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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: Emily.Brook@moredun.ac.uk (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.

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