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Comparison of diagnostic methods for assessment of Ostertagia ostertagi exposure in Norwegian dairy herds



The gastrointestinal nematode (GIN) Ostertagia ostertagi can cause severe disease in first season grazers (FSG) and impaired performance due to subclinical infections in adult cows. Diagnostic methods to assess exposure include faecal egg count and detection of specific antibodies using antibody-ELISAs resulting in an optical density ratio (ODR). Using the ELISA test on bulk tank milk (BTM) allows for a herd level diagnosis. Appropriate use of diagnostic methods for evaluation of O. ostertagi exposure is required to optimize herd parasite surveillance and aid in a sustainable control regime. The aim of this study was to describe the relationship between different diagnostic tests used to assess GIN exposure in Norwegian production systems. A cross-sectional field study was carried out in twenty herds in Norway in the fall of 2020. Serum and faecal samples were taken from 380 individuals, of which 181 were FSG and 199 were cows. In addition, milk was collected from every cow and one BTM sample was taken from each herd. Faecal egg counts were performed. The distribution of ODR values in individual samples within and between herds and the associations between BTM ODR and individual ODR values were described. The data were analysed using visual assessment of scatter plots, Pearson correlation coefficients and linear regression.


A high variability of the within-herd individual ODR values in serum and milk in every herd was detected. The ODR in BTM explained a low degree of the variation in the individual serum and milk samples. When plotting the ODR results in milk or serum according to four BTM categories, the distribution of ODR values were notably different in the highest and lowest BTM categories. The correlation between individual milk and serum samples was moderate (r = 0.68), while the highest correlation (r = 0.81) was between the BTM ODR and the group average individual milk samples.


A poor predictive ability for BTM ODR to assess individual ODR values in both FSG and cows was demonstrated. However, the study indicates that the evaluation by ELISA test on BTM to assess exposure to GIN could be useful in herds with a very high or low BTM ODR.


The gastrointestinal nematode (GIN) Ostertagia ostertagi is among the most important parasites contributing to bovine parasitic gastroenteritis in temperate and subtropical regions [1]. The developing larvae destroy the glandular tissue in the abomasum compromising digestion [2]. Severe disease can occur in first season grazers (FSG), whereas in adult animals, subclinical infections associated with economic losses due to impaired performance including reduced milk yield are common [3,4,5,6]. Treatment with anthelmintics has been extensively used to control parasite infection, however, as reviewed by Rose et al. [7], an evolving anthelmintic resistance has been detected in several countries. Due to strict regulations concerning food safety and ecotoxicity concerns, the development of new anthelmintic products is not considered to keep pace [8]. To optimize herd parasite surveillance and target treatment to reduce unnecessary use of anthelmintics, knowledge of appropriate and correct use of diagnostic methods is required [9, 10].

Ostertagiosis can be diagnosed by faecal egg counts (FEC) of nematode eggs and reported in eggs per gram (EPG), determination of serum pepsinogen levels, or by measuring parasite-specific serum antibody levels [11]. Molecular methods, such as qPCR, ddPCR, automated PCR platforms and DNA sequencing technologies, are more recent methods for detection and quantification, as well as detailed studies into GIN species diversity [12,13,14,15]. The use of FEC is the most widely used diagnostic technique for monitoring infection patterns in FSG as it is non-invasive, relatively cost-effective and does not require sophisticated laboratory equipment [16]. However, it correlates poorly with worm burden and subclinical production losses [17]. The relationship between FEC and worm burden may only be consistent until 2 months after onset of the pasture period. After that time period, the method loses diagnostic value as the acquired immunity restricts the fecundity of established adult worms [18, 19]. Performing FEC is still applicable to estimate pasture contamination with parasite eggs and to monitor the efficacy of anthelmintic treatment by interpretation of a FEC reduction test [8, 20, 21]. Previous exposure to O. ostertagi can be assessed by measuring serum pepsinogen levels, which increase due to release of accumulated pepsinogen into the blood stream as a sequela to abomasal worm activity [22]. A rising serum pepsinogen level has shown significant correlation with O. ostertagi adult-worm burden at slaughter [23], but the titre decreases rapidly in the absence of continuous exposure to the abomasal worm. Conversely, the antibody level may further increase after housing, due to the lag phase between infection and the appearance of antibodies [11]. An enzyme-linked immunosorbent assay (ELISA) using a crude adult O. ostertagi antigen can be used to detect antibodies against O. ostertagi in serum and milk. The results are expressed as optical density ratios (ODR). The antibody level has shown a weak correlation with parasite loads [24] and reflects the level of previous exposure rather than active infection [9]. On an individual level, associations with lactation number, days in milk (DIM) and milk yield have been described [25,26,27]. Furthermore, the specificity of the O. ostertagi antibody ELISA test is affected by cross reactivity with antibodies raised against other helminths such as Cooperia oncophora [28] and Fasciola hepatica [17]. For adult cows, both individual milk (IM) samples as well as bulk tank milk (BTM) samples can be tested by ELISA. The use of BTM testing instead of individual samples to assess exposure has both practical and economic benefits, as BTM sampling is easy, non-invasive, and often routinely performed. However, the choice of sampling schemes and the antibody titre being affected by the number and relative seropositivity of contributors are among many factors to consider when interpreting the ODR in BTM [29]. Animals not producing milk do not contribute to the sample, which may be a factor to consider when evaluating the results of a BTM test. Nonetheless, studies have shown a significant positive association between herd exposure to pasture and increased OD-values in the BTM [6, 30,31,32], even when considering heifers [3]. This encourages the use of BTM ELISA as a herd-level test at the end of the pasture season. Moreover, vast regional differences in exposure level have been detected in the northwestern European dairy herds, where the BTM ODR were associated with pasture management and climatic factors [32, 33]. Screening of BTM to identify high infection clusters could thus be used to inform monitoring and control programs in these regions. Additionally, calculations of economic loss due to GIN infections have been documented [9, 34,35,36,37] and may encourage farmers to perform diagnostic testing in their herds [15]. A conversion chart indicating milk yield loss when BTM ODR > 0.5 has been developed [38, 39]. This threshold is only validated for some European countries, and it has been emphasised that regional epidemiological surveys are required to validate diagnostic assessments that are applicable in different production systems [38].

Current knowledge about pasture parasites in Norway is limited. No national prevalence studies of O. ostertagi have been performed and the availability of commercial diagnostic tests is scarce. Norwegian cattle herds are relatively small, with a mean herd-size of 29.3 cow-years in 2020 [40]. The pasture season for Norwegian dairy cattle, occurring from May to October, has an average duration of 4.3 months [41], which is short compared to the average grazing season duration in other northwestern European countries [33]. Prior to implementing new methods for quantification of GIN infections, there is a need to investigate how different diagnostic tests can assess GIN exposure in Norwegian production systems. This is an important step before these can be established as a herd-health management tool to aid in the identification of which cows, herds or regions would benefit from improvements in pasture parasite management. The aims of this study were: (1) to describe the correlation between ODR values in milk and serum samples using a O. ostertagi antibody ELISA and to describe the distribution of ODR values within and between herds, (2) to investigate associations between BTM ODR and individual ODR values and (3) to evaluate the FEC in samples from these herds.


Study population

A cross-sectional field study was carried out in 20 herds in Norway in the autumn of 2020. Herd visits were conducted from August 30th to November 2nd 2020. Each herd was visited once. The herds were located in the eastern counties of Oslo (n = 1), Viken (n = 12), and Telemark (n = 4), as well as in the county Rogaland (n = 3) on the southwest coast of Norway. Farmers were recruited between March and September of 2020, primarily through acquaintance with project participants and via webpages targeting both dairy farmers and local veterinarians. Purposive convenience sampling was used to select herds in which exposure to parasites was expected based on information provided by the farmers. All included herds were members of the Norwegian Dairy Herd Recording System (NDHRS), allowing extraction of health and production data from farm records. The animals sampled had all been on pasture during the previous grazing season, which, according to Norwegian animal welfare legislation, is a requirement for all female cattle older than 6 months. The FSG in the study were heifers between 9 months and 2 years of age. They mostly grazed on home pastures that had been used for FSG every year, with no systematic approach to rotational grazing. Farmers that had treated with anthelmintics in 2020 were excluded. The farmers were provided with an information letter describing the use of data for research and publication and agreed to sampling and sharing of farm records.

The selected herds were all visited shortly (1–21 days) after housing. The sampling was done by the first author, or by local veterinarians following a written protocol provided by the project. At each herd visit, blood samples from the tail vein and IM samples were taken from a convenience sample of 10 lactating cows, including cows of different ages and lactation stages, as far as possible. Additionally, blood samples were taken from preferably 10, but at least 5 FSG. The number of samples was maximised within the constrains in terms of time and budget. Faecal samples were collected with a gloved hand from the rectum from all individuals. The plastic glove was immediately inverted and tied allowing storage of the faecal sample in the glove used for collection. One BTM sample was taken in each herd. The milk and blood samples were collected by the milk truck driver at the ordinary pick-up of milk and cooled at a temperature of 2–4 °C until freezing at the laboratory (TINE Laboratory, Molde, Norway). The blood samples were centrifuged to obtain serum, and milk and serum samples were subsequently stored at − 20 °C for up to 5 months before analysis. The faecal samples were brought to the Parasitology Laboratory at the Faculty of Veterinary Medicine at the Norwegian University of Life Sciences immediately after collection, or for faecal samples collected by local veterinarians, shipped to the laboratory by overnight express mail.

The collection of samples for this study was considered a non-experimental clinical procedures and did not require ethical approval in Norway.

Laboratory analyses of faeces, blood and milk

Egg counts in 3 g of faeces, recorded as EPG, were determined using a modified McMaster method [42, 43]. Levecke et al. [44] investigated the analytical properties of a modified McMaster technique similar to the one used in the present study and reports an analytical sensitivity of 10–50 EPG. Two of the project participants were responsible for the FEC, which was performed within 4 days after sampling. After analysis, the faecal samples were stored in vacuum sealed plastic bags. The analyses of serum and milk were carried out at TINE Mastitis Laboratory in Molde. Specific antibodies against O. ostertagi in BTM, IM, and individual serum (IS) samples were detected using the SVANOVIR® Ostertagia-Ab ELISA kit (Svanova Biotech, Uppsala, Sweden), which is a semi-quantitative test based on a crude adult-worm capture antigen [45]. The test is commercially available and marketed for use on BTM samples, however, it has been used for serum and milk samples [4, 46, 47] and a recent review confirmed its suitability to assess past GIN exposure in FSGs after housing [48]. The IM samples were used undiluted, and the IS samples were diluted 1:140 before being tested. Results from all tests were expressed as ODRs, which were calculated following the formula ODR=(OD / NC)/(PC /NC), where NC and PC are the OD values of the negative control and positive control, respectively.

Production data

To collect information about the herds from a period relevant to the time of sampling, we chose to extract data from the NDHRS for a study period from May 1st 2020 to April 31st 2021. The retrieved herd level variables were: herd size (cow-years, where one cow-year equals 365 days for a cow in a herd, calculated for each cow from date of first calving) and group average milk yield (kg energy corrected milk (ECM)/cow/day). Individual level variables were: lactation number, days in milk and individual milk yield (kg ECM/day) of the included individuals. Herd size was defined as the herds’ mean number of cow-years in the study period. For DIM, values above 600 days were deemed unlikely and removed. Mean individual milk yields were calculated based on the milk production of the cow given at 6–12 monthly test days, measured as kg ECM/day. One mean milk yield value of 3 kg ECM/day was deemed unlikely and removed.

The EPG, ODR values, and production data were received as Excel files, Excel Office 365 (Microsoft Inc) and exported to STATA 16 [49] for data management and statistical analysis.

Statistical analysis

A descriptive table was made for production data and herd demographics. The medians, means, standard deviations and ranges of the ODR and FEC values were calculated for the individual values as well as the herd level averages. Histograms and Shapiro-Wilk test were used to assess the data for normal distribution. The FEC were log transformed to reduce the impact of the few high values.

A graph matrix of scatter plots was made to visually assess the correlation between ODR values of IM and IS, as well as the correlation between IS and IM ODR values and BTM. A scatter plot with a lowess-smoothed curve was generated to visualize the association between BTM ODR and IM ODR. Pearson correlation coefficients were calculated between log EPG and IS ODR, log EPG and IM ODR, IS ODR and IM ODR, the group averages of IS ODR and IM ODR, and the group average ODR values and BTM ODR. The mean BTM ODR in the study sample was applied as a cut-off to divide the herds into high and low BTM ODR-categories. For the resulting 2 categories, a t-test was performed to evaluate the statistical significance when comparing the means of the IM ODR values in the high and low BTM ODR herds. Furthermore, the herds were divided into four categories depending on the BTM ODR result. The following cut-points were used to define categories of equal intervals: 0.20–0.40, 0.41–0.60, 0.61–0.80 and 0.81–1.0. The distributions of the IM and IS ODR values of the individuals in the four BTM categories were illustrated using 2 box plots. The associations between ODR in individual milk/serum samples and individual milk yield (average ECM kg/day), DIM and lactation number were visually assessed using scatter plots (results not shown). The ability of a BTM test to predict the IS ODR values was explored using a mixed linear regression model with BTM ODR, DIM and group (FSG, first lactation, second lactation, third and higher lactation) as the candidates for explanatory variables and IS ODR as the response variable. A herd random effect was included to account for dependence between cows from the same herd. The model was fit using a manual backward stepwise elimination procedure, with a selection threshold of P < 0.05. The final model, after elimination, included BTM ODR and group together with the random effect of herd and can be written as:

$$Yij = {\beta _0} + {\beta _1}{X^{BTM}}j{\text{ }} + {\beta _2}{X^{group}}_{ij} + {\upsilon _j} + {\varepsilon _{ij}},$$

where subscripts i and j denote the ith individual of the jth herd, respectively Yij = individual serum ODR, XBTM is the BTM ODR variable, Xgroup is the group variable, υj as the herd random effect and \({\epsilon }_{i }\)are identically and independently distributed \(N(0,{\sigma }^{2})\) error terms.

The intraclass correlation coefficient was calculated. All models were checked for residual normality and homoscedasticity by visually inspecting plots of residuals against fitted values and quantile-quantile plots of residuals.


The results from 13 samples were missing at the time of analysis. 6 IS and 5 IM samples lacked the O. ostertagi-Ab ELISA test result, and 2 faecal samples were not returned to the lab.

Herd and individual data

Of the 20 herds, 19 were conventional and one was organic. The mean herd size during the study period was 33.4 cow years. Of 380 included individuals, 181 were FSG and 199 were lactating cows. The distribution of included individuals from every herd was a mean of 9.95 cows (range 9–10) and a mean of 9.4 FSG (range 5–10). Production data from 19 of the 20 herds were provided. The average herd milk yield was 25.6 kg ECM/cow/day (Table 1). Of the 191 cows for which production data was provided, 82 (43%) were in their 1st lactation, and 109 (57%) were in second lactation or higher. The average individual milk yield in the study period, calculated for 181 individual cows, was 24.3 kg ECM/day.

Table 1 Production data of 20 herds included in a field study in autumn 2021. These data were retrieved from the Norwegian dairy herd recording system from May 1st 2020 till April 31st 2021

Relationship between antibody levels in BTM and individual samples

The mean BTM ODR value for O. ostertagi of the 20 herds in the study was 0.65, with a range of 0.22–0.92 (Table 2). A wide variation in the individual ODR values was detected in all study herds, as illustrated in Figs. 1 and 2, and 3. The t-test showed a significant difference in IM ODR between the low and high BTM ODR categories (t(192) = 16.63, P < 0.001) with a mean difference in IM ODR values between groups of 0.42, 95% CI [0.37,0.47]. By visual evaluation of the IM and IS ODR values according to the four BTM categories (Fig. 3), the distribution was notably different in the highest and lowest BTM category. Nevertheless, there were large within-herd variations resulting in cows with high ODR values in the lowest BTM-category (ODR: 0.20–0.39) and cows with low ODR values in the highest bulk tank category (ODR: 0.80–1.00). The correlation between group average IS ODR in FSG and BTM ODR (Table 3) was moderate (r = 0.63), while a higher correlation was observed between the group average IM ODR samples and BTM ODR (r = 0.81). The BTM ODR was higher than the group average IM ODR in 90% (18/20) of the samples, with an average increase of 25% in BTM ODR compared to group average IM ODR.

Table 2 Descriptive statistics of faecal egg counts (FEC) of strongyle eggs and ELISA tests using the SVANOVIR® Ostertagia-Ab ELISA kit
Fig. 1
figure 1

Graph matrix showing the relationship of the ODR values using an indirect antibody ELISA for Ostertagia ostertagi test on bulk tank milk, individual milk (n = 194) and cow serum samples (n = 197) from 20 herds in Norway

Fig. 2
figure 2

Scatterplot of bulk tank milk ODR values versus individual milk ODR values with a lowess fitted line using an indirect antibody ELISA for Ostertagia ostertagi in 194 cattle from 20 herds in Norway

Fig. 3
figure 3

Distribution of (a) individual serum (n = 374) and (b) individual milk (n = 194) ODR values according to four categories of bulk tank milk (BTM) using an indirect antibody ELISA for Ostertagia ostertagi in 20 herds in Norway

Table 3 Pearson correlation coefficients (r) of pairwise comparisons between ODR values in milk, serum and bulk tank milk on individual and group average levels from 20 Norwegian dairy herds

Results from the mixed effects linear regression model using all the serum ODR values are shown in Table 4. There was a small positive association between BTM ODR and the IS ODR (p < 0.001). Cows in their 1st lactation had higher estimated antibody levels than FSG. The intraclass correlation coefficient was 0.07, reflecting that only 7% of the variation in ODR from IS samples was explained by herd as a random effect. No major shortcomings of the model were detected through assessment of the residual plots. Based on visual assessment of scatter plots there were no indications of an association between individual ODR values and individual milk yield. In order to conduct a more comprehensive analysis of this relationship, it would have been imperative to gather supplementary data. However, this task was beyond the scope of this study.

Table 4 Results of the mixed linear regression model to estimate the association between the bulk tank ODR and individual ODR values from serum samples of 374 cows and first season grazers

Relationship between individual samples of serum, milk, and faeces

The individual log transformed EPGs had a very low correlation with both IM ODR in cows (r = 0.03) and IS ODR in both FSG (r = 0.11) and cows (r = 0.01). The group average IS ODR was higher than the group average IM ODR in 70% (14/20) of the herds. The correlation between the IS ODR and IM ODR, visually assessed in Fig. 1, was moderate (r = 0.58; Table 3). The correlation was higher in low-yielding cows with an average milk yield of < 25 kg ECM/day (r = 0.61) versus in high-yielding cows with an average milk yield of > 25 kg ECM/day (r = 0.56).


There was a large within-herd variation in ODR values for both IS and IM samples. The Pearson correlation coefficient in this study was highest between the BTM ODR and the group average IM ODR, while a moderate correlation was detected between the individual ODR values in IS and IM samples. The evaluation of the regression model further demonstrated that BTM explained a low degree of the variation in individual ODR values in both FSG and adult cows. Nevertheless, the distribution of individual ODR levels was clearly different when comparing the herds close to the minimum and maximum BTM ODR values. Due to relatively few study herds being included, the possible inferences that can be made from this are limited. However, these data indicate that BTM testing might be of value to identify herds or regions with a substantial exposure to O. ostertagi or to single out herds where the level of exposure does not appear to be of concern.

The farms included in this study had a mean BTM ODR value of 0.65, which is comparable to the results in other European surveys where cows were pastured [33, 38]. In a survey of 5 countries located in northwestern Europe in 2010, which also used the SVANOVIR® Ostertagia-Ab ELISA kit, an average of 0.66 ODR in BTM on country-level was classified as “intermediate” when considering exposure to GIN [33]. Thus, a moderate exposure is indicated in our study sample based on the ODR levels in their BTM.

The high correlation detected between the BTM ODR and the group average IM samples (r = 0.81), indicates that BTM ODR may estimate the average level of GIN exposure in cows. This correlation was found to be moderate in other studies, with coefficients of r = 0.45 and r = 0.54 [30, 50], while a correlation of r = 0.72 was detected using the mean of two IM samples collected from all lactating animals two months apart [25]. The higher correlation obtained in our study might be related to the small size of Norwegian herds. The sampled cows in our study were a relatively large proportion of the cows contributing to the bulk tank, thus the difference between the mean ODR of collected IM samples and the ODR from a pooled sample of all lactating cows (i.e., BTM) could be smaller than for studies with larger herds. The higher BTM ODR relative to the group average IM ODR is similar to results from previous studies. A consistently higher BTM ODR than the group average IM ODR has been described [25], while other studies have reported that the BTM ODR was 22% [30] and 53% [51] higher than the average IM ODR. These findings have been attributed to the plateau phase in the ELISA test reaction; as the antibody level in the sample exceeds a certain antibody concentration, a recognized non-linear relationship develops between antibody concentration and the ELISA test result [25].

The moderate correlation between the ODR values in IS and IM corroborates results from other studies, ranging from r = 0.45 [52] to r = 0.53 [30]. The relationship between serum and milk can be complex. Antibodies appear earlier in serum than in milk, and the concentration of antibodies in serum has previously been shown to be approximately 30 times greater than in milk [53]. Additionally, a dilution effect in high-yielding cows was suggested by Sanchez et al. (2002) [30]. Our results support this suggestion, as the correlation between IM and IS ODR was reduced in high-yielding cows compared to low-yielding cows.

As expected from the large within-herd variation, the regression model showed poor predictive ability for BTM ODR to assess individual ODR values in both FSG and cows. The majority of the variation in individual ODR values was found between cows, while only 7% is explained by a herd effect. The significant effect of lactation group in this study has also been found in other studies [25, 27, 30], and may reflect higher levels of acquired immunity in older cows [27]. However, evaluations of lactation number and other cow factors (DIM, somatic cell count) in relation to ODR in individual samples have provided differing results and the effects of these variables are often reported as low [25,26,27]. This implies that additional factors are important in explaining individual variation in antibody response in animals grazing the same pasture. Genetic traits have been significantly associated with the ability of dairy cows to mount an immune response to O. ostertagi [54, 55]. In addition, the uneven distribution of GINs among hosts may reflect the varying grazing behaviours [56]. It should be noted that the statistical model in this study relates inherently correlated values (ODR in individuals vs. ODR in a pooled sample), in addition to the use of an exposure variable on population level, while the outcome is on the individual level. The a priori correlation between these measurements of antibodies was not accounted for in the analysis. There is thus a potential bias in assuming causal association at an individual level.

The use of FEC in adult cows is generally not considered useful, and the low fraction of adult faecal samples with a positive EPG is similar to results from other studies [24, 57]. The lack of correlation between log EPG and IS ODR values has also previously been reported in calves [58]. Furthermore, a poor sensitivity and high variability using the McMaster technique at low egg count levels is recognised [59], and in our study herds, this might have caused an underestimation of the FEC in some samples.

The findings in this study are similar to those from previous research, which support the use of individual sampling to assess exposure [51] or the inclusion of both individual and BTM tests in a monitoring programme [30], rather than relying on BTM tests. On the other hand, testing individual animals is time consuming and less practical than testing a single BTM sample per herd, and may not be feasible or economically sustainable in many dairy herds. Another option, not explored in the current study, could be repeated BTM-testing, as a more robust approach than relying on a single BTM sample. The potential value of this would need to be assessed in further studies. As collection and testing of BTM is performed routinely, the result of the Ostertagia test could easily be incorporated as part of the herd-health surveillance programme. Moreover, when plotting the IS/IM ODR results according to four BTM categories, the finding of differing ODR values indicates that the evaluation of the BTM test to assess exposure to GIN could be useful in herds with very high or very low BTM ODR values. However, as there were only five herds in our study that are in these categories, we recommend a cautious approach to making inferences about this.

The current study was performed using data from a single sampling occasion and relatively few farms compared with other studies [25, 30, 52]. The small sample size is an important limitation and means that the results are less conclusive results than if more herds had been included. In order to obtain enough samples from FSG, it was necessary to select herds that were larger than the average Norwegian milk production unit, which may have caused a selection bias. The antibody titre could have been influenced by the fact that the average milk production in the studied herds was slightly higher than that of the typical Norwegian dairy farms. Herds were also selected based on a likely greater exposure to GINs, meaning the ODR values in our study might be overall higher than in the target population, and the presented results should not be interpreted as estimates of seroprevalence. The possible impact on the ODR results due to cross-reactions of the ELISA test antigen with F. hepatica may have affected the results. Conversely, cross-reactions with C. oncophora is not deemed to be a disadvantage, since the ELISA may be considered as an estimation of total GIN exposure [3]. The inclusion of one organic herd was not considered to affect the results, as the probability of parasite exposure was the same in all herds in the study regardless of the production system. Despite the included herds covering the scale from low to high BTM ODR values, the study would have benefited from a larger sample size for better coverage of low and high ODR herds. A future screening of many herds could be one approach to obtaining new knowledge of prevalence and associations between O. ostertagi antibodies and herd characteristics.


There was high variability between the within-herd individual ODR values of both serum and milk in every herd. Although poor predictive ability for BTM ODR to assess individual ODR values in both FSG and cows was demonstrated, the BTM test might be useful to differentiate between levels of GIN exposure in herds with high and low ODR value in BTM. Future studies should include more herds with low and high antibody levels to further evaluate the use of BTM in these herds. This work provides knowledge to guide further studies on GIN, including prevalence studies, investigation of effect on production impact, and risk-factor analysis.

Data Availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.



Bulk tank milk


Days in milk


Energy corrected milk


Enzyme linked immune sorbent assay


Eggs per gram


Faecal egg count


First season grazers


Gastrointestinal nematodes


Individual milk


Individual serum


Optical density ratio


  1. Myers GH, Taylor RF. Ostertagiasis in cattle. J Vet Diagn Invest. 1989;1:195–200.

    Article  CAS  PubMed  Google Scholar 

  2. Taylor LM, Parkins JJ, Armour J, Holmes PH, Bairden K, Ibarra-Silva AM, et al. Pathophysiological and parasitological studies on Ostertagia ostertagi Infections in calves. Res Vet Sci. 1989;46:218–25.

    Article  CAS  PubMed  Google Scholar 

  3. Charlier J, Claerebout E, Mûelenaere ED, Vercruysse J. Associations between dairy herd management factors and bulk tank milk antibody levels against Ostertagia ostertagi. Vet Parasitol. 2005;133:91–100.

    Article  PubMed  Google Scholar 

  4. Blanco-Penedo I, Höglund J, Fall N, Emanuelson U. Exposure to pasture borne nematodes affects individual milk yield in Swedish dairy herds. Vet Parasitol. 2012;188:93–8.

    Article  CAS  PubMed  Google Scholar 

  5. Sanchez J, Dohoo I. A bulk tank milk survey of Ostertagia ostertagi antibodies in dairy herds in Prince Edward Island and their relationship with herd management factors and milk yield. Can Vet J. 2002;43:454–9.

    PubMed Central  PubMed  Google Scholar 

  6. Guitián FJ, Dohoo IR, Markham RJ, Conboy G, Keefe GP. Relationships between bulk-tank antibodies to Ostertagia ostertagi and herd-management practices and measures of milk production in Nova Scotia dairy herds. Prev Vet Med. 1999;47:79–89.

    Article  PubMed  Google Scholar 

  7. Rose Vineer H, Morgan ER, Hertzberg H, Bartley DJ, Bosco A, Charlier J, et al. Increasing importance of anthelmintic resistance in European livestock: creation and meta-analysis of an open database. Parasite. 2020;27:69.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Sabatini GA, de Almeida Borges F, Claerebout E, Gianechini LS, Höglund J, Kaplan RM, et al. Practical guide to the diagnostics of ruminant gastrointestinal nematodes, liver fluke and lungworm Infection: interpretation and usability of results. Parasit Vectors. 2023;16:58.

    Article  PubMed Central  PubMed  Google Scholar 

  9. Charlier J, van der Voort M, Kenyon F, Skuce P, Vercruysse J. Chasing helminths and their economic impact on farmed ruminants. Trends Parasitol. 2014;30:361–7.

    Article  PubMed  Google Scholar 

  10. Vercruysse J, Charlier J, Van Dijk J, Morgan ER, Geary T, von Samson-Himmelstjerna G, Claerebout E. Control of helminth ruminant Infections by 2030. Parasitology. 2018;145:1655–64.

    Article  PubMed  Google Scholar 

  11. Berghen P, Hilderson H, Vercruysse J, Dorny P. Evaluation of pepsinogen, gastrin and antibody response in diagnosing ostertagiasis. Vet Parasitol. 1993;46:175–95.

    Article  CAS  PubMed  Google Scholar 

  12. Baltrusis P, Halvarsson P, Hoglund J. Molecular detection of two major gastrointestinal parasite genera in cattle using a novel droplet digital PCR approach. Parasitol Res. 2019;118:2901–7.

    Article  PubMed Central  PubMed  Google Scholar 

  13. Avramenko RW, Redman EM, Lewis R, Yazwinski TA, Wasmuth JD, Gilleard JS. Exploring the gastrointestinal nemabiome: deep amplicon sequencing to quantify the species Composition of Parasitic Nematode communities. PLoS ONE. 2015;10:e0143559.

    Article  PubMed Central  PubMed  Google Scholar 

  14. Roeber F, Hassan EB, Skuce P, Morrison A, Claerebout E, Casaert S, et al. An automated, multiplex-tandem PCR platform for the diagnosis of gastrointestinal nematode Infections in cattle: an australian-european validation study. Vet Parasitol. 2017;239:62–75.

    Article  CAS  PubMed  Google Scholar 

  15. Santos LL, Salgado JA, Drummond MG, Bastianetto E, Santos CP, Brasil BSAF, et al. Molecular method for the semiquantitative identification of gastrointestinal nematodes in domestic ruminants. Parasitol Res. 2020;119:529–43.

    Article  PubMed  Google Scholar 

  16. Vercruysse J, Claerebout E. Treatment vs non-treatment of helminth Infections in cattle: defining the threshold. Vet Parasitol. 2001;98:195–214.

    Article  CAS  PubMed  Google Scholar 

  17. Eysker M, Ploeger HW. Value of present diagnostic methods for gastrointestinal nematode Infections in ruminants. Parasitology. 2000;120:109–19.

    Article  Google Scholar 

  18. Ploeger HW, Kloosterman A, Rietveld FW, Berghen P, Hilderson H, Hollanders W. Quantitative estimation of the level of exposure to gastrointestinal nematode Infection in first-year calves. Vet Parasitol. 1994;55:287–315.

    Article  CAS  PubMed  Google Scholar 

  19. Shaw DJ, Vercruysse J, Claerebout E, Agneessens J, Dorny P. Gastrointestinal nematode Infections of first-season grazing calves in Belgium: general patterns and the effect of chemoprophylaxis. Vet Parasitol. 1997;69:103–16.

    Article  CAS  PubMed  Google Scholar 

  20. Charlier J, Williams DJ, Ravinet N, Claerebout E. To treat or not to treat: diagnostic thresholds in subclinical helminth Infections of cattle. Trends Parasitol. 2023;39:139–51.

    Article  PubMed  Google Scholar 

  21. Denwood MJ, Kaplan RM, McKendrick IJ, Thamsborg SM, Nielsen MK, Levecke B. A statistical framework for calculating prospective sample sizes and classifying efficacy results for faecal egg count reduction tests in ruminants, horses and swine. Vet Parasitol. 2023;314:109867.

    Article  PubMed  Google Scholar 

  22. Anderson N, Armour J, Jennings FW, Ritchie JS, Urquhart GM. The sequential development of naturally occurring ostertagiasis in calves. Res Vet Sci. 1969;10:18–28.

    Article  CAS  PubMed  Google Scholar 

  23. Dorny P, Shaw DJ, Vercruysse J. The determination at housing of exposure to gastrointestinal nematode Infections in first-grazing season calves. Vet Parasitol. 1999;80:325–40.

    Article  CAS  PubMed  Google Scholar 

  24. Borgsteede FHM, Tibben J, Cornelissen JBWJ, Agneessens J, Gaasenbeek CPH. Nematode parasites of adult dairy cattle in the Netherlands. Vet Parasitol. 2000;89:287–96.

    Article  CAS  PubMed  Google Scholar 

  25. Charlier J, Vercruysse J, Smith J, Vanderstichel R, Stryhn H, Claerebout E, Dohoo I. Evaluation of anti-Ostertagia ostertagi antibodies in individual milk samples as decision parameter for selective anthelmintic treatment in dairy cows. Prev Vet Med. 2010;93:147–52.

    Article  PubMed  Google Scholar 

  26. Almería S, Adelantado C, Charlier J, Claerebout E, Bach A. Ostertagia ostertagi antibodies in milk samples: relationships with herd management and milk production parameters in two Mediterranean production systems of Spain. Res Vet Sci. 2009;87:416–20.

    Article  PubMed  Google Scholar 

  27. Sanchez J, Markham F, Dohoo I, Sheppard J, Keefe G, Leslie K. Milk antibodies against Ostertagia ostertagi: relationships with milk IgG and production parameters in lactating dairy cattle. Vet Parasitol. 2004;120:319–30.

    Article  CAS  PubMed  Google Scholar 

  28. Keus A, Kloosterman A, Van den Brink R. Detection of antibodies to Cooperia ssp. and Ostertagia ssp. in calves with the enzyme linked immunosorbent assay (ELISA). Vet Parasitol. 1981;8:229–36.

    Article  Google Scholar 

  29. Sekiya M, Zintl A, Doherty ML. Bulk milk ELISA and the diagnosis of parasite Infections in dairy herds: a review. Ir Vet J. 2013;66:14.

    Article  PubMed Central  PubMed  Google Scholar 

  30. Sanchez J, Dohoo I, Nødtvedt A, Keefe G, Markham F, Leslie K, et al. A longitudinal study of gastrointestinal parasites in Canadian dairy farms: the value of an indirect Ostertagia ostertagi ELISA as a monitoring tool. Vet Parasitol. 2002;107:209–26.

    Article  PubMed  Google Scholar 

  31. Bosco A, Amadesi A, Morandi N, Pepe P, Maurelli MP, Cringoli G, Rinaldi L. Ostertagia ostertagi antibodies in bulk tank milk from dairy cattle in Italy: a nation-wide survey. Vet Parasitol Reg Stud Reports. 2018;13:166–70.

    PubMed  Google Scholar 

  32. Frey CF, Eicher R, Raue K, Strube C, Bodmer M, Hentrich B, et al. Apparent prevalence of and risk factors for Infection with Ostertagia ostertagi, Fasciola hepatica and Dictyocaulus Viviparus in Swiss dairy herds. Vet Parasitol. 2018;250:52–9.

    Article  CAS  PubMed  Google Scholar 

  33. Bennema SC, Vercruysse J, Morgan E, Stafford K, Höglund J, Demeler J, et al. Epidemiology and risk factors for exposure to gastrointestinal nematodes in dairy herds in northwestern Europe. Vet Parasitol. 2010;173:247–54.

    Article  PubMed  Google Scholar 

  34. Sanchez J, Dohoo I, Carrier J, DesCôteaux L. A meta-analysis of the milk-production response after anthelmintic treatment in naturally infected adult dairy cows. Prev Vet Med. 2004;63:237–56.

    Article  PubMed  Google Scholar 

  35. Fanke J, Charlier J, Steppin T, von Samson-Himmelstjerna G, Vercruysse J, Demeler J. Economic assessment of Ostertagia ostertagi and Fasciola hepatica Infections in dairy cattle herds in Germany using Paracalc®. Vet Parasitol. 2017;240:39–48.

    Article  PubMed  Google Scholar 

  36. van der Voort M, Charlier J, Lauwers L, Vercruysse J, Van Huylenbroeck G, Van Meensel J. Conceptual framework for analysing farm-specific economic effects of helminth Infections in ruminants and control strategies. Pre Vet Med. 2013;109:228–35.

    Article  Google Scholar 

  37. Charlier J, Van der Voort M, Hogeveen H, Vercruysse J. ParaCalc®—A novel tool to evaluate the economic importance of worm Infections on the dairy farm. Vet Parasitol. 2012;184:204–11.

    Article  PubMed  Google Scholar 

  38. Forbes AB, Charlier JV. A survey of the exposure to Ostertagia ostertagi in dairy cow herds in Europe through the measurement of antibodies in milk samples from the bulk tank. Vet Parasitol. 2008;157:100–7.

    Article  CAS  PubMed  Google Scholar 

  39. Charlier J, Claerebout E, Duchateau L, Vercruysse J. A survey to determine relationships between bulk tank milk antibodies against Ostertagia ostertagi and milk production parameters. Vet Parasitol. 2005;129:67–75.

    Article  PubMed  Google Scholar 

  40. Mikalsen V. In: Østerås O, Roalkvam T, editors. Statistikksamling fra Husdyrkontrollen Og Helsekortordningen. TINE SA; 2020. Access date 25.04.2023.

  41. Opsal T, Toftaker I, Nødtvedt A, Robertson LJ, Tysnes KR, Woolsey I, Hektoen L. Gastrointestinal nematodes and Fasciola hepatica in Norwegian cattle herds: a questionnaire to investigate farmers’ perceptions and control strategies. Acta Vet Scand. 2021;63:52.

    Article  PubMed Central  PubMed  Google Scholar 

  42. Henriksen SA, Aagaard K. A simple flotation and McMaster method (author’s transl). Nord Vet Med. 1976;28:392–7.

    CAS  PubMed  Google Scholar 

  43. Deplazes P, Eckert J, Mathis A, Samson-Himmelstjerna GV, Zahner H. Parasitology in Veterinary Medicine. 2016. Page 529.

  44. Levecke B, Rinaldi L, Charlier J, Maurelli MP, Morgoglione ME, Vercruysse J, Cringoli G. Monitoring drug efficacy against gastrointestinal nematodes when faecal egg counts are low: do the analytic sensitivity and the formula matter? Parasitol Res. 2011;109:953–7.

    Article  PubMed  Google Scholar 

  45. Charlier J, Troeng J, Höglund J, Demeler J, Stafford K, Coles G, et al. Assessment of the within- and between-laboratory repeatability of a commercially available Ostertagia ostertagi milk ELISA. Vet Parasitol. 2009;164:66–9.

    Article  PubMed  Google Scholar 

  46. Verschave SH, Vercruysse J, Forbes A, Opsomer G, Hostens M, Duchateau L, Charlier J. Non-invasive indicators associated with the milk yield response after anthelmintic treatment at calving in dairy cows. BMC Vet Res. 2014;10:264.

    Article  PubMed Central  PubMed  Google Scholar 

  47. Bellet C, Green MJ, Bradley AJ, Kaler J. A longitudinal study of gastrointestinal parasites in English dairy farms. Practices and factors associated with first lactation heifer exposure to Ostertagia ostertagi on pasture. J Dairy Sci. 2018;101:537–46.

    Article  CAS  PubMed  Google Scholar 

  48. Charlier J, Wang T, Verschave SH, Höglund J, Claerebout E. Review and evaluation of Ostertagia ostertagi antibody ELISA for application on serum samples in first season grazing calves. Animals. 2023;13:2226.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Stata Statistical Software. : Release 16. College Station, TX: StataCorp LP.

  50. Bellet C, Green MJ, Bradley AJ, Kaler J. Short- and long-term association between individual levels of milk antibody against Ostertagia ostertagi and first-lactation heifer’s production performances. Vet Parasitol. 2018;256:1–8.

    Article  CAS  PubMed  Google Scholar 

  51. Charlier J, Camuset P, Claerebout E, Courtay B, Vercruysse J. A longitudinal survey of anti-Ostertagia ostertagi antibody levels in individual and bulk tank milk in two dairy herds in Normandy. Res Vet Sci. 2007;83:194–7.

    Article  CAS  PubMed  Google Scholar 

  52. Kloosterman A, Verhoeff J, Ploeger HW, Lam TJGM. Antibodies against nematodes in serum, milk and bulk milk samples as possible estimators of Infection in dairy cows. Vet Parasitol. 1993;47:267–78.

    Article  CAS  PubMed  Google Scholar 

  53. Butler JE. Bovine immunoglobulins: an augmented review. Vet Immunol Immunopathol. 1983;4:43–152.

    Article  CAS  PubMed  Google Scholar 

  54. Hayhurst C, Bradley A, Forbes AB, Hunter K, Royal MD. Short communication: genetic and nongenetic factors influencing Ostertagia ostertagi antibodies in UK Holstein-Friesian cattle. J Dairy Sci. 2010;93:2239–43.

    Article  CAS  PubMed  Google Scholar 

  55. Warburton EM, Vonhof MJ. From individual heterogeneity to population-level overdispersion: quantifying the relative roles of host exposure and parasite establishment in driving aggregated helminth distributions. Int J Parasitol. 2018;48:309–18.

    Article  PubMed  Google Scholar 

  56. Charlier J, Höglund J, Morgan ER, Geldhof P, Vercruysse J, Claerebout E. Biology and Epidemiology of gastrointestinal nematodes in cattle. Vet Clin North Am Food Anim Pract. 2020;36:1–15.

    Article  PubMed  Google Scholar 

  57. Agneessens J, Claerebout E, Dorny P, Borgsteede FHM, Vercruysse J. Nematode parasitism in adult dairy cows in Belgium. Vet Parasitol. 2000;90:83–92.

    Article  CAS  PubMed  Google Scholar 

  58. Gasbarre LC, Leighton EA, Cavies CJ. Influence of host genetics upon antibody responses against gastrointestinal nematode Infections in cattle. Vet Parasitol. 1993;46:81–91.

    Article  CAS  PubMed  Google Scholar 

  59. Mes THM, Ploeger HW, Terlou M, Kooyman FNJ, Van Der Ploeg MPJ, Eysker M. A novel method for the isolation of gastro-intestinal nematode eggs that allows automated analysis of digital images of egg preparations and high throughput screening. Parasitology. 2001;123:309–14.

    Article  CAS  PubMed  Google Scholar 

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The authors are grateful for all participating farmers who contributed to this study. We also thank for all assistance provided by the contributing veterinarians in collection of samples for this study.


The study is a part of the project “Sustainable management of pasture parasites in Norwegian beef and dairy cattle”, funded by the Foundation for Research Levy on Agricultural Products and the Agricultural Agreement Research Fund, grant number: NFR 294727, TINE BA and Animalia – Norwegian Meat and Poultry Research Centre. TINE BA was involved in the collection of samples. Analysis and interpretation of milk, serum and bulk tank milk samples were done at the TINE Mastitis laboratory.

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TO was responsible for the main part of the collection of faecal, blood and milk samples. Laboratory analysis of faecal samples was done by TO and IW. All authors participated in the revision of the manuscript and read and approved the final version of the manuscript.

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Correspondence to Tonje Opsal.

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The authors declare that they have no competing interests.

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Informed consent was obtained from all farmers in the study. Furthermore, confidentiality was assured as the participants personal information will not be identified at any dissemination or publication of the results. Data were collected and stored according to the General Rata Protection Regulation (Regulation (EU), 2016/679).

Ethics approval

The animals were handled according to high ethical standards and national legislation. The sampling of animals was considered non-experimental clinical procedures and did not require ethical approval in Norway (Norwegian Ministry of Agriculture and Food, 2015).

Prior publication

Data included in this article have previously been published in the Proceedings of the 16th International Symposium on Veterinary Epidemiology and Economics Congress, Halifax, Canada, August 7th - 12th 2022.

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Opsal, T., Toftaker, I., Robertson, L. et al. Comparison of diagnostic methods for assessment of Ostertagia ostertagi exposure in Norwegian dairy herds. Acta Vet Scand 65, 52 (2023).

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