Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 https://doi.org/10.1186/s13071‑019‑3704‑4 REVIEW Cryptosporidium infections in terrestrial ungulates with focus on livestock: a systematic review and meta‑analysis Kareem Hatam‑Nahavandi1, Ehsan Ahmadpour2* , David Carmena3, Adel Spotin4,5, Berit Bangoura6 and Lihua Xiao7* Abstract Background: Cryptosporidium spp. are causative agents of gastrointestinal diseases in a wide variety of vertebrate hosts. Mortality resulting from the disease is low in livestock, although severe cryptosporidiosis has been associated with fatality in young animals. Methods: The goal of this systematic review and meta‑analysis was to review the prevalence and molecular data on Cryptosporidium infections in selected terrestrial domestic and wild ungulates of the families Bovidae (bison, buffalo, cattle, goat, impala, mouflon sheep, sheep, yak), Cervidae (red deer, roe deer, white‑tailed deer), Camelidae (alpaca, camel), Suidae (boar, pig), Giraffidae (giraffes) and Equidae (horses). Data collection was carried out using PubMed, Scopus, Science Direct and Cochran databases, with 429 papers being included in this systematic analysis. Results: The results show that overall 18.9% of ungulates from the investigated species were infected with Crypto- sporidium spp. Considering livestock species (cattle, sheep, goats, pigs, horses and buffaloes), analysis revealed higher Cryptosporidium infection prevalence in ungulates of the Cetartiodactyla than in those of the Perissodactyla, with cattle (29%) being the most commonly infected farm animal. Conclusions: Overall, the investigated domestic ungulates are considered potential sources of Cryptosporidium con‑ tamination in the environment. Control measures should be developed to reduce the occurrence of Cryptosporidium infection in these animals. Furthermore, literature on wild populations of the named ungulate species revealed a widespread presence and potential reservoir function of wildlife. Keywords: Cryptosporidiosis, Livestock, Cattle, Sheep, Goat, Pig, Horse, Wildlife © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Background Cryptosporidium, the causative agent of cryptosporidi- osis, is an ubiquitous protozoan parasite. It causes gas- trointestinal disease in a wide variety of vertebrate hosts, including ungulates of the orders Artiodactyla and Perissodactyla, as well as humans. Several Crypto- sporidium species are known to be zoonotic with ani- mals as major reservoirs [1]. In resource-limited settings, cryptosporidiosis is a leading cause of diarrhoeal death in children younger than five years across the globe, only second to rotaviral enteritis [2]. Cryptosporidiosis is also a significant contributor to health care cost in developed countries. It is estimated that in the USA 748,000 cases of human cryptosporidiosis occur annually [3]. Resi- dents of and travelers to developing countries may be at greater risk of infection due to poor water treatment and food sanitation [4, 5]. Cryptosporidiosis typically induces self-limiting diarrhea in immunocompetent individu- als, but the infection can be severe and life-threatening in immunocompromised subjects [6]. It is one of the most important diseases in young ruminants, espe- cially neonatal calves [7, 8]. The clinical presentation of Open Access Parasites & Vectors *Correspondence: ehsanahmadpour@gmail.com; lxiao1961@gmail.com 2 Infectious and Tropical Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran 7 College of Veterinary Medicine, South China Agricultural University, Guangzhou, China Full list of author information is available at the end of the article Page 2 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 cryptosporidiosis varies from asymptomatic to deadly, leading to important economic losses due to growth retardation, reduced productivity and mortality [9, 10]. Considering that an infected bovine calf can shed up to 1.1 × 108 oocysts per gram of feces at the peak of the infection, cattle (and very likely wild ruminants) are sig- nificant contributors of environmental Cryptosporidium oocysts [11, 12], causing water-borne [13–15] and food- borne [16, 17] diarrhea outbreaks in humans worldwide. The worldwide annual excretion of Cryptosporidium spp. oocysts by livestock has been calculated to be 3.2 × 1023 [18], with cattle being the host species causing most environmental contamination. Cattle are able to carry different species including C. hominis which implies an associated significant public health risk [19]. In addition, Cryptosporidium oocysts are infective at the time they are passed in feces and are highly resilient to a wide range of environmental factors including disinfection and water treatment processes. Moreover, low infection doses are sufficient to cause disease in suitable hosts, e.g. 10‒100 oocysts are described to provoke diarrhea in humans [20, 21]. Over the past few decades, a major subject of debate and controversy in the epidemiology of Cryptosporidium is whether, and to what extent, domestic and wildlife species may act as natural reservoirs of human crypto- sporidiosis [22, 23]. This is principally due to the fact that the genus Cryptosporidium encompasses nearly 40 valid species with marked differences in host range, among which over 10 (mainly C. hominis, C. parvum and C. meleagridis) have been reported in humans [24] with a variety of genotypes being zoonotic [1, 22, 25]. The pub- lic health significance of animal cryptosporidiosis varies greatly depending on factors such as geographical vari- ation in prevalence and genotype distribution, seasonal- ity, load of environmental contamination with oocysts and access to surface waters intended for human con- sumption or recreation [9, 26]. In particular, genotyping data from epidemiological surveys conducted globally indicate that infected calves are the major reservoir for zoonotic C. parvum in many areas [26, 27], with lambs, kids and foals being potential additional sources of C. parvum infection for humans in some areas of the world [28–31]. Pigs are only sporadically infected with zoonotic Cryptosporidium species and are therefore considered minor contributors to the zoonotic transmission of cryptosporidiosis in humans [32]. Adult livestock typi- cally harbor low level and asymptomatic infections but are epidemiologically important as cryptic carriers of the parasite, enabling re-infections at the herd level. Little is known of the molecular epidemiology and transmission cycles of cryptosporidiosis in wild ungulates. However, recent surveys have revealed the presence of C. parvum in wild hoofed species including the American mustang (Equus ferus caballus) [33], Scottish roe deer (Capreolus capreolus) and red deer (Cervus elaphus) [34], and Span- ish wild boars (Sus scrofa scrofa) [35], which may repre- sent a threat to water quality and public health [34]. In the present study, we conducted a systematic review of publications on the prevalence of Cryptosporidium infections and Cryptosporidium species distribution in domestic and wild ungulates in order to ascertain the extent to which hoofed animals should be considered as relevant reservoirs of human infection. Methods Search strategy To evaluate the prevalence of Cryptosporidium infec- tion in hoofed animals, we performed a comprehensive review of literatures (full text or abstracts) published online. English databases including PubMed, Scopus, Sci- ence Direct and Cochran were searched for publications related to Cryptosporidium infection of animals world- wide, from 1984 to 2016. We used the following MeSH terms alone or in combination: “Cryptosporidium” or “cryptosporidiosis” and “prevalence” and “livestock” or “cattle” or “buffaloes” or “sheep” or “pigs” or “camels” or “alpacas” or “horses” or “ruminants” or “wildlife”. To iden- tify additional published articles, we used the PubMed option of “related articles” and checked the reference lists of the original and review articles. The more agri- cultural and veterinary focused database CAB abstracts was searched using the following search terms: “Crypto- sporidium” or “cryptosporidiosis” and “prevalence” and “cattle” or “cows” or “calves” or “buffaloes” or “sheep” or “lambs” or “goats” or “kids” or “camels” or “alpacas” or “crias” or “llamas” or “pigs” or “piglets” or “horses” or “foals” or “deer” or “fawns” or “farm animals” or “rumi- nants” or “livestock” or “wildlife”. A protocol for the liter- ature review was devised (Fig. 1) in accordance with the PRISMA guidelines [36] (Additional file 1: Table S1). Inclusion and exclusion criteria As part of the eligibility for inclusion, titles that suggested the topic Cryptosporidium in domestic and wild hoofed animals were selected. The abstracts from the selected reference titles were reviewed by two independent reviewers to determine if the studies met the inclusion criteria and, if so, the entire articles were reviewed in full. If more than one report was published from the same study, only one was included. Exclusion criteria included studies only on human cryptosporidiosis or case reports. Studies on epidemiology of Cryptosporidium spp. in groups unrelated to hoofed animals, or studies present- ing overall prevalence estimates, where samples were collected from the ground, and data from each animal Page 3 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 were not independently retrievable, were also excluded. The language of data collection was limited to English. In order to provide contemporaneous and representative estimates, studies were excluded if they presented data collected prior to 1984. On several occasions, we con- tacted the authors for the collection of raw data. Data extraction and tabulation A data extraction form was used to collect the follow- ing data from each study: first author, year of publica- tion, location of study, period of study, host species, age range, clinical signs (diarrhoeic versus non-diarrhoeic), population nature (e.g. domestic, captive or wild), total number of fecal samples, utilized detection method (conventional microscopy, CM; immunofluorescence antibody test, IFA; enzyme-linked immunosorbent assay, ELISA; immunochromatographic test, ICT; quantitative latex agglutination, QLAT; and polymerase chain reaction, PCR), number of Cryptosporidium-pos- itive samples and identity of Cryptosporidium species and genotypes. Retrieving sequences and phylogenetic analyses To examine the genetic relationships among Crypto- sporidium spp. (C. hominis, C. felis, C. parvum, C. erinacei, C. xiaoi, C. ryanae, C. scrofarum, C. muris, C. andersoni, C. ubiquitum, C. bovis and C. suis) in ungulates, a phylogenetic tree was constructed using the program Splits Tree v.4.0 based on the Neighbor- Net method and Median-Joining analysis of sequences Fig. 1 Flow diagram describing the paper selection process according to PRISMA guidelines Page 4 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 of the 18S rRNA gene [37]. For this purpose, the sequences of the 18S rRNA gene of these Cryptosporid- ium spp. were retrieved from the GenBank database in the FASTA format. These sequences were initially obtained from various herbivores, including cattle, buf- faloes, yaks, camels, goats, sheep and deer, as well as pigs. Meta‑analysis A meta-analysis was performed for studies describ- ing Cryptosporidium infection prevalence in domestic animals that are common in many parts of the world, i.e. cattle, sheep, goats, buffaloes, horses and pigs. This analysis was performed to enhance knowledge on the potential role of livestock in zoonotic Cryptosporidium Table 1 Summarized Cryptosporidium prevalence data for major domestic farmed animals. Data for wild populations of the given species not included (see for full datasets and other host species in Additional file 2: Table S2) a Multiple studies revealed the same prevalence data Abbreviation: ns, not stated Host species Region No. of studies Utilized diagnostic methods Retrieved minimum prevalence (%) Retrieved maximum prevalence (%) Buffalo (Bubalus bubalis) Africa 6 CM, PCR 1.3 (CM) 52.0 (CM) Asia 16 CM, ICT, PCR 3.6 (CM) 50.0 (CM) Australia 2 PCR 13.1 (PCR) 30.0 (PCR) Europe 1 ELISA 14.7 (ELISA) South America 2 CM, PCR 9.4 (CM) 48.2 (PCR) Cattle (Bos taurus) Africa 29 CM, ELISA, PCR 0.5 (CM) 86.7 (CM) Asia 74 CM, ICT, IFA, PCR 1.5 (CM) 93.0 (CM) Australia 7 CM, IFA, PCR 3.6 (IFA) 73.5 (PCR) Europe 60 CM, ELISA, ICT, IFA, PCR, QLAT 0.0 (CM) 71.7 (CM) New Zealand 5 CM, IFA 2.6 (IFA) 21.2 (CM) North America 29 CM, IFA, PCR 1.1 (IFA) 78.0 (CM) South America 11 CM, ICT, PCR 3.0 (CM) 56.1 (CM) Goat (Capra hircus) Africa 10 CM, ELISA 0.0 (CM) 76.5 (ELISA) Asia 15 CM, ICT, IFA 0.0 (IFA) 42.9 (CM) Australia 1 PCR 4.4 (PCR) Europe 22 CM, ELISA, IFA 0.0 (CM) 93.0 (IFA) North America 3 CM 20.0 (CM) 72.5 (CM) South America 3 CM 4.8 (CM) 100 (CM) Sheep (Ovis aries) Africa 10 CM, ELISA, PCR 1.3 (CM) 41.8 (ELISA) Asia 17 CM, ELISA, ICT, PCR 1.8 (CM) 66.6 (CM) Australia 7 PCR 2.2 (PCR) 81.3 (PCR) Europe 22 CM, IFA, ELISA 1.4 (CM) 100.0 (CM) North America 9 CM, IFA, PCR 20.0 (CM) 77.4 (PCR) South America 5 CM, PCR 0.0 (CM) 25.0 (PCR) Pig (Sus scrofa) Africa 5 CM, ELISA, IFA, PCR 13.6 (CM) 44.9 (ELISA) Asia 13 CM, IFA, PCR 0.4 (IFA) 55.8 (PCR) Australia 3 CM, PCR 0.3 (CM) 22.1 (PCR) Europe 13 CM, IFA, PCR 0.1 (CM) 40.9 (IFA) North America 6 CM, IFA 2.8 (ns) 19.6 (CM) South America 3 CM, PCR 0.0 (CM) 2.2 (PCR) Horse (Equus caballus) Africa 3 CM, PCR 0.0 (CM) 2.9 (PCR) Asia 7 CM, PCR 2.7 (PCR) 37.0 (CM) Europe 10 CM, ELISA, IFA, PCR 3.4 (PCR) 25.0 (IFA) New Zealand 2 CM 18.0 (CM) 83.3 (CM) North America 6 CM, IFA, PCR 0.0 (IFA/PCRa) 17.0 (IFA) South America 7 CM 0.0 (CM) 100.0 (CM) Page 5 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Ta bl e 2 St at is tic al a na ly si s of C ry pt os po rid iu m in fe ct io n pr ev al en ce in d om es tic u ng ul at es u si ng C M a nd P C R m et ho ds M et ho d/ ho st C M PC R Po ol ed (% ) O R (9 5% C I) H et er og en ei ty Pu bl ic at io n bi as Po ol ed (% ) O R (9 5% C I) H et er og en ei ty Pu bl ic at io n bi as Q s ta tis tic df I2 (% ) Eg ge r b ia s (P -v al ue ) Q s ta tis tic df I2 (% ) Eg ge r b ia s (P -v al ue ) Ca tt le 22 .5 19 .6 –2 5. 6 11 ,0 38 .9 12 7 98 .8 10 .5 1 (P < 0 .0 00 1) 29 .1 23 .1 –3 5. 6 15 91 .1 34 97 .9 11 .5 2 (P < 0 .0 00 1) Sh ee p 20 .7 15 .2 –2 6. 8 13 91 .9 30 97 .8 6. 77 (P = 0 .0 08 6) 24 .4 16 .4 –3 3. 4 91 6. 7 14 98 .5 8. 18 (P = 0 .0 14 ) G oa t 18 .7 12 .3 6– 26 .2 18 52 .1 28 98 .5 9. 01 (P = 0 .0 00 4) 8. 2 3. 7– 14 .3 11 .2 2 82 .2 – Pi g 15 .5 10 .5 –2 1. 3 15 45 .4 21 98 .6 12 .4 2 (P = 0 .0 48 5) 22 .6 13 .7 –3 3. 0 99 .8 5 95 .0 2. 36 (P = 0 .6 45 2) H or se 13 .8 6. 6– 22 .9 62 1. 6 16 97 .4 6. 71 (P = 0 .0 00 2) 4. 7 2. 0– 8. 4 22 .5 4 82 .3 3. 67 (P = 0 .0 45 2) Bu ffa lo 18 .6 11 .1 –2 7. 4 99 1. 4 17 98 .3 8. 76 (P = 0 .0 00 4) 26 .0 12 .2 –4 2. 8 15 2. 4 4 97 .4 9. 28 (P = 0 .1 43 4) Page 6 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Fig. 2 Forest plot of prevalence of Cryptosporidium spp. infection in cattle using molecular methods (first author, year and country) Page 7 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 transmission since these animals feature a close con- tact to humans. The pooled prevalence of Cryptosporid- ium infection as well as its 95% confidence interval (CI) was calculated for each study. A forest plot was gener- ated to display the summarized results and heterogene- ity among the included studies. To ensure comparable sensitivity of tests used in analyzed studies, only results from studies based on PCR as a diagnostic method were included. Studies using PCR methods only for molecu- lar Cryptosporidium species/genotype identification but utilizing alternative diagnostic methods to determine prevalence were not included. The heterogeneity was expected in advance and statistical analyses including I2 and Cochrane’s Q test (with a significance level of P < 0.1) were used to quantify these variations. The meta-analy- sis considering the random effects model [38] was per- formed using the Stats Direct statistical software (http:// www.stats direc t.com). Results The initial database search retrieved 14,970 publications. The screening of these records enabled us to exclude 14,456 studies due to not meeting the inclusion criteria. Altogether, 514 studies were retained for further investi- gation. During the secondary assessment of these papers, another 85 were excluded because of one of the follow- ing reasons: other host species including wild hoofed animals; report of the same results as another paper pub- lished by the same author; and language of publication (e.g. Chinese, Spanish, etc.). Papers evaluating crypto- sporidiosis in camels, yaks, donkeys, alpacas and llamas were excluded in the secondary analysis of data, as the meta-analysis focused on Cryptosporidium infection in cattle, sheep, goats, pigs, buffaloes and horses. Eventually, 429 studies which evaluated Cryptosporidium infection during three decades met our eligibility criteria and were retained for analysis (Fig. 1). Different diagnostic procedures were used for the detection of Cryptosporidium oocysts to a varying extent in the different studies. The included publications fea- tured CM examination (n = 371), IFA (n = 107), ELISA (n = 25), ICT (n = 9), quantitative latex agglutination (QLAT) (n = 1) and polymerase chain reaction (PCR) (n = 99) (Additional file 2: Table S2). In total, 196,638 stool samples from Artiodactyla and Perissodactyla ungulates were evaluated, of which 37,206 (18.9%) subjects were positive for Cryptosporidium infec- tion. Among the 196,638 stool samples, 90,744 were associated with the domestic hoofed animals (includ- ing camels, yaks, donkeys, alpacas and llamas), display- ing a Cryptosporidium infection prevalence of 13.6% (n = 12,377) (Table 1 and Additional file 2: Table S2). All subsequent analyses included only the studies that focused on Cryptosporidium infection in cattle, sheep, Fig. 3 Forest plot of prevalence of Cryptosporidium spp. infection in goats using molecular methods (first author, year and country) Page 8 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 goats, pigs, buffaloes and horses (n = 429). Among them, 201 provided data on cattle, 66 on sheep, 55 on goats, 39 on pigs, 37 on horses and 28 on buffaloes (Additional file 2: Table S2). A total of 105,894 samples from 245 studies on com- mon livestock, defined as cattle, sheep, goats, pigs, horses and buffaloes, were examined for Cryptosporidium infection, with 24,829 (23.4%) being positive for Crypto- sporidium spp. using CM and PCR methods. Most of the studies were conducted on cattle (n = 163) and sheep (n = 46). The pooled prevalence rates using the CM method were 22.5% (95% CI: 19.6–25.6%), 20.7% (95% CI: 15.2– 26.8%), 18.7% (95% CI: 12.36–26.2%), 15.5% (95% CI: 10.5–21.3%), 13.8% (95% CI: 6.6–22.9%) and 18.6% (95% CI: 11.1–27.4%) for cattle, sheep, goats, pigs, horses and buffaloes, respectively (Table  2). The pooled preva- lence rates using the PCR method were 29.1% (95% CI: 23.1–35.6%), 24.4% (95% CI: 16.4–33.4%), 8.2% (95% CI: 3.7–14.3%), 22.6% (95% CI: 13.7–33%), 4.7% (95% CI: 2–8.4%) and 26.0% (95% CI: 12.2–42.8%) for cattle, sheep, goats, pigs, horses and buffaloes, respectively (Table  2). Analysis of available data by regions (continents and New Zealand) showed a moderate geographical varia- tion of observed prevalence (Table 1). Although diagnos- tic tests varied among regions, the observed prevalence mostly fell within the 5–30% range (Table 2). Regarding cattle, a considerably lower maximum prevalence was seen in New Zealand compared to other regions. Crypto- sporidium prevalence in goat tended to be lower in Asia; however, only one study was available for Australia. For sheep it was the highest in the regions with most inten- sive sheep production, i.e. Australia, Europe and North America (Table  1). Cryptosporidium prevalence in pigs was the highest in Asia, Africa and Europe. In horses, Fig. 4 Forest plot of prevalence of Cryptosporidium spp. infection in sheep using molecular methods (first author, year and country) Page 9 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 studies in South America reported the highest Crypto- sporidium prevalence. The forest plot diagrams of prevalence of Crypto- sporidium infection in domestic hoofed animals derived from studies using a PCR method are shown in Figs. 2, 3, 4, 5, 6, 7. As forest plots show, there is a considerable variation of study numbers and observed prevalence in a given host species within each defined geographical region, even if only studies based on PCR methodology are included. Considering a wider range of studies, i.e. studies that use either CM or PCR (Table 2), cattle are most commonly infected globally while horses feature the lowest Cryptosporidium prevalence. The highest and lowest prevalence rate of Crypto- sporidium infection in domestic hoofed animals was observed in America (26%) and Africa (14%) continents, respectively (Table  3, Fig.  8). Among 53 countries with data, Canada (60%) showed the highest infection rate whereas China, Thailand and Germany (8%) had the low- est infection rate (Table 3, Fig. 8). The distribution of Cryptosporidium species/genotypes by host and geographical region is summarized in Table 4. Cryptosporidium parvum (monoinfections 4172/10,583; 39.4%) and C. andersoni (monoinfections 1992/10,583; 18.8%) were the most commonly detected Cryptosporid- ium species (Table 4). A phylogenetic network was con- structed based on sequences of Cryptosporidium spp. (Fig. 9) using the Neighbor-Net method. On the basis of this phylogenetic analysis, 10 clades (I, II, III, IV, V, VI, VII, VIII, IX and X) containing 12 Cryptosporidium spp. were identified (Fig. 9). Interestingly, C. andersoni and C. muris were placed together in Clade I, and C. xiaoi and C. bovis were both placed in Clade III. It further demon- strated a pairwise sister relationship between clades III and IV (clustering C. xiaoi, C. bovis, and C. ryanae), VI and VII (containing C. ubiquitum and C. suis) and VIII and IX (containing C. hominis and C. erinacei), respec- tively. Interestingly, the result of the phylogenetic analy- sis indicated that clades II (C. scrofarum), III (C. bovis and C. xiaoi) and IV (C. ryanae) could have originated from a common ancestor. The distribution of Crypto- sporidium spp. in a wide range of domestic and wild ungulates is presented in Table 4. The C. parvum is the most common genotype in cattle (54.1%), goats (42.1%) and horses (40.2%), followed by C. ryanae in buffaloes (66.6%), C. suis in pigs (54.1%), and C. xiaoi in sheep (48.9%). In terms of transmission dynamics and clinical importance of zoonotic Cryptosporidium spp., C. homi- nis, C. parvum, C. andersoni, C. bovis and C. ubiquitum were identified in sheep/goats, cattle/goats/horses/pigs/ sheep, cattle/camels/sheep/yaks, buffaloes/cattle/sheep/ pigs/red deer and alpacas/buffaloes/cattle/goats/impalas/ sheep/red deers, respectively (Table 4). Fig. 5 Forest plot of prevalence of Cryptosporidium spp. infection in pigs using molecular methods (first author, year and country) Page 10 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Fig. 6 Forest plot of prevalence of Cryptosporidium spp. infection in horses using molecular methods (first author, year and country) Fig. 7 Forest plot of prevalence of Cryptosporidium spp. infection in buffaloes using molecular methods (first author, year and country) Page 11 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Table 3 The prevalence of Cryptosporidium infection in terrestrial ungulates (cattle, sheep, goat, pig, horse and buffalo) using conventional microscopic methods. Data are presented separately by continent and country Continent Country Prevalence, pooled proportion (95% CI) (%) Africa (43 studies; 17,424 samples) Egypt 10 (4.44–19.32) Ethiopia 17 (7.15–30.13) Ghana 29a Kenya 15 (10.72–21.30) Malawi 18 (10.48–28.78) Nigeria 17 (13.07–22.33) South Africa 0.5a Tanzania 11 (1.59–29.29) Tunisia 14 (2.09–44.93) Total prevalence in Africa: 14 (11.12–18.31) America (37 studies; 15,860 samples) Argentina 25 (18.83–33.58) Brazil 16 (5.82–30.23) Canada 60 (23.32–91.14) Chile 56a Costa Rica 11a Mexico 41 (31.81–52.23) Trinidad 32 (6.47–67.24) USA 11 (2.84–24.39) Total prevalence in America: 26 (18.41–34.67) Asia (90 studies; 37,458 samples) Bangladesh 9 (2.93–20.36) China 8 (5.62–12.95) India 21 (16.02–28.47) Iran 16 (11.96–20.68) Iraq 17 (11.36–25.23) Japan 24 (0.02–72.52) Malaysia 24 (8.43–46.55) Myanmar 56a Nepal 35 (28.81–43.45) Pakistan 16 (9.05–25.96) South Korea 17 (11.53–23.57) Sri Lanka 28a Taiwan 35 (32.44–38.15) Thailand 8 (3.08–17.41) Vietnam 18a Total prevalence in Asia: 17 (14.94–20.30) Australia (4 studies; 923 samples) Australia 23 (0.00–71.85) New Zealand 20 (15.42–25.92) Total prevalence in Australia: 21 (7.28–40.02) Europe (71 studies, 34,229 samples) Austria 11 a Czech Republic 17 (9.87–27.11) Denmark 33 (14.90–55.60) France 17 (2.56–41.08) Germany 8 (3.62–48.31) Greece 17 (9.87–27.11) Ireland 23 (3.84–52.25) Netherlands 60a Poland 11 (3.62–21.85) Portugal 17a Page 12 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Discussion In this systematic review and meta-analysis, we found that 18.9% of the overall populations of the investigated ungulate species were infected with Cryptosporidium spp. Our study showed that although the prevalence of Cryptosporidium infection was higher in ungulates of the Cetartiodactyla than in Perissodactyla, the prevalence in the latter was not negligible and needs to be considered in terms of pathogen transmission and cycling. From the data collected and summarized on wild animals (as included in Table 4, and Additional file 2: Table S2), it is obvious that sylvatic cycles play a major role in Crypto- sporidium transmission. Wild terrestrial ungulates are likely serving as important reservoir for the parasite, and certainly the infection of livestock and humans may occur by contact to wildlife feces. For meta-analysis, worldwide Cryptosporidium prevalence and species/genotype iden- tity common livestock species have been scrutinized. Overall, Cryptosporidium prevalence in farmed ani- mals is the highest in the Americas and Europe (Table 3) which could be attributed to the intensive farm animal production in these regions. More specifically, consid- ering domestic farm animals, the pooled prevalence of equine Cryptosporidium infection was 4.7%, compared to the pooled prevalence of 29.1%, 26.0%, 24.4%, 22.6% and 8.2% in cattle, buffaloes, sheep, pigs and goats, respec- tively. Regarding the number of studies published for the different geographical regions, our analysis does not sup- port under investigation of certain regions (e.g. Asia) as cause of a detection bias. This reinforces the suggestion that animal production intensity affects the prevalence of Cryptosporidium spp. Concentrated animal feed- ing operations (CAFOs) are most common in cattle and pigs. For example, in the USA, in 2002 more than 71% of all produced beef were derived from operations hold- ing more than 5000 heads of cattle each. It is known that CAFOs pose a major problem due to the high amounts of manure that are released to the environment, facilitating potential pathogen transmission to humans, wildlife and other agricultural operations [39]. Furthermore, patho- gen transmission within a CAFO seems much more likely than in more extensive farming systems. Accordingly, a high prevalence of Cryptosporidium was observed in ani- mals from countries with many CAFO operations, espe- cially in studies in Asia and Europe, with both regions harboring the majority of the commercial pig raising industry [40]. High prevalences in pigs in Africa may be attributed to the opposite effect of extensive farming with high exposure to environmental contamination. Other host animals displaying a high prevalence, such as buf- faloes and sheep, are also generally kept in larger groups on commercial operations. The comparatively low prev- alence rates in equines and goats may potentially result from smaller animal groups and free-range nature of the animal management. Between wild and domestic animals, it appears that Cryptosporidium prevalence is lower in wild populations than in farmed populations in the same host species. For example, Zahedi et  al. [41] reported Cryptosporidium infection rates of 30% in farmed buffalo but 12% in wild buffalo. This suggests that animal density and confine- ment to the same (contaminated) environment facili- tate Cryptosporidium transmission in domestic animals, and there is no clear host species disposition in terms of general susceptibility to infection with the genus Crypto- sporidium despite the observed variation in Crypto- sporidium infection rates among host species (Table 4). Cryptosporidiosis in ungulates, especially ruminants, has several economic and health implications. Crypto- sporidiosis in neonatal calves can lead to profuse watery diarrhea, loss of appetite, lethargy, dehydration and even death, thus may require costly treatments [42]. Moreo- ver, as shown in sheep and goats, cryptosporidiosis can exhibit long-term effects on the growth of animals [43, 44]. Additionally, infected calves can shed over 1 × 1010 oocysts each day, which can survive in the environments for months. The ingestion of very few oocysts can cause infection in susceptible hosts, including humans [23, 45]. a One study was performed in these countries Table 3 (continued) Continent Country Prevalence, pooled proportion (95% CI) (%) Romania 21 (15.02–27.97) Serbia 40 (31.95–49.48) Spain 29 (19.80–39.75) Sweden 8a Switzerland 55a Turkey 34 (19.82–50.61) UK 34 (0.59–85.50) Total prevalence in Europe: 23 (20.37–27.68) Page 13 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 It has been shown that the median infection dose of C. parvum for humans range from below 10 to over 1000 oocysts [22]. Zoonotic transmission of Cryptosporidium spp. can easily occur seasonally from young animals such as bovine calves to humans, frequently as an occupa- tional hazard [45, 46]. Nearly 40 Cryptosporidium species have been recog- nized based on molecular, morphological and biologi- cal characteristics of the parasites. Previous studies have shown that four major species are responsible for bovine cryptosporidiosis, namely C. parvum, C. andersoni, C. bovis and C. ryanae [1]. We showed that the most preva- lent Cryptosporidium species in ungulates are C. par- vum and C. andersoni, comprising 39.4% and 18.8% of detected parasites, respectively. The data also suggest that some Cryptosporidium spe- cies are shared among ungulate hosts (Table 4). This indi- cates the occurrence of some inter-species transmission of Cryptosporidium spp. among ungulate species, making wildlife an important reservoir for infections in domes- tic animals. Currently, most data on the distribution of Cryptosporidium species and genotypes are available on domestic animal populations. Amazingly, there are clear differences in the distribution of Cryptosporidium spe- cies within the same host species among geographical regions. For example, studies from Ethiopia and Nige- ria indicate that C. andersoni and C. bovis are the most prevalent species in cattle. In contrast, in countries with concentrated animal feeding operations (CAFO) such as Australia, Iran, Japan and New Zealand, as well as many European and North American countries, C. parvum is prevalent in cattle (Table  4). Similarly, alpacas in their region of origin are mostly infected with C. parvum and C. ubiquitum, while alpacas in the UK only tested posi- tive for C. parvum (Table  4). Calves, lambs and goat kids in areas with more human activities can even have C. hominis infections [19, 41, 47, 48]. Thus, it might be speculated that husbandry systems and contact to other livestock and humans strongly influence the distribution of Cryptosporidium species in an ungulate population. Our meta-analysis had several limitations. We observed a substantial heterogeneity among the included studies. Heterogeneity in the meta-analyses of prevalence is not uncommon, and the random-effect Fig. 8 Overall prevalence of Cryptosporidium in different geographical regions in the world. The prevalence in each country was determined from conventional microscopy data in farmed animals (cattle, sheep, goats, pigs, horses and buffaloes) Page 14 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Ta bl e 4 W or ld w id e oc cu rr en ce o f Cr yp to sp or id iu m s pe ci es o r ge no ty pe s in s el ec te d do m es tic a nd w ild p op ul at io ns o f un gu la te s pe ci es ; w he re a pp lic ab le , a va ila bl e da ta a re su m m ar iz ed fr om d iff er en t s ou rc es p er c ou nt ry H os t Co un tr y N o. o f i so la te s N o. o f C ry pt os po rid iu m s pe ci es /g en ot yp es Re fe re nc e M on oi nf ec tio n (n ) M ix ed in fe ct io n (n ) A lp ac a Pe ru 3 C. p ar vu m (2 ); C. u bi qu itu m (1 ) – G óm ez ‑C ou so e t a l. [5 1] A lp ac a U K 9 C. p ar vu m (9 ) – Tw om ey e t a l. [5 2] ; W es se ls e t a l. [5 3] Bi so n Po rt ug al 1 C. ty zz er i ( 1) – A lv es e t a l. [5 4] Bo ar C ze ch R ep ub lic 32 C. su is (1 3) ; C . s cr of ar um (7 ) C. su is + C. sc ro fa ru m (1 2) N ěm ej c et a l. [5 5] Bu ffa lo Eg yp t 70 C. p ar vu m (4 1) ; C . r ya na e (1 7) ; C . b ov is (2 ) C. p ar vu m + C . r ya na e (7 ); C. p ar vu m + C . bo vi s ( 3) A m er e t a l. [5 6] ; H el m y et a l. [5 7] ; M ah fo uz e t a l. [5 8] ; I br ah im e t a l. [5 9] Bu ffa lo So ut h A fri ca 2 C. u bi qu itu m (1 ); C. b ov is (1 ) – A bu S am ra e t a l. [6 0] Bu ffa lo A us tr al ia 72 C. p ar vu m (9 ); C. ry an ae (5 8) ; C . s cr of ar um (1 ); C. b ov is (4 ) – A be yw ar de na e t a l. [6 1] ; Z ah ed i e t a l. [6 2] Bu ffa lo Ita ly 6 C. p ar vu m (6 ) – Ca cc io e t a l. [6 3] Bu ffa lo Br az il 63 C. p ar vu m (1 ); C. ry an ae (6 0) ; u nk no w n ge no ty pe (2 ) – A qu in o et a l. [6 4] Ca m el C hi na 3 C. a nd er so ni (3 ) – W an g et a l. [6 5] ; L iu e t a l. [6 6] Ca tt le Eg yp t 23 8 C. p ar vu m (1 46 ); C. a nd er so ni (7 ); C. ry an ae (3 5) ; C . b ov is (1 5) C. p ar vu m + C . r ya na e (1 5) ; C . p ar vu m + C . bo vi s ( 10 ); C. p ar vu m + C . a nd er so ni (3 ); C. ry an ae + C . b ov is (7 ) A m er e t a l. [5 6] ; H el m y et a l. [5 7] ; M ah fo uz e t a l. [5 8] ; I br ah im e t a l. [5 9] Ca tt le Et hi op ia 71 C. a nd er so ni (5 4) ; C . r ya na e (3 ); C. b ov is (1 4) – W eg ay eh u et a l. [6 7] Ca tt le Ke ny a 27 C. p ar vu m (1 7) ; C . a nd er so ni (3 ); C. ry an ae (6 ); C. u bi qu itu m (1 ) – Sz on yi e t a l. [6 8] ; K an gʼet he e t a l. [6 9] Ca tt le M ad ag as ca r 17 C. su is (1 7) – Bo da ge r e t a l. [7 0] Ca tt le N ig er ia 65 C. a nd er so ni (5 ); C. ry an ae (1 3) ; C . b ov is (3 2) C. ry an ae + C . b ov is (1 1) ; C . b ov is + C. a nd er - so ni (4 ) A yi nm od e et a l. [7 1] ; M ai ka i e t a l. [7 2] Ca tt le So ut h A fri ca 6 C. p ar vu m (1 ); C. a nd er so ni (2 ); C. u bi qu itu m (3 ) – A bu S am ra e t a l. [6 0] ; A bu S am ra [7 3] Ca tt le Tu ni si a 7 C. p ar vu m (7 ) – So lta ne e t a l. [7 4] Ca tt le Za m bi a 45 C. p ar vu m (2 9) ; C . u bi qu itu m (1 ); C. b ov is (1 5) – G eu rd en e t a l. [7 5] Ca tt le C hi na 29 9 C. p ar vu m (6 9) ; C . a nd er so ni (1 00 ); C. ry an ae (1 9) ; C . b ov is (8 9) C. p ar vu m + C . b ov is (6 ); C. p ar vu m + C . ry an ae (4 ); C. p ar vu m + C . a nd er so ni (3 ); C. bo vi s + C . r ya na e (9 ) W an g et a l. [7 6, 7 7] ; H ua ng e t a l. [7 8] Ca tt le In di a 21 C. p ar vu m (6 ); C. a nd er so ni (3 ); C. ry an ae (3 ); C. bo vi s ( 8) ; C . o cc ul tu s ( 1) – Kh an e t a l. [7 9] Ca tt le Ira n 54 C. p ar vu m (5 0) ; C . a nd er so ni (4 ) – M ea m ar e t a l. [8 0] ; F ot ou hi e t a l. [8 1] ; P ire st an i et a l. [8 2] Ca tt le Is ra el 61 C. p ar vu m (6 1) Ta nr iv er di e t a l. [8 3] Ca tt le Ja pa n 33 C. p ar vu m (3 2) ; C . b ov is (1 ) Ka ra ni s et a l. [8 4] Ca tt le M al ay si a 14 C. p ar vu m (1 1) ; C . r ya na e (3 ) H al im e t a l. [8 5] Page 15 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Ta bl e 4 (c on ti nu ed ) H os t Co un tr y N o. o f i so la te s N o. o f C ry pt os po rid iu m s pe ci es /g en ot yp es Re fe re nc e M on oi nf ec tio n (n ) M ix ed in fe ct io n (n ) Ca tt le A us tr al ia 43 9 C. p ar vu m (2 97 ); C. a nd er so ni (2 0) ; C . r ya na e (3 0) ; C . b ov is (7 2) ; C . h om in is (3 ) C. p ar vu m + C . b ov is (1 2) ; C . p ar vu m + C . ry an ae (4 ); C. b ov is + C. ry an ae (1 ) W al dr on e t a l. [8 6] ; N ol an e t a l. [8 7] ; F er gu so n et a l. [8 8] ; N g et a l. [8 9] ; M cC ar th y et a l. [9 0] ; O ’B rie n et a l. [9 1] ; R al st on e t a l. [9 2] Ca tt le N ew Z ea la nd 12 7 C. p ar vu m (8 5) ; C . b ov is (4 2) – Le ar m on th e t a l. [9 3] ; G rin be rg e t a l. [9 4] ; A l‑ M aw ly e t a l. [9 5] Ca tt le Be lg iu m 11 4 C. p ar vu m (1 05 ); C. su is (1 ); C. b ov is (8 ) G eu rd en e t a l. [9 6] Ca tt le C ze ch R ep ub lic 20 19 C. p ar vu m (6 99 ); C. a nd er so ni (1 31 5) ; C . b ov is (5 ) – Kv ac e t a l. [9 7] ; K va c et a l. [9 8] ; O nd ra ck ov a et a l. [9 9] Ca tt le D en m ar k 24 4 C. p ar vu m (1 00 ); C. a nd er so ni (5 9) ; C . r ya na e (1 1) ; C . b ov is (5 7) ; C . o cc ul tu s ( 3) ; u nk no w n ge no ty pe (4 ) C. p ar vu m + C . a nd er so ni (1 0) La ng kj ae r e t a l. [1 00 ]; En em ar k et a l. [1 01 ] Ca tt le Fr an ce 91 C. p ar vu m (3 2) ; C . r ya na e (1 4) ; C . u bi qu itu m (1 ); C. b ov is (1 1) C. p ar vu m + C . r ya na e (1 2) ; C . p ar vu m + C . bo vi s ( 11 ); C. ry an ae + C . b ov is (8 ); C. p ar - vu m + C . r ya na e + C . p ar vu m (2 ) Fo lle t e t a l. [1 02 ] Ca tt le H un ga ry 22 C. p ar vu m (2 1) ; C . r ya na e (1 ) – Pl ut ze r e t a l. [1 03 ] Ca tt le U K (N or th er n Ire la nd ) 22 4 C. p ar vu m (2 13 ); C. ry an ae (3 ); C. b ov is (8 ) – Th om ps on e t a l. [1 04 ] Ca tt le Ita ly 10 1 C. p ar vu m (1 01 ) – D ur an ti et a l. [1 05 ] Ca tt le Po la nd 11 3 C. p ar vu m (3 6) ; C . a nd er so ni (1 7) ; C . r ya na e (8 ); C. b ov is (5 2) – Rz eż ut ka & K au pk e [1 06 ] Ca tt le Po rt ug al 82 C. p ar vu m (8 2) – M en do nc a et a l. [1 07 ] Ca tt le Ro m an ia 65 C. p ar vu m (6 5) – Im re e t a l. [1 08 ] Ca tt le Sc ot la nd 41 1 C. p ar vu m (4 09 ); C. h om in is (2 ) – Sm ith e t a l. [1 09 ] Ca tt le Se rb ia 62 C. p ar vu m (6 2) – M is ic & A be [1 10 ] Ca tt le Sp ai n 26 7 C. p ar vu m (2 55 ); C. a nd er so ni (1 ); C. b ov is (4 ); C. fe lis (4 ); un kn ow n ge no ty pe (3 ) – M en do nc a et a l. [1 07 ]; Q ui le z et a l. [1 11 ]; Ca rd on a et a l. [1 12 ] Ca tt le Sw ed en 35 9 C. p ar vu m (3 3) ; C . a nd er so ni (4 ); C. ry an ae (4 0) ; C. b ov is (2 62 ); C. u bi qu itu m (1 ) C. p ar vu m + C . b ov is (1 3) ; C . p ar vu m + C . ry an ae (6 ) Si lv er la s et a l. [1 13 ]; Si lv er la s et a l. [1 14 ]; Si lv er ‑ la s et a l. [1 15 ]; Bj or km an e t a l. [1 16 ] Ca tt le Sw itz er la nd 81 C. p ar vu m (8 1) – U hd e et a l. [1 17 ] Ca tt le Tu rk ey 15 C. p ar vu m (1 5) – Ta nr iv er di e t a l. [8 3] Ca tt le U K 30 6 C. p ar vu m (2 40 ); C. a nd er so ni (2 0) ; C . r ya na e (1 ); C. b ov is (3 1) C. p ar vu m + C . r ya na e + C . b ov is (1 ); C. ry an ae + C . b ov is (5 ); C. p ar vu m + C . b ov is (1 ); C. p ar vu m + C . r ya na e (1 ); C. a nd er - so ni + C . r ya na e (6 ) Th om ps on e t a l. [1 04 ]; Br oo k et a l. [1 18 ]; Fe at he rs to ne e t a l. [1 19 ]; M or ia rt y et a l. [1 20 ]; Sm ith e t a l. [ 1 21 ] Ca tt le Ca na da 13 4 C. p ar vu m (5 1) ; C . a nd er so ni (3 8) ; C . r ya na e (1 1) ; C . b ov is (3 4) – Co kl in e t a l. [1 22 ]; Co kl in e t a l. [1 23 ]; Bu du ‑ A m oa ko e t a l. [1 24 ]; Bu du ‑A m oa ko e t a l. [1 25 ] Page 16 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Ta bl e 4 (c on ti nu ed ) H os t Co un tr y N o. o f i so la te s N o. o f C ry pt os po rid iu m s pe ci es /g en ot yp es Re fe re nc e M on oi nf ec tio n (n ) M ix ed in fe ct io n (n ) Ca tt le U SA 69 8 C. p ar vu m (2 40 ); C. a nd er so ni (2 03 ); C. ry an ae (8 3) ; C . b ov is (1 71 ); C. su is (1 ) Sa nt ín e t a l. [1 26 ]; Fa ye r e t a l. [1 27 –1 29 ]; Sz on yi et a l. [1 30 ] Ca tt le Br az il 57 C. p ar vu m (1 5) ; C . a nd er so ni (3 3) ; C . r ya na e (4 ); C. b ov is (5 ) M ei re le s et a l. [1 31 ]; Se vá e t a l. [1 32 ]; Si lv a et a l. [1 33 ] G ira ffe C ze ch R ep ub lic 1 C. m ur is (1 ) – Ko dá dk ov á et a l. [1 34 ] G oa t Ta nz an ia 5 C. x ia oi (5 ) – Pa rs on s et a l. [1 35 ] G oa t Za m bi a 1 C. p ar vu m (1 ) – G om a et a l. [1 36 ] G oa t C hi na 44 C. a nd er so ni (1 6) ; C . u bi qu itu m (2 4) ; C . x ia oi (4 ) – W an g et a l. [1 37 ] G oa t Pa pu a N ew G ui ne a 10 C. p ar vu m (2 ); C. h om in is (6 ); C. x ia oi (1 ); ra t ge no ty pe II (1 ) – Ko in ar i e t a l. [1 38 ] G oa t Be lg iu m 11 C. p ar vu m (1 1) G eu rd en e t a l. [1 39 ] G oa t Fr an ce 31 C. p ar vu m (1 ); C. u bi qu itu m (1 2) ; C . x ia oi (1 8) – Ri eu x et a l. [1 40 ]; Pa ra ud e t a l. [1 41 ] G oa t G re ec e 14 C. p ar vu m (2 ); C. u bi qu itu m (5 ); C. x ia oi (7 ) – Tz an id ak is [1 42 ] G oa t Sp ai n 68 C. p ar vu m (6 1) ; C . x ia oi (7 ) – D ía z et a l. [1 43 ]; D ía z et a l. [1 44 ] G oa t U K 1 C. h om in is (1 ) – G ile s et a l. [4 6] H or se A lg er ia 4 C. e rin ac ei (4 ) – La at am na e t a l. [1 45 ] H or se C hi na 2 C. a nd er so ni (2 ) – Li u et a l. [1 46 ] H or se N ew Z ea la nd 9 C. p ar vu m (9 ) – G rin be rg e t a l. [3 1] H or se C ze ch R ep ub lic 12 C. p ar vu m (1 ); C. m ur is (9 ); C. ry an ae (1 ); ho rs e ge no ty pe (1 ) – W ag ne ro vá e t a l. [3 3] H or se Ita ly 35 C. p ar vu m (5 ); ho rs e ge no ty pe (2 1) H or se g en ot yp e + C. p ar vu m (9 ) G al up pi e t a l. [1 47 ] H or se U K 3 C. p ar vu m (3 ) – Sm ith e t a l. [1 21 ]; C ha lm er s et a l. [1 48 ] H or se U SA 29 C. p ar vu m (2 0) ; h or se g en ot yp e (9 ) – W ag ne ro vá e t a l. [][ 33 ]; Bu rt on e t a l. [1 49 ] Im pa la So ut h A fri ca 2 C. u bi qu itu m (2 ) – A bu S am ra e t a l. [6 0] M ou flo n sh ee p C ze ch R ep ub lic 1 C. m ur is (1 ) – Ko tk ov á et a l. [1 50 ] Pi g M ad ag as ca r 4 C. p ar vu m (1 ); C. su is (3 ) – Bo da ge r e t a l. [7 0] Pi g A us tr al ia 87 C. sc ro fa ru m (4 8) ; C . s ui s ( 35 ); C, b ov is (4 ) – M cC ar th y et a l. [9 0] ; [ M or ga n et a l. [1 51 ]; Jo hn ‑ so n et a l. [1 52 ]; Ry an e t a l. [1 53 ] Pi g C ze ch R ep ub lic 10 31 C. p ar vu m (2 ); C. m ur is (5 ); C. sc ro fa ru m (3 74 ); C. su is (6 21 ) C. su is + C. sc ro fa ru m (2 9) Vi to ve c et a l. [1 54 ]; Kv áč e t a l. [1 55 , 1 56 ]; N ěm ej c et a l. [1 57 ] Pi g D en m ar k 23 9 C. sc ro fa ru m (1 71 ); C. su is (6 8) La ng kj ae r e t a l. [1 00 ]; Pe te rs en e t a l. [1 58 ] Pi g Ire la nd 28 C. p ar vu m (2 ); C. m ur is (1 ); C. sc ro fa ru m (1 1) ; C. su is (1 4) – Zi nt l e t a l. [3 2] Pi g U K 42 C. p ar vu m (1 1) ; C . s cr of ar um (2 5) ; C . s ui s ( 6) – Sm ith e t a l. [1 21 ]; Fe at he rs to ne e t a l. [1 59 ] Page 17 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Ta bl e 4 (c on ti nu ed ) H os t Co un tr y N o. o f i so la te s N o. o f C ry pt os po rid iu m s pe ci es /g en ot yp es Re fe re nc e M on oi nf ec tio n (n ) M ix ed in fe ct io n (n ) Pi g Br az il 2 C. sc ro fa ru m (2 ) – Fi uz a et a l. [1 60 ] Re d de er C ze ch R ep ub lic 6 C. m ur is (1 ); C. u bi qu itu m (5 ) – Ko tk ov á et a l. [1 50 ] Ro e de er Sp ai n 6 C. ry an ae (3 ); C. b ov is (3 ) – G ar cí a‑ Pr es ed o et a l. [1 61 ] Sh ee p Eg yp t 3 C. x ia oi (3 ) – M ah fo uz e t a l. [5 8] Sh ee p Ta nz an ia 2 C. x ia oi (2 ) – Pa rs on s et a l. [1 35 ] Sh ee p Tu ni si a 3 C. b ov is (3 ) – So lta ne e t a l. [7 4] Sh ee p Za m bi a 6 C. p ar vu m (5 ); C. u bi qu itu m (1 ) – G om a et a l. [1 36 ] Sh ee p C hi na 12 5 C. a nd er so ni (4 ); C. u bi qu itu m (7 8) ; C . x ia oi (4 3) – W an g et a l. [1 62 ]; Li e t a l. [1 63 ] Sh ee p A us tr al ia 10 05 C. p ar vu m (7 8) ; C . a nd er so ni (6 ); Sh ee p ge no ty pe I (7 ); C. sc ro fa ru m (8 ); C. su is (2 ); C. u bi qu itu m (1 48 ); C. h om in is (1 ); C. x ia oi (6 41 ); C. b ov is (6 6) ; C . m ac ro po du m (4 ); un kn ow n ge no ty pe (1 ) C. p ar vu m + C . x ia oi (4 2) ; C . p ar vu m + C . ub iq ui tu m (1 ) Sw ee ny e t a l. [4 3] ; Y an g et a l. [1 64 ]; Ry an e t a l. [1 65 ]; Ya ng e t a l. [1 66 , 1 67 ] Sh ee p Pa pu a N ew G ui ne a 6 C. p ar vu m (4 ); C. a nd er so ni (1 ); C. sc ro fa ru m (1 ) – Ko in ar i e t a l. [1 38 ] Sh ee p Be lg iu m 9 C. p ar vu m (9 ) G eu rd en e t a l. [1 39 ] Sh ee p G re ec e 10 C. p ar vu m (7 ); C. u bi qu itu m (3 ) – Tz an id ak is [1 42 ] Sh ee p Ro m an ia 24 C. p ar vu m (2 0) ; C . u bi qu itu m (2 ); C. x ia oi (2 ) – Im re e t a l. [1 68 ] Sh ee p Sc ot la nd 16 C. p ar vu m (1 6) – G al up pi e t a l. [1 47 ] Sh ee p Sp ai n 57 C. p ar vu m (4 6) ; C . u bi qu itu m (1 1) – D ía z et a l. [1 44 , 1 69 ] Sh ee p U K 13 3 C. p ar vu m (1 21 ); C. h om in is (2 ); C. b ov is (1 0) – M ue lle r‑ D ob lie s et a l. [2 8] ; G ile s et a l. [4 6] ; Sm ith e t a l. [1 21 ]; Pr itc ha rd e t a l. [1 70 ] Sh ee p Br az il 42 C. p ar vu m (3 ); C. u bi qu itu m (2 4) ; C . x ia oi (1 5) – Fi uz a et a l. [1 71 ]; Pa z e Si lv a et a l. [1 72 ]; Zu ca tt o et a l. [1 73 ] W hi te ‑t ai le d de er C ze ch R ep ub lic 3 C. m ur is (1 ); C. ry an ae (2 ) Ko tk ov á et a l. [1 50 ] Ya k C hi na 15 8 C. a nd er so ni (7 2) ; C . r ya na e (3 7) ; C . b ov is (4 7) ; C. o cc ul tu s ( 2) – Ya ng e t a l. [1 64 ] N ot es : C . s ui s ( pr ev io us ly k no w n as p ig g en ot yp e I); C . s cr of ar um (p re vi ou sl y kn ow n as p ig g en ot yp e II) ; C . r ya na e (p re vi ou sl y de er ‑li ke g en ot yp e) ; C . e rin ac ei (p re vi ou sl y de sc rib ed a s he dg eh og g en ot yp e) ; C . b ov is (p re vi ou sl y bo vi ne g en ot yp e B) ; C . m ac ro po du m (p re vi ou sl y m ar su pi al g en ot yp e II) ; C . x ia oi (p re vi ou sl y bo vi s‑ lik e ge no ty pe ); C. h om in is (s yn on ym : C . p ar vu m g en ot yp e 1) ; C . p ar vu m (s yn on ym : C . p ar vu m g en ot yp e 2) ; C . ub iq ui tu m (p re vi ou sl y id en tifi ed a s Cr yp to sp or id iu m c er vi ne g en ot yp e) Ab br ev ia tio ns : n , n um be rs in p ar en th es es a re n um be r o f p os iti ve s am pl es g en ot yp es fo r e ac h sp ec ie s or g en ot yp e Page 18 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 model implicitly incorporates some of the heterogene- ity [49]. Nevertheless, we investigated several factors that can contribute to the observed heterogeneity. The diagnostic method used for the detection of Crypto- sporidium infection was one of the main confounding variables. For example, the pooled prevalence of bovine Cryptosporidium infection was estimated 29.1% using PCR compared to 22.5% using conventional micros- copy. This seems to indicate that molecular methods such as PCR are highly sensitive and specific for the detection of Cryptosporidium infection, but compared with conventional microscopic methods, they are more expensive and require a higher degree of expertise [50]. There are geographical differences in the estimated pooled prevalence of Cryptosporidium infection. The prevalence was highest in the continent of America, followed by Europe, Australia, Asia and Africa. Canada had the highest prevalence among countries. Study design, time of sampling, age of animals, and condi- tions of keeping animals are other factors that can contribute to the observed heterogeneity in crypto- sporidiosis prevalence, in addition to the nature of ani- mal management. The outcome of our study is probably affected by the publication bias. Publication bias occurs when the results of studies affect the likelihood of their inclusion in the systematic review and meta-analysis [49]. Our systematic review was limited to studies published after 1984 in English. Moreover, many studies did not provide enough information to be included in the meta-analysis. Conclusions Results of the meta-analysis suggest that Crypto- sporidium infection is highly prevalent in ungu- lates, especially ruminants. Geographical differences in Cryptosporidium prevalence and distribution of Cryptosporidium species are seen for most domestic ungulate hosts. These within-host-species differences could be partially attributed to differences in animal management among geographical regions. The high- est prevalence in farmed ungulates occurs in America and Europe where CAFO is widely practiced. The major farm animal hosts of Cryptosporidium spp. appear to be cattle, buffalo, sheep and pigs. These farm animals are potent reservoirs for a variety of Cryptosporidium spe- cies. Cryptosporidium prevalence is also clearly higher in farmed animals than in wild ungulate populations. Inter-species transmission of Cryptosporidium spp. appears to be affected by contact with other host spe- cies (humans or other animals) and infection pressure (intensive farming), rendering the investigated ungulate hosts capable of propagating both zoonotic and non- zoonotic Cryptosporidium species. Fig. 9 The phylogeny of Cryptosporidium spp Page 19 of 23Hatam‑Nahavandi et al. Parasites Vectors (2019) 12:453 Supplementary information Supplementary information accompanies this paper at https ://doi. org/10.1186/s1307 1‑019‑3704‑4. Additional file 1: Table S1. PRISMA checklist. Additional file 2: Table S2. Worldwide prevalence of Cryptosporidium spp. in herbivorous animals. Abbreviations CM: conventional microscopy; ELISA: enzyme‑linked immunosorbent assay; ICT: immunochromatographic test; PCR: polymerase chain reaction; PRISMA: Preferred Reporting Items for Systematic Reviews and Meta‑Analyses; QLAT: quantitative latex agglutination test. Acknowledgements We would like to thank Mr. F. Shahrivar and Dr. A.S. Pagheh for their assistance and kind help. Authors’ contributions KHN, EH, DC and LX contributed to the design of the study. KHN, EA and AS conducted the systematic review of the literature and extracted data. EA, AS, DC and LX analyzed data and drafted the first version of the manuscript. EA, DC, BB and LX contributed to the interpretation of data and writing of the first draft. All authors read and approved the final manuscript. Funding The authors received no financial support for the research. Availability of data and materials Data supporting the conclusions of this article are included within the article and its additional files. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Iranshahr University of Medical Sciences, Iranshahr, Iran. 2 Infectious and Tropical Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. 3 Parasitology Reference and Research Laboratory, National Centre for Microbiology, Carlos III Health Institute, Ctra Majadahonda‑Pozuelo Km 2, 28220 Majadahonda, Madrid, Spain. 4 Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. 5 Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. 6 Department of Veterinary Sciences, College of Agriculture and Natural Resources, University of Wyoming, Laramie, WY, USA. 7 College of Veterinary Medicine, South China Agricultural University, Guangzhou, China. Received: 9 May 2019 Accepted: 5 September 2019 References 1. Feng Y, Ryan UM, Xiao L. Genetic diversity and population structure of Cryptosporidium. Trends Parasitol. 2018;34:997–1011. 2. Khalil IA, Troeger C, Rao PC, Blacker BF, Brown A, Brewer TG, et al. 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