivity in the diagnostic procedures of
PMWS early in the course of the outbreak – especially
concerning the awareness of the disease and lack of
clinical experiences among the veterinary practi-
tioners. In general, identification of disease outbreaks
are often characterised by a lag phase, ranging from
few days to several months. The lag phase is a
consequence of initial low levels of suspicion of the
disease. Later in the course of an outbreak the intensity
of reporting often increased because of increased
awareness of the disease resulting in more submis-
sions for laboratory diagnostic examination. However,
at the same time confirmation rates may have
decreased (and subsequently also the registered
number of herds diagnosed with PMWS) because of
increased confidence in clinical diagnosis, which does
not request additional laboratory diagnostic examina-
tion.
5. Conclusion
The spatial and spatio-temporal analysis in this
study were not able to differentiate definitively
between presented hypotheses, because, spatial and
temporal variation in the sensitivity of the diagnostic
procedures of the disease might have contributed to
some extent in the pattern of spread of PMWS through
space and time. However, we conclude that the study
results support the hypothesis that PMWS is caused by
introduction a new, yet unidentified, pathogen into the
Danish pig production more than they support the
hypothesis that PMWS is caused by changes in
management.
H. Vigre et al. / Veterinary Microbiology 110 (2005) 17–2626
Acknowledgement
The authors thank Birgitta Svensmark and Kristian
Barfod at the Danish Bacon and Meat Council for
supporting the project by making data from the
laboratory, run by Danish Bacon and Meat Council at
Kjellerup, available.
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