<i>Cryptosporidium</i> spp. Infections in Combination with Other Enteric Pathogens in the Global Calf Population

• Main Authors: Beate Conrady, Michael Brunauer, Franz-Ferdinand Roch
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Animals
• Subjects: bovine rotavirus; bovine coronavirus; concurrent-infection; <i>Cryptosporidium</i> spp.; <i>Escherichia coli</i> F5 (K99); epidemiology
• Online Access: https://www.mdpi.com/2076-2615/11/6/1786

The most common worldwide diarrhoea-causing agents in neonatal calves are <i>Cryptosporidium</i> spp. (Crypto), bovine rotavirus (BRV), bovine coronavirus (BCoV), and enterotoxigenic <i>Escherichia coli</i> F5 (K99) (ETEC). Crypto is a zoonotic pathogen of diarrhoea in humans, particularly for children and immunocompromised adults. Four weighted-stratified random-effect meta-analyses including meta-regression analyses were performed to calculate the worldwide mean prevalence of Crypto and associated concurrent infections with BRV, BCoV and ETEC, as well as their potential influencing factors. The meta-analysis incorporated 28 studies (56 substudies) in 17 countries that determined the presence or absence of concurrent infections with Crypto in the global calf population. Approximately half of all considered studies presented here were conducted in Europe independently of the type of infections with Crypto. Within Europe, the highest estimated mean Crypto-BRV prevalence was identified in Ireland (16.7%), the highest estimated mean Crypto-BCoV prevalence was detected in the United Kingdom (4.3%), and the highest estimated mean Crypto-ETEC prevalence across the literature was determined in Turkey (4.7%). The chance of detecting BRV, BCoV, and ETEC in calves with diarrhoea was 0.8 (confidence interval (CI): 0.6–1.0), 0.7 (CI: 0.5–1.0) and 0.6 (CI: 0.4–0.9) lower in the presence of Crypto compared to calves without Crypto. This may indicate an inhibitory effect between BRV, BCoV, ETEC, and Crypto in calves. The variance in the published prevalence across the literature can mainly be explained by the “diagnostic” factor (R² min–max: 0.0–40.3%), followed by the “health status of the sampled animals” (R² min–max: 1.4–27.3%) and “geographical region” (R² min–max: 5.9–23.6%).