Evaluation of factors associated with the risk of infection with Cryptosporidium parvum in dairy calves

Barbara Szonyi Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.

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Yung-Fu Chang Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.

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Susan E. Wade Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.

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Hussni O. Mohammed Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.

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Abstract

Objective—To identify risk factors associated with Cryptosporidium parvum infection in dairy calves.

Animals—108 case animals and 283 control animals.

Procedures—Case animals were calves infected with C parvum, and controls were infected with Cryptosporidium bovis (n = 67) or calves not infected with Cryptosporidium spp. Fecal samples were tested via the flotation concentration method for Cryptosporidium spp. Samples were genotyped by sequencing of the 18s rRNA gene. Associations between host, management, geographic, and meteorologic factors and Cryptosporidium genotype were assessed.

Results—Younger calves and calves housed in a cow barn were more likely to be infected with both genotypes. Herd size and hay bedding were associated with an increased risk of infection with C parvum, and Jersey breed was a risk factor for C bovis infection. Compared with a flat surface, a steeper slope was significantly associated with a decreased likelihood of infection with both genotypes, and precipitation influenced the risk of C parvum infection only.

Conclusions and Clinical Relevance—Risk factors for calf infection with C parvum differed from those for infection with C bovis. Results may be useful to help design measures that reduce animal exposure and decrease public health risk and economic losses associated with C parvum infection in cattle.

Abstract

Objective—To identify risk factors associated with Cryptosporidium parvum infection in dairy calves.

Animals—108 case animals and 283 control animals.

Procedures—Case animals were calves infected with C parvum, and controls were infected with Cryptosporidium bovis (n = 67) or calves not infected with Cryptosporidium spp. Fecal samples were tested via the flotation concentration method for Cryptosporidium spp. Samples were genotyped by sequencing of the 18s rRNA gene. Associations between host, management, geographic, and meteorologic factors and Cryptosporidium genotype were assessed.

Results—Younger calves and calves housed in a cow barn were more likely to be infected with both genotypes. Herd size and hay bedding were associated with an increased risk of infection with C parvum, and Jersey breed was a risk factor for C bovis infection. Compared with a flat surface, a steeper slope was significantly associated with a decreased likelihood of infection with both genotypes, and precipitation influenced the risk of C parvum infection only.

Conclusions and Clinical Relevance—Risk factors for calf infection with C parvum differed from those for infection with C bovis. Results may be useful to help design measures that reduce animal exposure and decrease public health risk and economic losses associated with C parvum infection in cattle.

Cryptosporidium parvum is a protozoan parasite that causes gastrointestinal tract disease in humans and neonatal cattle. Infection with C parvum in calves can range from subclinical shedding of oocysts to severe diarrhea, dehydration, and death.1 Furthermore, damage to the intestinal epithelium can cause prolonged malnutrition and reduced growth rates in calves, resulting in considerable economic losses in dairy operations.2

Although cattle are commonly infected with 4 species of Cryptosporidium (C parvum, Cryptosporidium bovis, Cryptosporidium ryanae, and Cryptosporidium andersoni), only C parvum is of primary concern from the public health perspective and in terms of economic losses to the dairy industry caused by morbidity in calves.1 Preweaned calves are thought to be the main source of C parvum contamination in watersheds that threatens the quality of drinking water supplies. Preweaned calves may also be infected with the nonzoonotic and nonpathogenic species C bovis and C ryanae, both of which are morphologically indistinguishable from C parvum.3,4 Occasionally, preweaned calves may also be shedding structurally distinct C andersoni oocysts, although this species is considered to mainly infect the abomasum of older cattle, without apparent clinical signs.5 One of the main challenges to identifying risk factors for C parvum infections in cattle is that molecular techniques are required to distinguish C parvum from the microscopically identical oocysts of C bovis and C ryanae (commonly referred to as the C parvum–like species).1 It is necessary to decrease the risk of infection with C parvum in cattle to protect water consumers and prevent economic losses in the dairy industry. To effectively control the occurrence of C parvum infection in cattle, molecular techniques need to be incorporated into epidemiological studies to identify relevant risk factors associated with this parasite.

Cryptosporidium oocysts are tolerant to most disinfectants and can survive for months in the environment under favorable conditions.6 Temperature, moisture, and UV radiation are among the most important factors affecting oocyst survival in the environment.7 Although environmental conditions influence oocyst survival, their effect on the risk of infection has not been determined. It is also not known whether various environmental or ecological conditions facilitate infection with C parvum, compared with C bovis in calves. It is important to identify ecological niches that favor oocyst survival and lead to increased risk of infection with the zoonotic genotype because this information could be used to design preventive measures to decrease the risk of exposure.

The purpose of the study reported here was to identify risk factors associated with infection with C parvum in dairy calves. In addition, risk factors for C bovis infection were also investigated by use of C bovis–positive case animals and Cryptosporidium-negative controls.

Materials and Methods

Design—Unmatched retrospective case-control studies were performed to identify potential risk factors for infection with C parvum and C bovis among dairy calves. The study population was recruited from animals that were enrolled in ongoing studies8,9 in this target population on 44 dairy farms in the New York City Watershed. Briefly, fecal samples were collected from 1,072 preweaned calves (< 65 days of age) and screened for the presence of Cryptosporidium oocysts. There were 205 animals shedding C parvum–like oocysts as determined by use of a flotation technique. Subsequently, PCR assay and sequencing of the 18s rRNA gene were performed on all specimens with positive results of fecal flotation to determine the Cryptosporidium species in the sample. Sample size estimationa revealed that with 2 controls/case animal for exposures of 25% in the control population and at 95% confidence level, a study with 69 case animals would have 80% power and a study with 91 case animals would have 90% power to detect ORs ≥ 2.5. The protocol for the studies was approved by the Institutional Animal Care and Use Committee at Cornell University.

Sample collection and screening—Rectal fecal samples were collected from each animal into a plastic cup that was immediately capped and labeled to identify source on the basis of the ear tag number. For each sample, 1 g of feces was processed by use of sugar as the flotation medium (specific gravity, 1.33) with a standard quantitative centrifugation flotation technique.10 Cryptosporidium oocysts were confirmed at 400× magnification with bright-field and phase contrast illumination. A sample was considered to have a positive result when at least 1 oocyst with the correct morphological characters was identified at 400× magnification by use of bright-field and phase contrast illumination (C parvum–like oocysts are 4 to 6 μm in diameter and spherical, contain a residuum and sporozoites, appear pink and refractile in sugar medium, and have a halo).8

Molecular typing of specimens—The DNA was extracted from all samples that had a positive test result for C parvum–like oocysts and a select group of controls.b A 2-step nested PCR protocol was used to amplify an 830-bp fragment of the 18S rRNA gene by use of primers 5′-TTCTAGAGCTAATACATGCG-3′ and 5′-CCCATTTCCTTCGAAACAGGA-3′ for the primary PCR and 5′-GGAAGGGTTGTATTTATTAGATAAAG-3′ and 5′AAGGAGTAAGGAACAACCTCCA-3′ for the secondary PCR.11 The primary reaction volume was 25 μL, including 1 μL of genomic DNA, 10.8 μL of reverse osmosis water, 2 μL of 10× PCR buffer,c 4.8 μL of MgCl2 (25mM), 0.4 μL of deoxynucleotide triphosphates (10mM), 0.4 μL of each forward and reverse primer (10μM), and 0.2 μL of Taq DNA polymerase (5 U/μL). The secondary reaction volume included 1 μL of the product from the primary reaction added to a mixture containing 13.2 μL of reverse osmosis water, 2 μL of 10× PCR buffer, 2.4 μL of MgCl2 (25mM), 0.4 μL of dinucleotide triphosphates (10mM), 0.4 μL of each forward and reverse primer (10μM), and 0.2 μL of Taq DNA polymerase (5 U/μL). Both the primary and secondary reactions were run under the same conditions: initial denaturation (94°C for 3 minutes), followed by 35 cycles of amplification (94°C for 45 seconds, 55°C for 45 seconds, and 72°C for 1 minute) and a final extension cycle (72°C for 7 minutes). The PCR products were evaluated after electrophoresis on 1% agarose gel stained with ethidium bromide. After purification of PCR products by use of exonuclease I-shrimp alkaline phosphatase,d the products were sequenced by use of the internal primers in 9-μL reactions in an automated sequencer.e After bidirectional sequencing, the sequence chromatograms were aligned by use of software.12,f The DNA sequences were compared with GenBank DNA sequences to determine the species of Cryptosporidium in the sample by use of a bioinformatics search tool.g

Definition of cases and controls—A case animal was defined as a preweaned calf (< 65 days of age) that had positive results of flotation testing for Cryptosporidium organisms and for which subsequent sequencing of the 18s rRNA gene revealed 100% homology with C parvum (GenBank accession No. AF093490). Two sets of controls were used in this study. The first set of controls, Cryptosporidium-negative controls, were selected randomly from preweaned calves (< 65 days of age) that had negative results of the flotation concentration assay and the PCR assay targeting the 18s rRNA gene. Random selection was performed by use of the animal's unique identification number and a random number generated by use of spreadsheet software.h The second set of controls consisted of all preweaned calves (< 65 days of age) that had positive results of flotation testing for Cryptosporidium organisms and for which subsequent sequencing of the 18s rRNA gene revealed 100% homology with C bovis (GenBank accession No. AY120911). Specimens that had positive results of flotation testing for Cryptosporidium organisms but either subsequently failed to yield an 18s rRNA gene product or for which the amplicon did not have 100% homology with either C bovis or C parvum were excluded from the study.

Data collection—A questionnaire was used to collect information on host factors and farm management practices. The data were collected by personal interview of the farm owner or manager. Geospatial coordinates for each farm were recorded at the calf housing facility with a handheld global positioning system device.i Digital elevation models for the study area were obtained from the New York State Geographic Information System Clearinghouse. The global positioning system data for farm locations and the digital elevation models were imported into geographic information system softwarej and reprojected into the Universal Transverse Mercator coordinate system (zone 18N), North American Datum 1983. The digital elevation models and farm coordinates were overlaid to obtain information on geomorphological features (ie, elevation, slope, and aspect) associated with each farm location. Elevation was converted from feet to meters (1 foot = 0.305 m), and slope was expressed as percentage gradient. The numeric slope aspect values were converted to categorical values to reflect the direction in which the slope faces (ie, slope aspect was equal to south [≥ 112° and < 292° azimuth] or north [0° to 111°; 292° to 360° azimuth]). To determine whether the geographic position of farms with respect to cardinal directions had effect on genotype, we included the easting and northing coordinates in the logistic regression analyses. Easting refers to the eastward measured distance from the false east reading, which is uniquely defined in each Universal Transverse Mercator zone, and northing refers to the northward measured distance from the equator. Information on precipitation and temperature was obtained from the US Department of Commerce National Climatic Data Center. For the analyses, all the rainfall measurements were converted from inches to millimeters (1 inch = 25.4 mm), and all the temperature measurements were converted from Fahrenheit to Celsius (°C = [°F − 32]/9 × 5). Because oocyst excretion starts 2 to 7 days after infection and lasts for up to 2 weeks,6 precipitation and perhaps temperature are thought to exert an influence on the length of oocyst shedding intervals as well as the timing of shedding; therefore, mean temperature and precipitation 1 month before the detection of oocyst shedding were considered because this time frame appeared to be the most biologically plausible.

Statistical analysis—A systematic approach for data analysis was adopted. First, the bivariable association between shedding C parvum (zoonotic genotype) and each of the putative factors was examined in 2 sets of controls: Cryptosporidium-negative controls and C bovis–infected animals. The association between the putative risk factors and the likelihood of shedding C bovis was also evaluated by use of Cryptosporidium-negative controls. All analyses were conducted by use of standard statistical software.k Continuous variables were grouped into categories on the basis of equal intervals. The functional form of the relationship between continuous variables and outcome was explored by plotting log ORs on a graph. If a linear pattern was suggested by visual inspection, the variable was also considered as continuous. Screening of variables was performed by use of univariable logistic regression analysis to assess the effects of all variables on the outcome of infection. When the number of observations at a certain level of the independent variable and infection status was < 5, a Fisher exact test was used to evaluate the significance of association in the univariable analysis; such variables were omitted from the multivariable models. All other variables were considered for inclusion in the multivariable logistic regression models. Variables were retained in the model if the Wald test P value was < 0.05 or the variable significantly (likelihood ratio statistic < 0.05) improved the model fit. Biologically plausible interaction terms were tested among final model variables at the first-order level.

Because the sampling units, the animals in this study, were clustered in herds, it was assumed that this clustering would lead to a correlation in the likelihood of infection within the study population. This correlation among responses occurs because they are dependent on exogenous factors that are associated with these responses (ie, infection with the organism). Conditioning on an observed set of these factors by controlling for their effect in the analysis and including them as covariates in the logistic regression analysis will sometimes achieve approximate conditional independence. However, more often, this correlation in the response occurs because of observed and unobserved risk factors. It was assumed that the unobserved risk factors were randomly distributed among farms, and the overall importance of this assumption was evaluated by use of a mixed-effect logistic regression model.

The final multivariable models were examined for goodness of fit by use of the Hosmer-Lemeshow statistics,13 and the stability of the models were assessed by examining the delta betas.14 The models were considered stable if removal of observations with the largest delta-beta values (ie, those observations whose exclusions were predicted to have the largest influence on model fit) altered the OR by < 25% and did not affect the significance of individual variables. Values of P < 0.05 were considered significant.

Results

Descriptive data—A total of 108 C parvum-positive animals and 67 C bovis–positive animals met the inclusion criteria and were enrolled in their respective category. In addition, 216 Cryptosporidium-negative controls were randomly selected. The 44 study farms were located within a distance of 66 km in the east-west direction and 31 km in the north-south direction. The mean elevation on the farms was 512 m (range, 376 to 570 m) with a 6% slope (range, 0% to 17.6%). Mean monthly precipitation ranged from 47 to 198 mm, and mean monthly temperature ranged from −8.5° to 21.5°C. Total herd size (including all ages) ranged from 46 to 800 cattle.

C parvum–infected case animals versus Cryptosporidium-negative controls—A graphic examination of the frequency distribution of the continuous variables (age, herd size, geographic location, and meteorologic features) and their relation to the log ORs of being a case animal indicated a nonlinear pattern. Thus, these variables were retained as ordered categorical variables.

In the univariable analysis, calves that were > 1 month of age were at significantly decreased risk of C parvum infection, compared with younger calves (Table 1). Herd size, outdoor housing, dirt flooring, and being housed in a pen were associated with increased risk, whereas keeping calves in a greenhouse was associated with a decreased risk of infection with C parvum. Among geographic factors, steeper slope was significantly associated with a lower risk of C parvum infection. The likelihood of infection was higher with a monthly precipitation of 100 to 150 mm, compared with < 100 mm; however, precipitation > 150 mm was not associated with increased risk.

Table 1—

Results of univariable logistic regression analysis for associations between putative risk factors and Cryptosporidium parvum infection in preweaned calves, compared with Cryptosporidium-negative controls.

VariableNo. (%) of case animalsNo. (%) of controlsOR95% CIWald P valueLRS P value
Host factors      
   Age     < 0.001
      < 1 mo100 (59.1)70 (40.9)1  
      1–2 mo8 (5.2)146 (94.8)0.0380.01–0.08< 0.001 
   Breed     0.29
      Holstein101 (33.5)201 (66.5)1  
      Jersey7 (46.7)8 (53.3)1.740.61–4.930.29 
Herd management factors      
   Herd size     < 0.001
      < 10012 (13.6)76 (86.4)1  
      100–20039 (24.2)122 (75.8)21.0–4.10.05 
      > 20057 (76.0)18 (24.0)20.18.9–44.9< 0.001 
   Greenhouse     0.04
      No96 (35.8)172 (64.2)1  
      Yes12 (21.4)44 (78.6)0.480.24–0.960.04 
   Outdoors     < 0.001
      No67 (26.2)189 (73.8)1  
      Yes41 (65.1)22 (34.9)5.252.91–9.46< 0.001 
   In cow barn     0.06
      No64 (41)92 (59)1  
      Yes42 (30.2)97 (69.8)0.620.38–1.00.06 
   Flooring     < 0.001
      Cement50 (49.0)52 (51.0)1  
      Dirt58 (26.1)164 (73.9)2.711.66–4.49< 0.001 
   Hay bedding     0.88
      No36 (33.0)73 (67.0)1  
      Yes68 (33.8)133 (66.2)1.030.63–1.70.88 
   Dust bedding     0.86
      No56 (33.1)113 (66.9)1  
      Yes48 (34.0)93 (66.0)1.040.64–1.670.86 
   Tied     0.11
      No57 (40.14)85 (59.86)1  
      Yes51 (31.5)111 (68.5)0.680.42–1.00.11 
   Pen     0.015
      No55 (30)128 (70)1  
      Yes53 (43.8)68 (56.2)1.811.12–2.920.015 
Geographic features      
   Easting (km)     0.061
      < 50029 (41.4)41 (58.6)1  
      500–52052 (34.9)97 (65.1)0.750.42–1.350.351 
      > 52026 (25.0)78 (75.0)0.470.24–0.900.023 
   Northing (km)     0.21
      < 4,67519 (43.2)25 (56.8)1  
      4,675–4,68540 (34.5)76 (65.5)0.690.34–1.40.3 
      > 4,68548 (29.4)115 (70.6)0.540.27–1.080.08 
   Elevation (m)     0.37
      < 47045 (58.4)32 (41.6)1  
      470–57036 (20.8)137 (79.2)1.190.68–2.080.52 
      > 57027 (36.5)47 (63.5)0.790.44–1.410.43 
   Slope (%)     0.01
      < 551 (42.9)68 (57.1)1  
      5–1043 (29.5)103 (70.5)0.550.33–0.920.02 
      > 1014 (23.7)45 (76.3)0.410.21–0.830.01 
   Aspect     0.87
      South48 (33.8)94 (66.2)1  
      North60 (33.0)122 (67.0)0.960.65–1.650.87 
Meteorologic factors      
   Temperature (°C)     0.46
      < 037 (33.6)73 (66.4)1  
      0–1122 (36.6)38 (63.4)1.140.59–2.20.69 
      > 1149 (31.8)105 (68.2)0.920.54–1.550.75 
   Precipitation (mm)     0.005
      < 10046 (28.8)114 (71.3)1  
      100–15041 (47.7)45 (52.3)2.251.31–3.890.003 
      > 15021 (26.9)57 (73.1)0.910.49–1.670.76 

— = Not applicable. CI = Confidence interval. LRS = Likelihood ratio statistic.

The final multivariable model for C parvum infection by use of Cryptosporidium-negative controls revealed that the risk of being a case animal was significantly increased with herd size > 200, compared with < 100; being housed in the cow barn; and the use of hay bedding, whereas older age was associated with lower risk (Table 2). Compared with a flat surface, a slope of 5% to 10% was associated with a decreased risk; however, a slope > 10% was not significant in the model. There appeared to be a lower risk at higher latitudes (northing), although this was not significant. Precipitation of 100 to 150 mm, compared with < 100 mm, remained a significant risk factor in the multivariable model. No significant interactions were identified. The model was robust to the exclusion of observations with the highest delta-beta values (extreme values) and provided adequate goodness of fit, as measured by the Hosmer-Lemeshow statistics (P = 0.58).

Table 2—

Results of a final multivariable logistic regression model for associations between C parvum infection and explanatory variables in preweaned calves, compared with Cryptosporidium-negative controls.

VariableCoefficientSEOR95% CIWald P valueLRS P value
Age     < 0.001
   < 1 mo01  
   1–2 mo−4.470.770.010.003–0.051< 0.001 
Herd size     < 0.001
   < 10001  
   100–2001.050.642.870.81–10.10.099 
   > 2005.670.9329246–1,836< 0.001 
In cow barn     < 0.001
   No01  
   Yes2.640.88142.5–78.810.003 
Hay     < 0.001
   No01  
   Yes1.950.537.052.4–20.1< 0.001 
Northing (km)     0.002
   < 4,67501  
   4,675–4,685−1.50.90.220.03–1.30.096 
   > 4,685−1.40.760.240.05–1.120.071 
Slope (%)     0.003
   < 501  
   5–10−1.950.590.140.044–0.450.001 
   > 10−0.550.850.570.1–3.00.51 
Precipitation (mm)     0.042
   < 10001  
   100–1501.210.533.351.2–9.50.02 
   > 1500.0060.6110.3–3.30.99 

See Table 1 for key.

C parvum–infected case animals versus C bovis–infected controls—After visual inspection, none of the continuous variables were deemed to have a linear relationship with the log odds of C parvum infection by use of C bovis–infected controls. Thus, all continuous variables were retained as ordered categorical variables.

Univariable analysis revealed that compared with C bovis–infected controls, the likelihood of C parvum infection increased significantly with herd size, outdoor housing, dirt flooring, and being in a pen, whereas older age, Jersey breed, being tied, and being housed in the cow barn were associated with a lower risk (Table 3). The risk appeared to increase at higher latitudes, but this was not consistent across the categories. The univariable effects of the meteorologic factors were not significant.

Table 3—

Results of univariable logistic regression analysis for associations between putative risk factors and C parvum infection in preweaned calves, compared with Cryptosporidium bovis–infected controls.

VariableNo. (%) of case animalsNo. (%) of controlsOR95% CIWald P valueLRS P value
Host factors      
Age     < 0.001
   < 1 mo100 (70.9)41 (29.1)  
   1–2 mo8 (23.5)26 (76.5)0.120.05–0.30< 0.001 
Breed     < 0.001
   Holstein101 (69.2)45 (30.8)  
   Jersey7 (25.0)21 (75.0)0.140.05–0.37< 0.001 
Herd management factors      
Herd size     < 0.001
   < 10012 (33.3)24 (66.7)1  
   100–20039 (61.9)24 (38.1)3.251.37–7.670.007 
   > 20057 (75.0)19 (25.0)62.52–14.26< 0.001 
Greenhouse     0.86
   No96 (61.5)59 (38.1)1  
   Yes12 (60.0)8 (40.0)0.920.35–2.380.86 
Outdoors*      
   No67 (50.8)65 (49.2)1  
   Yes41 (95.3)2 (4.7)19.84.62–85.53< 0.001 
In cow barn     < 0.001
   No65 (80.2)16 (19.8)1  
   Yes43 (51.2)41 (48.8)0.250.12–0.51< 0.001 
Flooring*      
   Cement58 (47.5)64 (52.5)1  
   Dirt50 (94.3)3 (5.7)18.45.4–62.1< 0.001 
Hay bedding     0.15
   No36 (54.5)30 (45.5)1  
   Yes68 (65.4)36 (34.6)1.570.83–2.950.15 
Dust bedding     0.62
   No56 (62.9)33 (37.1)1  
   Yes48 (59.3)33 (37.1)0.850.46–1.580.62 
Tied     < 0.001
   No57 (52.78)16 (23.88)1  
   Yes51 (47.22)51 (76.12)0.280.14–0.55< 0.001 
Pen     < 0.001
   No55 (50.0)55 (50.0)1  
   Yes53 (81.54)12 (18.46)4.412.12–9.16< 0.001 
Geographic features Easting (km)     0.079
   < 50029 (76.3)9 (23.7)1  
   500–52052 (59)36 (41)0.440.18–1.050.068 
   > 52026 (54.2)22 (45.8)0.360.14–0.930.036 
Northing (km)     0.01
   < 4,67519 (50.0)19 (50.0)1  
   4,675–4,68540 (78.4)11 (21.6)3.631.44–9.140.006 
   > 4,68548 (56.5)37 (43.5)1.290.6–2.790.5 
   Elevation (m)     0.07
   < 47045 (68.2)21 (31.8)1  
   470–57036 (51.4)34 (48.6)0.490.24–0.990.04 
   > 57027 (69.2)12 (30.8)1.050.44–2.460.91 
Slope (%)     0.66
   < 551 (65.4)27 (34.6)1  
   5–1043 (58.9)30 (41.1)0.750.39–1.460.41 
   > 1014 (58.3)10 (41.7)0.740.29–1.880.53 
Aspect     0.07
   South48 (55.2)39 (44.8)1  
   North60 (68.2)28 (31.8)1.740.94–3.220.07 
Meteorologic factors Temperature (°C)     0.25
   < 037 (67.3)18 (32.7)1  
   0–1122 (68.7)10 (31.3)1.070.41–2.720.887 
   > 1149 (55.7)39 (44.3)0.610.3–1.230.17 
   Precipitation (mm)     0.32
   < 10046 (56.1)36 (43.9)1  
   100–15041 (68.3)19 (31.7)1.680.84–3.390.14 
   > 15021 (63.6)12 (36.4)1.360.59–3.140.45 

Fisher exact test was used to assess the significance of the association.

See Table 1 for remainder of key.

The multivariable model for C parvum infection analyzed by use of C bovis–infected controls revealed that the risk of being a case animal significantly increased with herd size and the use of hay bedding, whereas older age and Jersey breed were associated with lower risk (Table 4). The risk seemed to increase with precipitation, although this was not significant across the categories (ie, only 100 to 150 mm was associated with a significantly higher risk, compared with the reference category of < 100 mm). Similarly, there appeared to be an increased risk associated with latitude (northing), but this was not consistently observed across all categories of this variable. There were no significant interactions. The model was robust to the exclusion of observations with highest delta-beta values and fitted the data adequately as measured by the Hosmer-Lemeshow goodness of fit statistics (P = 0.44).

Table 4—

Results of a final multivariable logistic regression model for associations between C parvum infection and explanatory variables in preweaned calves, compared with C bovis–infected controls.

VariableCoefficientSEOR95% CIWald P valueLRS P value
Age     < 0.001
   < 1 mo01  
   1–2 mo−30.570.050.01–0.15< 0.001 
Breed     < 0.001
   Holstein01  
   Jersey−3.70.870.0230.004–0.132< 0.001 
Herd size     < 0.001
   < 10001  
   100–2001.970.627.12.0–24.60.002 
   > 2003.10.8522.34.1–119< 0.001 
Hay bedding     < 0.001
   No01  
   Yes1.730.785.661.21–26.30.027 
   Northing (km)     0.001
   < 4,67501  
   4,675–4,685−0.560.880.560.1–3.230.52 
   > 4,685−1.90.620.140.04–0.490.002 
   Precipitation (mm)     0.018
   < 10001  
   100–1501.430.674.211.13–15.680.032 
   > 1500.120.761.130.25–5.00.86 

See Table 1 for key.

C bovis–infected controls case animals versus Cryptosporidium-negative controls—On the basis of visual inspection, none of the continuous variables appeared to have a linear relationship with the log odds of C bovis infection. Thus, all continuous variables were retained as ordered categorical variables.

Univariable analysis indicated that herd size > 200 cattle, compared with < 100; Jersey breed; being housed in the cow barn; and being tied were significantly associated with an increased risk of C bovis infection, whereas older age, dirt flooring, and being in a pen were associated with a decreased risk (Table 5). In addition, latitude (northing), elevation, and northern aspect were associated with a decreased likelihood of C bovis infection.

Table 5—

Results of univariable logistic regression analysis for associations between putative risk factors and C bovis infection in preweaned calves, compared with Cryptosporidium-negative controls.

VariableNo. (%) of case animalsNo. (%) of controlsOR95% CIWald P valueLRS P value
Host factors      
Age     < 0.001
   < 1 mo54 (33.1)109 (66.3)1  
   1–2 mo13 (10.8)107 (89.2)0.240.12–0.46< 0.001 
Breed     < 0.001
   Holstein45 (19.1)201 (81.71)1  
   Jersey21 (72.4)8 (27.6)11.724.88–28.15< 0.001 
Herd management factors      
Herd size     < 0.001
   < 10024 (24.0)76 (76.0)1  
   100–20024 (16.4)122 (83.6)0.620.33–1.170.14 
   > 20019 (51.4)18 (48.6)3.341.51–7.370.003 
Greenhouse     0.12
   No59 (25.5)172 (74.5)1  
   Yes8 (15.4)44 (84.6)0.530.23–1.190.12 
Outdoors*      
   No65 (25.6)189 (74.4)1  
   Yes2 (8.3)22 (91.7)0.260.006–1.150.076 
In cow barn     0.007
   No16 (14.8)92 (85.2)1  
   Yes41 (29.7)97 (70.3)2.431.27–4.620.007 
Flooring*      
   Cement64 (28.1)164 (71.9)1  
   Dirt3 (5.5)52 (94.5)0.140.04–0.490.002 
Hay bedding     0.14
   No30 (29.1)73 (70.9)1  
   Yes36 (21.3)133 (78.7)0.650.37–1.150.14 
Dust bedding     0.49
   No33 (22.6)113 (77.4)1  
   Yes33 (26.2)93 (73.8)1.210.69–2.110.49 
Tied     0.005
   No16 (15.84)85 (84.16)1  
   Yes51 (31.48)111 (68.52)2.431.3–4.570.005 
Pen     0.012
   No55 (30.)128 (69.95)1  
   Yes12 (15.0)68 (85.0)0.410.2–0.810.012 
Geographic features Easting (km)     0.38
   < 5009 (18)41 (82)1  
   500–52036 (27)97 (73)1.60.74–3.820.2 
   > 52022 (22)78 (78)1.280.54–3.00.56 
Northing (km)     < 0.001
   < 4,67519 (43.2)25 (56.8)1  
   4,675–4,68511 (12.6)76 (87.4)0.160.06–0.39< 0.001 
   > 4,68537 (24.3)115 (75.7)0.40.2–0.80.01 
   Elevation (m)     0.01
   < 47021 (39.6)32 (60.4)1  
   470–57034 (19.9)137 (80.1)0.370.19–0.730.004 
   > 57012 (20.3)47 (79.7)0.380.16–0.90.027 
Slope (%)     0.33
   < 527 (28.4)68 (71.6)1  
   5–1030 (22.6)103 (77.4)0.730.4–1.340.31 
   > 1010 (18.2)45 (81.8)0.550.24–1.260.16 
   Aspect     0.03
   South39 (29.3)94 (70.7)1  
   North28 (18.7)122 (81.3)0.550.09–0.310.03 
Meteorologic factors Temperature (°C)     0.38
   < 018 (19.8)73 (80.2)  
   0–1110 (20.8)38 (97.1)1.060.44–2.530.88 
   > 1139 (27.1)105 (72.9)1.50.79–2.830.2 
   Precipitation (mm)     0.24
   < 10036 (24.0)114 (76.0)  
   100–15019 (29.7)45 (70.3)1.330.69–2.570.38 
   > 15012 (17.4)57 (82.6)0.660.32–1.370.27 

See Tables 1 and 3 for key.

The final multivariable model for C bovis infection by use of Cryptosporidium-negative controls revealed that the risk of being a case animal was significantly increased with Jersey breed and being housed in the cow barn, whereas older age was associated with lower risk (Table 6). There appeared to be an increased risk associated with latitude (northing), but this was not consistently observed across all categories of this variable. Finally, the likelihood of infection was lower on steeper slopes. There were no significant interactions. The model was robust to the exclusion of observations with highest delta-beta values and fitted the data adequately, as measured by the Hosmer-Lemeshow goodness of fit statistics (P = 0.54).

Table 6—

Results of a final multivariable logistic regression model for associations between C bovis infection and explanatory variables in preweaned calves, compared with Cryptosporidium-negative controls.

   VariableRegression coefficientSEOR95% CIWald P valueLRS P value
Age     < 0.001
   < 1 mo01  
   1–2 mo−1.440.410.230.1–0.5< 0.001 
Breed     < 0.001
   Holstein01  
   Jersey1.30.653.691.02–13.310.046 
In cow barn     < 0.001
   No01  
   Yes2.040.637.692.21–26.710.001 
Northing (km)     < 0.001
   < 4,67501  
   4,675–4,685−1.90.730.140.03–0.620.009 
   > 4,685−0.430.680.650.17–2.510.537 
Slope (%)     < 0.001
   < 501  
   5–10−1.440.420.230.09–0.560.001 
   > 10−2.110.390.120.04–0.39< 0.001 

See Table 1 for key.

Discussion

Recent studies15–17 have been conducted to identify risk factors for Cryptosporidium infection in cattle. The results of these investigations greatly differ and are often contradictory, which has been attributed to differences in study design, management practices, and climatic conditions.18 Arguably, the use of diagnostic techniques that do not distinguish the different species or genotypes of Cryptosporidium could also have contributed to inconsistent findings. To date, few studies19,20 have incorporated DNA sequencing to distinguish risk factors for infection with zoonotic versus nonzoonotic strains of Cryptosporidium in cattle. Although these studies had limited scope regarding the putative risk factors examined, their findings suggested that there was a difference in the subset of factors that predispose to infection by either genotype. The important and unique aspect of the present study was the examination of a range of host, management, and ecological factors at the genotype level, which allowed the identification of differences in risk factors associated with C parvum and C bovis infection. For the purpose of designing cost-effective strategies to mitigate the potential risk associated with various genotypes of Cryptosporidium, it is critical to compare and contrast the risk factors for zoonotic and nonzoonotic strains. We attempted to address this goal by comparing risk factors between animals shedding C parvum and the ones shedding C bovis.

Calves < 1 month of age were at greater risk of infection with both C parvum and C bovis. Younger calves were also more likely to be infected with C parvum, compared with C bovis. These findings are partially in agreement with results of previous studies that detected a relationship between age and Cryptosporidium infection in cattle. Specifically, the results concurred with a previous report21 that most C parvum infections occur in cattle < 1 month of age. However, our finding that younger age was also a risk factor for C bovis infection seems to contradict another report,21 which concluded that C bovis infection was acquired later in life and was thus considered to be predominant in older calves.

Jersey breed was a significant risk factor for C bovis infection in both multivariate models that included C bovis infection as outcome. However, breed was not a significant risk factor for C parvum infection. These results suggest that Jersey calves are more susceptible to C bovis infection but not less susceptible to C parvum infection, compared with Holstein calves.

In the present study, we found that calves housed in the cow barn were at an increased risk of infection with both genotypes, which seems to support the idea of cow-to-calf transmission. In the past, cows were not regarded as an important source of infection for calves22; however, recent studies23,24 indicated that cow-to-calf transmission might be an important route for acquiring Cryptosporidium infection in calves. The use of hay bedding increased the likelihood of infection with C parvum but not with C bovis in the multivariable models. One study25 in Mexico found that hay bedding increased the odds of shedding Cryptosporidium oocysts in dairy calves, which was attributed to the humid and protective environment created by hay, favoring oocyst survival. Finally, herd size > 200, compared with < 100, was associated with an increased risk of infection with C parvum. However, herd size did not remain a significant risk factor for C bovis infection in the multivariable analysis. A previous study26 revealed a positive association between the size of the farm and the risk of shedding C parvum–like oocysts; however, that study did not use molecular typing to distinguish genotypes.

Among the geomorphological factors in the multivariable analyses, a slope of 5% to 10%, compared with a flat surface, was significantly associated with a decreased likelihood of infection with both genotypes. One possible explanation for the effect of slope on infection is that steeper slopes may not be favorable for the accumulation of oocysts around calf holding facilities. In addition, the risk of infection with both C parvum and C bovis appeared to be lower at higher latitudes, although this finding was not significant. Only precipitation had a significant effect in the multivariable models among the meteorologic factors. Specifically, precipitation of 100 to 150 mm, compared with < 100 mm, was found to be a significant risk factor for C parvum infection by use of both sets of controls. Desiccation is lethal for Cryptosporidium oocysts7; thus, precipitation may increase the risk of exposure by supporting oocyst survival. In wet periods, rain water could also transport feces from one calf to another, increasing the risk of calf-to-calf transmission.

Many possible sources of bias and error in case-control studies have been discussed in the literature. Among the most common concerns are the identification of an appropriate control group (selection bias) and availability of accurate information on infection status and potential risk factors (information bias).27 The present study attempted to minimize selection bias by use of a control group that was randomly drawn from the same source population as the case animals. Misclassification bias was kept to a minimum by the use of PCR assay, which is one of the most highly sensitive and specific diagnostic tests for Cryptosporidium infection.28 However, a potential source of bias was that conventional sequencing of the 18s rRNA gene does not attain detection of mixed Cryptosporidium infections.29 Thus, if any of the case animals were infected with both genotypes, only the dominant genotype would have been identified by this method. Because age affects the likelihood of Cryptosporidium infection in cattle and may also be associated with other hypothesized risk factors, we controlled for the confounding effect of age by considering all independent variables in the multivariable analysis, even if they were not significant at the univariable level.

The present study identified and compared several host, management, and ecological risk factors associated with C parvum and C bovis infection in cattle. The findings will be useful in designing measures that reduce animal exposure and effectively decrease the public health risk and economic losses associated with C parvum infection in cattle herds.

ABBREVIATIONS

OR

Odds ratio

a.

StatCalc, EpiInfo, version 6, CDC, Atlanta, Ga.

b.

QIAamp DNA Stool Mini Kit, Qiagen, Valencia, Calif.

c.

Fermentas, Glen Burnie, Md.

d.

Exo-SAP-IT, USB Corp, Cleveland, Ohio.

e.

3730 DNA Analyzer, Applied Biosystems, Foster City, Calif.

f.

Molecular Evolutionary Genetics Analysis, version 4.1, Biodesign Institute, Tempe, Az.

g.

BLAST, National Center for Biotechnology Information, National Institutes of Health, Bethesda, Md. Available at: blast.ncbi.nlm.nih.gov/. Accessed May 21, 2010.

h.

Excel 2000, Microsoft, Redmond, Wash.

i.

Garmin eTrex Summit, Garmin International Inc, Olathe, Kan.

j.

Manifold System 8.0 Ultimate Edition, Manifold Net Ltd, Carson City, NV.

k.

Intercooled STATA, version 11 for Mac, Stat Corp LP, College Station, Tex.

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