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Veterinary Parasitology 152 (2008) 46–52
Prevalence and risk factors for Cryptosporidium spp.
infection in young calves
Emily Brook a,b,*, C. Anthony Hart b, Nigel French c, Robert Christley a
a Department of Veterinary Clinical Science, University of Liverpool, Leahurst, Neston, UKb Department of Medical Microbiology and Genitourinary Medicine, University of Liverpool, Liverpool, UK
c EpiCentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand
Received 20 June 2007; received in revised form 13 November 2007; accepted 4 December 2007
Abstract
A cross-sectional study was designed to investigate the prevalence and risk factors for Cryptosporidium infection in young
calves. Forty-one farms in a discrete, densely farmed 100 km2 area of North West England were visited over a 3-week period and
215 faecal samples were collected from young calves. Farms were not selected on the basis of existing scour problems. At the time
of sampling, several investigator-observed variables were recorded at the pen, animal and stool levels. Samples were screened and
60/215 were confirmed as positive by PCR of the 18S rRNA gene. Risk factors for infection were explored using multilevel
multivariable logistic regression with farm as a random effect. Age was significant in the final model, with a higher risk of infection
in calves aged 8–21 days, when compared to those aged 0–7 days. The depth of the bedding was also significant in the final model,
with calves housed in bedding 11–15 cm deep being at lower risk of infection than those on beds 0–5 cm deep. Consistency of the
faeces was highly correlated with age and colour of the faeces and was not significantly associated with infection when these
variables, and clustering at farm-level, were accounted for. This is interesting as Cryptosporidium is considered to be a primary
enteropathogen. The results suggest that intervention strategies should be targeted at calves under 21 days old. These animals
represent a significant reservoir of infection on the farm and may also pose a risk to public health, assuming that the species and
genotypes shed are zoonotic pathogens.
# 2007 Elsevier B.V. All rights reserved.
Keywords: Cryptosporidium; Cattle; Prevalence; Risk factor; Epidemiology
1. Introduction
Cryptosporidium species belong to the Apicomplexa
phylum of parasites and have been detected in a wide
range of vertebrate hosts. Infection, which usually
causes self-limiting diarrhoea in humans and animals,
* Corresponding author at: Epidemiology and Population Biology
Division, Moredun Research Institute, Pentlands Science Park, Bush
Loan, Penicuik, Near Edinburgh EH26 0PZ, UK.
Tel.: +44 131 445 5111; fax: +44 131 445 6235.
E-mail address: [email protected] (E. Brook).
0304-4017/$ – see front matter # 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.vetpar.2007.12.003
can be fatal in immunocompromised individuals.
Infection in cattle is highly age-dependent, with young
calves showing the highest prevalence and intensity of
shedding of the organism (Garber et al., 1994; Quilez
et al., 1996). These young animals mainly shed the
species C. parvum, which has a wide host range and is
considered to be a potentially zoonotic agent.
A number of prevalence studies have been under-
taken. The results of these vary widely depending on the
sensitivity and specificity of screening methods used
and the management groups sampled. Some have
studied infection in post-weaned or adult cattle
(Lorenzo Lorenzo et al., 1993; Scott et al., 1995; Fayer
E. Brook et al. / Veterinary Parasitology 152 (2008) 46–52 47
et al., 2000) whereas, in the majority of cases, young
calves have been the focus of the study (Anderson and
Hall, 1982; Ongerth and Stibbs, 1989; Sakai et al., 2003;
McAllister et al., 2005; Gow and Waldner, 2006). In
some cases farms have been sampled that had a previous
history of scour (de la Fuente et al., 1999; Lefay et al.,
2000; Peng et al., 2003). No cross-sectional studies to
date have focused on farms in a single locality, although
some studies have compared the prevalences found in
disparate geographical areas of a particular region or
country (Atwill et al., 1999a; Lefay et al., 2000;
McAllister et al., 2005; Hamnes et al., 2006).
Risk factor studies have also been carried out,
usually focusing on management factors at the herd-
level, derived from farmer questionnaire (Garber et al.,
1994; Maldonado-Camargo et al., 1998; Mohammed
et al., 1999; Atwill et al., 1999b; Hamnes et al., 2006;
Maddox-Hyttel et al., 2006). Other cross-sectional
studies have investigated the relationship between
diarrhoea and shedding of Cryptosporidium spp.
(Quilez et al., 1996; Olson et al., 1997; Wade et al.,
2000; Hamnes et al., 2006; Castro-Hermida et al., 2006;
Geurden et al., 2006). However, the analysis of these
studies has often not accounted for confounding
between variables or clustering at the farm-level.
The aim of this study was to determine prevalence
and risk factors for Cryptosporidium infection in
unweaned calves from farms in an area of Cheshire,
UK.
2. Materials and methods
2.1. Participants
All known cattle farms in a 10 km � 10 km area of
Cheshire, UK (n = 63) were contacted and those with
unweaned calves on the holding at the proposed time of
sampling were invited to participate in the study. The
area is primarily dairy farming, although some beef
units are also present.
2.2. Sampling
On farms, the aim was to sample 50% of unweaned
animals or a minimum of five calves per farm. Freshly
voided faecal samples were collected into sterile
universal containers, given a unique identifying number
and held at 4 8C until processed. Numerous variables
were recorded. At the farm-level, the number of
unweaned calves on the holding on the day of sampling
was recorded. Pen level variables consisted of pen type
(individual pen/hutch, pen shared with one other calf,
mixed pen), stocking density (m2 per calf), depth of
bedding and cleanliness of bedding. Bedding depth was
measured at the most representative area of the pen,
using a skewer; the bedding was compressed at the
measuring site to reduce air-content. A bed hygiene
score was derived based on the percentage of the pen
that was completely clean; a score of 100 represented a
pen in which the total area was considered to be
completely clean. Animal level variables included calf
identification number, which was used to determine age
from farm records. Breed (dairy or non-dairy/dairy
cross) was also recorded. A scoring system was used to
grade the cleanliness of the tail, hindquarters and flank
(Hughes, 2001). The consistency of the faeces was also
scored (Hughes, 2001) and this, with the colour of the
faeces (cream/orange, creamy brown or ‘‘intermedi-
ate’’, brown), comprised the stool level variables.
2.3. Screening
Faecal samples were initially screened using the
modified Ziehl-Neelsen (MZN) staining method and a
commercial enzyme immunoassay (EIA) kit (ProSpecT
Cryptosporidium Microplate Assay; Remel, Lenexa,
Kansas, USA). The status of all samples giving a
positive result for either or both of these screening tests
was confirmed by PCR of the 18S rRNA gene locus
(Xiao et al., 1999).
2.4. Analysis
Overall prevalence was calculated by dividing the
number of positive isolates by the total number
sampled. Farm prevalence was calculated as the number
of farms with at least one positive animal sampled
divided by the total number of farms sampled. The
prevalence on positive farms was calculated by dividing
the number of positive animals identified on each
positive farm by the total number sampled on that farm.
Generalised additive models (GAM) were used to
evaluate the functional form of the relationship between
continuous variables and the presence or absence of
oocysts (S-PLUS 2000, MathSoft Inc.). Non-linear
continuous variables were converted to categorical
variables. Initially, the association between each
recorded variable and the presence of Cryptosporidium
was assessed using univariable multilevel logistic
regression (EGRET, Cytel, Cambridge, MA, USA)
with farm as a random effect. This was done in order to
account for non-independence of samples originating
from one farm. Variables with p < 0.2 were considered
for inclusion in the multivariable analysis.
E. Brook et al. / Veterinary Parasitology 152 (2008) 46–5248
Fig. 1. Generalised additive model plots demonstrating the relation-
ship between (a) age and (b) depth of bedding and the outcome (the
presence of Cryptosporidium oocysts in the faeces). The rug plot on
the X-axis indicates the number of observations; the dashed lines
represent the 95% confidence intervals.
At this stage, cross-tabulation and chi-squared tests
were performed on categorical variables to assess the
level of correlation. Correlation between continuous
variables was assessed by Pearson or Spearman
correlation coefficients, where appropriate. Variables
showing a high degree of correlation underwent
hierarchical cluster analysis using Ward’s method
(SPSS 12).
Multilevel multivariable models, also with farm as a
random effect, were developed by stepwise elimination,
with variables remaining in the model if they
significantly improved the fit of the model ( p < 0.05)
or if their removal substantially altered the effect of the
other variables (EGRET, Cytel, Cambridge, MA, USA).
Intraclass correlation was approximated using the
latent-variable approach, which assumes that the
variance (on the logit scale) at level 1 (the sample-
level) is equal to p2/3 (Goldstein et al., 2002).
3. Results
3.1. Participants
A total of 215 samples were collected from 41 farms
between April and May 2004 (mean number of calves
sampled per farm was five; the mean proportion of
unweaned calves sampled was 50%). Of the remaining
22 farms invited, five had incorrect contact details or
could not be contacted, 13 had no stock at all or no
young calves and four were too busy. The farms
sampled were approximately evenly distributed
throughout the study area, resulting in good coverage
of the region.
The ages of 174 calves were obtained from farm
records. Fourteen calves did not have an ear tag at the
time of sampling and these calves could therefore not be
adequately identified to obtain the age. These calves
were categorised as a separate (unknown) age category.
The remaining 27 samples could not be attributed to
individual calves, as they were ‘‘environmental’’
samples, collected from the floor of group pens. This
proved problematic in further analyses due to other
missing calf-level data. In final model building these
‘‘environmental’’ samples were excluded. Using avail-
able data, the median age of calves sampled was 26
days. The mode age was 14 days.
3.2. Screening
Of the 215 samples collected, 28% were confirmed
as positive by PCR (60/215). At least one positive
animal was sampled on 27/41 farms (66%). The
prevalence on positive farms ranged from 11 to 67%
(mean 36%). The majority of samples typed in our study
(50/54) were C. parvum, however 3/54 were C. bovis
and one was C. deer-like genotype (Brook et al., 2007).
3.3. Risk factors
GAMs suggested a non-linear relationship between
outcome and age (Fig. 1a), with log-odds of infection
increasing to a maximum at around 16 days before
decreasing. There was a suggestion of a second rise in
risk of infection amongst older calves but few calves
over 90 days were sampled (as indicated by the sparse
rug plot and wide confidence intervals). These GAM
plots were used to form age group categories used in
further analysis, with age being categorised into 0–7
days, 8–14 days, 15–21 days, 22–28 days, 29–35 days,
36–42 days and >42 days, the focus of the study being
on unweaned calves. Based on GAM plots, bedding
depth (Fig. 1b) and space per calf were also categorised
before further analysis.
E. Brook et al. / Veterinary Parasitology 152 (2008) 46–52 49
Table 1
Final multilevel multivariable model demonstrating the significant associations between recorded variables and shedding of Cryptosporidium
oocysts, with farm as a random effect
Cases Controls Odds ratio 95% CI p-value
Bed depth 0.01
0–5 cm 18 19 1
6–10 cm 20 70 0.32 0.11–0.95
11–15 cm 7 47 0.12 0.03–0.48
>15 cm 15 17 0.72 0.20–2.59
Age <0.001
0–7 days 4 21 1
8–14 days 20 15 6.02 1.56–23.25
15–21 days 11 8 5.11 1.14–23.01
22–28 days 5 10 1.73 0.36–8.33
29–35 days 1 16 0.21 0.02–2.39
36–42 days 1 14 0.29 0.03–3.21
>42 days 2 46 0.27 0.04–1.78
Unknown 8 6 5.48 1.06–28.23
There was no evidence of non-linearity between
cleanliness of bedding score and the outcome
( p = 0.29). Similarly, the total number of calves on
the holding appeared to be linearly associated with the
outcome ( p = 0.26).
Seven variables with p < 0.2 in multilevel univari-
able analysis were considered for inclusion in the
multilevel multivariable models: age group, space per
calf (m2), depth of bedding, cleanliness of the flank,
consistency of the faeces, colour of the faeces and
number of unweaned calves on the farm on the day of
sampling.
These variables were examined for correlation.
Colour and consistency of the faeces were correlated
with each other. Cluster analysis was performed to
determine a faecal variable comprising colour and
consistency.
Age and depth of bedding were significant in the
final multilevel multivariable model (Table 1).
There was little evidence of clustering within farms;
the estimate of the variance associated with level 2
(farm) in the intercept only multilevel model (the ICC)
was 4.4%.
4. Discussion
4.1. Prevalence
Cryptosporidium was prevalent (28%) in unweaned
calves in this area of Cheshire during the sampling
period. The overall prevalence detected correlates well
with previous cross-sectional studies where unweaned
calves have been the sample population. Reported
prevalences vary from 22 to 59% (Anderson and Hall,
1982; Garber et al., 1994; Quilez et al., 1996;
Maldonado-Camargo et al., 1998; Fonseca et al.,
2001; Trotz-Williams et al., 2005; Castro-Hermida
et al., 2006), although this depends on the age of the
target population and the diagnostic test used. One study
sampled all calves <6 months on 109 dairy herds and
found the prevalence of C. parvum to be 2.4% (Wade
et al., 2000). This low prevalence is probably due to the
fact that many of the calves sampled were weaned
animals.
The sensitivity of the screening tests must also be
considered. The MZN stain is a non-specific stain and
therefore false-positives have been reported. The EIA
was designed for use in human stools and therefore
may not detect bovine specific genotypes; false
negatives have been reported in cattle. However,
the combination of the two procedures, with
confirmation if just one suggested a positive result,
was considered acceptable. In our hands, both MZN
and EIA have been shown to perform well in cattle
faeces when used alone (Brook et al., 2008).
Sensitivity (and therefore prevalence) may have
increased had all the samples been subject to PCR
of the 18S rRNA gene. Most comparable prevalence
studies in cattle have also used non-molecular
screening methods, mainly due to limited resources
(Maldonado-Camargo et al., 1998; Geurden et al.,
2006).
In one study, pre-weaned calves (5 days to 2 months
old) had an overall prevalence of 50% whereas post-
weaned (3–11 months) had a prevalence of 20% (Santin
et al., 2004). The molecular typing in this paper also
highlights the issue that non-parvum species, C. bovis
and C. deer-like genotype may be found in calves.
E. Brook et al. / Veterinary Parasitology 152 (2008) 46–5250
Depending on screening methods used, previous studies
may not have detected these non-parvum species or may
have identified them as C. parvum.
Most other prevalence studies have collected faeces
directly from the rectum whereas in this study voided
samples were collected. It is possible that calves with an
increased frequency of defecation, due perhaps to
gastrointestinal disease or as a result of increased intake
of milk or other food, may have been over-represented.
Herd prevalence (at least one positive animal
detected) in this study was 66%, with positive herds
distributed approximately evenly throughout the study
area. The herd prevalence in previous cross-sectional
studies is frequently between 53 and 67% (Anderson
and Hall, 1982; Garber et al., 1994; Lefay et al., 2000;
Hamnes et al., 2006; Castro-Hermida et al., 2006)
occasionally rising to 77% (Trotz-Williams et al., 2005)
and 94 or 96% (Maldonado-Camargo et al., 1998;
Maddox-Hyttel et al., 2006). In addition to actual
variation between farms, other factors may affect
apparent herd prevalence. Again, the sensitivity of the
screening procedure will affect this value. In addition,
the small sample sizes obtained here will reduce the
probability of detecting a positive animal were it to be
present, particularly where the within-farm prevalence
is low. The sampling protocol used in the current study
would have been sufficient to be 90% confident of
detecting one positive animal per herd, had the within
farm prevalence been higher (around 50%) (Dohoo
et al., 2003). However, due to the lower prevalence
found in this study, the herd prevalence may have been
underestimated. Some authors have concluded that
finding oocysts in one calf indicates ‘‘previous,
concurrent or expected infection in all calves up to
30 days of age’’ (McCluskey et al., 1995). These authors
therefore conclude that sampling small numbers may be
sufficient to determine herd prevalence. Oocyst shed-
ding in infected calves may also be intermittent
(McCluskey et al., 1995).
4.2. Risk factors
Many previous studies have principally considered
herd-level risk factors for Cryptosporidium shedding,
utilising farmer questionnaires to generate data. The
present study concentrated on variables that could be
observed and recorded on the day of sampling. In
addition, prior studies have rarely accounted for
clustering at the farm-level. Multilevel models, with
farm as a random effect, provide one means of
accounting for the potential lack of independence of
samples from the same farm. There was little evidence
of clustering in the current study, despite cryptospor-
idiosis being an infectious disease. This suggests that
the majority of the variation in disease status was due to
the significant fixed-effect covariates, i.e. the age of the
animal and the depth of bedding, rather than any
unmeasured factors at the farm-level. The farms
sampled in this study were not randomly selected. In
fact, they were invited to participate because of their
proximity to one another in a 10 km � 10 km area of
Cheshire. Therefore we might hypothesise that they
were likely to be fairly similar in their management
practices due to shared topography, weather patterns
and source of veterinary advice.
The effect of age on risk of shedding Cryptosporidium
spp. is clearly highly significant, with calves between 8
and 21 days being most at risk. The age category that was
assigned to calves in the current study with no tag (i.e.
unknown age) was also significantly associated with an
increased risk of Cryptosporidium infection; the odds
ratio in the final model was 5.24, which is similar to that
in calves aged 8–14 days (6.11) and 15–21 days (5.77).
This suggests that these calves are mainly between the
ages of 8 and 21 days, with farmers being required by
legislation to tag animals by 20 days. Young calves play
an important role in maintaining infection in the herd and
represent the greatest zoonotic risk; intervention
strategies should be targeted towards this age group, a
finding which is supported by several other studies.
Calves aged 4 months or under were 13 times more likely
to be infected with C. parvum than older animals in one
study (Atwill et al., 1999b). It has also been reported that
risk of infection significantly decreases with increasing
age of the animal, when age is used as a continuous
variable (Maldonado-Camargo et al., 1998; Mohammed
et al., 1999).
The depth of the bedding was also associated with
infection: the odds of calves on bedding of 11–15 cm
being infected were significantly lower than those
calves on bedding 0–5 cm deep. The risk may then
increase again with deeper bedding but this finding was
not significant. A possible explanation for this may be
that very scanty bedding is not sufficient to maintain
hygienic conditions and may result in higher levels of
environmental contamination than deeper bedding.
Bedding depth may also be a marker for other poor
management practices, as straw can be expensive and
adding or replacing bedding is time-consuming and
labour intensive. Other risk factor studies have also
identified similar bedding or hygiene related variables
as significantly affecting the odds of infection. For
example, it has been reported that disinfecting the floor
of pens decreases the risk of shedding and that frequent
E. Brook et al. / Veterinary Parasitology 152 (2008) 46–52 51
cleaning also results in lower levels of infection
(Hamnes et al., 2006; Castro-Hermida et al., 2006).
Adding bedding and removing soiled bedding daily
were also protective in other models (Mohammed et al.,
1999). Deep litter has been found to be protective on
univariable analysis, although this did not remain
significant when confounding variables were accounted
for (Maddox-Hyttel et al., 2006).
Consistency of the faeces did not remain significant
in the final model. This was surprising; Cryptospor-
idium is considered to be a cause of diarrhoea in
neonates and many previous studies have shown a
significant association between diarrhoea and shedding
(Quilez et al., 1996; Wade et al., 2000; Maddox-Hyttel
et al., 2006; Castro-Hermida et al., 2006; Geurden et al.,
2006; Singh et al., 2006). However, none of these
studies accounted for confounding or clustering at the
farm-level; most have simply explored the univariable
association between shedding and diarrhoea. Using
these methods in the current study would also
demonstrate an apparently significant association
between diarrhoea and infection ( p = 0.03). However,
the methods used in the current study did account for the
effects of potential confounders, such as age of the
animal. Age is correlated with consistency of the faeces,
with younger animals tending to have looser faeces,
perhaps due to the liquid nature of the milk diet.
Clustering at the farm-level was also taken into account,
which might reduce the apparent association between
consistency and the outcome as calves from the same
farm are likely to have the same feeding regime and
may also share the same enteropathogens.
Even where Cryptosporidium appears to be asso-
ciated with loose faeces, it is possible that the parasite is
not the only or primary enteropathogen. The potential
for a multifactorial infectious cause of calf diarrhoea
has been highlighted (de la Fuente et al., 1999).
Previous studies may also have used a different scoring
system for faecal consistency to that in the present study
(O’Handley et al., 1999; Sturdee et al., 2003; McAllister
et al., 2005); classification of the consistency may also
be affected by the presence of large amounts of mucus.
The consistency of the faeces may be associated with
the intensity of Cryptosporidium infection, rather than
the presence/absence of any oocysts (Quilez et al.,
1996). A validated method of quantification of oocyst
load must be used to explore this relationship.
5. Conclusion
By targeting all eligible cattle farms within a defined
geographical area, this unique study gives an insight
into the microepidemiology of the parasite, something
that has been suggested is lacking in currently available
literature (Smith et al., 2006). Whilst there was no
evidence in the current study to suggest that Cryptos-
poridium spp. adversely affect the health of young
calves, the presumed zoonotic potential of the organism
must be acknowledged. On farms, the aim should be to
reduce the shedding of the parasite in calves, which in
turn will aid reduction of transmission within herds.
Transmission of the organism between farms should
also be controlled and ultimately zoonotic pathways
must be minimised.
Acknowledgments
The authors wish to thank the farmers who
participated in the study. This work was supported by
a BBSRC studentship to Emily Brook.
References
Anderson, B.C., Hall, R.F., 1982. Cryptosporidial infection in Idaho
dairy calves. J. Am. Vet. Med. Assoc. 181, 484–485.
Atwill, E.R., Johnson, E., Klingborg, D.J., Veserat, G.M., Markegard,
G., Jensen, W.A., Pratt, D.W., Delmas, R.E., George, H.A., Forero,
L.C., Philips, R.L., Barry, S.J., McDougald, N.K., Gildersleeve,
R.R., Frost, W.E., 1999a. Age, geographic, and temporal distribu-
tion of fecal shedding of Cryptosporidium parvum oocysts in cow-
calf herds. Am. J. Vet. Res. 60, 420–425.
Atwill, E.R., Johnson, E.M., Pereira, M.G., 1999b. Association of herd
composition, stocking rate, and duration of calving season with
fecal shedding of Cryptosporidium parvum oocysts in beef herds.
J. Am. Vet. Med. Assoc. 215, 1833–1838.
Brook, E.J., Christley, R.M., French, N.P., Hart, C.A., 2008. . Detec-
tion of Cryptosporidium oocysts in fresh and frozen cattle faeces:
comparison of three methods. Lett. Appl. Microbiol 46, 26–31.
Brook, E.J., Hart, C.A., French, N.P., Christley, R.M., 2007. Molecular
epidemiology of Cryptosporidium subtypes in cattle in England.
Vet. J., doi:10.1016/j.tvjl.2007.10.023.
Castro-Hermida, J.A., Carro-Corral, C., Gonzalez-Warleta, M., Mezo,
M., 2006. Prevalence and intensity of infection of Cryptospor-
idium spp. and Giardia duodenalis in dairy cattle in Galicia (NW
Spain). J. Vet. Med. B Infect. Dis. Vet. Public Health 53, 244–246.
de la Fuente, R., Luzon, M., Ruiz-Santa-Quiteria, J.A., Garcia, A.,
Cid, D., Orden, J.A., Garcia, S., Sanz, R., Gomez-Bautista, M.,
1999. Cryptosporidium and concurrent infections with other major
enterophatogens in 1–30-day-old diarrheic dairy calves in central
Spain. Vet. Parasitol. 80, 179–185.
Dohoo, I., Martin, W., Stryhn, H., 2003. Veterinary Epidemiologic
Research. AVC Inc., Charlottestown, pp. 47.
Fayer, R., Trout, J.M., Graczyk, T.K., Lewis, E.J., 2000. Prevalence of
Cryptosporidium, Giardia and Eimeria infections in post-weaned
and adult cattle on three Maryland farms. Vet. Parasitol. 93, 103–
112.
Fonseca, I.P., Fazendeiro, I.S.A.B., Antunes, F.R.A.N., 2001. Char-
acterization of Cryptosporidium parvum isolates from cattle in
Portugal: animal and human implications. J. Eukaryot. Microbiol.
48, 32s–33s.
E. Brook et al. / Veterinary Parasitology 152 (2008) 46–5252
Garber, L.P., Salman, M.D., Hurd, H.S., Keefe, T., Schlater, J.L., 1994.
Potential risk factors for Cryptosporidium infection in dairy
calves. J. Am. Vet. Med. Assoc. 205, 86–91.
Geurden, T., Goma, F.Y., Siwila, J., Phiri, I.G., Mwanza, A.M.,
Gabriel, S., Claerebout, E., Vercruysse, J., 2006. Prevalence
and genotyping of Cryptosporidium in three cattle husbandry
systems in Zambia. Vet. Parasitol. 138, 217–222.
Goldstein, H., Browne, W.J., Rasbash, J., 2002. Partitioning variation
in multilevel models. UndStat 1, 223–232.
Gow, S., Waldner, C., 2006. An examination of the prevalence of and
risk factors for shedding of Cryptosporidium spp. and Giardia spp.
in cows and calves from western Canadian cow-calf herds. Vet.
Parasitol. 137, 50–61.
Hamnes, I.S., Gjerde, B., Robertson, L., 2006. Prevalence of Giardia
and Cryptosporidium in dairy calves in three areas of Norway. Vet.
Parasitol. 140, 204–216.
Hughes, J., 2001. A system for assessing cow cleanliness. In Practice
23, 517–524.
Lefay, D., Naciri, M., Poirier, P., Chermette, R., 2000. Prevalence
of Cryptosporidium infection in calves in France. Vet. Parasitol.
89, 1–9.
Lorenzo Lorenzo, M.J., Ares-Mazas, M.E., Villacorta, I., 1993.
Detection of oocysts and IgG antibodies to Cryptosporidium
parvum in asymptomatic adult cattle. Vet. Parasitol. 47, 9–15.
Maddox-Hyttel, C., Langkjaer, R.B., Enemark, H.L., Vigre, H., 2006.
Cryptosporidium and Giardia in different age groups of Danish
cattle and pigs—occurrence and management associated risk
factors. Vet. Parasitol. 141, 48–59.
Maldonado-Camargo, S., Atwill, E.R., Saltijeral-Oaxaca, J.A., Her-
rera-Alonso, L.C., 1998. Prevalence of and risk factors for shed-
ding of Cryptosporidium parvum in Holstein Freisian dairy calves
in central Mexico. Prev. Vet. Med. 36, 95–107.
McAllister, T.A., Olson, M.E., Fletch, A., Wetzstein, M., Entz, T.,
2005. Prevalence of Giardia and Cryptosporidium in beef cows in
southern Ontario and in beef calves in southern British Columbia.
Can. Vet. J. 46, 47–55.
McCluskey, B.J., Greiner, E.C., Donovan, G.A., 1995. Patterns of
Cryptosporidium oocyst shedding in calves and a comparison of
two diagnostic methods. Vet. Parasitol. 60, 185–190.
Mohammed, H.O., Wade, S.E., Schaaf, S., 1999. Risk factors asso-
ciated with Cryptosporidium parvum infection in dairy cattle in
southeastern New York State. Vet. Parasitol. 83, 1–13.
O’Handley, R.M., Cockwill, C., McAllister, T.A., Jelinski, M., Morck,
D.W., Olson, M.E., 1999. Duration of naturally acquired giardiosis
and cryptosporidiosis in dairy calves and their association with
diarrhea. J. Am. Vet. Med. Assoc. 214, 391–396.
Olson, M.E., Guselle, N.J., O’Handley, R.M., Swift, M.L., McAllister,
T.A., Jelinski, M.D., Morck, D.W., 1997. Giardia and Cryptos-
poridium in dairy calves in British Columbia. Can. Vet. J. 38,
703–706.
Ongerth, J.E., Stibbs, H.H., 1989. Prevalence of Cryptosporidium
infection in dairy calves in western Washington. Am. J. Vet. Res.
50, 1069–1070.
Peng, M.M., Wilson, M.L., Holland, R.E., Meshnick, S.R., Lal, A.A.,
Xiao, L., 2003. Genetic diversity of Cryptosporidium spp. in cattle
in Michigan: implications for understanding the transmission
dynamics. Parasitol. Res. 90, 175–180.
Quilez, J., Sanchez-Acedo, C., del, C.E., Clavel, A., Causape, A.C.,
1996. Prevalence of Cryptosporidium and Giardia infections in
cattle in Aragon (northeastern Spain). Vet. Parasitol. 66, 139–146.
Sakai, H., Tsushima, Y., Nagasawa, H., Ducusin, R.J., Tanabe, S.,
Uzuka, Y., Sarashina, T., 2003. Cryptosporidium infection of cattle
in the Tokachi district, Hokkaido. J. Vet. Med. Sci. 65, 125–127.
Santin, M., Trout, J.M., Xiao, L., Zhou, L., Greiner, E., Fayer, R.,
2004. Prevalence and age-related variation of Cryptosporidium
species and genotypes in dairy calves. Vet. Parasitol. 122,
103–117.
Scott, C.A., Smith, H.V., Mtambo, M.M., Gibbs, H.A., 1995. An
epidemiological study of Cryptosporidium parvum in two herds of
adult beef cattle. Vet. Parasitol. 57, 277–288.
Singh, B.B., Sharma, R., Kumar, H., Banga, H.S., Aulakh, R.S., Gill,
J.P., Sharma, J.K., 2006. Prevalence of Cryptosporidium parvum
infection in Punjab (India) and its association with diarrhea in
neonatal dairy calves. Vet. Parasitol. 140, 162–165.
Smith, H.V., Caccio, S.M., Tait, A., McLauchlin, J., Thompson, R.C.,
2006. Tools for investigating the environmental transmission of
Cryptosporidium and Giardia infections in humans. Trends Para-
sitol. 22, 160–167.
Sturdee, A.P., Bodley-Tickell, A.T., Archer, A., Chalmers, R.M.,
2003. Long-term study of Cryptosporidium prevalence on a
lowland farm in the United Kingdom. Vet. Parasitol. 116,
97–113.
Trotz-Williams, L.A., Jarvie, B.D., Martin, S.W., Leslie, K.E., Pere-
grine, A.S., 2005. Prevalence of Cryptosporidium parvum infec-
tion in southwestern Ontario and its association with diarrhea in
neonatal dairy calves. Can. Vet. J. 46, 349–351.
Wade, S.E., Mohammed, H.O., Schaaf, S.L., 2000. Prevalence
of Giardia sp. Cryptosporidium parvum and Cryptosporidium
andersoni (syn. C. muris) (correction of Cryptosporidium parvum
and Cryptosporidium muris (C. andersoni)) in 109 dairy herds
in five counties of southeastern New York. Vet. Parasitol. 93,
1–11.
Xiao, L., Escalante, L., Yang, C., Sulaiman, I., Escalante, A.A.,
Montali, R.J., Fayer, R., Lal, A.A., 1999. Phylogenetic analysis
of Cryptosporidium parasites based on the small-subunit rRNA
gene locus. Appl. Environ. Microbiol. 65, 1578–1583.