Beyond stabilization: prevalence, risk factor and molecular identification of rumen flukes in cattle from northwestern Spain

Beyond stabilization: prevalence, risk factor and molecular identification of rumen flukes in cattle from northwestern Spain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Beyond stabilization: prevalence, risk factor and molecular identification of rumen flukes in cattle from northwestern Spain David García-Dios, Pablo Díaz, Susana Remesar, Carlota Fernández-González, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6804093/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2025 Read the published version in BMC Veterinary Research → Version 1 posted 11 You are reading this latest preprint version Abstract Background An upward trend of paramphistomid prevalence was detected in domestic ruminants from Europe in the last decades. Nevertheless, recent data from Ireland, which reports the highest prevalences in Europe, suggests that this trend may be stabilizing. This study analysed the current epidemiological situation of rumen fluke infections in cattle from northwestern Spain, focusing on two regions: Galicia, where increasing prevalences were reported, and Asturias, where data is limited. Between 2018 and 2022, 3,095 faecal samples from 137 farms were analysed using sedimentation coprological technique. Risk factor analysis was conducted through mixed logistic regression and ANOVA; paramphistomid species were molecularly identified. Results High individual (51.2%; 95% CI: 49.4–53.0) and herd (81.8%; 95% CI: 74.1–87.6) prevalences were found, representing the highest recorded prevalence of paramphistomids in cattle from Spain. Prevalence was significantly influenced by region (Asturias: OR 11.4), age (> 60 months: OR 17.1; 25–60 months: OR 5.8), co-infection with Fasciola hepatica (OR 13.1) and absence of slurry scrapers (OR 76.9). Egg shedding intensity was notably higher in older animals and those co-infected with F. hepatica as well as in farms from coastal areas and using extensive management. Molecular analysis confirmed Calicophoron daubneyi as the most common species. Conclusions Our findings reveal a continued upward trend in rumen fluke prevalence in cattle from north-western Spain, suggesting that a stabilization of prevalence is not occurring. The study highlights the urgent need for targeted surveillance, farmer education, and integrated control measures in north-western Spain, especially in Asturias, where infection rates are particularly high. Paramphistomids Calicophoron daubneyi cattle epidemiology risk factors molecular identification Spain Figures Figure 1 Figure 2 Figure 3 Background Cattle sector has a significant economic importance in north-western Spain. Over recent decades, this sector has experienced considerable changes. The traditional family farms, which once predominated in this region, have been replaced by larger, fewer, and significantly more professionalized farms [ 1 , 2 ]. In addition, in recent years, the growing concern on animal welfare and sustainable production practices has prompted changes in management systems, such as the return to grazing practices. These changes in management have influenced the assessment of on-farm parasitic risks, thereby raising concerns regarding gastrointestinal nematode or trematode infections, including those caused by paramphistomids. Paramphistomids, also known as rumen flukes, are digenean trematodes whose adults are located in pre-stomachs of their definitive hosts, including domestic and wild ruminants [ 3 ]. These parasites require an intermediate host, a freshwater snail [ 4 – 8 ]. In cattle, clinical signs are more frequent in young animals after a massive ingestion of metacercariae within a short time [ 9 ], leading to significant duodenal lesions and considerable economic losses [ 10 – 12 ]. Although these trematodes are prevalent in tropical and subtropical areas, their occurrence in Europe was considered unusual until the last decades of the 20th century. Nevertheless, during the 1990s, a significant rise in the prevalence was documented in France [ 13 ]. Since then, the spread of paramphistomids has been observed across nearly all the European continent [ 3 ], with the highest prevalences recorded in cattle from western Europe [ 14 , 15 ]. This increase in the prevalence rates has been mainly related to the spread of Calicophoron daubneyi across the European continent. This paramphistomid species has adapted effectively to Galba truncatula , an amphibious snail that is very abundant in Europe, also acting as an intermediate host of the liver fluke Fasciola hepatica [ 6 ]. In Spain, the presence of paramphistomids has been exhaustively studied in ruminants from the north-western area of the country, especially in cattle from Galicia, which is considered an endemic area for fasciolosis where G. truncatula is abundant [ 16 ]. In this region, a number of investigations reported low but stable prevalences of paramphistomids (12–17%) during the early 2000s [ 17 – 19 ]; nevertheless, a progressive increase in the prevalence of paramphistomids in cattle (18.8–26%) has been observed from 2010 onwards [ 11 , 20 ]. The most recent data on rumen fluke infections in cattle from northern Spain indicates an overall prevalence of 33.9% in organic farms from four regions: Galicia, Asturias, Cantabria, and the Basque Country [ 21 ]. Since then, no further epidemiological investigations on paramphistomid infection in cattle were performed in this area. It is worth noting that in countries with high prevalences, such as Ireland or France, recent data show a maintenance or reduction of infection rates suggesting a possible stabilization of the situation [ 13 , 14 , 22 , 23 ]. Thus, obtaining updated data on the presence of these trematodes in north-western Spain is strongly needed. In this context, the present study provides updated information on the prevalence of paramphistomids in cattle from two regions in north-western Spain. Thus, the findings from Galicia -where information on rumen fluke infections in cattle is available for more than 25 years and training of farmers on this issue was extensively performed- and from Asturias -where individualised data is not available and the parasite is less known among farmers- were compared. In addition, the risk factors influencing both the probability of infection and the egg shedding were detected, and the paramphistomid species present were identified using molecular methods. Methods Study area The present study was conducted in Galicia (41 o 49’ to 43 o 47’ N, 6 o 42’ to 9 o 18’W) and Asturias (42°54’ to 43°39’ N, 4°25’ to 7°11’ W), two regions located in north-western Spain and characterized by its oceanic climate, with high rainfall and moderate temperatures throughout the year [ 24 , 25 ]. The study area has a population of 1,297,012 heads of cattle, representing the 20.33% of the cattle population in Spain; it is worth noting that both regions are key dairy cattle areas, including more than half of the animals destined for milk production in the country (50.18%; 393,045 animals) [ 26 ]. Sampling and data collection The number of farms required for the study was calculated using the n.for.survey function from the EpiDisplay R statistical package [ 27 ] considering a 95% confidence interval, a precision of 0.1, and a prevalence of 50%, leading to the highest sampling. Although a minimum of 96 farms was required, additional 41 farms were included for maximizing the territorial coverage; thus, farms were located on 68 different municipalities (Fig. 1 ). The number of samples collected on each farm was also calculated considering a 95% confidence interval, a precision of 0.1 and a prevalence of 50%. In total, 3,095 faecal samples were collected between October 2018 and February 2022. All samples were collected directly from the rectum of the animals, stored at 4 o C and analysed individually within 24 hours. In each farm, information regarding the age of the animals was sourced from official records, while data on management and facilities was gathered using an epidemiological survey. Coproscopic analysis Trematode eggs were detected using a quantitative sedimentation technique [ 28 ] with a detection limit of 1 egg per gram of faeces (epg). In addition, a quantitative McMaster flotation technique using saturated saline solution [ 29 ] was performed to detect infections by coccidia, gastrointestinal nematodes or cestodes, with a detection limit of 50 eggs/oocysts per gram of faeces (epg/opg). Risk factor analysis A mixed logistic regression was performed for identifying factors influencing the probability of paramphistomid infection in cattle within the studied regions; the farm was included as a random variable. This analysis was performed using the glmer() function of the lme4 package [ 30 ] in the R statistical package [ 31 ]. Factors were manually removed using a stepwise approach (forward and backwards) based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) values until the best model was built. Odds ratios were calculated by raising the constant e to the estimators obtained from the model. The possible influence of different variables on paramphistomid egg shedding was assessed using a multivariate ANOVA; only positive animals (n = 1586) were included, and the logarithm of paramphistomid egg counts was used as the dependent variable. To avoid the potential impact of a larger number of animals on some farms, a mixed linear regression was performed -lmer() function in lme4 package-. The factors were similarly eliminated stepwise, forward and backwards, based on the AIC and BIC values until the best model was obtained. Results of the ANOVA over the linear model were obtained using the Anova() function of the car package [ 32 ]. Pairwise analyses were performed using the glht() function of the multicomp package [ 33 ], using Tukey’s adjustment method. Molecular analysis The sediment of all the positive samples of each positive farm was pooled; thus, a pool for each positive farm was obtained. DNA was extracted from those pools showing the highest mean egg counts (n = 41) using a commercial kit (QIAamp Fast DNA Stool Mini Kit, Quiagen N.V.®, Venlo, The Netherlands) and following the manufacturer’s instructions. DNA samples were stored at -20°C until analysis. The samples were tested using a PCR protocol for amplification of a partial region of the internal transcribed spacer of Trematoda described by García-Dios et al . [ 34 ]. All PCR-positive samples, including the positive control, were purified and sequenced in both senses on an ABI 3730xl (Applied Biosystems, Foster City, California, USA) using a Big Dye Terminator v3.1 cycle sequencing kit (Applied Biosystems, Foster City, California, USA) at the Sequencing and Fragment Analysis Unit of the Santiago de Compostela University (Spain). The sequences were aligned and edited using the software ChromasPro (Technelysium, Brisbane, Australia), and the consensus sequences were queried against the GenBank database using the Basic Local Alignment Search Tool (BLAST; http://blast.ncbi.nlm.nih.gov/Blast.cgi ). Results Overall prevalence and egg shedding Overall, the 51.2% (95%CI 49.4–53) of the animals shed paramphistomid eggs, showing a mean egg count of 96.6 epg (SD 178.2; range 1–2,744). Half of the positive animals presented counts below 50 epg; eliminations above 250 epg were considered atypical, although some animals exceeded 1,000 epg. At least one infected animal was found in the 81.8% (95%CI 74.1–87.6) of the farms. On positive farms, the mean intra-farm prevalence was 65.5% (95%CI 62.6–66.4), although it varied significantly, from 2.44 to 100% (Fig. 2 ). Risk factor analysis A total of 34 independent variables were included in the risk analysis (Table 1 ). Considering the different factors, the prevalence ranged from 0 to 92.7% and the mean egg shedding from 0 epg to 244 epg. Table 1 Variables and categories considered in the risk factor analysis for the prevalence and egg shedding of paramphistomids in cattle from northwestern Spain Variable Categories Positive animals/total (%; CI 95%*) Mean egg shedding (sd) Region Asturias 516/751 (68.7; 65.2–72) 107.3 (209.568) Galicia 1069/2343 (45.6; 43.6–47.7) 91.4 (160.697) Climatic area Plateau 481/1406 (34.2; 31.7–36.8) 74 (150.260) Coast 331/645 (51.3; 47.4–55.2) 123.7 (216.467) Mountain 773/1044 (74; 71.2–76.7) 99.1 (174.437) Management Extensive 421/476 (88.5; 85.1–91.1) 136.7 (217.597) Semiextensive 1070/2104 (50.9; 48.7–53) 85.4 (164.715) Intensive 94/515 (18.3; 15.1–21.9) 44.3 (59.420) Mean altitude 550 m 885/1557 (56.8; 54.3–59.3) 80.7 (149.412) Age (months) 1–24 89/251 (35.5; 29.6–41.8) 91.7 (150.613) 25–60 572/1325 (43.2; 40.5–45.9) 85.5 (135.862) > 60 892/1425 (62.6; 60-65.1) 185.2 (204.907) Breed group Allochthonous 595/1627 (36.6; 34.2–39) 92.3 (153.829) Autochthonous 258/352 (73.3; 68.3–77.8) 112.9 (199.492) Unknown 516/816 (63.2; 59.8–66.5) 84.9 (195.687) Mixed 216/300 (72; 66.5–76.9) 117 (167.626) Farm purpose Meat 909/1267 (71.7; 69.2–74.2) 93.3 (169.650) Dairy 676/1828 (36.8; 34.6–39) 101 (189.072) Introduction of animals from other farms No 755/1715 (44; 41.7–46.4) 99.7 (153.173) Yes 830/1380 (60.1; 57.5–62.7) 93.8 (198.249) Presence of other ruminants on the farm No 1533/3024 (50.7; 48.9–52.5) 95.8 (176.301) Yes 52/71 (73.2; 61.2–82.7) 121.5 (227.308) Presence of carnivores on the farm No 1011/1668 (60.6; 58.2–63) 96.1 (174.772) Yes 574/1427 (40.2; 37.7–42.8) 97.5 (184.167) Proximity to other farms (< 2km) No 60/103 (58.3; 48-67.8) 118.8 (133.528) Yes 1525/2992 (51; 49.2–52.8) 95.7 (179.682) Quarantine No 1363/2641 (51.6; 49.7–53.5) 99.8 (179.388) Yes 222/454 (48.9; 44.2–53.6) 77.3 (169.646) Use of anthelmintics No 267/536 (49.8; 45.5–54.1) 66.4 (90.794) Yes 1318/2559 (51.5; 49.5–53.5) 102.7 (190.512) Deworming frequency No 268/537 (49.9; 45.6–54.2) 66.3 (90.652) Once a year 443/1168 (37.9; 35.1–40.8) 98.6 (199.775) Twice a year 852/1318 (64.6; 62-67.2) 105.6 (187.334) Three times a year 22/72 (30.6; 20.5–42.7) 78.9 (111.845) Anthelmintic drugs used No treatment 268/537 (49.9; 45.6–54.2) 66.4 (90.794) Benzimidazoles 30/181 (16.6; 11.6–23) 114.8 (166.681) Macrocyclic lactones 689/1382 (49.9; 47.2–52.5) 104.4 (149.848) B + ML 296/458 (64.6; 60–69) 106.3 (203.313) Closantel + ML 206/324 (63.6; 58.1–58.8) 122 (298.563) LM + Oxyclozanide 81/162 (50; 42.4–57.6) 39.4 (81.737) Bedding material Extensive management 411/491 (83.7; 80.1–86.8) 126.1 (197.381) Lime 63/92 (68.5; 57.8–77.5) 56.8 (85.853) Mat 469/1018 (46.1; 43-49.2) 88.4 (155.893) Straw 168/312 (53.9; 48.1–59.5) 138.9 (287.219) Sawdust 105/540 (19.4; 16.2–23.1) 78.7 (139.017) Directly on the floor 369/642 (57.5; 53.5–61.3) 66.9 (122.753) Bed cleaning frequency Extensive management 423/506 (83.6; 80-86.7) 135.4 (196.160) 1–2 days 961/2264 (42.4; 40.4–44.5) 76.2 (153.395) 4–7 days 75/151 (49.7; 41.5–57.9) 130.6 (218.259) 14 days or more 126/174 (72.4; 65-78.8) 135.6 (235.001) Water troughs cleaning frequency No 84/166 (50.6; 42.8–58.4) 86 (128.405) According to use 810/1396 (58; 55.4–60.2) 98.8 (171.434) Daily 272/506 (53.8; 49.3–58.2) 112.7 (226.310) 2–4 days 162/477 (34; 29.8–38.4) 32.9 (45.002) 7–14 days 215/469 (45.8; 41.3–50.5) 127.3 (213.249) 15 days or more 9/44 (20.5; 10.3–35.8) 3.8 (3.308) Not applicable 33/37 (89.2; 73.6–96.5) 74.6 (72.034) Farm floor Extensive management 405/456 (88.8; 85.5–91.5) 127.9 (198.291) Cement 332/392 (84.7; 80.7–88) 62.8 (146.095) Straw 57/77 (71.4; 59.8–80.9) 234.9 (377.745) Slatted floor 791/1570 (50.4; 47.9–52.9) 84.8 (148.277) Corridors cleaning frequency According to use 41/71 (57.8; 45.5–69.2) 218.5 (248.033) Daily 957/2268 (42.2; 40.2–44.3) 80.6 (163.885) 3–7 days 105/175 (60; 52.3–67.2) 65.5 (93.389) 15 days or more 96/146 (65.8; 57.4–73.3) 155.4 (258.252) Not applicable 386/435 (88.7; 85.3–91.5) 117.2 (186.937) Use of slurry scraper No 1467/2355 (62.3; 60.3–64.3) 100.6 (183.168) Yes 118/740 (15.9; 13.4–18.8) 46.8 (81.873) Slurry scraper frequency No 1467/2355 (62.3; 60.3–64.3) 100.6 (183.168) 1–2 times/day 94/380 (24.7; 20.5–29.5) 49.5 (88.790) 3 or more times/day 24/360 (6.7; 4.4–9.9) 36.1 (45.530) Contact with fresh pasture Never 0/49 (0; 0-0.9) - Occasionally 70/432 (16.2; 12.9–20.1) 37.9 (53.671) Daily 1515/2614 (58; 56-59.9) 99.3 (181.428) Pasture rotation No 128/375 (34.1; 29.4–39.2) 85.6 (153.739) Yes 1433/2637 (54.3; 52.4–56.3) 98.2 (181.397) Not applicable 24/83 (28.9; 19.7–40.1) 63 (71.735) Presence of water courses on the pasture No 217/515 (42.1; 37.9–46.5) 124.8 (247.157) Yes 1358/2511 (54.1; 52.1–56) 92.8 (164.716) Not applicable 10/69 (14.5; 7.5–25.5) 6.5 (6.399) Presence of water troughs on the pasture No 393/630 (62.4; 58.5–66.2) 63.1 (93.601) Yes 1168/2382 (49; 47-51.1) 108.6 (198.750) Not applicable 24/83 (28.9; 19.7–40.1) 63 (71.735) Use of slurry as fertilizer No 11/55 (20; 10.9–33.4) 229.5 (205.874) Yes 1574/3040 (51.8; 50-53.6) 95.7 (177.692) Slurry origin External 7/49 (14.3; 6.4–27.6) 244 (213.115) Own 1578/3046 (51.8; 50-53.6) 96 (177.813) Introduction of youg animals on the pasture No 390/875 (44.6; 41.3–47.9) 92.6 (193.424) Yes 1195/2220 (53.8; 51.7–55.9) 97.9 (172.973) Age at start of grazing Not applicable 28/118 (23.7; 16.6–32.6) 61.8 (68.436) 0 days 489/647 (75.6; 72-78.8) 117.4 (189.725) 1–30 days 115/175 (65.7; 58.1–72.6) 49.9 (102.202) 2–5 months 172/293 (58.7; 52.8–64.4) 69.5 (178.410) 6–9 months 194/552 (35.1; 31.2–39.3) 128.5 (200.929) 10–14 months 348–620 (56.1; 52.1–60) 97.3 (198.264) 15–19 months 98/333 (29.4; 24.7–34.7) 71.9 (146.383) 24 months or more 141/357 (39.5; 34.4–44.8) 73.9 (103.528) Use of individual boxes for calves No 1265/2143 (59; 56.9–61.1) 91.9 (165.431) Yes 320/952 (33.6; 30.6–36.7) 115.1 (220.834) Positivity to gastrointestinal nematodes No 1149/2350 (48.9; 46.9–51) 95.6 (187.137) Yes 435/744 (58.7; 55.1–62.3) 99.6 (152.911) Positivity to Fasciola hepatica No 1483/2985 (49.7; 47.9–51.5) 94.2 (179.586) Yes 102/110 (92.7; 85.7–96.6) 130.9 (152.911) Positivity to Dicrocoelium dendriticum No 1535/3034 (50.6; 48.8–52.4) 94.3 (177.731) Yes 11/61 (18; 9.8–30.4) 167.7 (178.907) * The confidence interval was calculated using the prop.test() function of R Table 1 . Variables and categories considered in the risk factor analysis for the prevalence and egg shedding of paramphistomids in cattle from northwestern Spain (placed at the end of the document) The mixed logistic regression model extracted four factors influencing the probability of infection by paramphistomids: region, age, positivity to F. hepatica and use of slurry scraper (Table 2 ). Table 2 Model obtained by mixed logistic regression for the prevalence of paramphistomids in cattle from northwestern Spain Factor Estimator Z-value p-value OR CI 95% Region (Galicia) - - - - - Region (Asturias) 2.4364 3.234 0.001 11.4 2.6–50 Age 1 (1–24 months) - - - - - Age 2 (25–60 months) 1.7646 6.056 60 months) 2.8395 9.697 < 0.001 17.1 9.64–30.37 Negative to F. hepatica - - - - - Positive to F. hepatica 2.5737 3.711 < 0.001 13.1 3.4–51.1 Use of slurry scraper - - - - - No slurry scraper 4.3430 4.609 < 0.001 76.9 12.1-487.8 - Reference category Statistical analysis revealed that animals from Asturian farms are 11.4 times more likely to be infected with paramphistomids than those from Galicia. Prevalence increased with age; the probability of being positive in animals aged 0–24 months was 5.8 times lower than those aged 25 to 60 months and 17.1 times lower than cattle over 60 months. Significant differences were also found between animals aged 25–60 months and those over 60 months, with the older age group presenting a 2.97-fold higher probability of infection. In addition, infection with F. hepatica significantly influenced the occurrence of paramphistomid infections; thus, animals shedding liver fluke eggs showed a 13.1 times higher probability of having paramphistomid infections. Finally, the probability of infection was up to 76.9 times higher in farms that did not use scrapers. ANOVA analysis demonstrated that age, climatic area, type of management, and positivity to F. hepatica significantly influence egg shedding (Fig. 3 ). The analysis revealed a clear trend in age: egg counts increased progressively and significantly with age; thus, animals older than 60 months showed the highest egg shedding. Pairwise analyses detected significant differences among all age groups: between animals aged 1–24 months and 25–60 months (p = 0.003), between those aged 1–24 months and > 60 months groups (p 60 months (p < 0.001). In addition, animals from coastal areas showed significantly higher egg shedding than animals from the central area (p = 0.002). Regarding management, animals from extensive farms showed significant higher eliminations than those from farms with semiextensive (p < 0.001) or intensive (p = 0.002) management. Finally, animals positive to F. hepatica significantly shed more eggs of paramphistomids than those negative to the liver fluke (p = 0.006). Amplicons of the expected size were obtained in 31 out of 41 pools. Calicophoron daubneyi was the only paramphistomid species identified in all samples. All sequences were identical between them and with those previously obtained in domestic ruminants from different European countries [ 35 – 37 ] including sequences recently obtained in sheep from Galicia [ 34 ]. Discussion Our findings reveal that paramphistomid infections in cattle from north-western Spain keep increasing in recent years, suggesting that infection rates are not stabilizing. The prevalences found in the present study are noticeably higher than those previously reported in beef and dairy farms of Galicia [ 11 , 18 – 20 ]. Our data was even higher than that detected in dairy organic farms from north-western Spain, where the risk of parasite infection is particularly high due to outdoor rearing of livestock and legal restrictions on the use of anthelmintics [ 21 ]. In contrast, recent data on sheep from Galicia showed a substantially lower prevalence (14%)[ 34 ], which is consistent with previous reports in Europe [ 15 , 35 , 38 – 41 ]. These evidence, together with results of experimental infections [ 42 , 43 ], suggest that paramphistomids are better adapted to cattle. The infection rates recorded in this investigation are among the highest in Europe and similar to those reported in some countries of the British Islands such as Ireland (48.8–53.8%)[ 22 , 38 , 44 ] or Scotland (43.3%) [ 35 ]. However, infection rates in cattle across the rest of Europe are noticeably lower; a thorough review of available data suggests that countries located further east tend to report lower prevalence rates. For example, a recent study in western France (Normandy) showed a prevalence of 29.9% [ 23 ], although in France, prevalences as high as 50% were recorded in the 1990’s (Mage et al., 2002). Similar infection rates were found in Belgium (28.8%; [ 45 ] and the Czech Republic (29.9%; Červená et al., 2022) whereas lower percentages of infection were recorded in cattle from Germany (12.7%;[ 41 ] or Italy (10.9%; [ 39 ]. The highest prevalences detected in the westernmost part of Europe may be related to its climate, since the abundant rainfall and moderate temperatures throughout the year favour the development of the life cycle of rumen flukes. This climate could enhance the survival of the external stages of rumen flukes in the environment, as they are less exposed to desiccation and extreme temperatures [ 15 ]. In this sense, it has been proven that miracidia can infect snails in a temperature range of 1-35 o C [ 46 ]. In addition, moderate temperatures throughout the year can extend the period of activity of intermediate hosts [ 47 ]. A mean egg shedding of 96.6 epg was recorded. Comparing egg counts can be very complex, as the sensitivity of the techniques used can lead to marked differences in epg values. However, a good correlation has been demonstrated between the shedding of paramphistomid eggs and the parasite burden in the host’s forestomachs, in contrast to that reported for hepatic trematodes [ 48 ]. Regarding risk analysis, the logistic regression revealed that the probability of cattle being infected with rumen flukes was significantly higher in Asturias than in Galicia. Specifically, higher individual (68.7%), herd (91.2%) and intra-herd (80%) prevalences were found in Asturias than in Galicia (45.6%, 78.6% and 60.2%, respectively). Since both regions have a similar climate, the observed differences may be due to variations in health management practices. In Spain, animal health issues mainly depend on regional governments, leading to substantial differences between regions. In addition, the availability of previous data on cattle in Galicia [ 11 , 18 , 20 ] along with the education and training provided to farmers and veterinarians through numerous informative sessions in this region (Díaz, personal communication) has increased awareness of the presence of paramphistomids, favouring the implementation of more suitable preventive and control measures. The risk analysis also revealed a significant and direct relationship between the age of the animals and both the probability of infection and egg shedding, being consistent with previous studies [ 10 , 49 – 52 ]. All this data suggests that cattle only develop a partial protective immunity against future infections, at least against juvenile paramphistomids, since clinical cases usually occur in young animals [ 9 ] This immunity may prevent the establishment of large numbers of paramphistomids in the duodenum but does not prevent some of them from completing their development. In this regard, the longevity of paramphistomids must be also considered, since their life span can extend up to 10 years, leading to an accumulation of parasites throughout the animal's lifetime [ 43 , 53 , 54 ] and, consequently, to the higher prevalence and egg shedding values found in the oldest animals. Thus, further research is needed to explore the influence of age on rumen fluke infection and the dynamics between these parasites and the host's immune system. Animals infected with F. hepatica had a significant higher risk of testing positive for paramphistomids and shed a significantly higher number of paramphistomid eggs. Although this effect on egg shedding had not been previously documented in cattle, it has recently been observed in F. hepatica positive sheep from Galicia [ 34 ]. Moreover, a similar effect on F. hepatica egg shedding had been described in animals infected by paramphistomids [ 55 ]. In Europe, both parasites share their intermediate host, G. truncatula . Consequently, if an animal is infected with F. hepatica , it is likely to have frequented the same areas where the metacercariae of paramphistomids are primarily found. In addition, it has been demonstrated that F. hepatica exerts an immunomodulatory effect on the host that facilitates infection by other agents [ 56 ], so its influence in this regard cannot be ruled out. The statistical analysis also indicated that animals from farms that did not use scrapers were 76.9 times more likely to be infected with paramphistomids. This factor may be related to the level of professionalization, which is challenging to quantify but clearly influences prevalence rates [ 57 ]. In the studied area, the farms using scrapers tend to be more professionalized, and their animals spend more time indoors. In addition, faeces collected by the scrapers fall into a slurry pit, where fermentative processes destroy the parasitic forms [ 58 ]. Thus, this slurry can be used as fertilizer without contributing to increase the environmental contamination. In any case, further studies are needed to unravel the effect of farm professionalization on the prevalence of rumen flukes. The differences in egg elimination between climatic areas, particularly between the coastal and the central area, may be due to more favourable conditions for the survival of the external stages of the parasite in coastal areas [ 59 ], where winters are milder and humidity is high throughout the year [ 60 ]. These conditions enhance the survival of the parasite in the environment, leading to an increased parasite burden in pastures and, therefore, in the animals grazing there. The greater elimination of eggs observed in extensive farms compared to semi-extensive or intensive farms is probably due to the daily and continuous contact with the parasite [ 10 , 61 ]; in contrast, cattle from semi-extensive farms have intermittent contact with metacercariae, while those from intensive farms are only exposed to the parasite when fed with fresh grass. In addition, in extensive farming, sanitary control and deworming are more challenging and less frequent, which may result in higher parasite burdens. Finally, our results confirm that C. daubneyi is the most common paramphistomid species in northwestern Spain, agreeing with previous studies in both cattle and sheep [ 11 , 34 ]. Although Paramphistomum leydeni has been previously found in cattle, sheep and wild ruminants from different European countries [ 36 , 37 , 62 , 63 ], further investigations are needed for determining whether C. daubneyi is the only paramphistomid species present in north-western Spain. Conclusions Our results reveal a growing trend in the prevalence of paramphistomid infections in cattle from Galicia over the last years, despite the availability of data and ongoing information campaigns. The situation in Asturias is particularly noteworthy, given the lack of prior individual data and the significantly high prevalence rates observed. Consequently, it is imperative to continue monitoring the situation in these regions, enhance awareness among livestock farmers and veterinarians, and carry out farm-specific risk assessments to implement the most effective pharmacological and management interventions. Abbreviations AIC Akaike information criterion ANOVA Analysis of variance BIC Bayesian Information Criterion BLAST Basic Local Alignment Search Tool epg Eggs per gram of faeces ITS-2 Internal transcribed spacer 2 opg Oocysts per gram of faeces SD Standard deviation Declarations Ethics approval and consent to participate All faecal samples used in this study were collected with the permission of the farm owners. All experimental procedures fully complied with European and Spanish ethics regulations on the protection of animals used for scientific purposes (European Directive 2010/63/EU and Spanish Royal Decree 53/2013) and approved by the ethical committee of the University of Santiago de Compostela. Consent for publication Not applicable Availability of data and materials The data supporting the conclusions of this article are included within the article. A more detailed dataset used during the current study is available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study has received funding from the Program for consolidating and structuring competitive research groups (ED431C 2019/04 and ED431C2023/16, Xunta de Galicia, Spain). Authors’ contributions Conceptualization: PD, CML; Methodology: CML, PD; Formal Analysis: DGD, CML; Investigation: DGD, PD, AS, SR; Resources: CF, DGD, PD, NMC; Writing original draft: DGD; Writing, revision and Editing: PD, CML, RP, PM; Visualization: DGD; Funding: PM, RP. All authors reviewed and accepted the final manuscript. Acknowledgements The authors would like to thank the veterinarians and farmers of the participating farms for their collaboration. References Vázquez González I. Situación actual, dinámica y estrategias de las explotaciones con bovino en el norte de España. Thesis dissertation. Universidade de Santiago de Compostela; 2013. García-Suárez E, García-Arias AI, Vázquez-González I. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6804093","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471762814,"identity":"98d31315-f48b-4ecd-96c0-2203542f4034","order_by":0,"name":"David García-Dios","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"García-Dios","suffix":""},{"id":471762815,"identity":"6fff4b91-0e69-4d62-bea6-4d5e3dec0527","order_by":1,"name":"Pablo Díaz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFACHsYDDAzMPEB8gGgtDFAtbAmkaQExDIjTYM5+9sCBDwzWMvz9Z75J/qhhkJNvIKDFsicv4eAMhnQeiQNnt0nzHGMwNjhAQIvBgRyDwzwMQHSwd5s0YwND4gZCDjM4/waiRf4wzzPJnw0M9fMJOczgBtQWg2M8bBK8DQwJDIQcZjnjHdAvBuk8hmfYjK15jkkYbiCkxZw/9+CDDxXW9nLnDz+8+aPGRp5giBkgkSAgQUA9iuJRMApGwSgYBbgAAJ5ZPKaFJEEDAAAAAElFTkSuQmCC","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":true,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Díaz","suffix":""},{"id":471762816,"identity":"0404c798-cd25-4233-b1e0-f7308744da7d","order_by":2,"name":"Susana Remesar","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Susana","middleName":"","lastName":"Remesar","suffix":""},{"id":471762817,"identity":"816512f3-a521-4769-9926-276c536026aa","order_by":3,"name":"Carlota Fernández-González","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Carlota","middleName":"","lastName":"Fernández-González","suffix":""},{"id":471762818,"identity":"b76fccba-5b96-4989-9189-b0325861f476","order_by":4,"name":"Néstor Martínez-Calabuig","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Néstor","middleName":"","lastName":"Martínez-Calabuig","suffix":""},{"id":471762819,"identity":"ce67afc1-bbc8-44d5-b145-6dce877af6bd","order_by":5,"name":"Ana Saldaña","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Saldaña","suffix":""},{"id":471762820,"identity":"9b5a25c9-6125-4d54-ac55-da12eacd9f72","order_by":6,"name":"Rosario Panadero","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Rosario","middleName":"","lastName":"Panadero","suffix":""},{"id":471762821,"identity":"6ac735c1-3ea4-4061-82ee-a02838364505","order_by":7,"name":"Patrocinio Morrondo","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Patrocinio","middleName":"","lastName":"Morrondo","suffix":""},{"id":471762822,"identity":"a3462b20-d113-45e1-b8d6-f716b87728b1","order_by":8,"name":"Ceferino Manuel López-Sández","email":"","orcid":"","institution":"University of Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Ceferino","middleName":"Manuel","lastName":"López-Sández","suffix":""}],"badges":[],"createdAt":"2025-06-02 16:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6804093/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6804093/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12917-025-05009-y","type":"published","date":"2025-10-02T15:58:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84789759,"identity":"235991e5-ded0-4915-b450-06c36e64601f","added_by":"auto","created_at":"2025-06-17 11:08:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":380298,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the cattle farms sampled. The numbers indicate how many farms were sampled in each municipality\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6804093/v1/55f83d6de8914354307a7679.jpg"},{"id":84789763,"identity":"18be0238-8d92-4ea7-b7b1-d817f297389f","added_by":"auto","created_at":"2025-06-17 11:08:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159833,"visible":true,"origin":"","legend":"\u003cp\u003eIntra-herd prevalence of paramphistomid infections within positive farms in total (a) and by region (b: Galicia; c: Asturias)\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6804093/v1/8855c6825b4ef7e8d171bb59.jpg"},{"id":84790387,"identity":"54c470d7-5ea0-499d-85a7-1672c08e419b","added_by":"auto","created_at":"2025-06-17 11:16:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199150,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot representing the distribution of paramphistomid egg shedding according to age, climatic area, management and infection with F. hepatica\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6804093/v1/b3149b70042fa1fc810633c1.jpg"},{"id":92884150,"identity":"cfc5af35-7be3-4ab8-8944-dd684933c7d5","added_by":"auto","created_at":"2025-10-06 16:12:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1820319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6804093/v1/cfcf1da3-0d58-4a15-ac11-4f87b7f79cf7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond stabilization: prevalence, risk factor and molecular identification of rumen flukes in cattle from northwestern Spain","fulltext":[{"header":"Background","content":"\u003cp\u003eCattle sector has a significant economic importance in north-western Spain. Over recent decades, this sector has experienced considerable changes. The traditional family farms, which once predominated in this region, have been replaced by larger, fewer, and significantly more professionalized farms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition, in recent years, the growing concern on animal welfare and sustainable production practices has prompted changes in management systems, such as the return to grazing practices. These changes in management have influenced the assessment of on-farm parasitic risks, thereby raising concerns regarding gastrointestinal nematode or trematode infections, including those caused by paramphistomids.\u003c/p\u003e \u003cp\u003eParamphistomids, also known as rumen flukes, are digenean trematodes whose adults are located in pre-stomachs of their definitive hosts, including domestic and wild ruminants [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These parasites require an intermediate host, a freshwater snail [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In cattle, clinical signs are more frequent in young animals after a massive ingestion of metacercariae within a short time [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], leading to significant duodenal lesions and considerable economic losses [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although these trematodes are prevalent in tropical and subtropical areas, their occurrence in Europe was considered unusual until the last decades of the 20th century. Nevertheless, during the 1990s, a significant rise in the prevalence was documented in France [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Since then, the spread of paramphistomids has been observed across nearly all the European continent [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], with the highest prevalences recorded in cattle from western Europe [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This increase in the prevalence rates has been mainly related to the spread of \u003cem\u003eCalicophoron daubneyi\u003c/em\u003e across the European continent. This paramphistomid species has adapted effectively to \u003cem\u003eGalba truncatula\u003c/em\u003e, an amphibious snail that is very abundant in Europe, also acting as an intermediate host of the liver fluke \u003cem\u003eFasciola hepatica\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Spain, the presence of paramphistomids has been exhaustively studied in ruminants from the north-western area of the country, especially in cattle from Galicia, which is considered an endemic area for fasciolosis where \u003cem\u003eG. truncatula\u003c/em\u003e is abundant [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this region, a number of investigations reported low but stable prevalences of paramphistomids (12\u0026ndash;17%) during the early 2000s [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; nevertheless, a progressive increase in the prevalence of paramphistomids in cattle (18.8\u0026ndash;26%) has been observed from 2010 onwards [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The most recent data on rumen fluke infections in cattle from northern Spain indicates an overall prevalence of 33.9% in organic farms from four regions: Galicia, Asturias, Cantabria, and the Basque Country [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Since then, no further epidemiological investigations on paramphistomid infection in cattle were performed in this area. It is worth noting that in countries with high prevalences, such as Ireland or France, recent data show a maintenance or reduction of infection rates suggesting a possible stabilization of the situation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, obtaining updated data on the presence of these trematodes in north-western Spain is strongly needed.\u003c/p\u003e \u003cp\u003eIn this context, the present study provides updated information on the prevalence of paramphistomids in cattle from two regions in north-western Spain. Thus, the findings from Galicia -where information on rumen fluke infections in cattle is available for more than 25 years and training of farmers on this issue was extensively performed- and from Asturias -where individualised data is not available and the parasite is less known among farmers- were compared. In addition, the risk factors influencing both the probability of infection and the egg shedding were detected, and the paramphistomid species present were identified using molecular methods.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe present study was conducted in Galicia (41\u003csup\u003eo\u003c/sup\u003e 49\u0026rsquo; to 43\u003csup\u003eo\u003c/sup\u003e 47\u0026rsquo; N, 6\u003csup\u003eo\u003c/sup\u003e 42\u0026rsquo; to 9\u003csup\u003eo\u003c/sup\u003e 18\u0026rsquo;W) and Asturias (42\u0026deg;54\u0026rsquo; to 43\u0026deg;39\u0026rsquo; N, 4\u0026deg;25\u0026rsquo; to 7\u0026deg;11\u0026rsquo; W), two regions located in north-western Spain and characterized by its oceanic climate, with high rainfall and moderate temperatures throughout the year [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The study area has a population of 1,297,012 heads of cattle, representing the 20.33% of the cattle population in Spain; it is worth noting that both regions are key dairy cattle areas, including more than half of the animals destined for milk production in the country (50.18%; 393,045 animals) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling and data collection\u003c/h3\u003e\n\u003cp\u003eThe number of farms required for the study was calculated using the n.for.survey function from the EpiDisplay R statistical package [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] considering a 95% confidence interval, a precision of 0.1, and a prevalence of 50%, leading to the highest sampling. Although a minimum of 96 farms was required, additional 41 farms were included for maximizing the territorial coverage; thus, farms were located on 68 different municipalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The number of samples collected on each farm was also calculated considering a 95% confidence interval, a precision of 0.1 and a prevalence of 50%. In total, 3,095 faecal samples were collected between October 2018 and February 2022. All samples were collected directly from the rectum of the animals, stored at 4\u003csup\u003eo\u003c/sup\u003e C and analysed individually within 24 hours. In each farm, information regarding the age of the animals was sourced from official records, while data on management and facilities was gathered using an epidemiological survey.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCoproscopic analysis\u003c/h3\u003e\n\u003cp\u003eTrematode eggs were detected using a quantitative sedimentation technique [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] with a detection limit of 1 egg per gram of faeces (epg). In addition, a quantitative McMaster flotation technique using saturated saline solution [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was performed to detect infections by coccidia, gastrointestinal nematodes or cestodes, with a detection limit of 50 eggs/oocysts per gram of faeces (epg/opg).\u003c/p\u003e\n\u003ch3\u003eRisk factor analysis\u003c/h3\u003e\n\u003cp\u003eA mixed logistic regression was performed for identifying factors influencing the probability of paramphistomid infection in cattle within the studied regions; the farm was included as a random variable. This analysis was performed using the glmer() function of the lme4 package [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] in the R statistical package [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Factors were manually removed using a stepwise approach (forward and backwards) based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) values until the best model was built. Odds ratios were calculated by raising the constant e to the estimators obtained from the model.\u003c/p\u003e \u003cp\u003eThe possible influence of different variables on paramphistomid egg shedding was assessed using a multivariate ANOVA; only positive animals (n\u0026thinsp;=\u0026thinsp;1586) were included, and the logarithm of paramphistomid egg counts was used as the dependent variable. To avoid the potential impact of a larger number of animals on some farms, a mixed linear regression was performed -lmer() function in lme4 package-. The factors were similarly eliminated stepwise, forward and backwards, based on the AIC and BIC values until the best model was obtained. Results of the ANOVA over the linear model were obtained using the Anova() function of the car package [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Pairwise analyses were performed using the glht() function of the multicomp package [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], using Tukey\u0026rsquo;s adjustment method.\u003c/p\u003e\n\u003ch3\u003eMolecular analysis\u003c/h3\u003e\n\u003cp\u003eThe sediment of all the positive samples of each positive farm was pooled; thus, a pool for each positive farm was obtained. DNA was extracted from those pools showing the highest mean egg counts (n\u0026thinsp;=\u0026thinsp;41) using a commercial kit (QIAamp Fast DNA Stool Mini Kit, Quiagen N.V.\u0026reg;, Venlo, The Netherlands) and following the manufacturer\u0026rsquo;s instructions. DNA samples were stored at -20\u0026deg;C until analysis. The samples were tested using a PCR protocol for amplification of a partial region of the internal transcribed spacer of Trematoda described by Garc\u0026iacute;a-Dios \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. All PCR-positive samples, including the positive control, were purified and sequenced in both senses on an ABI 3730xl (Applied Biosystems, Foster City, California, USA) using a Big Dye Terminator v3.1 cycle sequencing kit (Applied Biosystems, Foster City, California, USA) at the Sequencing and Fragment Analysis Unit of the Santiago de Compostela University (Spain). The sequences were aligned and edited using the software ChromasPro (Technelysium, Brisbane, Australia), and the consensus sequences were queried against the GenBank database using the Basic Local Alignment Search Tool (BLAST; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://blast.ncbi.nlm.nih.gov/Blast.cgi\u003c/span\u003e\u003cspan address=\"http://blast.ncbi.nlm.nih.gov/Blast.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOverall prevalence and egg shedding\u003c/h2\u003e \u003cp\u003eOverall, the 51.2% (95%CI 49.4\u0026ndash;53) of the animals shed paramphistomid eggs, showing a mean egg count of 96.6 epg (SD 178.2; range 1\u0026ndash;2,744). Half of the positive animals presented counts below 50 epg; eliminations above 250 epg were considered atypical, although some animals exceeded 1,000 epg. At least one infected animal was found in the 81.8% (95%CI 74.1\u0026ndash;87.6) of the farms. On positive farms, the mean intra-farm prevalence was 65.5% (95%CI 62.6\u0026ndash;66.4), although it varied significantly, from 2.44 to 100% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk factor analysis\u003c/h3\u003e\n\u003cp\u003eA total of 34 independent variables were included in the risk analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Considering the different factors, the prevalence ranged from 0 to 92.7% and the mean egg shedding from 0 epg to 244 epg.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables and categories considered in the risk factor analysis for the prevalence and egg shedding of paramphistomids in cattle from northwestern Spain\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive animals/total (%; CI 95%*)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean egg shedding (sd)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsturias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e516/751 (68.7; 65.2\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.3 (209.568)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalicia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1069/2343 (45.6; 43.6\u0026ndash;47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.4 (160.697)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eClimatic area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e481/1406 (34.2; 31.7\u0026ndash;36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (150.260)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e331/645 (51.3; 47.4\u0026ndash;55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.7 (216.467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMountain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e773/1044 (74; 71.2\u0026ndash;76.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.1 (174.437)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eManagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421/476 (88.5; 85.1\u0026ndash;91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136.7 (217.597)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSemiextensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1070/2104 (50.9; 48.7\u0026ndash;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.4 (164.715)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94/515 (18.3; 15.1\u0026ndash;21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.3 (59.420)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean altitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;550 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700/1538 (45.5; 43\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116.7 (207.322)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;550 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e885/1557 (56.8; 54.3\u0026ndash;59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.7 (149.412)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89/251 (35.5; 29.6\u0026ndash;41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.7 (150.613)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e572/1325 (43.2; 40.5\u0026ndash;45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.5 (135.862)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e892/1425 (62.6; 60-65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185.2 (204.907)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBreed group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAllochthonous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e595/1627 (36.6; 34.2\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.3 (153.829)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutochthonous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e258/352 (73.3; 68.3\u0026ndash;77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.9 (199.492)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e516/816 (63.2; 59.8\u0026ndash;66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.9 (195.687)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216/300 (72; 66.5\u0026ndash;76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117 (167.626)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFarm purpose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e909/1267 (71.7; 69.2\u0026ndash;74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.3 (169.650)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDairy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e676/1828 (36.8; 34.6\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (189.072)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIntroduction of animals from other farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e755/1715 (44; 41.7\u0026ndash;46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.7 (153.173)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e830/1380 (60.1; 57.5\u0026ndash;62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.8 (198.249)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePresence of other ruminants on the farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1533/3024 (50.7; 48.9\u0026ndash;52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.8 (176.301)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52/71 (73.2; 61.2\u0026ndash;82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.5 (227.308)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePresence of carnivores on the farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1011/1668 (60.6; 58.2\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.1 (174.772)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e574/1427 (40.2; 37.7\u0026ndash;42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.5 (184.167)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProximity to other farms (\u0026lt;\u0026thinsp;2km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60/103 (58.3; 48-67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118.8 (133.528)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1525/2992 (51; 49.2\u0026ndash;52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.7 (179.682)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuarantine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1363/2641 (51.6; 49.7\u0026ndash;53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.8 (179.388)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e222/454 (48.9; 44.2\u0026ndash;53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.3 (169.646)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUse of anthelmintics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e267/536 (49.8; 45.5\u0026ndash;54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.4 (90.794)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1318/2559 (51.5; 49.5\u0026ndash;53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.7 (190.512)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDeworming frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e268/537 (49.9; 45.6\u0026ndash;54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.3 (90.652)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e443/1168 (37.9; 35.1\u0026ndash;40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.6 (199.775)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwice a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e852/1318 (64.6; 62-67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.6 (187.334)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThree times a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22/72 (30.6; 20.5\u0026ndash;42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.9 (111.845)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eAnthelmintic drugs used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e268/537 (49.9; 45.6\u0026ndash;54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.4 (90.794)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenzimidazoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30/181 (16.6; 11.6\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.8 (166.681)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacrocyclic lactones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e689/1382 (49.9; 47.2\u0026ndash;52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104.4 (149.848)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u0026thinsp;+\u0026thinsp;ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e296/458 (64.6; 60\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.3 (203.313)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClosantel\u0026thinsp;+\u0026thinsp;ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e206/324 (63.6; 58.1\u0026ndash;58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (298.563)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLM\u0026thinsp;+\u0026thinsp;Oxyclozanide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81/162 (50; 42.4\u0026ndash;57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.4 (81.737)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eBedding material\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtensive management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e411/491 (83.7; 80.1\u0026ndash;86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.1 (197.381)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63/92 (68.5; 57.8\u0026ndash;77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.8 (85.853)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e469/1018 (46.1; 43-49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.4 (155.893)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168/312 (53.9; 48.1\u0026ndash;59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.9 (287.219)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSawdust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105/540 (19.4; 16.2\u0026ndash;23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.7 (139.017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirectly on the floor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e369/642 (57.5; 53.5\u0026ndash;61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.9 (122.753)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBed cleaning frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtensive management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e423/506 (83.6; 80-86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.4 (196.160)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e961/2264 (42.4; 40.4\u0026ndash;44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.2 (153.395)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;7 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75/151 (49.7; 41.5\u0026ndash;57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130.6 (218.259)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 days or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126/174 (72.4; 65-78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.6 (235.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eWater troughs cleaning frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84/166 (50.6; 42.8\u0026ndash;58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (128.405)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccording to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e810/1396 (58; 55.4\u0026ndash;60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.8 (171.434)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e272/506 (53.8; 49.3\u0026ndash;58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.7 (226.310)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;4 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162/477 (34; 29.8\u0026ndash;38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.9 (45.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026ndash;14 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215/469 (45.8; 41.3\u0026ndash;50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127.3 (213.249)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 days or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9/44 (20.5; 10.3\u0026ndash;35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8 (3.308)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33/37 (89.2; 73.6\u0026ndash;96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.6 (72.034)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFarm floor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtensive management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e405/456 (88.8; 85.5\u0026ndash;91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127.9 (198.291)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332/392 (84.7; 80.7\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.8 (146.095)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57/77 (71.4; 59.8\u0026ndash;80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234.9 (377.745)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlatted floor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e791/1570 (50.4; 47.9\u0026ndash;52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.8 (148.277)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCorridors cleaning frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccording to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41/71 (57.8; 45.5\u0026ndash;69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e218.5 (248.033)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e957/2268 (42.2; 40.2\u0026ndash;44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.6 (163.885)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;7 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105/175 (60; 52.3\u0026ndash;67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.5 (93.389)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 days or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96/146 (65.8; 57.4\u0026ndash;73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155.4 (258.252)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e386/435 (88.7; 85.3\u0026ndash;91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.2 (186.937)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUse of slurry scraper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1467/2355 (62.3; 60.3\u0026ndash;64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.6 (183.168)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118/740 (15.9; 13.4\u0026ndash;18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.8 (81.873)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSlurry scraper frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1467/2355 (62.3; 60.3\u0026ndash;64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.6 (183.168)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94/380 (24.7; 20.5\u0026ndash;29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.5 (88.790)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 or more times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24/360 (6.7; 4.4\u0026ndash;9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.1 (45.530)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eContact with fresh pasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0/49 (0; 0-0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70/432 (16.2; 12.9\u0026ndash;20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.9 (53.671)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1515/2614 (58; 56-59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.3 (181.428)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePasture rotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128/375 (34.1; 29.4\u0026ndash;39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.6 (153.739)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1433/2637 (54.3; 52.4\u0026ndash;56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.2 (181.397)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24/83 (28.9; 19.7\u0026ndash;40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (71.735)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePresence of water courses on the pasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217/515 (42.1; 37.9\u0026ndash;46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.8 (247.157)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1358/2511 (54.1; 52.1\u0026ndash;56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.8 (164.716)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10/69 (14.5; 7.5\u0026ndash;25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5 (6.399)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePresence of water troughs on the pasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e393/630 (62.4; 58.5\u0026ndash;66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.1 (93.601)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1168/2382 (49; 47-51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108.6 (198.750)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24/83 (28.9; 19.7\u0026ndash;40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (71.735)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUse of slurry as fertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11/55 (20; 10.9\u0026ndash;33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229.5 (205.874)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1574/3040 (51.8; 50-53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.7 (177.692)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSlurry origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExternal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7/49 (14.3; 6.4\u0026ndash;27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244 (213.115)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOwn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1578/3046 (51.8; 50-53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (177.813)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIntroduction of youg animals on the pasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e390/875 (44.6; 41.3\u0026ndash;47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.6 (193.424)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1195/2220 (53.8; 51.7\u0026ndash;55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.9 (172.973)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eAge at start of grazing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28/118 (23.7; 16.6\u0026ndash;32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.8 (68.436)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e489/647 (75.6; 72-78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.4 (189.725)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;30 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115/175 (65.7; 58.1\u0026ndash;72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.9 (102.202)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;5 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172/293 (58.7; 52.8\u0026ndash;64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.5 (178.410)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;9 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194/552 (35.1; 31.2\u0026ndash;39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128.5 (200.929)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;14 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e348\u0026ndash;620 (56.1; 52.1\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.3 (198.264)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98/333 (29.4; 24.7\u0026ndash;34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.9 (146.383)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 months or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141/357 (39.5; 34.4\u0026ndash;44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.9 (103.528)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUse of individual boxes for calves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1265/2143 (59; 56.9\u0026ndash;61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.9 (165.431)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e320/952 (33.6; 30.6\u0026ndash;36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.1 (220.834)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePositivity to gastrointestinal nematodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1149/2350 (48.9; 46.9\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.6 (187.137)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e435/744 (58.7; 55.1\u0026ndash;62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.6 (152.911)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePositivity to \u003cem\u003eFasciola hepatica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1483/2985 (49.7; 47.9\u0026ndash;51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.2 (179.586)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102/110 (92.7; 85.7\u0026ndash;96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130.9 (152.911)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePositivity to \u003cem\u003eDicrocoelium dendriticum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1535/3034 (50.6; 48.8\u0026ndash;52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.3 (177.731)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11/61 (18; 9.8\u0026ndash;30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167.7 (178.907)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* The confidence interval was calculated using the prop.test() function of R\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cem\u003eVariables and categories considered in the risk factor analysis for the prevalence and egg shedding of paramphistomids in cattle from northwestern Spain (placed at the end of the document)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe mixed logistic regression model extracted four factors influencing the probability of infection by paramphistomids: region, age, positivity to \u003cem\u003eF. hepatica\u003c/em\u003e and use of slurry scraper (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel obtained by mixed logistic regression for the prevalence of paramphistomids in cattle from northwestern Spain\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (Galicia)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (Asturias)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 1 (1\u0026ndash;24 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 2 (25\u0026ndash;60 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u0026ndash;10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 3 (\u0026gt;\u0026thinsp;60 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.64\u0026ndash;30.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative to \u003cem\u003eF. hepatica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive to \u003cem\u003eF. hepatica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4\u0026ndash;51.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of slurry scraper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo slurry scraper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.1-487.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cp\u003e- Reference category\u003c/p\u003e \u003cp\u003eStatistical analysis revealed that animals from Asturian farms are 11.4 times more likely to be infected with paramphistomids than those from Galicia. Prevalence increased with age; the probability of being positive in animals aged 0\u0026ndash;24 months was 5.8 times lower than those aged 25 to 60 months and 17.1 times lower than cattle over 60 months. Significant differences were also found between animals aged 25\u0026ndash;60 months and those over 60 months, with the older age group presenting a 2.97-fold higher probability of infection. In addition, infection with \u003cem\u003eF. hepatica\u003c/em\u003e significantly influenced the occurrence of paramphistomid infections; thus, animals shedding liver fluke eggs showed a 13.1 times higher probability of having paramphistomid infections. Finally, the probability of infection was up to 76.9 times higher in farms that did not use scrapers.\u003c/p\u003e \u003cp\u003eANOVA analysis demonstrated that age, climatic area, type of management, and positivity to \u003cem\u003eF. hepatica\u003c/em\u003e significantly influence egg shedding (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis revealed a clear trend in age: egg counts increased progressively and significantly with age; thus, animals older than 60 months showed the highest egg shedding. Pairwise analyses detected significant differences among all age groups: between animals aged 1\u0026ndash;24 months and 25\u0026ndash;60 months (p\u0026thinsp;=\u0026thinsp;0.003), between those aged 1\u0026ndash;24 months and \u0026gt;\u0026thinsp;60 months groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and between cattle aged 25\u0026ndash;60 months and \u0026gt;\u0026thinsp;60 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, animals from coastal areas showed significantly higher egg shedding than animals from the central area (p\u0026thinsp;=\u0026thinsp;0.002). Regarding management, animals from extensive farms showed significant higher eliminations than those from farms with semiextensive (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or intensive (p\u0026thinsp;=\u0026thinsp;0.002) management. Finally, animals positive to \u003cem\u003eF. hepatica\u003c/em\u003e significantly shed more eggs of paramphistomids than those negative to the liver fluke (p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmplicons of the expected size were obtained in 31 out of 41 pools. \u003cem\u003eCalicophoron daubneyi\u003c/em\u003e was the only paramphistomid species identified in all samples. All sequences were identical between them and with those previously obtained in domestic ruminants from different European countries [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] including sequences recently obtained in sheep from Galicia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings reveal that paramphistomid infections in cattle from north-western Spain keep increasing in recent years, suggesting that infection rates are not stabilizing. The prevalences found in the present study are noticeably higher than those previously reported in beef and dairy farms of Galicia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our data was even higher than that detected in dairy organic farms from north-western Spain, where the risk of parasite infection is particularly high due to outdoor rearing of livestock and legal restrictions on the use of anthelmintics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In contrast, recent data on sheep from Galicia showed a substantially lower prevalence (14%)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which is consistent with previous reports in Europe [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These evidence, together with results of experimental infections [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], suggest that paramphistomids are better adapted to cattle.\u003c/p\u003e \u003cp\u003eThe infection rates recorded in this investigation are among the highest in Europe and similar to those reported in some countries of the British Islands such as Ireland (48.8\u0026ndash;53.8%)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] or Scotland (43.3%) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, infection rates in cattle across the rest of Europe are noticeably lower; a thorough review of available data suggests that countries located further east tend to report lower prevalence rates. For example, a recent study in western France (Normandy) showed a prevalence of 29.9% [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], although in France, prevalences as high as 50% were recorded in the 1990\u0026rsquo;s (Mage et al., 2002). Similar infection rates were found in Belgium (28.8%; [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and the Czech Republic (29.9%; Červen\u0026aacute; et al., 2022) whereas lower percentages of infection were recorded in cattle from Germany (12.7%;[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] or Italy (10.9%; [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The highest prevalences detected in the westernmost part of Europe may be related to its climate, since the abundant rainfall and moderate temperatures throughout the year favour the development of the life cycle of rumen flukes. This climate could enhance the survival of the external stages of rumen flukes in the environment, as they are less exposed to desiccation and extreme temperatures [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this sense, it has been proven that miracidia can infect snails in a temperature range of 1-35\u003csup\u003eo\u003c/sup\u003eC [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, moderate temperatures throughout the year can extend the period of activity of intermediate hosts [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA mean egg shedding of 96.6 epg was recorded. Comparing egg counts can be very complex, as the sensitivity of the techniques used can lead to marked differences in epg values. However, a good correlation has been demonstrated between the shedding of paramphistomid eggs and the parasite burden in the host\u0026rsquo;s forestomachs, in contrast to that reported for hepatic trematodes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding risk analysis, the logistic regression revealed that the probability of cattle being infected with rumen flukes was significantly higher in Asturias than in Galicia. Specifically, higher individual (68.7%), herd (91.2%) and intra-herd (80%) prevalences were found in Asturias than in Galicia (45.6%, 78.6% and 60.2%, respectively). Since both regions have a similar climate, the observed differences may be due to variations in health management practices. In Spain, animal health issues mainly depend on regional governments, leading to substantial differences between regions. In addition, the availability of previous data on cattle in Galicia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] along with the education and training provided to farmers and veterinarians through numerous informative sessions in this region (D\u0026iacute;az, personal communication) has increased awareness of the presence of paramphistomids, favouring the implementation of more suitable preventive and control measures.\u003c/p\u003e \u003cp\u003eThe risk analysis also revealed a significant and direct relationship between the age of the animals and both the probability of infection and egg shedding, being consistent with previous studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50 CR51\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. All this data suggests that cattle only develop a partial protective immunity against future infections, at least against juvenile paramphistomids, since clinical cases usually occur in young animals [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] This immunity may prevent the establishment of large numbers of paramphistomids in the duodenum but does not prevent some of them from completing their development. In this regard, the longevity of paramphistomids must be also considered, since their life span can extend up to 10 years, leading to an accumulation of parasites throughout the animal's lifetime [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and, consequently, to the higher prevalence and egg shedding values found in the oldest animals. Thus, further research is needed to explore the influence of age on rumen fluke infection and the dynamics between these parasites and the host's immune system.\u003c/p\u003e \u003cp\u003eAnimals infected with \u003cem\u003eF. hepatica\u003c/em\u003e had a significant higher risk of testing positive for paramphistomids and shed a significantly higher number of paramphistomid eggs. Although this effect on egg shedding had not been previously documented in cattle, it has recently been observed in \u003cem\u003eF. hepatica\u003c/em\u003e positive sheep from Galicia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, a similar effect on \u003cem\u003eF. hepatica\u003c/em\u003e egg shedding had been described in animals infected by paramphistomids [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In Europe, both parasites share their intermediate host, \u003cem\u003eG. truncatula\u003c/em\u003e. Consequently, if an animal is infected with \u003cem\u003eF. hepatica\u003c/em\u003e, it is likely to have frequented the same areas where the metacercariae of paramphistomids are primarily found. In addition, it has been demonstrated that \u003cem\u003eF. hepatica\u003c/em\u003e exerts an immunomodulatory effect on the host that facilitates infection by other agents [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], so its influence in this regard cannot be ruled out.\u003c/p\u003e \u003cp\u003eThe statistical analysis also indicated that animals from farms that did not use scrapers were 76.9 times more likely to be infected with paramphistomids. This factor may be related to the level of professionalization, which is challenging to quantify but clearly influences prevalence rates [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In the studied area, the farms using scrapers tend to be more professionalized, and their animals spend more time indoors. In addition, faeces collected by the scrapers fall into a slurry pit, where fermentative processes destroy the parasitic forms [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Thus, this slurry can be used as fertilizer without contributing to increase the environmental contamination. In any case, further studies are needed to unravel the effect of farm professionalization on the prevalence of rumen flukes.\u003c/p\u003e \u003cp\u003eThe differences in egg elimination between climatic areas, particularly between the coastal and the central area, may be due to more favourable conditions for the survival of the external stages of the parasite in coastal areas [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], where winters are milder and humidity is high throughout the year [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These conditions enhance the survival of the parasite in the environment, leading to an increased parasite burden in pastures and, therefore, in the animals grazing there.\u003c/p\u003e \u003cp\u003eThe greater elimination of eggs observed in extensive farms compared to semi-extensive or intensive farms is probably due to the daily and continuous contact with the parasite [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]; in contrast, cattle from semi-extensive farms have intermittent contact with metacercariae, while those from intensive farms are only exposed to the parasite when fed with fresh grass. In addition, in extensive farming, sanitary control and deworming are more challenging and less frequent, which may result in higher parasite burdens.\u003c/p\u003e \u003cp\u003eFinally, our results confirm that \u003cem\u003eC. daubneyi\u003c/em\u003e is the most common paramphistomid species in northwestern Spain, agreeing with previous studies in both cattle and sheep [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although \u003cem\u003eParamphistomum leydeni\u003c/em\u003e has been previously found in cattle, sheep and wild ruminants from different European countries [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], further investigations are needed for determining whether \u003cem\u003eC. daubneyi\u003c/em\u003e is the only paramphistomid species present in north-western Spain.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results reveal a growing trend in the prevalence of paramphistomid infections in cattle from Galicia over the last years, despite the availability of data and ongoing information campaigns. The situation in Asturias is particularly noteworthy, given the lack of prior individual data and the significantly high prevalence rates observed. Consequently, it is imperative to continue monitoring the situation in these regions, enhance awareness among livestock farmers and veterinarians, and carry out farm-specific risk assessments to implement the most effective pharmacological and management interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANOVA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBLAST\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBasic Local Alignment Search Tool\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eepg\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEggs per gram of faeces\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eITS-2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternal transcribed spacer 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eopg\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOocysts per gram of faeces\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll faecal samples used in this study were collected with the permission of the farm owners. All experimental procedures fully complied with European and Spanish ethics regulations on the protection of animals used for scientific purposes (European Directive 2010/63/EU and Spanish Royal Decree 53/2013) and approved by the ethical committee of the University of Santiago de Compostela.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the conclusions of this article are included within the article. A more detailed dataset used during the current study is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study has received funding from the Program for consolidating and structuring competitive research groups (ED431C 2019/04 and ED431C2023/16, Xunta de Galicia, Spain).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: PD, CML; Methodology: CML, PD; Formal Analysis: DGD, CML; Investigation: DGD, PD, AS, SR; Resources: CF, DGD, PD, NMC; Writing original draft: DGD; Writing, revision and Editing: PD, CML, RP, PM; Visualization: DGD; Funding: PM, RP. All authors reviewed and accepted the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the veterinarians and farmers of the participating farms for their collaboration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eV\u0026aacute;zquez Gonz\u0026aacute;lez I. Situaci\u0026oacute;n actual, din\u0026aacute;mica y estrategias de las explotaciones con bovino en el norte de Espa\u0026ntilde;a. Thesis dissertation. Universidade de Santiago de Compostela; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Su\u0026aacute;rez E, Garc\u0026iacute;a-Arias AI, V\u0026aacute;zquez-Gonz\u0026aacute;lez I. Situaci\u0026oacute;n productiva reciente de las explotaciones con bovino en Espa\u0026ntilde;a: el caso de la Cornisa Cant\u0026aacute;brica. 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Ir Veterinary J. 2024;77(1):21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusin V, Geddes E, Robertson G, Mitchell G, Skuce P, Waine K, Millins C, Forbes A. A study into the identity, patterns of infection and potential pathological effects of rumen fluke and the frequency of co-Infections with liver fluke in cattle and sheep. Ruminants. 2023;3(1):38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Toole A, Browne JA, Hogan S, Bassi\u0026egrave;re T, DeWaal T, Mulcahy G, Zintl A. Identity of rumen fluke in deer. Parasitol Res. 2014;113(11):4097\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiedermann S, Harl J, Fuehrer H, Mayr S, Schmid J, Hinney B, Rehbein S. DNA barcoding of rumen flukes (Paramphistomidae) from bovines in Germany and Austria. Parasitol Res. 2021;120(12):4061\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToolan DP, Mitchell G, Searle K, Sheehan M, Skuce PJ, Zadoks RN. Bovine and ovine rumen fluke in Ireland - Prevalence, risk factors and species identity based on passive veterinary surveillance and abattoir findings. Vet Parasitol. 2015;212(3\u0026ndash;4):168\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanna G, Varcasia A, Serra S, Salis F, Sanabria R, Pipia AP, Dore F, Scala A. \u003cem\u003eCalicophoron daubneyi\u003c/em\u003e in sheep and cattle of Sardinia, Italy. Helminthologia (Poland). 2016;53(1):87\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlstedt U, Voigt K, J\u0026auml;ger MC, Knubben-Schweizer G, Zablotski Y, Strube C, Wenzel C. Rumen and Liver Fluke Infections in Sheep and Goats in Northern and Southern Germany. Animals 2022, 12(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMay K, Raue K, Blazejak K, Jordan D, Strube C. Pasture rewetting in the context of nature conservation shows no long-term impact on endoparasite infections in sheep and cattle. Parasit Vectors. 2022;15(1):33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorak IG. Host-parasite relationships of \u003cem\u003eParamphistomum microbothrium\u003c/em\u003e Fischoeder, 1901, in experimentally infested ruminants, with particular reference to sheep. Onderstepoort J Vet 1967, 34(2):451\u0026ndash;540.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorak IG. Paramphistomiasis of Domestic Ruminants. Adv Parasitol. 1971;9:33\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtcheson E, Lagan B, McCormick R, Edgar H, Hanna REB, Rutherford NH, McEvoy A, Huson KM, Gordon A, Aubry A, Vickers M, Robinson MW, Barley JP. The effect of naturally acquired rumen fluke infection on animal health and production in dairy and beef cattle in the UK. Front Vet Sci 2022 Aug 18:9968753.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalrait K, Verschave S, Skuce P, Van Loo H, Vercruysse J, Charlier J. Novel insights into the pathogenic importance, diagnosis and treatment of the rumen fluke (\u003cem\u003eCalicophoron daubneyi\u003c/em\u003e) in cattle. Vet Parasitol. 2015;207(1\u0026ndash;2):134\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamnaliev P, Vassilev I. Ecology of the larval and parthenite stages of \u003cem\u003eParamphistomum microbothrium\u003c/em\u003e. I. effect of the temperature, UV [ultraviolet] rays and X ray irradiation on the development of eggs. Khelmintologiia. 1976;1:88\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones RA, Williams HW, Mitchell S, Robertson S, Macrelli M. Exploration of factors associated with spatial\u0026thinsp;\u0026ndash;\u0026thinsp;temporal veterinary surveillance diagnoses of rumen fluke (\u003cem\u003eCalicophoron daubneyi\u003c/em\u003e) infections in ruminants using zero-inflated mixed modelling. Parasitology. 2022;149(2):253\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRieu E, Recca A, B\u0026eacute;net JJ, Saana M, Dorchies P, Guillot J. Reliability of coprological diagnosis of \u003cem\u003eParamphistomum\u003c/em\u003e sp. infection in cows. Vet Parasitol. 2007;146(3):249\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul AK, Talukder M, Begum K, Rahman MA. Epidemiological investigation of Paramphistomiasis in cattle at selected areas of Sirajgonj district of Bangladesh. J Bangladesh Agril Univ. 2011;9(2):4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzam MG, Begum N, Ali MH. Status of amphistomiasis in cattle at Joypurhat district of Bangladesh. Bang J Anim Sci. 2012;40(1\u0026ndash;2):34\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreethi M, Venu R, Srilatha C, Rao KS, Rao PV. Prevalence of paramphistomosis in domestic ruminants in Chittoor district of Andhra Pradesh, India. Agr Sci Digest. 2020;40(1):61\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeguini MN, Righi S, Bouchekhchoukh M, Sedraoui S, Benakhla A. Investigation of flukes (\u003cem\u003eFasciola hepatica\u003c/em\u003e and \u003cem\u003eParamphistomum\u003c/em\u003e sp.) parasites of cattle in north-eastern Algeria. Ann Parasitol. 2021;67(3):455\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDinnik JA. Intestinal paramphistomiasis and \u003cem\u003eParamphistomum microbothrium\u003c/em\u003e Fischoeder in Africa. Bull Epizoot Dis Afr 1964, 12(4):439\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuson KM, Atcheson E, Oliver NAM, Best P, Barley JP, Hanna REB, McNeilly TN, Fang Y, Haldenby S, Paterson S, Robinson MW. 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Rumen (\u003cem\u003eCalicophoron\u003c/em\u003e/\u003cem\u003eParamphistomum\u003c/em\u003e spp.) and Liver Flukes (\u003cem\u003eFasciola hepatica\u003c/em\u003e) in Cattle-Prevalence, Distribution, and Impact of Management Factors in Germany. Animals 2021, 11(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Ibeas AM, Munita MP, Lawlor K, Sekiya M, Mulcahy G, Sayers R. Rumen fluke in Irish sheep: prevalence, risk factors and molecular identification of two paramphistome species. BMC Vet Res. 2016;12(1):143.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorariu S, S\u0026icirc;rbu CB, T\u0026oacute;th AG, Dărăbuș G, Oprescu I, Mederle N, Ilie MS, Imre M, S\u0026icirc;rbu BA, Solymosi N, Florea T, Imre K. First Molecular Identification of \u003cem\u003eCalicophoron daubneyi\u003c/em\u003e (Dinnik, 1962) and \u003cem\u003eParamphistomum leydeni\u003c/em\u003e (Nasmark, 1937) in Wild Ruminants from Romania. Vet Sci. 2023;10(10):603.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Paramphistomids, Calicophoron daubneyi, cattle, epidemiology, risk factors, molecular identification, Spain","lastPublishedDoi":"10.21203/rs.3.rs-6804093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6804093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAn upward trend of paramphistomid prevalence was detected in domestic ruminants from Europe in the last decades. Nevertheless, recent data from Ireland, which reports the highest prevalences in Europe, suggests that this trend may be stabilizing. This study analysed the current epidemiological situation of rumen fluke infections in cattle from northwestern Spain, focusing on two regions: Galicia, where increasing prevalences were reported, and Asturias, where data is limited. Between 2018 and 2022, 3,095 faecal samples from 137 farms were analysed using sedimentation coprological technique. Risk factor analysis was conducted through mixed logistic regression and ANOVA; paramphistomid species were molecularly identified.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigh individual (51.2%; 95% CI: 49.4\u0026ndash;53.0) and herd (81.8%; 95% CI: 74.1\u0026ndash;87.6) prevalences were found, representing the highest recorded prevalence of paramphistomids in cattle from Spain. Prevalence was significantly influenced by region (Asturias: OR 11.4), age (\u0026gt;\u0026thinsp;60 months: OR 17.1; 25\u0026ndash;60 months: OR 5.8), co-infection with \u003cem\u003eFasciola hepatica\u003c/em\u003e (OR 13.1) and absence of slurry scrapers (OR 76.9). Egg shedding intensity was notably higher in older animals and those co-infected with \u003cem\u003eF. hepatica\u003c/em\u003e as well as in farms from coastal areas and using extensive management. Molecular analysis confirmed \u003cem\u003eCalicophoron daubneyi\u003c/em\u003e as the most common species.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings reveal a continued upward trend in rumen fluke prevalence in cattle from north-western Spain, suggesting that a stabilization of prevalence is not occurring. The study highlights the urgent need for targeted surveillance, farmer education, and integrated control measures in north-western Spain, especially in Asturias, where infection rates are particularly high.\u003c/p\u003e","manuscriptTitle":"Beyond stabilization: prevalence, risk factor and molecular identification of rumen flukes in cattle from northwestern Spain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 11:08:12","doi":"10.21203/rs.3.rs-6804093/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-25T07:15:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T08:57:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T15:21:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270930775885749047684564872079511207768","date":"2025-06-25T06:19:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93894360314135363601143542835889303401","date":"2025-06-24T13:14:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16886294131976603322143690741084188911","date":"2025-06-24T04:21:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T11:45:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-04T11:21:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T05:43:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-04T05:38:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-06-02T16:36:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c14831d-db45-48b4-ae4e-bab556661171","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:08:14+00:00","versionOfRecord":{"articleIdentity":"rs-6804093","link":"https://doi.org/10.1186/s12917-025-05009-y","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2025-10-02 15:58:15","publishedOnDateReadable":"October 2nd, 2025"},"versionCreatedAt":"2025-06-17 11:08:12","video":"","vorDoi":"10.1186/s12917-025-05009-y","vorDoiUrl":"https://doi.org/10.1186/s12917-025-05009-y","workflowStages":[]},"version":"v1","identity":"rs-6804093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6804093","identity":"rs-6804093","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}