Spatial analysis of BSE cases in the Netherlands.

Abstract

In many of the European countries affected by Bovine Spongiform Encephalopathy (BSE), case clustering patterns have been observed. Most of these patterns have been interpreted in terms of heterogeneities in exposure of cattle to the BSE agent. Here we investigate whether spatial clustering is present in the Dutch BSE case data.
We have found three spatial case clusters in the Dutch BSE epidemic. The clusters are geographically distinct and each cluster appears in a different birth cohort. When testing all birth cohorts together, only one significant cluster was detected. The fact that we found stronger spatial clustering when using a cohort-based analysis, is consistent with the evidence that most BSE infections occur in animals less than 12 or 18 months old.
Significant spatial case clustering is present in the Dutch BSE epidemic. The spatial clusters of BSE cases are most likely due to time-dependent heterogeneities in exposure related to feed production.

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BMC Veterinary Research
Open Access
Research article
Spatial analysis of BSE cases in the Netherlands
Lourens Heres1,4, Dick J Brus2 and Thomas J Hagenaars*3
Address: 1Department of Bacteriology and TSEs, Central Veterinary Institute of Wageningen UR, Lelystad, The Netherlands, 2Alterra, Wageningen
University and Research Centre, Wageningen, The Netherlands, 3Department of Virology, Central Veterinary Institute of Wageningen UR, Lelystad,
The Netherlands and 4Current Address: VION Fresh Meat West, Boxtel, The Netherlands
Email: Lourens Heres - lourens.heres@vionfood.nl; Dick J Brus - dick.brus@wur.nl; Thomas J Hagenaars* - thomas.hagenaars@wur.nl
* Corresponding author
Abstract
Background: In many of the European countries affected by Bovine Spongiform Encephalopathy
(BSE), case clustering patterns have been observed. Most of these patterns have been interpreted
in terms of heterogeneities in exposure of cattle to the BSE agent. Here we investigate whether
spatial clustering is present in the Dutch BSE case data.
Results: We have found three spatial case clusters in the Dutch BSE epidemic. The clusters are
geographically distinct and each cluster appears in a different birth cohort. When testing all birth
cohorts together, only one significant cluster was detected. The fact that we found stronger spatial
clustering when using a cohort-based analysis, is consistent with the evidence that most BSE
infections occur in animals less than 12 or 18 months old.
Conclusion: Significant spatial case clustering is present in the Dutch BSE epidemic. The spatial
clusters of BSE cases are most likely due to time-dependent heterogeneities in exposure related to
feed production.
Background
BSE case clustering
Disease clustering patterns may provide important clues
to the nature of disease transmission. In the context of BSE
in cattle, clustering can be defined as cases not being com-
pletely randomly distributed amongst farms. We speak of
spatial clustering if cases are more likely to occur in geo-
graphic proximity to other cases. after correcting for spa-
tial differences in density of cattle.
BSE cases first occurred in the Netherlands in 1997. After
a peak of 24 cases in 2002 the epidemic has now much
declined. In Table 1 we list the chronology of introduction
of the different BSE control measures in the Netherlands.
The majority of Dutch BSE cases were born after the intro-
duction of a ban on ruminant Meat-and-Bone Meal
(MBM) in ruminant feed in 1989, and the results of a case-
control study [1] indicate that cross-contamination of
ruminant feed with MBM from pig or poultry feed has
been the main cause of these cases.
Possible clustering mechanisms
As the transmission of BSE is at least predominantly feed-
borne, case clustering patterns differ from patterns caused
by infectious spread via direct transmission between
hosts. In particular, none of the farms with BSE in The
Netherlands (82 farms in total) has had more than one
animal diagnosed [1].
Published: 17 June 2008
BMC Veterinary Research 2008, 4:21 doi:10.1186/1746-6148-4-21
Received: 27 December 2007
Accepted: 17 June 2008
This article is available from: http://www.biomedcentral.com/1746-6148/4/21
© 2008 Heres et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Although this paper deals with a spatial analysis of the
BSE case data, we find it useful to discuss the possible clus-
tering mechanisms from the broader perspective of case
clustering in general (i.e. not necessarily spatial case clus-
tering). The possible mechanisms generating clustering of
BSE cases are numerous. All of them are associated with
some form of heterogeneity. For spatial clustering of BSE
cases, the perhaps most direct mechanism would be
regional differences in disease surveillance intensity.
However, this mechanism can not apply to the time
period we are studying here (from 2001 onwards), during
which all cattle over 30 months of age were tested at
slaughter, as well as all animals over 24 months of age
sent for emergency slaughter or for rendering. It is instruc-
tive to distinguish between two remaining possible types
of heterogeneity, namely exposure heterogeneity and
population heterogeneity.
Exposure heterogeneity, i.e. variation in exposure to infec-
tious feed, may be caused by several mechanisms. The first
one is the presence of between-producer differences in the
feed processing methods and/or in the origin of MBM.
Differences in feed processing may, for example, result in
different levels of cross-contamination (after introduction
of a ban on MBM in ruminant feed). Differences in origin
of MBM can arise if feed producers have different MBM
suppliers. Suppliers of MBM may differ in the offal they
have processed, and in their processing procedures. The
second underlying mechanism is between-farm variation
in feeding practice, for example in the amount of feed
used or the age of animals at which protein-enriched feed-
stuff is fed. These first two mechanisms can be viewed as
farm-level risk factors, as they relate to the farmer's choice
of feed supplier and feeding practice. Regional variation
in the presence of these risk factors results in spatial clus-
tering.
The third mechanism causing exposure heterogeneity is
aggregation of infectivity within MBM [2]. This mecha-
nism is a more complex heterogeneity between feed
"units", where the scale of a unit might range from a bite
to bag or batch [2]. Spatial locality in the production and
distribution of MBM and feed (i.e. "regional recycling") is
a fourth exposure-based mechanism generating cluster-
ing. This type of clustering is necessarily spatial. This
mechanism is most powerful if the transmission potential
is above threshold level [3], such that the regional recy-
cling of BSE infection can produce regional epidemics of
BSE infection.
The third type of heterogeneity capable of generating case
clustering of a feed-borne disease is population heteroge-
neity. Genetically determined variations in cattle suscepti-
bility, as well as between-farm variation in age-specific
animal replacement probabilities are examples of popula-
tion heterogeneity that may cause spatial clustering of BSE
cases. The latter mechanism may cause clustering because
the later an animal is replaced by the farmer, the less likely
a BSE infection will remain undetected. We note that spa-
tial variation in population density can not (trivially) lead
to spatial clusters of BSE cases in our analyses, as the test-
ing methods applied in this paper account for differences
in population density.
By theoretically working out the characteristics of the clus-
tering patterns associated with the different possible
mechanisms, expected differences in these patterns can be
identified. Subsequently, if the data are informative
enough, then these differences may allow us to exclude
certain candidate clustering mechanisms, thus producing
insight into the properties of the transmission cycle. For
example, Hagenaars et al. [2] used mathematical model-
ling of feed-borne transmission to show that infectivity
aggregation within MBM would give clustering character-
istics different from those observed in the BSE epidemic in
Great Britain (GB). Thus infectivity aggregation in feed
could be excluded as underlying mechanism of BSE clus-
tering in GB. Feed processing heterogeneities and hetero-
geneity in feeding practice (i.e. variation in the per-animal
feed uptake) remained candidate mechanisms in GB, as
they do produce clustering patterns consistent with
observed patterns [2].
Table 1: BSE control measures in the Netherlands.
YearMeasure
1989
1990
1994
1996
1997
April 1999
December 2000
Ban on ruminant MBM in ruminant feed
Passive surveillance for BSE; Ban on export of ruminant MBM from GB
Ban on mammalian MBM in ruminant feed
EU ban on import of MBM from GB
Removal and rendering of SRM
Zero tolerance for MBM in ruminant feed, i.e. separation of feed production facilities.
EU ban on use of MBM and other animal protein in feed of farm animals, excluding dicalcium phosphate, fishmeal, and gelatine for
non-ruminants.
Active surveillance in cattle over 24 months of age (fallen stock and emergency slaughter) or over 30 months of age (healthy
slaughter)
January 2001

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In this paper we will focus on the question whether there
is spatial clustering present in the Dutch BSE epidemic.
Scientifically explaining the results in terms of underlying
mechanisms was impossible due to insufficient data.
However, in the discussion we will argue that the patterns
observed are likely to be generated by exposure heteroge-
neities related to feed production that occurred during a
limited period.
Observed BSE clustering patterns
In order to be able to relate our results for the Dutch epi-
demic to the ones in GB, France, Switzerland, Ireland, and
Spain, we now briefly review the BSE clustering patterns
observed in those countries. For a comparison with The
Netherlands, the most interesting clustering patterns are
those in animals born after a ban on ruminant MBM in
ruminant feed was introduced in the country.
Both in GB and in France, farm type (dairy versus beef
suckler) has been identified as an important risk factor
[4,5]. Furthermore, holding size was identified as a risk
factor in GB [4,6] and Switzerland [7]. In several countries
including Spain, regional differences or spatial clusters
were found in BSE incidence [4,8-18]. These spatial clus-
tering patterns were hypothesized to be generated by var-
ious kinds of exposure heterogeneity. The spatial
clustering pattern of BSE in GB has been studied in [19-
21]. Donnelly and Ferguson [21] found that clustering is
present at all spatial scales ranging from 100 to 819 km.
They concluded that there must have been a number of
processes, operating at different scales, causing exposure
heterogeneity. The spatial clustering pattern of BSE in
Switzerland has been studied in two papers [14,16]. Swiss
spatial clusters of cases born after a ban on MBM in rumi-
nant feed were found to be correlated with higher pig den-
sities, consistent with the assumption that these cases
were due to cross-contamination of cattle feed with feed
containing meat and bone meal, intended for other spe-
cies such as pigs [16]. Sheridan et al. [17] detected spatial
BSE clusters in Ireland, and provided support for the
hypothesis that these are associated with feed-producer
related heterogeneity. Finally, spatial clustering of BSE
was observed in France [12,13,15,22,23] and some evi-
dence was presented for correlations between the BSE
clustering pattern and the density of pigs and/or poultry,
consistent with the hypothesis that cross-contamination
caused recycling of the BSE infection in French cattle born
after a ban on MBM in ruminant feed [22].
Research question
In Great Britain evidence was found that most BSE infec-
tions occur in animals less than 12 or 18 months old
[24,25]. This implies that the BSE cases born in a given
period of 1 to 1.5 year provide information of the BSE
exposure levels during that period. It also implies that any
spatial case clustering arising from exposure heterogenei-
ties lasting for about a year only, would be best detectable
by analyzing the case data on a cohort-by-cohort basis.
Such an analysis gives insight into where and when expo-
sure levels were relatively high. Finally, a cohort-specific
analysis is motivated by the possibility that when several
cohorts are analyzed together, spatial clusters can be
obscured if the elevated exposure is not persistent but
shifts across the study area. Research questions of this
study therefore are:
1. Are the BSE cases in the Netherlands spatially clustered,
and if so where are these clusters located?
2. Are BSE cases born in a given period of one year spa-
tially clustered, and if so where are these clusters located?
3. Can we relate any observed case clustering to one or
more possible underlying heterogeneities?
Methods
Case and population data
We analyzed the BSE cases in the period from the start of
active surveillance (1 January 2001) until 31 December
2004 (69 cases in total). The locations and sizes (number
of animals) of all Dutch dairy farms were available from
the agricultural census data [26]. The case data for 2005
and 2006, five cases in total, were not included as no cen-
sus data were available yet for these years. Table 2 lists
how the 69 cases are distributed over birth cohorts, and
over age at detection. The estimated local population sizes
were obtained on the basis of the average number of cows
older than two years in the period 2001–2004, and an
estimated age distribution (used for the birth-cohort spe-
cific analysis). This age distribution, shown in Table 3, is
based on the population structure observed on 100 Dutch
dairy farms, randomly selected from the full Dutch dairy
farm population. As the average age of dairy cattle in
Dutch herds has been stable during the last 5 years and no
major changes occurred in the dairy industry during these
years, we assume the age distribution to be the same for
the different birth cohorts. The average and median age
observed in the random set of farms were very similar to
those for another set of farms, comprising the Dutch BSE
herds and more than 120 control herds [1]. This provides
confidence that the estimated age distribution is a good
representation of the Dutch dairy population. We assume
that there are no regional differences in animal replace-
ment policies, as the dairy farm management culture is
known to be highly uniform across the Netherlands.
Table 4 shows summary statistics of the data. The majority
of BSE cases were born, raised and culled at the same farm.
In the few instances where this was not the case, we used

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the location of the farm where the animal stayed during
the first half year of its life.
Testing for clustering
We first tested whether clustering is present (global clus-
tering test), and then detected and tested local clusters. To
test whether spatial clusters are present, we used Diggle
and Chetwynd's method [27]. The Kulldorff scan test
[28,29] was used to detect and test local clusters. Both
methods account for spatial variation in population den-
sity, which may cause deviations from a completely ran-
dom spatial point pattern. Both the global and local
clustering tests were performed in two ways: on the overall
case data and on the case data for each birth cohort sepa-
rately. In order to see if and how the results of the cohort-
wise analyses are influenced by the definition of the
cohorts, we have carried out two variants of these analy-
ses: one using calendar years as birth years and one using
1 July to 30 June periods as birth years. Hereafter, birth
cohorts starting on 1 January will be denoted by a single
year (e.g. 1996), while birth cohorts starting on 1 July will
be denoted by two years (e.g. 1996/1997).
The K-functions in Diggle and Chetwynd's method were
estimated for distances 5 km, 10 km, ..., 50 km. For the K-
function of the controls we selected 2000 controls from
the population at risk by random sampling with probabil-
ities proportional to size. We used the R-package
SPLANCS for this global clustering test [30]. 999 Monte
Carlo runs were performed to test the null hypothesis of
no spatial clustering.
To reduce computing time of the Kulldorff scan test, the
number of cases and animals at risk were aggregated to
totals for 1 km2 square grid cells. For 999 Monte Carlo
runs computing time was 7 h. 49 min when the original
farm-level data were used, whereas with aggregated data
this was 1 h 24 min (using a standard computer with a
2.60 Ghz processor and a RAM of 504 MB). This enabled
the increase of the number of Monte Carlo runs to 9999
(computing time 10 h 23 min). The effect of the spatial
aggregation on the location, size, and p-value of the clus-
ters was negligible. The systematic scanning of the spatial
data and the statistical testing were carried out using the
freeware SaTScan, version 7.0.3 [29]. Data were treated as
spatial data (no time dimension). The population size
changes over the years, and therefore the time-averaged
population size was used in the analysis. The centres of
the 1 × 1 km grid were used as centres for the circular win-
dows (19030 nodes). The maximum circle size was set by
requiring the window to contain at most 25% of the pop-
ulation. Note that the shape of the scanned neighbour-
hoods is often not circular but irregular, especially near
the nation's boundary. However, the shape of the neigh-
bourhood is immaterial; what counts are the sizes of the
populations at risk inside and outside the scanned neigh-
bourhood. The p-values were computed from 9999 Monte
Carlo replications.
Results
For all distances considered, the difference between the
estimated K-functions of cases and controls based on all
69 cases is far beyond the upper confidence bound, calcu-
lated as 1.96 times the standard error of the difference.
This indicates strong spatial clustering at all distances,
which is confirmed by the very small p-value of the test
statistic (p = 0.001, Table 5). Also for the 1996 birth
cohort (32 cases) and the 1996/1997 birth cohort (25
cases) the differences between the K-functions of cases
and controls are well above the upper confidence bounds,
indicating strong spatial clustering of BSE cases for these
birth cohorts. The p-values of the test statistic are again
0.001 (Table 5). For the 1995/1996 birth cohort (17
cases) the difference of the K-function is in between the
confidence bounds for all distances considered, indicating
Table 4: Population size and case incidence. Mean total
population size and overall case incidence for the study period 01/
01/2001 – 31/12/2004.
Size of the population older than 2 yr (2001–2004 average)
Total number of cases in the study period
Average yearly number of cases per 100000 cattle older
than 2 yr
1815449
69
0.95
Table 3: Cattle age distribution. Age distribution of cattle, based
on population data from 100 randomly selected Dutch dairy
farms.
AgeTotal dairy populationDairy > 2 yr
0-1
1-2
2–3
3–4
4–5
5–6
6–7
7–8
8–9
9–10
10–11
11–12
20.8%
18.7%
16.2%
13.3%
10.1%
8.1%
5.3%
3.7%
2.0%
1.0%
0.4%
0.2%
26.83%
21.92%
16.62%
13.38%
8.76%
6.09%
3.26%
1.67%
0.73%
0.37%
Table 2: Dutch BSE cases. BSE cases in the period 2001–2004, by
cohort and by age at detection.
Cohort
88 91 9293 94 9596 9798 99
Number of cases
Age at detection
Number of cases
1
4
9
2
5
2
6
2
7
9
5
8
3
7
9
2
32
10
1
12
11
51
1213
1 212102

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that there is no significant spatial clustering for this
cohort, which is confirmed by the large p-value of 0.360.
For the remaining birth cohorts the numbers of cases are
considered too small to allow valid testing of global clus-
tering.
Figures 1 and 2 and Tables 6 and 7 show the results of the
local clustering tests (Kulldorff scan tests). The analysis
not accounting for birth cohort (Figure 1, Table 6)
resulted in one significant cluster (p = 0.0387). A second
cluster was found that was not significant (p = 0.1795).
Cohort-wise clustering analysis (Figure 2, Table 7)
resulted in significant clusters for the birth cohorts 1994,
1994/1995, 1996, 1996/1997, 1997. and 1997/1998
(Table 7). The 1996 cohort cluster in the eastern part of
the Netherlands (Figure 2) coincides with the first cluster
of the combined analysis (Figure 1). The clusters of the
1996 birth cohort and of the 1996/1997 birth cohort
overlap to a large extent: they share 15 BSE cases (which
are all born in the period 1 July 1996 – 31 December
1996). All 6 cases of the 1997/1998 cohort cluster are
born between 1 July 1997 and 31 December 1997 and are
also part of the 1997 birth-cohort cluster. This cluster is
located in the middle-west part of The Netherlands. It cor-
responds to the second (non-significant) cluster in the
combined analysis. The 1994 and 1994/1995 cohort clus-
ters have two BSE cases in common, and are situated in
the centre of the Netherlands.
In a non-temporal, non-spatial analysis of Dutch case-
control data [1], it was found that the "group of feed pro-
ducers" is a significant risk factor for BSE. This previous
study suggested that the feed-production heterogeneities
have most likely arisen due to both origin of MBM and
cross-contamination on mixed production lines. We were
therefore interested in the question whether feed suppli-
ers can be identified that are associated with the three
clusters of the cohort-wise analysis. For the farms with BSE
cases we have collected feed supplier information. The
four farms in the cluster of the 1994 birth cohort were
supplied by three different (farmer owned) feed mills. In
each of the two cohorts 1996 and 1997, we observed that
one particular feed mill is more frequently used as sup-
plier inside the spatial cluster than outside. The cluster of
1996 birth cohort contained 20 cases, 11 of which were
supplied by producer A (proportion = 55%). In this birth
cohort there were 12 cases outside the cluster, and only
one of these was supplied by A (proportion = 8.3%).
Using Fisher's exact test on these proportions revealed a
significant association between the 1996 cluster and feed
producer A (p = 0.021, two-tailed probability). Note that
the association with the 1996 cluster cannot serve as evi-
dence for producer A being associated with BSE risk. How-
ever, producer A did belong to the group of feed producers
found to be associated with BSE risk in a former study [1].
The 1997 case cluster contained seven cases, five of which
were supplied by producer K (71.4%), another member of
the group of feed producers found to be associated with
BSE risk in the former study [1]. In the 1997 birth cohort
there were five cases outside the cluster, and two of these
were supplied by K (40%). These numbers do not reveal a
significant association between the 1997 cluster and feed
producer K (p = 0.558, Fisher's exact test). Figure 3 and 4
show the locations of all farms with BSE cases supplied by
the producers A and K, respectively.
Discussion
We have found three spatial case clusters in the Dutch BSE
epidemic. The clusters are geographically distinct and
each cluster appears in a different birth cohort. The fact
that we found stronger spatial clustering when using a
cohort-based analysis, is consistent with the evidence that
most BSE infections occur in animals less than 12 or 18
months old [24,25]. As a result of the infection at a young
age, temporal changes in BSE exposure are seen most
clearly by comparing cohort-wise incidence levels. The
fact that each of the three significant clusters in the cohort-
based analysis occurs in a different birth cohort, suggests
that the causes of the enhanced infection levels each
occurred within a limited time frame of at most about a
year.
In the Introduction we discussed the possible mecha-
nisms that may produce clustering of BSE cases. The can-
didate mechanisms that could underlie the observed
spatial clusters in the Netherlands are feeding practice and
on-farm cross-contamination, and heterogeneities in ren-
dering and feed processing. Local recycling is not likely as
the number of rendering plants in the Netherlands was as
low as two and each of these supplied nationwide to feed
producers. Population heterogeneity is unlikely because
in previous work [1] neither genetic differences between
regions have been found nor differences in management.
Population heterogeneity as a cause of spatial clustering is
also unlikely in view of the limited time frame in which
the causes of the clusters seem to have been present.
In the same previous work, the factor "group of feed pro-
ducers" was found to be a significant risk factor in a non-
temporal, non-spatial analysis of case-control data [1].
Based on this previous result we therefore interpret the
observed clustering to be at least in part due to regional
Table 5: Results of a global clustering analysis using Diggle and
Chetwynd's test. The p-value is of Diggle and Chetwynd's test
statistic Dc,k.
All cohorts1995/199619961996/19971997
p-value 0.0010.361 0.0010.001 0.001

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differences in feed production. As the information from
the previous study suggested, the feed-production hetero-
geneities have most likely arisen due to both origin of
MBM and production on mixed production lines. Feed
producers were different in their sourcing of MBM and the
use of mixed or dedicated production lines. Separation of
production lines was not obligatory up until 1999 (Table
4), and both producers A and K have used mixed produc-
tion lines up until then. Variation in feeding practice (i.e.
between-farm variation in the per-animal feed uptake) is
a less likely mechanism to have contributed to the cluster-
ing, as the amount of feed fed was not significantly asso-
ciated with BSE in the previous study. Furthermore, a
contribution due to on-farm cross-contamination as a
Spatial BSE case clusters
Figure 1
Spatial BSE case clusters. Spatial clusters of BSE cases detected in an analysis not accounting for birth cohort.

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consequence of mixed farming has not been detected.
Indications against such a contribution are the fact that
the 1997 cluster is in an area with small numbers of pigs
and the observation that the southern part of the Nether-
lands with dense populations of pigs has a relatively small
number of BSE cases.
Cohort-specific spatial BSE case clusters
Figure 2
Cohort-specific spatial BSE case clusters. Spatial clusters of BSE cases detected in a cohort-wise analysis in the 1 January
1994 – 31 December 1994 birth cohort (red), the 1 January 1996 – 31 December 1996 birth cohort (blue) and the 1 January
1997 – 31 December 1997 birth cohort (magenta).

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Also in some other European countries where spatial clus-
ters of BSE have been found, the most likely mechanisms
were suggested to be related to exposure heterogeneity
[4,10-19,23]. The feature of different spatial clustering
occurring in different birth cohorts has also been observed
elsewhere in Europe [10,12,14,15]. In Switzerland, France
and Great Britain it was difficult to distinguish effects due
to feed processing differences from those arising from dif-
ferences in feeding practices between mixed farms and
farms with only ruminants, because typically feed produc-
ers with mixed production lines and mixed farms were
spatially correlated. In a recent analysis of Swiss data for
the period after the introduction of a ban on MBM in cat-
tle feed, Schwermer et al. [31] found evidence of spatial
association between BSE cases and feed producers where
cattle feed was found MBM positive by cross-contamina-
tion. Cross-contamination in the feed-production process
was also implicated in a recent study by Paul et al. [23], in
which a spatial analysis of the French feed industry and
BSE case data showed that BSE risk in France after a ban
on MBM in ruminant feed is spatially linked to the use of
MBM in non-ruminant feed.
Conclusion
We have identified three spatial case clusters in the Dutch
BSE epidemic. The clusters are geographically distinct and
each cluster appears in a different birth cohort. In a former
study the factor "group of feed producers" was found to be
a significant risk factor in a non-temporal, non-spatial
analysis of case-control data [1]. Based on this result we
interpret the spatial clustering observed here to be at least
in part due to regional differences in feed production. We
have found that the 1996 cluster is significantly associated
with one particular feed producer. These results suggest
that many of the BSE cases in the Netherlands may have
been due to incidental infectivity releases, that are both
temporally and spatially confined.
Authors' contributions
LH conceived of the study, collected the feed supplier
information of BSE case farms, participated in the cluster-
ing analysis and the interpretation of the results, and
drafted the manuscript. DJB identified the relevant statis-
tical methods for the clustering analysis, carried out the
statistical calculations, participated in the interpretation
of the results and helped to draft the manuscript. TJH par-
ticipated in the clustering analysis and in the interpreta-
tion of the results, helped to draft and finalized the
manuscript. All authors read and approved the final man-
uscript.
Table 7: Results of a local clustering analysis using Kulldorff's scan test. Cohort-wise analysis.
Cohort Population size
inside cluster
Total number
of cases
Number of cases
inside cluster
Log likelihood
ratio
p-value Centre of
cluster
Radius of
cluster (km)
1994
1994/1995
1995
1995/1996
1996
1996/1997
1997
1997/1998
55545
69535
No significant clustering
No significant clustering
145764
171601
206348
242090
5
5
4
3
9.941
13.17
0.0484
0.0053
(181500,444500)
(162500,456500)
25.079
4.472
32
25
12
10
20
18
7
6
16.55
18.40
19.60
17.04
0.0007
0.0002
0.0001
0.0004
(252500,443500)
(252500,443500)
(139500,424500)
(139500,424500)
57.870
57.870
12.806
12.806
Table 6: Results of a local clustering analysis using Kulldorff's
scan test. All 69 BSE cases (no distinction between cohorts).
First cluster Second cluster
Population size inside cluster
Number of cases in cluster
Expected number of cases
Annual number of cases/100000
Observed/expected
Relative Risk
Log likelihood ratio
p-value
329786
30
12.53
2.3
2.393
3.47
11.75
0.0387
26611
8
1.01
7.5
7.910
8.82
9.93
0.1795

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Case farms supplied by one particular feed producer
Figure 3
Case farms supplied by one particular feed producer. Locations of all farms that had BSE cases and were supplied by
feed producer A [1].

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Case farms supplied by one particular feed producer
Figure 4
Case farms supplied by one particular feed producer. Locations of all farms that had BSE cases and were supplied by
feed producer K [1].

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Acknowledgements
We would like to thank Aline de Koeijer for scientific advice, Edo Gies
(Alterra) for GIS work and for producing Figures 1 and 2, and three anon-
ymous referees for their valuable comments on a previous version of this
manuscript. This research was financed by funds from the Ministry of Agri-
culture, Nature and Food Quality and the NeuroPrion Network of Excel-
lence (FOOD-CT-2004-506579).
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