Investigating the association between socioeconomic and agricultural risk factors and the incidence of Salmonella Heidelberg and Salmonella Typhimurium in Ontario in 2015: A multi-level ecological approach.

The incidence of salmonellosis, and other enteric zoonotic diseases, has been associated with various food and environmental exposures and socioeconomic factors. Increasingly, there is interest in exploring differences among serotypes of Salmonella to better inform public health prevention efforts. Consequently, we investigated whether rates of Salmonella Heidelberg and Typhimurium in Ontario communities in 2015 were influenced by household characteristics, agricultural factors, and the presence of meat plants. Data for each reported case of S. Heidelberg and S. Typhimurium in Ontario in 2015 were collected. Expected cases of each serotype were calculated, stratified by age group and sex, according to the underlying population distribution from the Canadian census. Socioeconomic, agricultural census data, and data concerning provincial and federally inspected meat plants were combined with observed and expected case counts. The association between community-level agricultural, meat processing, and socioeconomic variables, serotype, and the rate of salmonellosis in each census subdivision (CSD) was explored using multilevel Poisson models, with random intercepts for CSD and census division (CD). Rates of S. Heidelberg and S. Typhimurium were associated with the proportion of married individuals in a CSD, and were higher in CSDs with the highest quantile of labour participation compared to those in the lowest quantile. There was an interaction effect between cattle, poultry and swine farm density in a CD and serotype, with rates of either serotype decreasing as cattle, poultry, or swine farm density in the encompassing CD increased. The rate of the decrease varied by serotype. Our findings concerning community-level household characteristics may be explained by the influence of family structure and occupation on food consumption patterns and environmental exposures. Rates of S. Heidelberg and S. Typhimurium may be lower in areas with increased animal farm density due to naturally acquired immunity from routine exposure to Salmonella via livestock.

ATQ: alert time quality, an evaluation metric for assessing timely epidemic detection models within a school absenteeism-based surveillance system.

BACKGROUND: Wellington-Dufferin-Guelph Public Health (WDGPH) has conducted an absenteeism-based influenza surveillance program in the WDG region of Ontario, Canada since 2008, using a 10% absenteeism threshold to raise an alert for the implementation of mitigating measures. A recent study indicated that model-based alternatives, such as distributed lag seasonal logistic regression models, provided improved alerts for detecting an upcoming epidemic. However model evaluation and selection was primarily based on alert accuracy, measured by the false alert rate (FAR), and failed to optimize timeliness. Here, a new metric that simultaneously evaluates epidemic alert accuracy and timeliness is proposed. The alert time quality (ATQ) metric is investigated as a model selection criterion on both a simulated and real data set. METHODS: The ATQ assessed alerts on a gradient, where alerts raised incrementally before or after an optimal day were considered informative, but were penalized for lack of timeliness. Summary statistics of ATQ, average alert time quality (AATQ) and first alert time quality (FATQ), were used for model evaluation and selection. Alerts raised by ATQ and FAR selected models were compared. Daily elementary school absenteeism and laboratory-confirmed influenza case data collected by WDGPH were used for demonstration and evaluation of the proposed metric. A simulation study that mimicked the WDG population and influenza demographics was conducted for further evaluation of the proposed metric. RESULTS: The FATQ-selected model raised acceptable first alerts most frequently, while the AATQ-selected model raised first alerts within the ideal range most frequently. CONCLUSIONS: Models selected by either FATQ or AATQ would more effectively predict community influenza activity with the local community than those selected by FAR.

Exploring the geographical distribution of human cryptosporidiosis in Southern Ontario from 2011 to 2014.

Cryptosporidium is a protozoan parasite of increasing global public health concern because of its ability to cause disease in both humans and animals through contaminated food and water supplies. In Canada, most human cryptosporidiosis cases are due to Cryptosporidium hominis; however, the presence of zoonotic Cryptosporidium parvum has been observed. Since 2005, the incidence of cryptosporidiosis in Ontario has been consistently higher than the national average; however, it is not understood why, suggesting an incomplete understanding of the pathogen's ecology, epidemiology and transmission pathways. The goal of this study was to explore the spatial distribution of human cryptosporidiosis across the 29 Public Health Unit (PHU) areas of Southern Ontario from 2011 to 2014. Surveillance data on human cryptosporidiosis were obtained from Public Health Ontario. Choropleth and isopleth maps were used to display the distribution of incidence rates of human cryptosporidiosis. High-rate clusters of human cryptosporidiosis were identified. Poisson and spatial Poisson regression models were used to determine the relationship between the incidence of human cryptosporidiosis, cattle density and the smoothed farm-level prevalence of bovine cryptosporidiosis at the PHU level. The annual incidence of reported human cryptosporidiosis in Southern Ontario ranged from 1.62 (95% CI: 1.41-1.86) to 1.82 (95%CI: 1.60-2.06) cases per 100,000 population, with an overall cumulative incidence of 6.91 (95%CI: 6.47-7.39) cases per 100,000 for the 4-year study period. High-risk clusters of human cryptosporidiosis were identified in each year. The relative risk for the clusters ranged from 2.03 (95% CI: 1.63-2.55) to 6.87 (95% CI: 5.07-9.30). A relationship was found between the incidence of cryptosporidiosis and dairy cattle density. Based on this study, the Central West region would be an ideal ecological system to conduct further targeted surveillance to identify factors that may be contributing to the higher burden of cryptosporidiosis in the human and bovine populations in the region.

Detection of spatial and spatio-temporal Salmonella Heidelberg and Salmonella Typhimurium human case clusters focused around licensed abattoirs in Ontario in 2015, and their potential relation to known outbreaks.

Salmonellosis is one of several zoonotic diseases for which individuals with occupational animal contact, including abattoir workers, are at an increased risk. If meat is contaminated during slaughter, this can increase the risk of enteric illness for consumers. In this study, we investigated whether reported cases of Salmonella Heidelberg and Typhimurium were clustered around abattoirs in Ontario in 2015 and whether there was any evidence (laboratory/exposure) to suggest an abattoir at the centre of a cluster might be the source of exposure. Data for each reported case of S. Heidelberg and S. Typhimurium in Ontario in 2015 were collected. Multi-focused and non-focused spatial and space-time cluster detection tests were performed for each serotype, with and without cases linked to known outbreaks, using Poisson and space-time permutation models. Focused tests included the location of abattoirs operational in all or part of 2015. Laboratory data and exposure information were used to explore the relatedness of cases within identified clusters. Focused spatial tests identified clusters of S. Heidelberg and S. Typhimurium around abattoirs. Focused space-time permutation tests identified 2 significant space-time clusters of S. Heidelberg; one cluster (n = 11 cases) included 8 of 9 cases associated with a known outbreak and the other cluster (n = 18 cases) was not part of a previously identified outbreak. Review of laboratory and risk factor information suggested that cases within each cluster shared a common exposure. Cases were not asked about goat or sheep meat consumption. The focused cluster test, particularly with the space-time permutation model, could assist in identifying outbreaks associated with a particular physical location, such as an abattoir. Improvements to the current case investigation process, such as consistent collection and reporting of high-risk occupation information and more detailed food consumption history, could assist in outbreak identification when coupled with this statistic.

Good times bad times: Automated forecasting of seasonal cryptosporidiosis in Ontario using machine learning.

BACKGROUND: The rise of big data and related predictive modelling based on machine learning algorithms over the last two decades have provided new opportunities for disease surveillance and public health preparedness. Big data come with the promise of faster generation of and access to more precise information, potentially facilitating predictive precision in public health ("precision public health"). As an example, we considered forecasting of the future course of the monthly cryptosporidiosis incidence in Ontario. METHODS: The traditional statistical approach to forecasting is the seasonal autoregressive integrated moving-average (SARIMA) model. We applied SARIMA and an artificial neural network (ANN) approach, specifically a feed-forward neural network, to predict monthly cryptosporidiosis incidence in Ontario in 2017 using 2005-2016 data as a training set. Both forecasting approaches are automated to make them relevant in a disease surveillance context. We compared the resulting forecasts using the root mean squared error (RMSE) and mean absolute error (MAE) as measures of predictive accuracy. RESULTS: Cryptosporidiosis is a seasonal disease, which peaks in Ontario in late summer. In this study, the SARIMA model and ANN forecasting approaches captured the seasonal pattern of cryptosporidiosis well. Contrary to similar studies reported in the literature, the ANN forecasts of cryptosporidiosis were slightly less accurate than the SARIMA model forecasts. CONCLUSION: The ANN and SARIMA approaches are suitable for automated forecasting of public health time series data from surveillance systems. Future studies should employ additional algorithms (e.g. random forests) and assess accuracy by using alternative diseases for case studies and conducting rigorous simulation studies. Difference between the forecasts from the machine learning algorithm, that is, the ANN, and the statistical learning model, that is, the SARIMA, should be considered with respect to philosophical differences between the two approaches.

Detection of spatial, temporal and space-time Salmonella Heidelberg and Salmonella Typhimurium clusters in Ontario in 2015, and comparisons to known outbreaks.

PURPOSE: Salmonellosis is one of several reportable diseases in Ontario (ON). Two or more cases of the same serotype that are linked to a common exposure or related to one another in time and/or space are considered a potential outbreak. While laboratory data can help to determine the molecular relatedness of cases, results may take up to several weeks. This study aimed to assess the utility of the retrospective spatial scan statistic in detecting clusters of Salmonella Heidelberg and Salmonella Typhimurium cases using data from ON in 2015. Identified clusters were validated by laboratory data (where available) to determine whether identified clusters were likely outbreaks. METHODS: Data representing the location of each reported S. Heidelberg or S. Typhimurium case in 2015, responsible serotype and symptom onset date were exported to SaTScan for retrospective spatial, temporal, and space-time analyses using the spatial scan statistic with Bernoulli models and a space-time permutation model. Analyses were performed with and without those cases linked to known outbreaks. Laboratory subtyping data (i.e. pulsed field gel electrophoresis (PFGE) and/or phage type) and food and environmental exposure information (e.g. travel, animal contact, poultry and other food item consumption) were used to explore the relatedness of cases within identified clusters. RESULTS: Spatial, temporal and space-time analyses identified a known outbreak of S. Heidelberg in 2015 (n = 9 cases) and a previously unidentified cluster of S. Heidelberg cases. Most cases (94%) within a cluster detected via a space-time permutation model of S. Heidelberg cases shared an identical PFGE pattern and appeared to represent a true outbreak. CONCLUSIONS: The spatial scan statistic, and particularly the space-time permutation model, could assist in outbreak identification before laboratory data are available, allowing for faster cluster identification and implementation of control measures.

A case-case study comparing the individual risk factors and symptomatology of Salmonella Heidelberg and Salmonella Typhimurium in Ontario in 2015, following implementation of the Ontario Investigation Tools.

Salmonella Heidelberg and Salmonella Typhimurium are among the most common serotypes responsible for human salmonellosis in Ontario. Introduction of the Ontario Investigation Tools (OIT) in 2014 allowed for standardized case investigation and reporting. This study compared the risk factors and symptomatology for sporadic S. Heidelberg and S. Typhimurium cases reported in Ontario in 2015, following implementation of the OIT. Multilevel logistic regression models were applied to assess associations between serotype and individual-level demographic characteristics, exposures and symptoms for sporadic confirmed cases of S. Heidelberg and S. Typhimurium in Ontario in 2015. There were 476 sporadic cases of S. Typhimurium (n = 278) and S. Heidelberg (n = 198) reported in Ontario in 2015. There were significant associations between the odds of the isolate from a case being one of these serotypes, and travel, consumption of sprouts (any type), contact with reptiles and development of malaise, fever or bloody diarrhoea. The S. Typhimurium and S. Heidelberg cases differed in both symptom presentation and risk factors for illness. Case-case comparisons of Salmonella serotypes have some advantages over case-control studies in that these are less susceptible to selection and recall bias while allowing for rapid comparison of cases to identify potential high-risk exposures that are unique to one of the serotypes when compared to the other. Comparing cases of two different Salmonella serotypes can help to highlight risk factors that may be uniquely associated with one serotype, or more strongly associated with one serotype compared to another. This information may be useful for understanding relative source attribution between common serotypes of Salmonella.

Examining the factors related to bacteriological testing of private wells in Southern Ontario.

The incidence of infectious waterborne disease in Canada continues to be a public health issue and can be associated with the source of drinking water. Millions of Canadians relying on unregulated private well water are at increased risk of disease. This study examined relationships between well and owner characteristics and the frequency of microbial testing of private wells in two southern-Ontario counties. Using multi-level logistic regression models, testing frequency (i.e., at least once per year vs. less) was modeled, as both self-reported and laboratory-validated, for associations with owner and well characteristics. For the self-reported outcome, a previous adverse test result significantly increased the odds of being classified as a frequent tester, and owners with a well-head more than 16 inches (40.6 cm) above the ground were at significantly higher odds of being classified as frequent testers compared to those with well-heads less than 16 inches above the ground and those below ground level. For the model based on the laboratory-validated outcome, the odds of an owner being a frequent tester significantly varied with the length of occupancy and the occurrence of a previous adverse result. The absence of associations between other well characteristics and testing frequency suggests that well safety education could benefit these communities.

Exploring the geographical distribution of cryptosporidiosis in the cattle population of Southern Ontario, Canada, 2011-2014.

Cryptosporidiosis is an infectious disease of relevance to the cattle industry. The southern region of the Canadian province of Ontario is characterised by widespread cattle farming that is a key contributor to the Canadian dairy industry. Given Ontario's key role in the Canadian dairy industry and the potential impact that cryptosporidiosis can have on cattle operations, identifying areas of increased risk for bovine cryptosporidiosis is important. The primary goal of this study was to explore the distribution of bovine cryptosporidiosis, across the geographical areas served by the 29 Public Health Units (PHUs) of Southern Ontario, in the period 2011-2014. Laboratory data on bovine cryptosporidiosis were collected from the Animal Health Laboratory at the University of Guelph, Canada. Using veterinary clinic locations as a proxy for farm location, choropleth and isopleth maps were produced. Highrisk clusters of bovine cryptosporidiosis were identified using the flexible spatial scan test. Assessment of the potential for spatial misclassification bias resulting from a proxy location variable was conducted. The overall raw farm-level prevalence of bovine cryptosporidiosis was 45% [95% confidence interval, CI: 42%-48%]. A cluster was identified in the central-west region of Southern Ontario (relative risk 1.30 [95% CI: 1.07-1.54, P=0.026]) meaning that cattle in the areas served by the Bruce-Grey-Owen Sound, Huron, Wellington-Dufferin Guelph and Waterloo PHUs were at a higher risk for infection. Given that this area is known for having a high-density of dairy cattle, it should be considered as a target for further surveillance.

Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system.

BACKGROUND: School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics. METHODS: Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness. RESULTS: All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827. CONCLUSIONS: School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.

Pork implicated in a Shiga toxin-producing Escherichia coli O157:H7 outbreak in Ontario, Canada.

OBJECTIVES: To describe an outbreak of Shiga toxin-producing Escherichia coli (STEC) O157:H7 infection following a four-day family gathering in Ontario. This is the first published account of a STEC O157 outbreak in Canada linked to consumption of pork. METHODS: The outbreak investigation included interviews with food handlers and other key associated persons, inspection of food preparation premises, traceback investigations, case finding, analysis of data from an outbreak questionnaire, and laboratory analysis of samples collected from various sources associated with the outbreak. RESULTS: Several meals, including pork from a pig roast, were served to the 59 attendees, 29 of whom developed gastrointestinal illness following the event. Six cases developed bloody diarrhoea and seven were hospitalized. Leftover pork served the day after the pig roast was the item most significantly associated with an increased risk of illness (p<0.001). STEC O157:H7 was isolated from 11 of the 29 ill attendees, and also from the pork. By pulse-field gel electrophoresis (PFGE), all STEC O157:H7 pork isolates were either identical or closely related to the 11 clinical isolates. No STEC was detected in any other samples. Three Clostridium perfringens isolates, unrelated by PFGE, were obtained from two STEC-positive cases and the pork. CONCLUSION: This outbreak highlights the need for increased awareness of pork as a potential source of STEC O157 infection, and for enhanced education regarding the safe handling, cooking and storage of food, specifically where large cuts of meat are cooked outdoors at events such as pig roasts, a cultural norm in some communities.

Occurrence of Anoplocephala perfoliata infection in horses in Ontario, Canada and associations with colic and management practices.

Infection with the tapeworm Anoplocephala perfoliata has been found to be associated with equine colic in horses in the United Kingdom. Using a matched case-control study design, data collected from 117 pairs of horses in Ontario were examined for evidence of associations between risk of colic and A. perfoliata infection, and between seropositivity to infection and management practices. Cases were horses in southern Ontario diagnosed with colic by local veterinarians, and control horses were from the same stables as cases and were matched by age, breed and gender where possible. Infection status was defined on the basis of positive results upon coprological examination, and/or seropositivity to a 12/13 kDa A. perfoliata secretory protein. Fifty-six percent of the 234 horses were seropositive for A. perfoliata, but eggs were found in samples from only 6% of horses. Horses dependent on pasture for a large part of their diet were significantly more likely to have ELISA optical density levels above 0.600 compared to other horses (odds ratio [OR]=6.38; p=0.029). This finding identified exposure to pasture as an important source of A. perfoliata infection in the horses used in the study. In a subset of 46 pairs of horses for which control horses had no known history of colic, a statistically significant negative association was found between the risk of colic and optical density (OD) levels >0.200-0.600, relative to OD levels < or = 0.090 (OR=0.08; p=0.017). There was no other statistical evidence of an association between the risk of colic and A. perfoliata infection.

Association between management practices and within-herd prevalence of Cryptosporidium parvum shedding on dairy farms in southern Ontario.

To identify management practices associated with an increased within-herd prevalence of Cryptosporidium parvum shedding on dairy farms in southern Ontario, fecal samples were taken from 1089 calves aged 7-28 days, from 119 herds. Information on management practices was obtained by administering a questionnaire compiled using a modified Delphi technique. Data were analyzed using univariable and multivariable negative binomial regression. Overall, 30% of the calves in the study were shedding C. parvum oocysts, with at least one positive calf detected in 77% of herds. Within-herd prevalence ranged from 0 to 80%. Predictors significantly associated with an increased prevalence of shedding in multivariable modelling were the use of calf scour prophylaxis in cows (risk ratio [RR] 1.70, P<0.01) and calves (RR 1.38, P=0.02) and the feeding of milk replacer in the first week of life (RR 1.40, P=0.02). In contrast, the presence of concrete flooring in calf housing areas (RR 0.59, P<0.01) and the use of soap or detergent when washing calf feeding utensils (RR 0.61, P<0.01) appeared to be protective.

Calf-level risk factors for neonatal diarrhea and shedding of Cryptosporidium parvum in Ontario dairy calves.

This work was conducted to investigate calf-level factors that influence the risk of neonatal diarrhea and shedding of Cryptosporidium parvum oocysts in calves, on dairy farms in Ontario with histories of calf diarrhea or cryptosporidiosis. Fecal samples were collected weekly for 4 weeks from each of 1045 calves under 30 days of age on 11 dairy farms in south-western Ontario during the summer of 2003 and the winter of 2004. A questionnaire designed to gather information on calf-level management factors was administered on farm for each calf in the study. Samples were examined for C. parvum oocysts by microscopy, and a subset of specimens was also tested for enterotoxigenic Escherichia coli, Salmonella, bovine rotavirus and bovine coronavirus. The consistency of each sample was scored and recorded at the time of collection in order to assess the presence or absence of diarrhea. In addition, a blood sample was taken from each calf upon enrollment in the study, for assessment of maternal antibody transfer and for polymerase chain reaction testing for persistent bovine viral diarrhea virus infection. Using the GLLAMM function in Stata 9.0, multilevel regression techniques were employed to investigate associations between management practices and the risk of C. parvum shedding or diarrhea. C. parvum oocysts were detected in the feces of 78% of the 919 calves from which all four fecal samples had been collected. Furthermore, 73% of the 846 calves for which all four fecal consistency scores had been recorded were diarrheic at the time of collection of at least one sample. Significant predictors of the calf-level risk of C. parvum shedding included the use of calf diarrhea prophylaxis in pregnant cows, and the type of maternity facilities in which the calves were born. Factors associated with an increased risk of diarrhea were leaving the calf with the dam for more than an hour after birth, and the birth of a calf in the summer as opposed to winter. Calves shedding C. parvum oocysts had 5.3 (95% CI 4.4, 6.4) times the odds of diarrhea than non-shedding calves, controlling for other factors included in the final multivariable model. Furthermore, infected calves shedding more than 2.2 x 10(5) oocysts per gram of feces were more likely to scour than infected calves shedding lower numbers of oocysts (OR= 6.1, 95% CI 4.8, 7.8). The odds of diarrhea in calves shedding oocysts that had been allowed to remain with their dams for more than an hour were higher than the odds of diarrhea in shedding calves that had been separated from their dams within an hour after birth.

Clostridium difficile PCR ribotypes in calves, Canada.

We investigated Clostridium difficile in calves and the similarity between bovine and human C. difficile PCR ribotypes by conducting a case-control study of calves from 102 dairy farms in Canada. Fecal samples from 144 calves with diarrhea and 134 control calves were cultured for C. difficile and tested with an ELISA for C. difficile toxins A and B. C. difficile was isolated from 31 of 278 calves: 11 (7.6%) of 144 with diarrhea and 20 (14.9%) of 134 controls (p = 0.009). Toxins were detected in calf feces from 58 (56.8%) of 102 farms, 57 (39.6%) of 144 calves with diarrhea, and 28 (20.9%) of 134 controls (p = 0.0002). PCR ribotyping of 31 isolates showed 8 distinct patterns; 7 have been identified in humans, 2 of which have been associated with outbreaks of severe disease (PCR types 017 and 027). C. difficile may be associated with calf diarrhea, and cattle may be reservoirs of C. difficile for humans.

Multiattribute evaluation of two simple tests for the detection of Cryptosporidium parvum in calf faeces.

There is a need for simple and inexpensive diagnostic and screening tests for the detection of Cryptosporidium parvum infection in calves. A sucrose wet mount test and a lateral immunochromatography test were evaluated for epidemiological sensitivity and specificity, cost per test, simplicity, test time and ease of batching. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) of the Cryptosporidium oocyst wall protein (COWP) gene locus, with gel electrophoresis, was used as a gold standard. Cohen's kappa statistic of agreement (kappa) between the Ontario Veterinary College (OVC) sucrose wet mount test and COWP PCR-RFLP was 0.82, and the sensitivity and specificity of the OVC sucrose wet mount test were 88.6% and 93.8%, respectively. The sensitivity and specificity of the lateral immunochromatography test were 78.3% and 93.3%, respectively, and agreement between this test and PCR-RFLP was good (kappa=0.73). There was substantial agreement between the OVC sucrose wet mount test and the lateral immunochromatography test (kappa=0.84). Both tests were inexpensive and easy to use; however, the lateral immunochromatography test was faster and simpler to perform than the sucrose wet mount test, and was generally more user-friendly. These tests provide practitioners and researchers with cheap, quick and accurate methods of detecting C. parvum infection in young calves.

Prevalence of Cryptosporidium parvum infection in southwestern Ontario and its association with diarrhea in neonatal dairy calves.

Cryptosporidium parvum infection was detected in 203 (40.6%) of 500 Ontario dairy calves aged 7 to 21 d, on a convenience sample of 51 farms with a history of calf diarrhea. Within-farm prevalence ranged from 0% to 70%, and both shedding and intensity of shedding were significantly associated with diarrhea. This parasite appears to be common in Ontario dairy calves and important as a cause of dairy calf scours in the province.