Management-associated herd-level risk factors for cryptosporidiosis in dairy calves in Finland

1 Introduction

Infectious diarrheal diseases are major illnesses of neonatal calves, and the most common pathogens are the protozoan parasite Cryptosporidium parvum, bovine rotavirus, bovine coronavirus, and the bacterium enterotoxigenic Escherichia coli F5 (K99) (ETEC) (Conrady et al., 2021). Calf diarrhea is a major cause of economic losses; it affects the welfare of calves and can predispose them to other diseases (Cho and Yoon, 2014; Calderon-Amor and Gallo, 2020). The main consequences on farms include reduced calf growth rates, increased mortality, higher treatment costs, and additional labor requirements.

C. parvum is a prevalent cause of diarrheal diseases in young calves, and cryptosporidiosis may cause problems, especially on dairy farms (Olson et al., 2004). Other common Cryptosporidium spp. species in cattle include C. andersoni, C. bovis, and C. ryanae, which are mainly associated with asymptomatic infections in older animals (Santin, 2020). The prevalence of Cryptosporidium is high in calves less than one month old in several countries (Smith et al., 2014; Maddox-Hyttel et al., 2006; Trotz-Williams et al., 2008). The route of infection is fecal–oral (Santin, 2020), and the infectious dose of Cryptosporidium is low; ingestion of as few as six protozoan oocysts is sufficient to cause infection (Zambriski et al., 2013). C. parvum is also a zoonotic pathogen and can cause serious intestinal infections in humans, which might require hospitalization and can even lead to death in the case of immunocompromised individuals (Kosek et al., 2001). Contact with cattle has been associated with Cryptosporidium infections in humans in many countries (Mcdaniel et al., 2014; Enbom et al., 2023; Roy et al., 2004). Farm workers and veterinarians have an occupational health risk of cryptosporidiosis as they may become infected while taking care of calves (Enbom et al., 2023). Therefore, the prevention of cryptosporidiosis on cattle farms is essential.

The emergence of clinical cryptosporidiosis is influenced by multiple host, pathogen, management, and environmental factors, including the calf’s immunological status, infection load, nutrition, management practices, and housing conditions (Trotz-Williams et al., 2007; Gulliksen et al., 2009). Several herd-level factors may predispose calves to C. parvum infection, but study results have been inconsistent (Delafosse et al., 2015; Brainard et al., 2020; Deksne et al., 2022). These risk factors include age of the calf, contacts with other calves, larger herds, organic production, various management practices, poor hygiene, the depth of the pen bedding, and other occurring pathogens. In a systematic review, Brainard et al. (2020) identified fourteen studies of which only three were considered methodologically strong and found no convincing evidence for any specific risk or protective factors. Therefore, more information on potential factors predisposing to cryptosporidiosis is needed to target management practices correctly to reduce the incidence of the disease.

C. parvum is a common endemic pathogen worldwide, and Cryptosporidium is prevalent in calves in many European countries (Maddox-Hyttel et al., 2006; Silverlås et al., 2009; Smith et al., 2014). In Finland, outbreaks in calves and humans were relatively rare until recently (Suominen et al., 2023; Finnish Food Authority, 2022). Data of laboratory-confirmed cryptosporidiosis from Finnish cattle herds show that the number of cryptosporidiosis cases has increased over fivefold in calves since 2012 (Finnish Food Authority, 2022). Since 2018, over 35% of examined herds whose samples were analyzed at the laboratory of the Finnish Food Authority have tested positive for C. parvum each year (The Zoonosis Centre, 2026). No previous studies in Finland have examined factors that may contribute to the increased occurrence of cryptosporidiosis in calves. The aim of our study was to identify factors associated with the clinical disease caused by C. parvum infection on dairy farms using a case–control study. This research paper presents information on management practices that are associated with occurrence of clinical cryptosporidiosis in calves on dairy farms, and may have implications to animal welfare and occupational hygiene of personnel working on farms.

2 Materials and methods

2.1 Study population and study design

This study was conducted among Finnish dairy herds belonging to the voluntary Centralized Cattle Health Care Register (Naseva register). In 2005, the Naseva register was developed in cooperation with Finnish dairy companies and slaughterhouses, and it is administered by Animal Health ETT, an industry-based association. The Naseva register has several interfaces to different databases, such as the Bovine Register, the Finnish Dairy Herd Recording System, databases of mastitis testing laboratories, meat inspection databases of slaughterhouses, and veterinary practice management systems. Herds in the Naseva must have a contract with a herd health veterinarian, who visits the farm regularly and has access to the farm’s production and medication data. The farmer and the farm’s veterinarian establish and implement a management plan that is updated during a specific herd health visit, which must be done at least once a year. This herd health visit includes monitoring of production data and animal movements, as well as observations of the condition, health, and behavior of different age groups of animals. Mortality in different age groups is also recorded in the database. Occurrence of signs and cases of bovine diseases, including diarrhea, is monitored and documented, and sampling is recommended when needed. The herd health veterinarian also monitors disease control measures, feed hygiene, and biosecurity. The evaluation performed by the veterinarian is documented in the Naseva register in a structured form using national predefined criteria categorized on a scale of 1, 2, or 3 (e.g., 1 = good practice, 2 = satisfactory practice, 3 = insufficient practice) or not applicable. National guidelines have been developed for farms to manage various diseases, including farm-level control of cryptosporidiosis. On January 31, 2019, 85% of Finnish dairy farms and 92% of cows were in the Naseva register.

We conducted a case–control study to investigate potential risk factors for clinical cryptosporidiosis on farms. The study population comprised of Finnish dairy herds with at least 50 cows belonging to the Naseva register. The inclusion and exclusion criteria for case and control farms are presented in Figure 1.

A case farm was defined as a dairy farm with a calf diarrhea outbreak caused by laboratory confirmed C. parvum within the last two years. Farms with calf diarrhea outbreaks can send diagnostic samples from calves aged 0–5 weeks to the laboratory of the Finnish Food Authority for the detection of possible causative bacterial, parasitic or viral agents. Fecal samples were stained with the modified Ziehl–Neelsen stain for Cryptosporidium oocyst microscopy. The presence of C. parvum was confirmed by real time PCR. DNA was extracted using QIAamp Mini Kit (Qiagen, Hilden, German) according to the manufacturer’s protocol for tissue with overnight proteinase K treatment. The presence of C. parvum in samples was examined using real-time PCR and primers targeting the rRNA gene of C. parvum (CFX96 Touch Real-Time PCR Detection System, Bio-Rad Laboratories, CA, USA) as described previously (Burnet et al., 2013; Jothikumar et al., 2008). Commercially available plasmid pUC19 was used as an internal amplification control according to Fricker et al. (2007).

Farms that had a laboratory confirmed C. parvum calf diarrhea outbreak were contacted and invited to participate in this study.

A control farm was defined as a dairy farm without any calf diarrhea outbreaks including cryptosporidiosis outbreaks recorded in the most recent Naseva herd health visit (conducted within the past year). Status of calf health was checked from veterinarian’s annual health report from the Naseva register, and farms with no calf diarrhea or only sporadic cases of calf diarrhea were eligible to participate in the study. Control farms were not sampled for presence of C. parvum, because those farms had not had any calf diarrhea outbreaks. A total of 400 farms were randomly selected using the random number function in Excel and invited to participate in the study. To facilitate contact with the farms, the farms were selected within the biggest Finnish dairy company, which processes approximately 80% of milk produced in Finland.

The OpenEpi calculator for an unmatched case–control study (https://www.openepi.com/SampleSize/SSCC.htm) was used to determine the sample size to detect significant differences between case and control farms. The expected difference in the proportions of any management procedure or other feature between the case and control groups was set to 20% (40% vs. 60% in the calculations), and the control–case ratio was set at 2:1, the statistical power at 80%, and the confidence level at 95%. The calculated target sample size was 74 case farms and 147 control farms (and the least extreme odds ratio to be detected using this sample size was 2.25).

2.2 Data collection

Data on the study farms originated from several sources, and the participating farmers gave their informed consent to use their data in the study. The laboratory information management system of the Finnish Food Authority provided the information on C. parvum findings. Data on the number of animals on two separate dates with a 3-yr interval and on cattle transfers were obtained from the Bovine Register (Finnish Food Authority), herd health data from the Naseva register (ETT ry), and farm production data from the Finnish Dairy Herd Recording System operated by the Agricultural Data Processing Centre (ProAgria). Table 1 summarizes the data and their sources.

Table 1. Description of the data used to evaluate management-associated risk factors for calf diarrhea caused by Cryptosporidium parvum in dairy calves.

Number of cattle aged >24 mo at the time of the C. parvum outbreak (case farms) or survey completion (control farms).

Change in the number of cattle >24 mo of age in the three-year interval ending in the C. parvum outbreak (case farms) or survey (control farms).

During the year of the C. parvum outbreak (case farms) or year of the survey (control farms).

Information at the time of the C. parvum outbreak (case farms) or survey (control farms).

Water and feeding practices, general health, homogeneity of calf groups, air ventilation and temperature, space per animal, and bedding comfort evaluated by the herd health veterinarian in the Naseva register on a scale 1= good practice, 2= satisfactory practice, 3= insufficient practice.

Data on farm-level risk factors were collected with an online questionnaire with the Webropol 3.0 survey and reporting tool (Webropol Ltd, Helsinki, Finland) or via telephone. Telephone interview responses (n = 15) were entered into Webropol during the interviews. A structured questionnaire was developed to gather information about the workload of daily management, the disease status of the herd, and previous events on the farm that could have impacted, for example, the time spent on animal care. Information on calf management practices such as calving conditions, colostrum intake, and calf pens was also assessed. The questionnaire included a total of 51 questions. Content validation of the questionnaire was conducted through expert assessment of the clarity and relevance of the questions, complemented by pilot testing on five farms, two of which had experienced a C. parvum outbreak among calves. Subsequently, the questionnaire was modified based on the comments from the pilot respondents and experts, as well as observations made by the researchers. The questionnaire gathered data from the case farms concerning the time of the outbreak of C. parvum and from the control farms at the time of responding to the questionnaire. The data collection was conducted and the case and control farms completed the questionnaire between November 2019 and May 2020. Recall bias was possible because case farms answered with reference to the time when C. parvum was detected on the farm, whereas control farms responded according to the situation at the time of completing the questionnaire. The questionnaire is available on request in Finnish or Swedish from the first author.

2.3 Statistical analysis

We first calculated basic descriptive statistics on herd characteristics and the frequency of potential risk factors for cryptosporidiosis. We analyzed potential risk factors in calves using univariable analysis followed by multivariable logistic regression analysis. Variables with P < 0.2 in the univariable analysis were selected for inclusion in the multivariable logistic regression analysis. One commonly used method for reducing the number of predictor variables is to include in subsequent multivariable analyses only those variables that demonstrate a statistically significant unconditional association with the outcome in univariable screening, based on liberal significance criteria (e.g., P < 0.15 or P < 0.20) (Dohoo et al., 2014). The outcome of interest was a farm-level diagnosis of clinical cryptosporidiosis outbreak in calves (yes/no), in other words, a herd status as a case or a control farm. The associations between the outcome and potential farm-level risk factors were first assessed with univariable screening using the Mann–Whitney U-test for continuous variables or Fisher’s exact test for dichotomous and categorical variables with more than two levels.

Three variables with over five missing values were excluded from further analyses. The ​​responses were obtained from 77 case farms and 152 control farms. Thus, the total number of observations ​​was for 229 farms if the data for each farm were available. Variables with P < 0.2 in the univariable screening were assessed for collinearity with linear regression analysis using the variance inflation factor (VIF’) according to Shrestha (2020). No highly correlated variables were detected, as VIF was under 5 for every variable. Variables with P < 0.2 in the univariable analysis were first included in the multivariable logistic regression analysis simultaneously. Subsequently, a manual backward elimination of non-significant (P > 0.05) variables was performed with an examination of the results of the Wald test for individual variables, first dropping variables with the largest P-value, one at a time. Variables with P < 0.05 were considered significant and included in the final model. Biologically meaningful interactions were tested and kept in the final model if P < 0.05. No significant interactions were found. Statistical analyses were performed using IBM^® SPSS Statistics software Version 27 (27.0.1.0) (IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp).

3 Results

3.1 Descriptive statistics and risk factors

Altogether, 90 case and 175 control farms completed the questionnaire: the response rates were 52% (90/173) and 44% (175/400), respectively. From the analysis, 13 case farms were excluded because C. parvum had been diagnosed for the first time on the farm prior to 2017 and the survey was conducted between November 2019 and May 2020. This exclusion was made because the respondents would not have necessarily remembered the events at the time when C. parvum was first detected on the farm. Based on the inclusion and exclusion criteria in Figure 1, a total of 77 case farms and 152 control farms were included in the final analysis.

The median herd size was 93 cows on the case farms and 69 cows on the control farms (Table 2). Cattle transfers were more common on the case farms than on the control farms. During the three-year study period, the number of animals had increased considerably, by 41–180 cows, on 16.9% (13/77) of the case farms. On the control farms, only 5.3% (8/152) had a corresponding increase in the number of animals. On 69.7% of the control farms, the number of animals had remained relatively stable, increasing or decreasing by a maximum of 10 cows, and the corresponding proportion of case farms was 59.7%.

Table 2. Basic descriptive data for case and control farms, using Fisher’s exact test for categorical variables and the Mann–Whitney U-test for continuous variables when comparing case and control farms.

Number of cattle >24 mo of age at the time of the Cryptosporidium parvum outbreak (case farms) or survey (control farms).

Change in the number of cattle >24 mo of age in the three-year interval ending in the C. parvum outbreak (case farms) or survey (control farms).

No. of farms from which animals were purchased or obtained over a three-year period.

Results of dairy cattle production monitoring in 2019 from the Finnish Dairy Herd Recording System (ProAgria).

Missing data: 2 case farms, 10 control farms.

In the initial univariable screening, several variables were associated with clinical cryptosporidiosis (Table 2, 3). Results of the multivariable logistic regression analysis (Table 4) were obtained from 76 case farms and 150 control farms; one case farm and two control farms had some missing data and were thus omitted from the analysis. These results demonstrated that a herd size of more than 100 animals over 24 months of age (OR 3.4), cattle transfers from two or more herds (OR 3.0) or more than five transfers (OR 7.7), insufficient time for calf care (OR 45.2), and five or more individuals involved in daily cattle work (OR 8.3) increased the odds of being a case farm. Although not separating sick calves from healthy ones was not significantly associated with case farm status (adjusted OR = 3.1, 95% CI 0.97–9.83; P = 0.056) compared to always separating them, the practice still indicated a tendency toward clearly higher odds of being a case farm. Diarrhea in adult cattle in the herd reported by a farmer, more resources (6–10 or more than 10 persons) in cattle work during the preceding year, and if a respondent had experienced a stressful event over the previous year were observed to be associated with lower odds of being a case farm. The logistic regression model showed relatively strong explanatory power, with a Nagelkerke R² value of 0.46. The Hosmer–Lemeshow test yielded a P−value of 0.294, which is not statistically significant (P > 0.05), indicating that the model demonstrates good fit.

Table 3. Descriptive data on risk factors for cryptosporidiosis on case and control farms.

P-values are from univariable analysis comparing case and control farms using Fisher’s exact test.

Table 4. Results from a multivariable logistic regression model for the odds of being a case farm with cryptosporidiosis outbreak in calves.

The results include 76 case farms and 150 control farms.

β = estimated coefficient.

OR = odds ratio.

CI = confidence interval.

No. of cattle aged >24 mo.

No. of farms from which animals were purchased or obtained over a three-year period.

Has the person responsible for taking care of calves been affected by any stressful event over the past year.

ref. = reference.

* = P < 0.05.

4 Discussion

The study identified several factors associated with farms with clinical cryptosporidiosis in calves. These were a large herd size, more cattle transfers between farms, no separation of sick calves from healthy ones, many individuals involved in daily cattle work, and insufficient time for calf care. The odds of being a case farm decreased with increasing number of persons involved in cattle work during the preceding year. The odds were also lower if adult cattle in the farm had had diarrhea and if the respondents had experienced a stressful event during the previous year compared to a situation if they had not. However, these findings may reflect reporting or time frame differences, or residual confounding rather than true protective effects. Notably, data from the case farms were collected in relation to the time of the outbreak, whereas control farms reported the situation at the time of the survey. This difference in the time frame used for data collection may have affected the comparability of several management-related variables, such as time allocated to calf care, the respondent’s experience of a stressful event, diarrhea in adult cattle, and labor availability.

The control farms were not tested for C. parvum and therefore, they may have included farms with subclinical or unrecognized infections. Consequently, the findings of this study should be interpreted as factors associated with clinical, laboratory-confirmed outbreaks on dairy farms rather than with C. parvum infection more broadly. The inclusion criterion for farms in this study was a herd size of more than 50 dairy cows, indicating that the participating farms were slightly larger than the average Finnish dairy farm, as shown in the 2019 dairy production monitoring data from the Finnish Dairy Herd Recording System (Table 2). Nevertheless, the farms constitute a representative cross-section of the Finnish dairy population, and the results are likely generalizable to farms beyond the study population.

The finding that a herd size of more than 100 cows was associated with the clinical cryptosporidiosis compared to a herd size of less than 100 cows is consistent with previous reports. A herd size of more than 100 adult animals (Duranti et al., 2009), 500 cows (Urie et al., 2018), or 200 animals including all ages (Szonyi et al., 2012) has been found to increase the risk of C. parvum infection. Larger herds have a larger number of susceptible young calves, which may become infected with C. parvum, increasing the environmental contamination of the calf premises (Urie et al., 2018; Duranti et al., 2009) and leading to a chain of infection. Furthermore, management factors, which likely vary with herd size, are associated with the prevalence of Cryptosporidium (Urie et al., 2018). A large herd size is likely to be associated with many people involved in daily cattle work. Based on the univariable analyses, we also found that the number of cows had increased considerably during the three-year study period on many of the case farms. This is likely due to animal additions. Bringing new animals onto the farm is a biosecurity concern and may pose a risk of introducing more diseases to the farm. Many factors might influence the association between herd size and animal health and welfare, such as herd expansion, facilities, management, the training and experience of farm workers, and the ratio of workers to the number of animals (Barkema et al., 2015).

Cattle transfers were associated with the occurrence of clinical cryptosporidiosis. Expanding the herd by purchasing cattle, sending heifer calves to be raised on another farm, and reintroducing the farm’s own cattle upon returning, for example, from another farm, agricultural shows, common pastures, or contract rearing facilities are potential routes for the transfer of infectious diseases from one farm to another, and hence create a high biosecurity risk (Mee et al., 2012). However, import of cattle to Finland from other countries is nearly non-existent, contacts between animals from different farms are limited as common pastures and cattle shows are rare, and public livestock markets and auctions are non-existent (Autio et al., 2021). External biosecurity includes all measures to prevent pathogen entry to the farm, and an effective way to prevent the risk of infection is to maintain a closed herd without introducing new animals (Mee et al., 2012; Damiaans et al., 2020).

Even though our study did not demonstrate unambiguous significant associations between case farm status and separation of sick calves from healthy ones, this practice can be an important management procedure in preventing the spread of diseases with a herd. Internal farm biosecurity includes measures to prevent the spread of pathogens within the farm, and the separation of sick animals from healthy ones is crucial to prevent the spread of infection (Damiaans et al., 2020). In addition, hygiene measures, such as a dry and clean environment, are generally important components in disease control (Santin, 2020). The control of cryptosporidiosis is difficult, because oocysts are resistant to many disinfectants (Delling et al., 2017) and are well preserved, especially in humid conditions (Ryan et al., 2014). A calf can obtain oocysts from a contaminated pen, dirty water and feeding containers, or by licking other calves (Delafosse et al., 2015) or human clothing contaminated with manure (Smith et al., 2014). Silverlås et al. (2009) observed a higher prevalence of Cryptosporidium spp. in dairy herds using a continuous or a mixed system for moving young stock compared to an all-in/all-out system. This suggests that the spread of Cryptosporidium can at least partially be controlled by barriers. Hospital pens for diseased animals should be separate from healthy animals to prevent disease transmission (Gorden and Plummer, 2010), and it is advisable to have hospital pens or individual hutches in a separate area for sick calves. Additionally, efficient cleaning and disinfection procedures can help limit the spread of diseases on a farm. Investments in the design of calf premises should be made as a part of barn design to make farm solutions functional for disease control, considering that enough pens for calves exist, calf facilities are easy to clean, and the pen structures can be dried.

In the current study, farmer’s perceptions regarding inadequate time being allocated to caring for calves was also associated with case farm status. Therefore, sufficient labor and time should be reserved for calf care. However, it is important to note that the confidence intervals for the ‘no, not enough time for calf care’ category were wide, indicating substantial uncertainty around the estimate. Interestingly, 5 or more individuals being involved in daily cattle work compared to 1–2 people caring for cattle daily was associated with an increased odds of clinical cryptosporidiosis, albeit association was not statistically significant. However, having a greater number of different people (e.g., family members, substitute workers, trainees) working on a farm during the previous year, was associated with decreased odds of being a case farm. This may reflect the fact that the control farms reported more labor compared to the case farms, which may have reduced the workload per person on the farm. A higher proportion of respondents from the control than from the case farms reported that they had experienced some stressful event in the past year. This might have affected their use of time or resources. The questionnaire did not specify details about which stressful event(s) the respondent had experienced.

Diarrhea in adult cattle was negatively associated with the odds of being a case farm in our multivariable model. The control farms reported significantly more often diarrhea in adult cattle compared to the case farms. This was most likely related to the time of the survey, since epidemics of bovine coronavirus, both winter dysentery and respiratory disease, were commonly observed throughout Finland in the winter of 2019–2020 (Finnish Food Authority, 2022). That was the time when the control farms responded to the questionnaire. The case farms, instead, responded to the survey concerning the time when cryptosporidiosis had emerged in their herd, and for a large number of them it was before the winter of 2019–2020. Maybe winter dysentery had not occurred during the time period that the questionnaire concerned, or the respondents had forgotten about the diarrhea cases in adult cattle. Another possible hypothesis is that the control farms may be better at recognizing and treating sick animals, which could contribute to their greater effectiveness in limiting the spread of cryptosporidiosis among calves.

5 Limitations of the study

Recall bias is possible in case–control studies (Coughlin, 1990). In this study, this bias is possible because the case farms responded to the questionnaire regarding the situation at the time of the C. parvum outbreak whereas the control farms answered according to the situation at the time of the survey. According to the survey results, the respondents from the control farms reported that they had experienced some stressful events more frequently than those on the case farms. The difference in timing may have affected how well the respondents remembered stressful events and may have caused some recall bias. The responses relating to different times from the case and control farms probably also influenced the fact that diarrhea in adult cattle was found as a protective factor in the study. Ideally, the time frame for answering the questionnaire would have been consistent for both case and control farms, but in order to include a sufficient number of case farms, the retrospective time period had to be longer. The control group was asked to answer the questionnaire based on the situation at the time of responding and on events from the previous year in order to decrease recall bias. Thirteen case farms in which cryptosporidiosis had been diagnosed prior to 2017 were excluded from the study to prevent substantial differential timing of exposure assessment between cases and controls from affecting the results.

Due to voluntary participation in the survey, the possibility of selection bias cannot be excluded, which may limit the generalizability of the results to the entire Finnish cattle population. Nevertheless, we believe that our study population was quite representative of the Finnish dairy herds at the time of the study. The average milk yield in the study herds was similar to that in Finnish dairy population in general and it was very similar between case and control farms. The case farms, however, were larger and had a lower average parity than the control farms. Voluntary participation may have affected this.

Non-response bias is possible due to voluntary participation in the study, with the questionnaire response rate being 52% for the case farms and 44% for the control farms. Information bias and social desirability bias may also have occurred because farmers self-reported their responses by answering the questionnaire. Several variables, including the adequacy of time allocated to calf care, were based on farmers’ subjective assessments and may therefore represent imperfect or heterogeneous measures across farms. As evaluations of time use are inherently subjective, individual differences in perception, recall, and interpretation may have resulted in inconsistency in how respondents assessed the time demands of calf care.

Data of herd health visits documented in the Naseva register are based on the evaluations made by numerous herd health veterinarians. Therefore, there may be some variation in how different veterinarians evaluate farms against the national pre-defined criteria. However, this potential bias is likely to influence the data of both case and control farms in a similar way. Such non-differential information bias typically biases the results toward the null, thereby reducing the apparent effects of the factors under this study.

6 Conclusion

Cryptosporidiosis caused by C. parvum in calves is associated with a larger herd size, and the risk factors are management-related. Careful attention should be paid to external and internal biosecurity of dairy farms. Hospital pens in the barn indicate a potential role in reducing transmission of the infection by allowing separation of sick and healthy calves. Caring for calves requires labor and time, and the occurrence of cryptosporidiosis may be reduced by investing in calf care and risk management. Investing in calf management and health, as well as controlling factors associated with cryptosporidiosis in calves, may help reduce human cases.

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Footnotes

Edited by: Corinne Ong, Toronto Metropolitan University, Canada

Reviewed by: Daniel Esonu, Ahmadu Bello University, Nigeria

Wyckliff Ngetich, Egerton University, Kenya