Prepartal vaccination has no influence on mammary health and milk yield of dairy cows: a retrospective study addressing non-specific effects of vaccination
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Abstract Background: Prepartal vaccinations against newborn calf diarrhea pathogens are performed in the last weeks of pregnancy, with the intention to induce a maternal adaptive humoral immune response and to protect calves in the first weeks of life via colostral transferred pathogen-specific antibodies. There is evidence that vaccination-related innate immune responses can also affect the mother's immune system response to stressors. Whether such non-specific vaccine effects alter the disease susceptibility of dairy cows in the very sensitive transition period has not been addressed so far. In a retrospective cross-sectional study, we investigated the influence of prepartal maternal vaccination on mammary health and milk yield of the periparturient dairy cow. Herd record data from 73,378 dairy cows from 20 farms located in Eastern Germany, together with on-site-collected survey data, were analyzed using linear mixed-effects regression, quantile regression and random forest machine-learning algorithms. A total of 57,166 transition periods without prior vaccination, distributed along 16 herds, and 63,228 transition periods on 13 herds with prior vaccination are included. Additionally healthy primiparous cows from alternately vaccinated herds were analyzed. Results: Herd management-related factors, such as herd in general and calving year, as well as parity proved to be most influential for mammary health and milk yield, while prepartal vaccination occurred as least influential. Vaccinated cows did not show significant differences in mastitis prevalence and somatic cell count as compared with non-vaccinated cows. Also, energy corrected milk yield on first test day of milk recordings, as well as in 305-days of lactation, of healthy primiparous cows with and without prior vaccination showed no significant differences. Conclusions: This study presents evidence that prepartal vaccination against newborn calf diarrhea does not have significant non-specific effects on mammary health and milk yield parameters. Instead, the findings highlight the importance of herd management factors. This work emphasizes the significance of multivariable analysis based on a large database with high statistical power. It also recommends further research to explore potential non-specific vaccination effects on other organ systems, infectious diseases, and production metrics in cows.
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Prepartal vaccination has no influence on mammary health and milk yield of dairy cows: a retrospective study addressing non-specific effects of vaccination | 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 Prepartal vaccination has no influence on mammary health and milk yield of dairy cows: a retrospective study addressing non-specific effects of vaccination Caroline Kuhn, Holm Zerbe, Hans-Joachim Schuberth, Anke Römer, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4259469/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Prepartal vaccinations against newborn calf diarrhea pathogens are performed in the last weeks of pregnancy, with the intention to induce a maternal adaptive humoral immune response and to protect calves in the first weeks of life via colostral transferred pathogen-specific antibodies. There is evidence that vaccination-related innate immune responses can also affect the mother's immune system response to stressors. Whether such non-specific vaccine effects alter the disease susceptibility of dairy cows in the very sensitive transition period has not been addressed so far. In a retrospective cross-sectional study, we investigated the influence of prepartal maternal vaccination on mammary health and milk yield of the periparturient dairy cow. Herd record data from 73,378 dairy cows from 20 farms located in Eastern Germany, together with on-site-collected survey data, were analyzed using linear mixed-effects regression, quantile regression and random forest machine-learning algorithms. A total of 57,166 transition periods without prior vaccination, distributed along 16 herds, and 63,228 transition periods on 13 herds with prior vaccination are included. Additionally healthy primiparous cows from alternately vaccinated herds were analyzed. Results: Herd management-related factors, such as herd in general and calving year, as well as parity proved to be most influential for mammary health and milk yield, while prepartal vaccination occurred as least influential. Vaccinated cows did not show significant differences in mastitis prevalence and somatic cell count as compared with non-vaccinated cows. Also, energy corrected milk yield on first test day of milk recordings, as well as in 305-days of lactation, of healthy primiparous cows with and without prior vaccination showed no significant differences. Conclusions: This study presents evidence that prepartal vaccination against newborn calf diarrhea does not have significant non-specific effects on mammary health and milk yield parameters. Instead, the findings highlight the importance of herd management factors. This work emphasizes the significance of multivariable analysis based on a large database with high statistical power. It also recommends further research to explore potential non-specific vaccination effects on other organ systems, infectious diseases, and production metrics in cows. Transition period Maternal vaccination Trained immunity innate immune memory Dairy cow Peripartum Figures Figure 1 Figure 2 Figure 3 Background Prepartal vaccinations of cows are performed to protect calves from infectious diseases in the first weeks of life. During the last weeks of pregnancy, the mother is usually vaccinated with a killed vaccine to induce high levels of pathogen-specific antibodies (1-3). Prepartal vaccination of cows is regarded as safe and usually no adverse reactions are reported after vaccinations with killed vaccines containing different adjuvants (4, 5). Vaccinations of pregnant women against SARS-CoV-2 also revealed no harmful effects on pregnancy (6). In addition to the induction of an adaptive immune response intending to induce the generation of pathogen-specific antibodies, the immediate innate response after vaccinations of cows with killed vaccines resulted in an altered gene expression of circulating immune cells (7, 8). Thus, the inflammatory response after active vaccination can lead to an altered functionality of circulating immune cells. The innate response to vaccination has also been shown to modify fetal immune system development in utero via the induction of epigenetic changes (9). The altered immune response of newborns due to maternal vaccination implies that vaccination-induced epigenetic modification also changes the immune reactivity of the vaccinated mother. Such vaccination-induced, innate-response-mediated effects can lead to a status called innate immune memory, based on epigenetic modifications (10). Such effects are well-studied for certain vaccines, e. g. the Bacille Calmette Guerin (BCG) vaccine containing an avirulent Mycobacterium bovis strain (11, 12). This vaccine resulted in a trained functional phenotype (trained immunity) of circulating myeloid cells of calves (13), referring to primary stimulus-augmented innate immune responses towards a secondary stimulation (10). Vaccination-induced effects beyond antigen-specific induction of adaptive immune responses are known as non-specific effects (NSEs) of vaccines. NSEs can be positive or negative for the individual. Underlying mechanisms of NSEs are intensely discussed (14) and possible characteristics of live, attenuated, or killed vaccines in this regard are not finally elaborated (15). While live vaccinations in humans are in the center of investigations, there is a lack of research on NSEs of veterinary vaccine products (15). Whether contemporarily used veterinary vaccines, especially those applied to cows in the transition period (TP) during late pregnancy, have non-specific effects or not, has not been addressed so far. A number of studies explored variable effects of cow vaccinations with conventional vaccines on milk yield. No effect on milk yield was reported after vaccination with a core antigen vaccine against gram-negative bacteria (16). Lower milk yields were shown after vaccination with an inactivated Coxiella burnetii vaccine (17), whereas heifers vaccinated with a BCG vaccine showed higher milk yields (18). How these effects were mediated was not addressed so far and, to the best of our knowledge, no study addressed the potential NSEs of prepartal vaccination against neonatal calf diarrhea (NCD) on the prevalence of postpartal mastitis and the milk yield of cows. Therefore, the aim of this retrospective cross-sectional study was to analyze the effect of contemporary used prepartal vaccines on mammary health and milk yield of the periparturient cow. Results Descriptive Summary: vaccination management in participating herds and key figures 73,378 dairy cows in 22 herds across 20 farms were included in this study, of which 53,370 dairy cows in 21 herds across 19 farms could be further included for analysis after matching herd records with health documentation, milk records and the questionnaire conducted in this study. This first dataset (D1) consisted of 120,394 TP in total and could be divided into 57,166 TP without vaccination during the dry period (NON VACC) across 16 herds and 63,228 TP with prior vaccination (VACC) across 13 herds. The overall mastitis prevalence was 6.2% (VACC 6.6%, NON VACC 5.8%). Overall median somatic cell count on the first day of milk recording (SCC) was 69,000 (VACC 73,000; NON VACC 65,000). In VACC cows, the median energy corrected milk yield was 37kg on the first day of milk recording (ECM FTD) and 9,739kg in 305 days of lactation (ECM 305), compared to 36kg ECM FTD and 9,597kg ECM 305 in NON VACC cows. The overall median ECM FTD was 37kg, ECM 305 was 9,674kg. Dataset 2 (D2) represents a subset of D1, containing 1,002 TP from 1,002 primiparous, utmost healthy cows on four alternately vaccinated herds, thereof 525 NON VACC and 477 VACC. In D2 overall median SCC is 49,000 (VACC 50,000; NON VACC 47,000), ECM 305 8,748kg (VACC 8,797kg; NON VACC 8,696kg) and ECM FTD 29.9kg (VACC 30; NON VACC 29.8). An overview of included TP and descriptive results is provided in table 1 and 2 (see Table 1, Table 2). Table 1: Numbers of transition periods in individual herds with different vaccination managements. Dataset 1 NON VACC a (n = 57,166) VACC b (n = 63,228) Non-vaccinated herds c 1a 8,791 0 1b 4,459 0 1c 1,902 0 1d 3,531 0 1e 821 0 1f 636 0 1g 3,231 0 1h 5,476 0 Continuously vaccinated herds d 2a 0 1,305 2b 0 2,387 2c 0 8,093 2d 0 7,429 2e 0 18,453 Alternately vaccinated herds e 3a 1,744 1,545 3b 3,128 5,807 3c 4,137 227 3d 969 6,885 3e 1,478 327 3f 9,182 5,555 3g 5,847 4,007 3h 1,834 1,208 Dataset 2 NON VACC a (n = 525) VACC b (n = 477) Alternately vaccinated herds e 3a 159 109 3d 137 142 3f 141 141 3g 88 85 a no vaccination during the dry period; b vaccination between 2.5 and 8 weeks before expected calving date; c herds without vaccinations within the dry period; d herds with continuous prepartal vaccinations; e herds with prepartal vaccinations of parts of the herd or during defined time periods of time. Table 2 : Descriptive summary - mammary health and milk yield in herds with different prepartal vaccination status. Dataset 1 Prepartal vaccination status Overall (n = 120,394) NON VACC a (n = 57,166) VACC b (n = 63,228) Mastitis , % 6.2% 5.8% 6.6% SCC c , median (IQR d ) 69 (34 - 184) 65 (32 - 174) 73 (36 - 196) Unknown 27,839 10,136 17,703 ECM 305 e , median (IQR d ) 9,674 (7,884 - 11,236) 9,597 (7,851 - 11,352) 9,739 (7,914 - 11,161) Unknown 5,431 2,648 2,783 ECM FTD f , median (IQR d ) 37 (30 - 44) 36 (29 - 44) 37 (30 - 44) Unknown 27,839 10,136 17,703 Dataset 2 Prepartal vaccination status Overall (n = 1,002) NON VACC a (n = 525) VACC b (n = 477) SCC c , median (IQR d ) 49 (32 - 69) 47 (29 - 68) 50 (34 - 69) ECM 305 e , median (IQR d ) 8,748 (7,351 - 9,911) 8,696 (7,190 - 9,833) 8,797 (7,506 - 10,004) ECM FTD f , median (IQR d ) 29.9 (26.6 - 33.3) 29.8 (26.9 - 33.2) 30.0 (26.4 - 33.5) a no vaccination during the dry period; b vaccination between 2.5 and 8 weeks before expected calving date; c Somatic cell count on the first day of milk recording (in thousand); d interquartile range; e Energy corrected milk yield in 305 days lactation in kg; f Energy corrected milk yield on the first day of milk recording in kg. Prepartal vaccination has no significant influence on mammary health parameters To identify significant influencing parameters for the response variables mastitis and SCC , univariable mixed-effects logistic regression was performed, taking into account the available meaningful variables. In both analyses, the likewise significant variables herd and calving year were applied as random effects in a nested structure. Mastitis was significantly associated with parity , calving season and access to pasture . For the SCC , the variables parity , calving season, flooring and farmsize emerged as significant. All of these significant variables were further included for multivariable mixed-effects regression to investigate the influence of prepartal vaccination on mastitis and SCC. Multivariable analyses revealed that prepartal vaccination had no significant influence on both mastitis prevalence (see Table 3) and SCC (see Table 4). Table 3: Association between prepartal vaccination status and mastitis prevalence in multivariable linear mixed-effects logistic regression. Variables OR a 95% CI b p-value c Prepartal vaccination status VACC d / NON VACC e 0.99 0.86, 1.14 0.869 Parity secondiparous / primiparous 0.73 0.66, 0.80 <0.001 multiparous / primiparous 1.30 1.20, 1.40 <0.001 multiparous / secondiparous 1.78 1.64, 1.94 <0.001 Access to pasture f yes / no 1.85 1.61, 2.12 <0.001 Calving season summer / spring 1.32 1.21, 1.44 0.999 autumn / summer 0.76 0.70, 0.83 <0.001 winter / spring 0.96 0.87, 1.06 0.681 winter / summer 0.73 0.67, 0.79 <0.001 winter / autumn 0.96 0.87, 1.05 0.577 a Odds Ratio for pairwise contrasts; b Confidence Interval; c significant effects are marked in bold - threshold: 0.0014; d vaccination between 2.5 and 8 weeks before expected calving date; e no vaccination during the dry period; f access to pasture during dry period; herd and calving year were applied as random effects. Table 4: Prepartal vaccination status and somatic cell count (SCC a ) in multivariable linear mixed-effects regression. Model estimates 95% CI b p-value c Prepartal vaccination status VACC d - NON VACC e -0.06 -0.12, 0.00 0.036 Parity secondiparous - primiparous -0.18 -0.22, -0.15 <0.001 multiparous - primiparous 0.23 0.20, 0.26 <0.001 multiparous - secondiparous 0.41 0.38, 0.44 <0.001 Flooring deeplitter – slatted floor 0.16 0.11, 0.21 <0.001 Herdsize f small - medium -0.20 -0.40, -0.00 0.050 large - medium -0.31 -0.52, -0.10 0.001 large - small -0.11 -0.36, 0.14 0.551 Calving season g summer - spring 0.15 0.11, 0.19 <0.001 autumn - spring -0.01 -0.05, 0.03 0.971 autumn - summer -0.16 -0.19, -0.12 <0.001 winter - spring -0.03 -0.07, 0.01 0.171 winter - summer -0.18 -0.22, -0.14 <0.001 winter - autumn -0.02 -0.06, 0.01 0.339 a Somatic cell count was logarithmically transformed; b Confidence Interval; c significant effects are marked in bold - threshold: 0.0014; d prepartal vaccination during the dry period; e no vaccination during the dry period; f annual total number of cows in herd, segmentated into categories small [184, 987], medium [890, 1275] and large [1276, 2625]; g spring (March-May), summer (June-August), autumn (September-November), winter (December-February); herd and calving year were applied as random effects. Prepartal vaccination has no significant influence on milk yield in healthy primiparous cows Univariable linear mixed-effects models showed higher ECM FTD and ECM 305 in VACC, compared to NON VACC (see Additional file 1). Multivariable analysis was not possible with the same dataset due to missing values resulting from the initial joint of herd and milk records. As a solution, D2 was created to reduce the number of missing values and control confounding variables. Here, available disease-associated variables, such as mastitis , SCC above 100,000, retained placenta , metritis , and ketotic risk were used to exclude diseased cows. Further, only four herds were taken into account that were vaccinated alternately, to allow for a comparison between VACC and NON VACC cows in more identical environments. Additionally, the data was limited to the time period of twelve months before and after change of vaccination management. With primiparous, utmost healthy cows from alternately vaccinated herds, univariable and multivariable analysis was performed. No significant influence of prepartal vaccination on milk yield parameters could be confirmed in these more ideal conditions, neither in uni- nor in multivariable models. However, the variable replacement rate remained significant for ECM 305 and ECM FTD, as well as rest period for ECM 305 (see Table 5). Table 5: Prepartal vaccination status and energy corrected milk yield in multivariable linear mixed-effects logistic regression. ECM 305 a ECM FTD b Model estimates 95% CI c p-value d Model estimates 95% CI c p-value d Prepartal vaccination status VACC e - NON VACC f 5.0 -224, 234 0.966 -0.42 -0.99, 0.15 0.148 Replacement rate -65 -88, -42 <0.001 -0.15 -0.21, -0.09 <0.001 Rest period g 14 8.3, 21 <0.001 a Energy corrected milk yield in 305 days of lactation; b Energy corrected milk yield on first day of milk recordings; c Confidence Interval; d significant effects are marked in bold - threshold: 0.0158; e prepartal vaccination during the dry period; f no vaccination during the dry period; g rest period was only applied for response variable ECM 305 due to temporal overlap with ECM FTD; herd was applied as random effect. Quantile regression models for four individual herds clarified that other factors, such as herd itself , have higher influence on milk yield parameters than the prepartal vaccination status in healthy primiparous cows (see Figure 1). [ Figure 1] Herd management related factors are most relevant for mammary health In order to broaden the analysis for the mammary health parameters mastitis and SCC , random forest-analysis was performed. With this machine-learning algorithm, influencing variables were ranked by importance, allowing for the comparison between all variables, including herd and calving year , which were applied as random effects in previous analyses of the study. Findings suggest that herd management-related parameters are the most relevant influencing factors for both response variables, either directly as herd or indirectly as calving year and farmsize . Parity proofed to be among the top three influencing variables. Prepartal vaccination status, however, takes the last, or second last place in this ranking, as illustrated in figure 2. [ Figure 2 ] Discussion The high incidence of postpartal infectious diseases of the cow poses challenges for the current dairy industry. During TP, defined as three weeks before until three weeks after parturition (19, 20), the dairy cow’s health is challenged and typical disease symptoms accumulate. Mastitis, metritis, ketosis, digestive disorders and laminitis have their highest incidences during early lactation (21-23). The mammary gland is particularly susceptible to pathogens during colostrogenesis (24). Mammary disorders negatively affect animal welfare, milk yield and the financial situation of the dairy farm, especially in early lactation (25). Reasons for mammary disorders may originate from a poorly regulated, dysregulated or suppressed immune system (7, 26, 27). Therefore, ways are needed to modulate the immune system of the transition cow to cope better with infectious pathogens. An attractive possibility could be a vaccine-induced modulation of the immune system. The vaccination-induced mediator release after an initial activation of innate immune processes may have mid- or long-term effects on subsequent immune mechanisms depending on the duration of mediator-mediated epigenetic modifications in various cell types (10). Although vaccine-mediated epigenetic alterations were reported for distinct vaccines, it remained unknown whether contemporarily used prepartal vaccinations against NCD are able to induce such NSEs in cows. In this context, we investigated whether the prepartal vaccination against NCD of pregnant cows has an impact on the prevalence of mastitis, the somatic cell count and the short- and long-term milk production post-partum (p.p.). In univariable analysis no significant associations between prepartal vaccination and mammary health could be found, but higher ECM FTD and ECM 305 in VACC. However, multivariable analysis clearly showed that prepartal vaccination had no effect, neither on the mastitis prevalence (table 3), the SCC (table 4), nor the milk yield (table 5). These findings mirror in part those of Scott et al. (16), who found no effect on milk yield after vaccination with a core antigen vaccine against gram-negative bacteria, although the used vaccine contained a dedicated immune-enhancing/-modulating adjuvant (Immune Plus®). Our findings argue against a non-specific effect of mother cow vaccination with the used NCD vaccines. Such non-specific effects were reported by others after vaccination of heifers with a live-attenuated BCG-strain (18). Vaccination with this well-characterized, non-specific effect-inducing live vaccine resulted in higher milk yields in the first 100 days p.p. Another study reported on a farm-specific reduction of mastitis incidence after prepartal intranasal vaccination of dairy cows with a virus live vaccine (28). The use of inactivated killed vaccine in D2 suggests that the attenuated strains in the NCD vaccines may not have been able to induce the same mechanisms as the BCG vaccine or the intranasally administered modified live vaccine against BRSV and PI3. The influence of vaccine type and adjuvant was evaluated only in udder health analysis, where two different killed vaccines and one live vaccine with different adjuvant combinations were applied. However, no significant effects were observed. In contrast to Cortese et al. (28), milk yield losses were reported after vaccination with an inactivated Coxiella burnetii vaccine containing no dedicated adjuvant (17). Thus, the interaction of the cows with Coxiella-derived molecules could have induced a response leading to an altered secretion or synthesis capacity of mammary epithelial cells and strengthen the hypothesis that vaccine-induced mechanisms can have non-specific effects in cows. The comparison of the three mentioned studies with ours is limited, as methodologies differed. In addition to different vaccine types, and used adjuvants, prepartal vaccination was only performed by Retamal et al. (18), whereas Scott et al. (16) and Schulze et al. (17) vaccinated cows during lactation. A closer look at statistical methods and thorough examination of multiple predictors is substantial to reveal possible confounding events. In the present study, methodological reduction to univariable analysis would have resulted in higher milk yields of vaccinated cows, leading to spurious correlation. Through multivariable analysis, the effect of prepartal vaccination on milk yield was more accurately determined by excluding disease-associated variables and limiting the analysis to primiparity, which are commonly addressed variables in the analysis of herd records from transition cows (29). Pairing lactations by matching VACC and NON VACC from the same herd allows for more confident attribution of the differences between the groups to the actual exposure. Quantile regression provided insight into the interactions with other predictors. Here, the difference of milk yield between the herds was higher than between VACC and NON VACC. Similarly, the effect of prepartal vaccination had opposite effects depending on the herd. The Random Forest machine learning-algorithms, a model robust to collinearity, facilitated the ranking of importance of influencing factors. While herd and calving year were considered as random effects in regression models, random forest-analysis makes them comparable with other influencing variables. This study concludes that there were no significant effects of prepartal vaccination on milk yield. In the study of Retamal et al. (18) – according to the author’s assessment - these other influencing factors cannot be ruled out, as rather simple analysis was performed on milk yield variables with contingency tables and the Wilcoxon–Mann–Whitney test. In Schulze et al. (17) and Scott et al. (16), similar statistical methodology as in the present study were applied with linear mixed-effects models. Multiple influencing variables were considered, therefore, results are statistically more comparable to Schulze et al. (17) and Scott et al. (16), but still limited, according to the different study designs. Generalized linear mixed models have proven to be a good and flexible method, especially for transition cow analysis (29). A large number of observations lays the foundation for statistical power, enhances the precision of analysis and enables thorough investigation of subgroups and variables to control confounders. Simultaneously, large datasets entail several challenges. While even small effects can be discovered, the probability of type I errors and therefore false positive significant effects is increased. For this reason, we adjusted our p-value threshold in accordance with Goods’ (30) recommendations, resulting in higher hurdles of significance. Herd records and their retrospective nature, especially diagnostic data, bear the potential for documentation variability. On all farms, health management is conducted in close collaboration with the attendant veterinarians, diagnostics of acute diseased animals is performed by veterinarians, but identification of frequently occurring diseases, such as mastitis is usually subject to standard operating procedures of the farm and subsequently documented by managing or milking staff. Therefore, misclassification and differences in documentation between the farms, and respectively herds cannot be ruled out and might compromise accuracy and consistency. Hence, there is a risk, that for instance high numbers in mastitis documentation on a farm do not inevitably represent actual high occurrence of mastitis, but originate in precise documentation of e. g. subclinical mastitis. Precise documentation might lead to false high diagnostic frequency, whereas not recognized diseases due to farm management or inconsistent health documentation might lead to false low incidences. The discussion also considered whether the overall quality of farm management influenced the decision to vaccinate cows. It is unclear which direction this potential bias may take. On one hand, farms with excellent health management and superior animal health status may choose to vaccinate cows as a precautionary measure. On the other hand, farms with poorer health management may vaccinate cows to address the animals’ poor health status. Recognizing these possible variances across herds and risks of biased data, we have implemented comprehensive data cleaning and validation procedures. First, to gain a deeper understanding of the data and verify the comparability of herds, we conducted on-site farm visits. It is noteworthy that all included farms share a common history as former Agricultural Production Cooperatives (LPG) in the German Democratic Republic until 1989, making them more comparable than other farms in Germany. Secondly, we conducted an exploratory analysis of herd management related variables for each herd using contingency tables to better differentiate between systematic and random errors. Thirdly, we examined the diagnostic documentation for each herd across years and months, comparing different diagnostic results with overall patterns to understand and interpret the trends in health documentation and anticipate documentation variability. On some of the farms, more detailed diagnostic data is available with subordinate terms, such as the subdivision of mastitis into forms of inflammation or pathogen etiology. It was agreed upon the utilization of the generic term mastitis as the least common denominator of diagnostic health documentation across all herds. After excluding inconsistent time periods (e. g. initial phase/ first year of health documentation in a herd, allowing time for familiarization), diagnostic health documentation of mastitis was assessed reliable for analysis. Findings of overall exploratory analysis suggest that the variables herd and calving year inherit high influence on mastitis and other variables, consequently these were included as random effects in the subsequent linear mixed-effects regression. Additional to mastitis, we incorporated further robust and objective variables, which are less susceptible to variations in documentation. Originating from milk testing, SCC and milk yield parameters are hardly affected by humans. Thus, the authors regard such variables as more robust than mastitis . Given that milk recordings occur at monthly intervals on the respective farms, the variability in DIM among data points raises concerns about the comparability of the groups. To address this issue, we investigated the average milk performance in relation to the average DIM on the day of milk testing. Our findings suggest that the observed increase in average milk performance does not exceed the contrast estimates obtained through univariable analysis. As a result, we can reasonably conclude that the differing timing of data collection did not significantly impact the results. Conclusion Previous studies have suggested that vaccination in cows may lead to NSEs. However, this study does not provide significant evidence to support this assumption regarding NSEs of prepartal maternal vaccination against NCD in mammary health and milk yield parameters. Instead, findings underline the importance of herd management-related factors. This work provides insights from a large database in the field, particularly for the German dairy industry. The focus is on study design and statistical methodology. Further research is needed to explore the potential impact of vaccination on other infectious diseases, as well as any correlations with other organ systems and production metrics. It remains to be further elaborated whether vaccination ingredients, such as live or attenuated vaccines, as well as adjuvants, play a role in NSEs. Methods Study Population and Farm Selection Data was obtained from test herds of the RinderAllianz (RA), a breeding organization that supervises numerous dairy farms in Mecklenburg-Vorpommern, Saxony-Anhalt, and Brandenburg. The service includes sperm sales, mating, insemination, cattle marketing, milk control and analyses. In cooperation with the RA, comprehensive data from 20 test farms, were obtained in the context of this work. At the timepoint of data collection in June 2021, prepartal vaccination was performed on ten of the participating farms. Milk yield was leveled, so that each group of farms contained five high yielding (mean ECM 305 above 11,000kg) and five low yielding farms. Informed consent was obtained from all participating farms. All of these farms share a common history as former Agricultural Production Cooperatives (LPG) in the German Democratic Republic until 1989. Altogether, data of these 20 farms comprises herd records, reproduction data, milk recordings, health documentation and holding registers of 73,378 dairy cows of 22 herds located across all three operating regions of the RA between January 2007 and September 2020. On-farm data collection Each farm was visited on-site to collect contextual information and conduct an in-depth survey. The survey comprised information on vaccination history, dry-off management, housing system, health management, monitoring during birth, milking and colostrum management, hygiene and feeding management. On-site visits took place between October 2021 and August 2022 with the herd managers. Depending on the size, infrastructure of the farms and distribution of tasks between workers, responsible persons were consulted, if necessary. The questionnaire was always filled out by the same surveyor in order to ensure conformity of documentation. One farm was surveyed online, due to pandemic precaution-measures, all others were visited on-site, allowing a deeper understanding of the conditions of the location. Participating farmers were very cooperative and, with no exception, showed the surveyors around all relevant areas of the facility, housing of dairy cows in all stages of the production cycle – calves, heifers, dry cows, fresh and late-lactating cows -, milking installation and especially calving area was inspected carefully. Apart from data collection, the purpose of the visits ensured that at least no obvious problems in management and hygiene are apparent on the respective farms. Furthermore, attendant veterinarians were consulted to better estimate herd management. Depending on time availability, in some farm visits attendant veterinarians were participating in person, in other cases, they were consulted by phone and in all cases confirmed reliability of health management. Data cleaning and wrangling Step-by-step data was joined and cleaned according to the needs of the study, using the software R version 4.3.1 and R Studio. Herd record data were inspected for inconsistencies before converted into a format, where each observation corresponds to one lactation, whereby chronologic adaptions were made. Events during the dry period are assigned to the subsequent lactation, in order to tailor the database to the TP of the cow. Reproduction data of 148,268 lactations, milk yield and components from 1,561,273 recordings, 1,298,703 diagnoses from health documentation was added. Moreover, on-site survey data were manually transferred from paper-based forms filled out on-site into Excel TM -sheets and further integrated into the final database. Observations were excluded when on-site data or health documentation were not available. Information on vaccination management on each farm were assigned to the vaccination status on an individual cow basis and TP. All observations with other vaccinations than prepartal vaccination against neonatal calf diarrhea were excluded to reduce interaction with other vaccines. When a farm changed its vaccination management or didn’t vaccinate for a distinct time period, a buffer of one month (15 days prior to and 15 days after the date of change) was created, thus risk of false entries is minimized. Here, farm-specific time of vaccination was paid attention to, in order to correctly assign vaccination periods and calving dates. The original database, containing 73,378 cows on ten vaccinating farms and ten non-vaccinating farms, was finally adapted to 120,394 TP of 53,370 cows on 19 farms and distributed across non-vaccinated, continuously vaccinated and alternately vaccinated herds, resulting in overall 57,166 NON VACC and 63,228 VACC in D1 (table 1). Despite retrospectively adapting the groups according to the specification of vaccination periods through on-farm data collection, NON VACC and VACC are comparable in mammary health and milk yield parameters (table 2). The second and smaller dataset (D2) represents a subset of D1, containing 1,002 TP from 1,002 primiparous, utmost healthy cows on four alternately vaccinated herds. D2 allows analyses on rather ideal conditions, while presuming, that health and immunological status of primiparous cows differ from multiparous cows (29, 31). Furthermore, we intended to reduce the influences of pathologic processes by excluding diseased cows. Regarding mammary health, those cows diagnosed with mastitis, were excluded, taking into account, that mastitis can lead to lower milk yields (25). As SCC below 100,000 cells is considered physiological (32, 33) lactations were only included, when SCC of the first test day of milk recordings were below this threshold. Risk of ketosis and diagnosis of retained placenta and metritis could also be eliminated from D2. In alternately vaccinated herds a timeframe of twelve months before and twelve months after change of vaccination management (from vaccination to non-vaccination or vice versa) was subset. By this means, the comparison of VACC and NONVACC is less influenced by calving year. [ Figure 3 ] Elaboration of vaccination related parameters An additional table provides an overview of response and independent variables, definitions, composition, and values (see Additional file 2). Three vaccines were used: Bovilis® Rotavec® Corona (Intervet) (RC): n=27,769, containing inactivated bovine rotavirus (serotype G6 P5), inactivated bovine coronavirus (strain Mebus), E. coli (K99 Antigen), mineral oil and aluminium hydroxide. Scourguard® 3 (Zoetis) (SG): n=8,352, containing live attenuated bovine rotavirus (strain Lincoln), live attenuated bovine coronavirus (strain Hansen), inactivated E.coli (K99 Antigen), Alhydrogel. Bovigen® Scour (Forte Healthcare) (BS): n=8,004, containing inactivated bovine rotavirus (serotype G6 P1), inactivated bovine coronavirus (strain C-197), E. coli (K99 Antigen), Montanide ISA 206 VG. RC and BS are applicated once and SG is applicated twice, whereby the date of the first application was considered as time of vaccination. In 18,453 cases the vaccine product could not be associated (not specified). On the basis of the used vaccine products, the variable adjuvant was designed grouping animals in those vaccinated with alum (RC, SG) or montanide-containing (BS) vaccines. Elaboration of mammary health and milk yield parameters Each two response variables were elaborated to represent milk performance and mammary health: ECM 305 defines the energy corrected milk yield in 305 days of lactation and ECM FTD on the first test day of milk recording. For this, the milk recording parameters were consulted to calculate ECM as follows: milk yield in kg × (0.38 x fat content in % + 0.21 x protein content in % + 1.05) ÷ 3.28 (34, 35). To approach an adequate representation of mammary health status of the cows, we consulted parameters of different origin. Farm health documentation was transformed into the binary variable mastitis indicating the prevalence of mastitis diagnoses of each cow, when the diagnosis mastitis was registered at least once within the time period of 10 days p.p. We complemented the analysis with SCC as a further more stringent variable, less influential by personnel or documentation. It is defined as the somatic cell count on the first day of milk recording and was logarithmically transformed to stabilize variance. SCC is seen as a good parameter to identify intramammary infections (36), and is considered physiological below 100,000 (32, 33). The farms conducted milk recordings once a month. Thus, the first test day of milk recordings and therefore Days in Milk (DIM) at the time of data collection of the variables ECM FTD and SCC differs across the observations, resulting in limitations for these variables, as they represent only one single point of time during the first weeks of lactation, where the milk yield and cell count vary. The mean DIM was considered between the groups investigated, ascertaining comparability. Eligible influencing variables were defined, firstly the above-mentioned vaccination related variables, and further, farm management and cow related variables. Management related variables encompass the following variables: The herd itself representing the general farm management. Calving year denotes the year when cows calved, potentially impacting health due to environmental or management changes. The variables herd and calving year might not be understood in isolation, but rather in combination. Although e. g. climate or pathogen diversity might vary between the years, the fluctuations of the years are probably more subject to farm management than the year itself. For this reason, they were consequently combined as random effects in the statistical models. Herd Size indicates the annual total number of cows in the herd, potentially influencing disease spread and herd dynamics. The variable was transformed from numeric to character type by segmenting into the categories small, medium and large with the binning-function of the dlookr -package. The quantile method was employed to determine break points, ensuring an equitable distribution of farm sizes across the derived categories. Herd replacement rate of the previous year , affecting overall herd health and productivity in the respective year, was calculated as percentage of number of primiparous calvings, compared to the number of secondi- or multiparous calvings. Access to pasture reflects the availability and quality of grazing areas during the dry period, impacting especially the exercise level of the cow during calving and lactation. Flooring refers to the type of flooring within the barn during TP, impacting cow comfort and hoof health with the two categories deeplitter or slatted floor. Score of hygiene indicates the cleanliness level of the cows and density of possible pathogens. A score between 1 and 4 was documented on the date of on-site survey (37). Calving box describes the two options of calving in a group or in an individual box, influencing behavioral dynamics around calving. When calving in individual boxes, pen change was conducted during TP, which can affect the cows’ metabolism in combination with prepartal vaccination (38). Milking Frequency defines how often cows are milked daily, affecting mammary health. Here robot-milked cows could not be traced for this variable. Moreover, Type of dry-off describes the method used when cows are dried off from milking, impacting mammary gland health, by using or not using antibiotic dry-cow treatment. Additionally, supplements such as Energy Supplements , Calcium Supplements , Vitamin D3 Supplements , further Vitamin and Trace Element Supplements and Monensin , given shortly before, during or shortly after calving were provided, possibly impacting nutritional and immune status. Following aspects were assigned to cow related variables: Cows were categorized as primi-, secondi- or multiparous in the variable parity . Length of the dry period was provided partly in weeks during the on-site survey and calculated in days and can therefore deviate slightly. The first lactation age was calculated as the difference between calving date of first calving and the cows’ birthdate, assigned to categories: low first lactation age therefore is less than or equal to 700 days, medium between 701-750 days and high more than 750 days, taking into consideration that milk yield and ingredients can differ between these groups (39). The four calving seasons enable us to investigate potential seasonal variations in dairy cow health, while spring is defined as calving date between March and May, summer between June and August, autumn between September and November and winter between December and February. This variable not only refers to the calving itself, but also allows us to explore whether specific health events were more prevalent during certain times of the year. Finally, risk of ketosis allows a conclusion on the ketotic metabolic status, determined by the milk components on the first day of milk recording. Ketotic risk was assumed, if the fat-protein-ratio exceeds 1.4 and lower limits of protein content are undercut or upper limits of fat content are passed. Limits were calculated according to Glatz-Hoppe et al. (40). By assessing the risk of ketosis in this critical period, we aimed to predict and mitigate potential metabolic disruptions affecting the cows’ health. Statistical Analyses The software R version 4.3.1 and R Studio were used for statistical analyses (41). Descriptive analysis of D1 for the categories NON VACC and VACC was undertaken to get an overview of the data structure. All eligible variables were examined for any possible influence on the response variables mastitis, SCC, ECM FTD and ECM 305 in univariable analysis . The variables in question were further checked for missing value patterns. (Generalized) linear mixed-effects models were executed using the lme4- package. Here, we considered the variables herd and calving year as nested random effects. Given the nested structure of the data involving herds and calving years, we assessed different combinations of random effects in mixed-effects models to determine the most suitable formulation based on Akaike’s Information Criterion. P-value threshold was adjusted in accordance with Goods’ recommendations for large number of observations (30). By applying the function , p-value of each model outcome could be evaluated, resulting in significance thresholds between 0.0158 (D1) and 0.0014 (D2). All variables, that were significant in univariable (generalized) linear mixed-effects regression of the response variables mastitis and SCC , according to Goods’ p-value threshold, were further included in the subsequent multivariable analysis. When preparing multivariable analysis for the response variables ECM FTD and ECM 305, it soon became clear, that other than vaccination-related parameters overlap results. Furthermore, missing value patterns foreclosed multivariable analysis. Therefore, D2, a subset of D1 was elaborated, reducing influencing variables. The available disease-associated variables, mastitis , SCC above 100,000, retained placenta , metritis , and ketotic risk were used to exclude diseased cows, as these are known influencing factors on milk yield (42, 43). Parity, another known influencing variable on milk yield and the periparturient cow’s immune system (29, 44, 45) proved to be significant in univariable analysis of the present study. Thus, parity was reduced to primiparous cows. Furthermore, pairing of VACC and NON VACC on four alternately vaccinating farms was conducted to further control confounding effects. In addition, only data in the period of twelve months before and after change of vaccination management was selected to minimize the influence of calving year . With D2, containing primiparous, utmost healthy cows from alternately vaccinated herds, univariable and multivariable analysis was carried out. Quantile regression was conducted to model the relationship between predictor variables and median of the response variable. Additional broadening of the analysis with the random forest-algorithm (46) was conducted in order to obtain a ranking of importance of influencing variables for mastitis and SCC , including herd and calving year , previous inserted as random effects. Abbreviations BCG: Bacille Calmette Guerin BS : Bovigen® Scour D1: dataset 1 D2: dataset 2 DIM : days in milk ECM 305: energy corrected milk yield in 305 days of lactation (in kg) ECM FTD: energy corrected milk yield on the first test day of milk recording (in kg) ECM : energy corrected milk LPG : Agricultural Production Cooperatives NCD : neonatal calf diarrhea NON VACC: no vaccination during the dry period NSEs: non-specific effects p.p.: post-partum RA: RinderAllianz RC : Bovilis® Rotavec® Corona SG : Scourguard® 3 TP: transition period VACC : vaccination during dry period Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials All relevant data are within the paper and its supporting additional files. Competing interests The authors declare that they have no competing interests. Funding This study was funded by Intervet Deutschland GmbH. Authors’ contributions C.K. wrote the main manuscript text. Formal analysis and software were performed by C.K. and Y.Z., while Y.Z. and A.R. adapted the methodology and validated the analysis. C.K. and Y.Z. prepared figures and tables and H.Z., H-J.S., Y.Z. and A.R. edited the text. The study was supervised by Y.Z., H.Z., H-J.S. and A.R. Project administration and funding acquisition was ensured by H.Z., M.R. and A.S and detailed conceptualization was obtained by H.Z. H-J.S., Y.Z. and C.K. Data was obtained by D.K. and C.W. and curated by C.K., Y.Z., A.R. All authors discussed and reviewed the manuscript. Corresponding author: Caroline Kuhn ( [email protected] ) Acknowledgements The authors would like to thank all farmers who participated in the study. References Viidu D-A, Mõtus K. Implementation of a pre-calving vaccination programme against rotavirus, coronavirus and enterotoxigenic Escherichia coli (F5) and association with dairy calf survival. BMC Veterinary Research. 2022;18(1):59. Gonzalez R, Elvira L, Carbonell C, Vertenten G, Fraile L. The Specific Immune Response after Vaccination against Neonatal Calf Diarrhoea Differs between Apparent Similar Vaccines in a Case Study. Animals : an open access journal from MDPI. 2021;11(5). Durel L, Rose C, Bainbridge T, Roubert J, Dressel K-U, Bennemann J, et al. Immune response of mature cows subjected to annual booster vaccination against neonatal calf diarrhoea with two different commercial vaccines: A non-inferiority study. Livestock Science. 2017;204:52-8. Smith BI, Rieger RH, Dickens CM, Schultz RD, Aceto H. Anti-bovine herpesvirus and anti-bovine viral diarrhea virus antibody responses in pregnant Holstein dairy cattle following administration of a multivalent killed virus vaccine. American Journal of Veterinary Research. 2015;76(10):913-20. Vidlund J, Gelalcha BD, Gillespie BE, Agga GE, Schneider L, Swanson SM, et al. Efficacy of novel staphylococcal surface associated protein vaccines against Staphylococcus aureus and non-aureus staphylococcal mastitis in dairy cows. Vaccine. 2024;42(6):1247-58. Leik NKO, Ahmedy F, Guad RM, Baharuddin DMP. Covid-19 vaccine and its consequences in pregnancy: Brief review. Annals of Medicine and Surgery. 2021;72:103103. Weikard R, Demasius W, Hadlich F, Kühn C. Different Blood Cell-Derived Transcriptome Signatures in Cows Exposed to Vaccination Pre- or Postpartum. PloS one. 2015;10(8):e0136927. Demasius W, Weikard R, Hadlich F, Müller KE, Kühn C. Monitoring the immune response to vaccination with an inactivated vaccine associated to bovine neonatal pancytopenia by deep sequencing transcriptome analysis in cattle. Veterinary research. 2013;44(1):93. Amir M, Zeng MY. Immune imprinting in utero. Science. 2021;373(6558):967-8. Geckin B, Konstantin Föhse F, Domínguez-Andrés J, Netea MG. Trained immunity: implications for vaccination. Current Opinion in Immunology. 2022;77:102190. Kleinnijenhuis J, Quintin J, Preijers F, Joosten LAB, Ifrim DC, Saeed S, et al. Bacille Calmette-Guerin induces NOD2-dependent nonspecific protection from reinfection via epigenetic reprogramming of monocytes. Proceedings of the National Academy of Sciences of the United States of America. 2012;109(43):17537-42. Ziogas A, Netea MG. Trained immunity-related vaccines: innate immune memory and heterologous protection against infections. Trends in molecular medicine. 2022;28(6):497-512. Guerra-Maupome M, Vang DX, McGill JL. Aerosol vaccination with Bacille Calmette-Guerin induces a trained innate immune phenotype in calves. PloS one. 2019;14(2):e0212751. Benn CS, Fisker AB, Rieckmann A, Sørup S, Aaby P. Vaccinology: time to change the paradigm? The Lancet Infectious Diseases. 2020;20(10):e274-e83. Arega SM, Knobel DL, Toka FN, Conan A. Non-specific effects of veterinary vaccines: a systematic review. Vaccine. 2022;40(11):1655-64. Scott HM, Sargeant JM, Ireland MJ, Lissemore KD, Leslie KE, Kelton DF, et al. Effects of a core antigen vaccine against gram-negative bacteria on physiologic and yield parameters of dairy cows during late lactation and the dry period. Journal of Dairy Science. 1998;81(7):1928-35. Schulze LSC, Borchardt S, Ouellet V, Heuwieser W. Effect of a phase I Coxiella burnetii inactivated vaccine on body temperature and milk yield in dairy cows. Journal of Dairy Science. 2016;99(1):541-50. Retamal P, Ábalos P, Alegría-Morán R, Valdivieso N, Vordermeier M, Jones G, et al. Vaccination of Holstein heifers with Mycobacterium bovis BCG strain induces protection against bovine tuberculosis and higher milk production yields in a natural transmission setting. Transboundary and Emerging Diseases. 2022;69(3):1419-25. Grummer RR. Impact of changes in organic nutrient metabolism on feeding the transition dairy cow. Journal of animal science. 1995;73(9):2820-33. Drackley JK. Biology of dairy cows during the transition period: the final frontier? Journal of Dairy Science. 1999;82(11):2259-73. Ingvartsen KL, Dewhurst RJ, Friggens NC. On the relationship between lactational performance and health: is it yield or metabolic imbalance that cause production diseases in dairy cattle? A position paper. Livestock Production Science. 2003;83(2-3):277-308. LeBlanc SJ. Postpartum uterine disease and dairy herd reproductive performance: a review. The Veterinary Journal. 2008;176(1):102-14. Mulligan FJ, Doherty ML. Production diseases of the transition cow. The Veterinary Journal. 2008;176(1):3-9. Pyörälä S. Mastitis in post-partum dairy cows. Reproduction in domestic animals. 2008;43:252-9. Hagnestam C, Emanuelson U, Berglund B. Yield losses associated with clinical mastitis occurring in different weeks of lactation. Journal of Dairy Science. 2007;90(5):2260-70. Trevisi E, Minuti A. Assessment of the innate immune response in the periparturient cow. Research in Veterinary Science. 2018;116:47-54. Sordillo LM, Mavangira V. The nexus between nutrient metabolism, oxidative stress and inflammation in transition cows. Animal Production Science. 2014;54(9):1204. Cortese VS, Pinedo PJ, Manriquez D, Velasquez-Munoz A, Solano G, Short TH, et al. Effect of periparturient intranasal vaccination on post parturient health parameters in holstein cows. Concepts of Dainry and Veterinary Sciences. 2020;3(4):318-27. Wisnieski L, Norby B, Pierce SJ, Becker T, Sordillo LM. Prospects for predictive modeling of transition cow diseases. Anim Health Res Rev. 2019;20(1):19-30. Good IJ. The Interface Between Statistics and Philosophy of Science. Statistical Science. 1988;3(4):386-97. Gilbert RO, Gröhn YT, Miller PM, Hoffman DJ. Effect of parity on periparturient neutrophil function in dairy cows. Vet Immunol Immunopathol. 1993;36(1):75-82. Doggweiler R, Hess E. Zellgehalt in der Milch ungeschädigter Euter. Milchwissenschaft. 1983;38:5-8. Hamann J, Fehlings K. Leitlinien zur Bekämpfung der Mastitis des Rindes als Bestandsproblem. 4 ed. Gießen: Deutsche Veterinärmedizinische Gesellschaft e.V. (DVG), Fachgruppe "Milchhygiene"; 2002. Ernährung BfLu. Definitionen und Begriffe: Milch und Milcherzeugnisse 2021 [Available from: https://www.ble.de/DE/BZL/Daten-Berichte/Milch-Milcherzeugnisse/milch-milcherzeugnisse_node.html. Spiekers H, Potthast V. Erfolgreiche Milchviehfütterung. 4 ed. Frankfurt am Main: DLG-Verl.; 2004. Nyman AK, Emanuelson U, Waller KP. Diagnostic test performance of somatic cell count, lactate dehydrogenase, and N-acetyl-β-D-glucosaminidase for detecting dairy cows with intramammary infection. Journal of Dairy Science. 2016;99(2):1440-8. Cook N, Reinemann D. A Tool Box for Assessing Cow, Udder and Teat Hygiene. NMC annual Meeting Paper. 2007. Menichetti BT, Garcia-Guerra A, Lakritz J, Weiss WP, Velez JS, Bothe H, et al. Effect of timing of prepartum vaccination relative to pen change with an acidogenic diet on lying time and metabolic profile in Holstein dairy cows. Journal of Dairy Science. 2021;104(10):11059-71. Ettema JF, Santos JEP. Impact of Age at Calving on Lactation, Reproduction, Health, and Income in First-Parity Holsteins on Commercial Farms. Journal of Dairy Science. 2004;87(8):2730-42. Glatz-Hoppe J, Losand B, Kampf D, Onken F, Spiekers H. Nutzung von Milchkontrolldaten zur Fütterungs- und Gesundheitskontrolle bei Milchkühen 2022 [2:[Available from: https://www.dlg.org/de/landwirtschaft/themen/tierhaltung/futter-und-fuetterung/dlg-merkblatt-451. Team RC. R: A language and environment for statistical computing. Vienna, Austria2023. Hagnestam-Nielsen C, Emanuelson U, Berglund B, Strandberg E. Relationship between somatic cell count and milk yield in different stages of lactation. Journal of Dairy Science. 2009;92(7):3124-33. Heikkilä AM, Liski E, Pyörälä S, Taponen S. Pathogen-specific production losses in bovine mastitis. Journal of Dairy Science. 2018;101(10):9493-504. Lee J-Y, Kim I-H. Advancing parity is associated with high milk production at the cost of body condition and increased periparturient disorders in dairy herds. J Vet Sci. 2006;7(2):161-6. Ohtsuka H, Terasawa S, Watanabe C, Kohiruimaki M, Mukai M, Ando T, et al. Effect of parity on lymphocytes in peripheral blood and colostrum of healthy Holstein dairy cows. Can J Vet Res. 2010;74(2):130-5. Breiman L. Random Forests. Machine Learning. 2001;45(1):5-32. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additional file 1 (format: .xlsx): Prepartal vaccination status and energy corrected milk yield in univariable linear mixed-effects logistic regression. Additionalfile2.xlsx Additional file 2 (format: .xlsx): Composition, definition and source of response and independent variables, applied in the study. 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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-4259469","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293952898,"identity":"500c0572-f225-4291-a298-ea7056d06335","order_by":0,"name":"Caroline Kuhn","email":"data:image/png;base64,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","orcid":"","institution":"Clinic for Ruminants with Ambulatory and Herd Health Services, Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-University, Munich","correspondingAuthor":true,"prefix":"","firstName":"Caroline","middleName":"","lastName":"Kuhn","suffix":""},{"id":293952899,"identity":"c098a064-e4b5-417f-9895-38f61a2663c0","order_by":1,"name":"Holm Zerbe","email":"","orcid":"","institution":"Clinic for Ruminants with Ambulatory and Herd Health Services, Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-University, Munich","correspondingAuthor":false,"prefix":"","firstName":"Holm","middleName":"","lastName":"Zerbe","suffix":""},{"id":293952900,"identity":"08110253-3344-4954-a35c-f05ffd766518","order_by":2,"name":"Hans-Joachim Schuberth","email":"","orcid":"","institution":"Institute for Immunology, University of Veterinary Medicine, Hannover","correspondingAuthor":false,"prefix":"","firstName":"Hans-Joachim","middleName":"","lastName":"Schuberth","suffix":""},{"id":293952901,"identity":"4649a33a-5fcc-4cb8-89ac-37aa4ad39f69","order_by":3,"name":"Anke Römer","email":"","orcid":"","institution":"3Mecklenburg-Vorpommern Research Centre for Agriculture and Fisheries, Institute of Livestock Farming, Dummerstorf","correspondingAuthor":false,"prefix":"","firstName":"Anke","middleName":"","lastName":"Römer","suffix":""},{"id":293952902,"identity":"ae7a21ef-2c20-436c-93b1-44f7b9a6f42a","order_by":4,"name":"Debby Kraatz-van Egmond","email":"","orcid":"","institution":"RinderAllianz GmbH, Woldegk","correspondingAuthor":false,"prefix":"","firstName":"Debby","middleName":"Kraatz-van","lastName":"Egmond","suffix":""},{"id":293952903,"identity":"00dcb5fa-baa1-4314-8519-c700c4693493","order_by":5,"name":"Claudia Wesenauer","email":"","orcid":"","institution":"RinderAllianz GmbH, Woldegk","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Wesenauer","suffix":""},{"id":293952904,"identity":"3aa83f7b-d44d-4280-97d4-d71d1ce4d498","order_by":6,"name":"Martina Resch","email":"","orcid":"","institution":"Intervet Deutschland GmbH, Unterschleissheim","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Resch","suffix":""},{"id":293952905,"identity":"a859c9c7-5bfe-4dea-a0bf-386bd7e0a254","order_by":7,"name":"Alexander Stoll","email":"","orcid":"","institution":"Intervet Deutschland GmbH, Unterschleissheim","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Stoll","suffix":""},{"id":293952906,"identity":"dc5768f2-e690-4c6e-9c82-e2d31fea8fc8","order_by":8,"name":"Yury Zablotski","email":"","orcid":"","institution":"Clinic for Ruminants with Ambulatory and Herd Health Services, Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-University, Munich","correspondingAuthor":false,"prefix":"","firstName":"Yury","middleName":"","lastName":"Zablotski","suffix":""}],"badges":[],"createdAt":"2024-04-12 19:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4259469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4259469/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55260468,"identity":"3e36f44d-5c14-4898-80bf-7a8b751d1f22","added_by":"auto","created_at":"2024-04-24 22:26:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77396,"visible":true,"origin":"","legend":"\u003cp\u003eMedian energy corrected milk yield in 305 days of lactation (ECM 305) of four herds (Table 1: 3a, 3d, 3f, 3g), comparing prepartal vaccinated (VACC) and non-vaccinated (NON VACC) cows in quantile regression. The error bars display 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4259469/v1/d4feb21efefa3dec9a9421b7.png"},{"id":55260469,"identity":"038f891a-255a-4493-9a0a-d405911574d1","added_by":"auto","created_at":"2024-04-24 22:26:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":202204,"visible":true,"origin":"","legend":"\u003cp\u003eRanking of importance of influencing variables on mastitis prevalence and somatic cell count (\u003cem\u003eSCC\u003c/em\u003e) by a Random Forest model. The importance of the predicted variable is represented by the mean decrease of impurity, which is the reduction of uncertainty in the model. Importance is associated with the ability to predict the response variable.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4259469/v1/127b95e65f0462d90f83b5a2.png"},{"id":55260470,"identity":"2dbe564b-fdff-4acf-9aab-b815693d5a1b","added_by":"auto","created_at":"2024-04-24 22:26:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":610590,"visible":true,"origin":"","legend":"\u003cp\u003eSimplified flow diagram of the studies’ data cleaning process\u003c/p\u003e","description":"","filename":"Figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-4259469/v1/78b778f058865d76071d2520.png"},{"id":74094993,"identity":"ca1c4c99-a0db-4e34-bb24-4ef4239a2444","added_by":"auto","created_at":"2025-01-17 17:01:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2134075,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4259469/v1/581e5e1c-69b3-45d5-9776-2645b8e93099.pdf"},{"id":55260471,"identity":"d8831c36-f165-47cf-ba5f-70e9b01695f9","added_by":"auto","created_at":"2024-04-24 22:26:54","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1 \u003c/strong\u003e(format: .xlsx): Prepartal vaccination status and energy corrected milk yield in univariable linear mixed-effects logistic regression.\u003c/p\u003e","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4259469/v1/86322c9948fc517cb91c2ad7.xlsx"},{"id":55260472,"identity":"10db3a50-7433-4902-a222-50b59d7b8544","added_by":"auto","created_at":"2024-04-24 22:26:54","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2 \u003c/strong\u003e(format: .xlsx)\u003cstrong\u003e: \u003c/strong\u003eComposition, definition and source of response and independent variables, applied in the study.\u003c/p\u003e","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4259469/v1/e7ed3191739557ff78b936f4.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prepartal vaccination has no influence on mammary health and milk yield of dairy cows: a retrospective study addressing non-specific effects of vaccination","fulltext":[{"header":"Background","content":"\u003cp\u003ePrepartal vaccinations of cows are performed to protect calves from infectious diseases in the first weeks of life. During the last weeks of pregnancy, the mother is usually vaccinated with a killed vaccine to induce high levels of pathogen-specific antibodies (1-3). Prepartal vaccination of cows is regarded as safe and usually no adverse reactions are reported after vaccinations with killed vaccines containing different adjuvants (4, 5). Vaccinations of pregnant women against SARS-CoV-2 also revealed no harmful effects on pregnancy (6). In addition to the induction of an adaptive immune response intending to induce the generation of pathogen-specific antibodies, the immediate innate response after vaccinations of cows with killed vaccines resulted in an altered gene expression of circulating immune cells (7, 8). Thus, the inflammatory response after active vaccination can lead to an altered functionality of circulating immune cells. The innate response to vaccination has also been shown to modify fetal immune system development in utero via the induction of epigenetic changes (9). The altered immune response of newborns due to maternal vaccination implies that vaccination-induced epigenetic modification also changes the immune reactivity of the vaccinated mother. Such vaccination-induced, innate-response-mediated effects can lead to a status called innate immune memory, based on epigenetic modifications (10). Such effects are well-studied for certain vaccines, e. g. the Bacille Calmette Guerin (BCG) vaccine containing an avirulent Mycobacterium bovis strain (11, 12). This vaccine resulted in a trained functional phenotype (trained immunity) of circulating myeloid cells of calves (13), referring to primary stimulus-augmented innate immune responses towards a secondary stimulation (10). Vaccination-induced effects beyond antigen-specific induction of adaptive immune responses are known as non-specific effects (NSEs) of vaccines. NSEs can be positive or negative for the individual. Underlying mechanisms of NSEs are intensely discussed (14) and possible characteristics of live, attenuated, or killed vaccines in this regard are not finally elaborated (15). While live vaccinations in humans are in the center of investigations, there is a lack of research on NSEs of veterinary vaccine products (15). Whether contemporarily used veterinary vaccines, especially those applied to cows in the transition period (TP) during late pregnancy, have non-specific effects or not, has not been addressed so far. A number of studies explored variable effects of cow vaccinations with conventional vaccines on milk yield. No effect on milk yield was reported after vaccination with a core antigen vaccine against gram-negative bacteria (16). Lower milk yields were shown after vaccination with an inactivated Coxiella burnetii vaccine (17), whereas heifers vaccinated with a BCG vaccine showed higher milk yields (18). How these effects were mediated was not addressed so far and, to the best of our knowledge, no study addressed the potential NSEs of prepartal vaccination against neonatal calf diarrhea (NCD) on the prevalence of postpartal mastitis and the milk yield of cows. Therefore, the aim of this retrospective cross-sectional study was to analyze the effect of contemporary used prepartal vaccines on mammary health and milk yield of the periparturient cow.\u003c/p\u003e\n"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003eDescriptive Summary: vaccination management in participating herds and key figures\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e73,378 dairy cows in 22 herds across 20 farms were included in this study, of which 53,370 dairy cows in 21 herds across 19 farms could be further included for analysis after matching herd records with health documentation, milk records and the questionnaire conducted in this study. This first dataset (D1) consisted of 120,394 TP in total and could be divided into 57,166 TP without vaccination during the dry period (NON VACC) across 16 herds and 63,228 TP with prior vaccination (VACC) across 13 herds. The overall mastitis prevalence was 6.2% (VACC 6.6%, NON VACC 5.8%). Overall median somatic cell count on the first day of milk recording (SCC) was 69,000 (VACC 73,000; NON VACC 65,000). \u0026nbsp;In VACC cows, the median energy corrected milk yield was 37kg on the first day of milk recording (ECM FTD) and 9,739kg in 305 days of lactation (ECM 305), compared to 36kg ECM FTD and 9,597kg ECM 305 in NON VACC cows. The overall median ECM FTD was 37kg, ECM 305 was 9,674kg. Dataset 2 (D2) represents a subset of D1, containing 1,002 TP from 1,002 primiparous, utmost healthy cows on four alternately vaccinated herds, thereof 525 NON VACC and 477 VACC. In D2 overall median SCC is 49,000 (VACC 50,000; NON VACC 47,000), ECM 305 8,748kg (VACC 8,797kg; NON VACC 8,696kg) and ECM FTD 29.9kg (VACC 30; NON VACC 29.8). An overview of included TP and descriptive results is provided in table 1 and 2 (see Table 1, Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eNumbers of transition periods in individual herds with different vaccination managements.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDataset 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNON VACC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n = 57,166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVACC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n = 63,228)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNon-vaccinated herds\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e8,791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e4,459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e1,902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e1d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e3,531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e1e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e1f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e1g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e3,231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e1h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e5,476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuously vaccinated herds\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e1,305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e2,387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e2c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e8,093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e2d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e7,429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e2e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e18,453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlternately vaccinated herds\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e1,744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e1,545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e3,128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e5,807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e4,137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e6,885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e1,478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e9,182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e5,555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e5,847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e4,007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e1,834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e1,208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDataset 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNON VACC\u003c/strong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(n = 525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVACC\u003c/strong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(n = 477)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlternately vaccinated herds\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.811175337186896%\" valign=\"top\"\u003e\n \u003cp\u003e3g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.433526011560694%\" valign=\"top\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.75529865125241%\" valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eno vaccination during the dry period; \u003csup\u003eb\u003c/sup\u003evaccination between 2.5 and 8 weeks before expected calving date; \u003csup\u003ec\u003c/sup\u003eherds without vaccinations within the dry period; \u003csup\u003ed\u003c/sup\u003eherds with continuous prepartal vaccinations; \u003csup\u003ee\u003c/sup\u003eherds with prepartal vaccinations of parts of the herd or during defined time periods of time.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Descriptive summary - mammary health and milk yield in herds with different prepartal vaccination status.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDataset 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"50%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrepartal vaccination status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n = 120,394)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNON VACC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n = 57,166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVACC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n = 63,228)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMastitis\u003c/strong\u003e, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e6.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e5.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e6.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e, median\u0026nbsp;(IQR\u003csup\u003ed\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e69 (34 - 184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e65 (32 - 174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e73 (36 - 196)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e27,839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e10,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e17,703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eECM\u003c/strong\u003e \u003cstrong\u003e305\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e, median\u0026nbsp;(IQR\u003csup\u003ed\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e9,674 (7,884 - 11,236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e9,597 (7,851 - 11,352)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e9,739 (7,914 - 11,161)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e5,431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e2,648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e2,783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eECM\u003c/strong\u003e \u003cstrong\u003eFTD\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e, median\u0026nbsp;(IQR\u003csup\u003ed\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e37 (30 - 44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e36 (29 - 44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e37 (30 - 44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e27,839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e10,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e17,703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDataset 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrepartal vaccination status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n = 1,002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNON VACC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n = 525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVACC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n = 477)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e, median\u0026nbsp;(IQR\u003csup\u003ed\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e49 (32 - 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e47 (29 - 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e50 (34 - 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eECM\u003c/strong\u003e \u003cstrong\u003e305\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e, median\u0026nbsp;(IQR\u003csup\u003ed\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e8,748 (7,351 - 9,911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e8,696 (7,190 - 9,833)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e8,797 (7,506 - 10,004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eECM\u003c/strong\u003e \u003cstrong\u003eFTD\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e, median\u0026nbsp;(IQR\u003csup\u003ed\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e29.9 (26.6 - 33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.922118380062305%\" valign=\"top\"\u003e\n \u003cp\u003e29.8 (26.9 - 33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e30.0 (26.4 - 33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eno vaccination during the dry period; \u003csup\u003eb\u003c/sup\u003evaccination between 2.5 and 8 weeks before expected calving date; \u003csup\u003ec\u003c/sup\u003eSomatic cell count on the first day of milk recording (in thousand); \u003csup\u003ed\u003c/sup\u003einterquartile range; \u003csup\u003ee\u003c/sup\u003eEnergy corrected milk yield in 305 days lactation in kg; \u003csup\u003ef\u003c/sup\u003eEnergy corrected milk yield on the first day of milk recording in kg.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cu\u003ePrepartal vaccination has no significant influence on mammary health parameters\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo identify significant influencing parameters for the response variables \u003cem\u003emastitis\u003c/em\u003e and \u003cem\u003eSCC\u003c/em\u003e, univariable mixed-effects logistic regression was performed, taking into account the available meaningful variables. In both analyses, the likewise significant variables \u003cem\u003eherd\u003c/em\u003e and \u003cem\u003ecalving year\u003c/em\u003e were applied as random effects in a nested structure. \u003cem\u003eMastitis\u003c/em\u003e was significantly associated with \u003cem\u003eparity\u003c/em\u003e, \u003cem\u003ecalving season\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;access to pasture\u003c/em\u003e. For the \u003cem\u003eSCC\u003c/em\u003e, the variables \u003cem\u003eparity\u003c/em\u003e, \u003cem\u003ecalving season, flooring\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;farmsize\u003c/em\u003e emerged as significant. All of these significant variables were further included for multivariable mixed-effects regression to investigate the influence of prepartal vaccination on \u003cem\u003emastitis\u003c/em\u003e and \u003cem\u003eSCC.\u003c/em\u003e Multivariable analyses revealed that prepartal vaccination had no significant influence on both mastitis prevalence (see Table 3) and SCC (see Table 4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003eAssociation between prepartal vaccination status and mastitis prevalence in multivariable linear mixed-effects logistic regression.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrepartal vaccination status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003eVACC\u003csup\u003ed\u003c/sup\u003e / NON VACC\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e0.86, 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003esecondiparous / primiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e0.66, 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003emultiparous / primiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e1.20, 1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003emultiparous / secondiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e1.64, 1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccess to pasture\u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003eyes / no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e1.61, 2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalving season\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003esummer / spring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e1.21, 1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003eautumn / spring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e0.91, 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003eautumn / summer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e0.70, 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003ewinter / spring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e0.87, 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003ewinter / summer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e0.67, 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.10940919037199%\" valign=\"top\"\u003e\n \u003cp\u003ewinter / autumn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.25382932166302%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.35010940919037%\" valign=\"top\"\u003e\n \u003cp\u003e0.87, 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.286652078774615%\" valign=\"top\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eOdds Ratio for pairwise contrasts; \u003csup\u003eb\u003c/sup\u003eConfidence Interval; \u003csup\u003ec\u003c/sup\u003esignificant effects are marked in bold - threshold: 0.0014; \u003csup\u003ed\u003c/sup\u003evaccination between 2.5 and 8 weeks before expected calving date; \u003csup\u003ee\u003c/sup\u003eno vaccination during the dry period; \u003csup\u003ef\u003c/sup\u003eaccess to pasture during dry period; herd and calving year were applied as random effects.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003ePrepartal vaccination status and somatic cell count (SCC\u003csup\u003ea\u003c/sup\u003e) in multivariable linear mixed-effects regression.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel estimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrepartal vaccination status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003eVACC\u003csup\u003ed\u003c/sup\u003e - NON VACC\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.12, 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003esecondiparous - primiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.22, -0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003emultiparous - primiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e0.20, 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003emultiparous - secondiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e0.38, 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlooring\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003edeeplitter \u0026ndash; slatted floor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e0.11, 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHerdsize\u003c/strong\u003e\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003esmall - medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.40, -0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003elarge - medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.52, -0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003elarge - small\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.36, 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalving season\u003c/strong\u003e\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003esummer - spring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e0.11, 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003eautumn - spring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.05, 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003eautumn - summer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.19, -0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003ewinter - spring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.07, 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003ewinter - summer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.22, -0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.067796610169495%\" valign=\"top\"\u003e\n \u003cp\u003ewinter - autumn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.584745762711865%\" valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.610169491525422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.06, 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.73728813559322%\" valign=\"top\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eSomatic cell count was logarithmically transformed;\u003csup\u003e\u0026nbsp;b\u003c/sup\u003eConfidence Interval; \u003csup\u003ec\u003c/sup\u003esignificant effects are marked in bold - threshold: 0.0014; \u003csup\u003ed\u003c/sup\u003eprepartal vaccination during the dry period; \u003csup\u003ee\u003c/sup\u003eno vaccination during the dry period; \u003csup\u003ef\u003c/sup\u003eannual total number of cows in herd, segmentated into categories small [184, 987], medium [890, 1275] and large [1276, 2625]; \u003csup\u003eg\u003c/sup\u003espring (March-May), summer (June-August), autumn (September-November), winter (December-February); herd and calving year were applied as random effects.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003cu\u003ePrepartal vaccination has no significant influence on milk yield in healthy primiparous cows\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable linear mixed-effects models showed higher ECM FTD and ECM 305 in VACC, compared to NON VACC (see Additional file 1). Multivariable analysis was not possible with the same dataset due to missing values resulting from the initial joint of herd and milk records. As a solution, D2 was created to reduce the number of missing values and control confounding variables. Here, available disease-associated variables, such as \u003cem\u003emastitis\u003c/em\u003e, \u003cem\u003eSCC\u003c/em\u003e above 100,000, \u003cem\u003eretained placenta\u003c/em\u003e, \u003cem\u003emetritis\u003c/em\u003e, and \u003cem\u003eketotic risk\u003c/em\u003e were used to exclude diseased cows. Further, only four herds were taken into account that were vaccinated alternately, to allow for a comparison between VACC and NON VACC cows in more identical environments. Additionally, the data was limited to the time period of twelve months before and after change of vaccination management. With primiparous, utmost healthy cows from alternately vaccinated herds, univariable and multivariable analysis was performed. No significant influence of prepartal vaccination on milk yield parameters could be confirmed in these more ideal conditions, neither in uni- nor in multivariable models. However, the variable \u003cem\u003ereplacement rate\u003c/em\u003e remained significant for ECM 305 and ECM FTD, as well as rest period for ECM 305 (see Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003ePrepartal vaccination status and energy corrected milk yield in multivariable linear mixed-effects logistic regression.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.287319422150883%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.831460674157306%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eECM 305\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.881219903691814%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eECM FTD\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.2%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel estimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel estimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrepartal vaccination status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.2%\" valign=\"top\"\u003e\n \u003cp\u003eVACC\u003csup\u003ee\u003c/sup\u003e - NON VACC\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e-224, 234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e-0.99, 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReplacement rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e-88, -42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e-0.21, -0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRest period\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003eg\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e8.3, 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.56%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eEnergy corrected milk yield in 305 days of lactation; \u003csup\u003eb\u003c/sup\u003eEnergy corrected milk yield on first day of milk recordings; \u003csup\u003ec\u003c/sup\u003eConfidence Interval; \u003csup\u003ed\u003c/sup\u003esignificant effects are marked in bold - threshold: 0.0158; \u003csup\u003ee\u003c/sup\u003eprepartal vaccination during the dry period; \u003csup\u003ef\u003c/sup\u003eno vaccination during the dry period; \u003csup\u003eg\u003c/sup\u003erest period was only applied for response variable ECM 305 due to temporal overlap with ECM FTD; herd was applied as random effect.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eQuantile regression models for four individual herds clarified that other factors, such as \u003cem\u003eherd\u0026nbsp;\u003c/em\u003eitself\u003cem\u003e,\u003c/em\u003e have higher influence on milk yield parameters than the prepartal vaccination status in healthy primiparous cows (see Figure 1).\u003c/p\u003e\n\u003cp\u003e[\u003cstrong\u003eFigure 1]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eHerd management related factors are most relevant for mammary health\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eIn order to broaden the analysis for the mammary health parameters \u003cem\u003emastitis\u003c/em\u003e and \u003cem\u003eSCC\u003c/em\u003e, random forest-analysis was performed. With this machine-learning algorithm, influencing variables were ranked by importance, allowing for the comparison between all variables, including \u003cem\u003eherd\u003c/em\u003e and \u003cem\u003ecalving\u003c/em\u003e \u003cem\u003eyear\u003c/em\u003e, which were applied as random effects in previous analyses of the study. Findings suggest that herd management-related parameters are the most relevant influencing factors for both response variables, either directly as \u003cem\u003eherd\u003c/em\u003e or indirectly as \u003cem\u003ecalving year\u003c/em\u003e and \u003cem\u003efarmsize\u003c/em\u003e. \u003cem\u003eParity\u003c/em\u003e proofed to be among the top three influencing variables. Prepartal vaccination status, however, takes the last, or second last place in this ranking, as illustrated in figure 2.\u003c/p\u003e\n\u003cp\u003e[\u003cstrong\u003eFigure 2\u003c/strong\u003e\u003cstrong\u003e]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe high incidence of postpartal infectious diseases of the cow poses challenges for the current dairy industry. During TP, defined as three weeks before until three weeks after parturition\u0026nbsp;(19, 20), the dairy cow\u0026rsquo;s health is challenged and typical disease symptoms accumulate. Mastitis, metritis, ketosis, digestive disorders and laminitis have their highest incidences during early lactation\u0026nbsp;(21-23).\u0026nbsp;The mammary gland is particularly susceptible to pathogens during colostrogenesis\u0026nbsp;(24). Mammary disorders negatively affect animal welfare, milk yield and the financial situation of the dairy farm, especially in early lactation\u0026nbsp;(25). Reasons for mammary disorders may originate from a poorly regulated, dysregulated or suppressed immune system\u0026nbsp;(7, 26, 27). Therefore, ways are needed to modulate the immune system of the transition cow to cope better with infectious pathogens. An attractive possibility could be a vaccine-induced modulation of the immune system. The vaccination-induced mediator release after an initial activation of innate immune processes may have mid- or long-term effects on subsequent immune mechanisms depending on the duration of mediator-mediated epigenetic modifications in various cell types\u0026nbsp;(10). Although vaccine-mediated epigenetic alterations were reported for distinct vaccines, it remained unknown whether contemporarily used prepartal vaccinations against NCD are able to induce such NSEs in cows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this context, we investigated whether the prepartal vaccination against NCD of pregnant cows has an impact on the prevalence of mastitis, the somatic cell count and the short- and long-term milk production post-partum (p.p.). In univariable analysis no significant associations between prepartal vaccination and mammary health could be found, but higher ECM FTD and ECM 305 in VACC. However, multivariable analysis clearly showed that prepartal vaccination had no effect, neither on the mastitis prevalence (table 3), the SCC (table 4), nor the milk yield (table 5). These findings mirror in part those of Scott et al.\u0026nbsp;(16), who found no effect on milk yield after vaccination with a core antigen vaccine against gram-negative bacteria, although the used vaccine contained a dedicated immune-enhancing/-modulating adjuvant (Immune Plus\u0026reg;).\u0026nbsp;Our findings argue against a non-specific effect of mother cow vaccination with the used NCD vaccines. Such non-specific effects were reported by others after vaccination of heifers with a live-attenuated BCG-strain\u0026nbsp;(18). Vaccination with this well-characterized, non-specific effect-inducing live vaccine resulted in higher milk yields in the first 100 days p.p. Another study reported on a farm-specific reduction of mastitis incidence after prepartal intranasal vaccination of dairy cows with a virus live vaccine\u0026nbsp;(28). The use of inactivated killed vaccine in D2 suggests that the attenuated strains in the NCD vaccines may not have been able to induce the same mechanisms as the BCG vaccine or the intranasally administered modified live vaccine against BRSV and PI3. The influence of vaccine type and adjuvant was evaluated only in udder health analysis, where two different killed vaccines and one live vaccine with different adjuvant combinations were applied. However, no significant effects were observed.\u0026nbsp;In contrast to Cortese et al.\u0026nbsp;(28), milk yield losses were reported after vaccination with an inactivated Coxiella burnetii vaccine containing no dedicated adjuvant\u0026nbsp;(17). Thus, the interaction of the cows with Coxiella-derived molecules could have induced a response leading to an altered secretion or synthesis capacity of mammary epithelial cells and strengthen the hypothesis that vaccine-induced mechanisms can have non-specific effects in cows. The comparison of the three mentioned studies with ours is limited, as methodologies differed. In addition to different vaccine types, and used adjuvants, prepartal vaccination was only performed by Retamal et al.\u0026nbsp;(18), whereas Scott et al.\u0026nbsp;(16)\u0026nbsp;and Schulze et al.\u0026nbsp;(17)\u0026nbsp;vaccinated cows during lactation. A closer look at statistical methods and thorough examination of multiple predictors is substantial to reveal possible confounding events. In the present study, methodological reduction to univariable analysis would have resulted in higher milk yields of vaccinated cows, leading to spurious correlation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThrough multivariable analysis, the effect of prepartal vaccination on milk yield was more accurately determined by excluding disease-associated variables and limiting the analysis to primiparity, which are commonly addressed variables in the analysis of herd records from transition cows\u0026nbsp;(29). \u0026nbsp;Pairing lactations by matching VACC and NON VACC from the same herd allows for more confident attribution of the differences between the groups to the actual exposure. Quantile regression provided insight into the interactions with other predictors. Here, the difference of milk yield between the herds was higher than between VACC and NON VACC. Similarly, the effect of prepartal vaccination had opposite effects depending on the herd. The Random Forest machine learning-algorithms, a model robust to collinearity, facilitated the ranking of importance of influencing factors. While \u003cem\u003eherd\u003c/em\u003e and \u003cem\u003ecalving year\u003c/em\u003e were considered as random effects in regression models, random forest-analysis makes them comparable with other influencing variables. This study concludes that there were no significant effects of prepartal vaccination on milk yield. In the study of Retamal et al.\u0026nbsp;(18)\u0026nbsp;\u0026ndash; according to the author\u0026rsquo;s assessment - these other influencing factors cannot be ruled out, as rather simple analysis was performed on milk yield variables with contingency tables and the Wilcoxon\u0026ndash;Mann\u0026ndash;Whitney test. In Schulze et al.\u0026nbsp;(17)\u0026nbsp;and Scott et al.\u0026nbsp;(16), similar statistical methodology as in the present study were applied with linear mixed-effects models. Multiple influencing variables were considered, therefore, results are statistically more comparable to Schulze et al.\u0026nbsp;(17)\u0026nbsp;and Scott et al.\u0026nbsp;(16), but still limited, according to the different study designs. Generalized linear mixed models have proven to be a good and flexible method, especially for transition cow analysis\u0026nbsp;(29).\u003c/p\u003e\n\u003cp\u003eA large number of observations lays the foundation for statistical power, enhances the precision of analysis and enables thorough investigation of subgroups and variables to control confounders. Simultaneously, large datasets entail several challenges. While even small effects can be discovered, the probability of type I errors and therefore false positive significant effects is increased. For this reason, we adjusted our p-value threshold in accordance with Goods\u0026rsquo;\u0026nbsp;(30)\u0026nbsp;recommendations, resulting in higher hurdles of significance. Herd records and their retrospective nature, especially diagnostic data, bear the potential for documentation variability. On all farms, health management is conducted in close collaboration with the attendant veterinarians, diagnostics of acute diseased animals is performed by veterinarians, but identification of frequently occurring diseases, such as mastitis is usually subject to standard operating procedures of the farm and subsequently documented by managing or milking staff. Therefore, misclassification and differences in documentation between the farms, and respectively herds cannot be ruled out and might compromise accuracy and consistency. Hence, there is a risk, that for instance high numbers in mastitis documentation on a farm do not inevitably represent actual high occurrence of mastitis, but originate in precise documentation of e. g. subclinical mastitis. Precise documentation might lead to false high diagnostic frequency, whereas not recognized diseases due to farm management or inconsistent health documentation might lead to false low incidences. The discussion also considered whether the overall quality of farm management influenced the decision to vaccinate cows.\u0026nbsp;It is unclear which direction this potential bias may take.\u0026nbsp;On one hand, farms with excellent health management and superior animal health status may choose to vaccinate cows as a precautionary measure. On the other hand, farms with poorer health management may vaccinate cows to address the animals\u0026rsquo; poor health status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecognizing these possible variances across herds and risks of biased data, we have implemented comprehensive data cleaning and validation procedures. First, to gain a deeper understanding of the data and verify the comparability of herds, we conducted on-site farm visits. It is noteworthy that all included farms share a common history as former Agricultural Production Cooperatives (LPG) in the German Democratic Republic until 1989, making them more comparable than other farms in Germany. Secondly, we conducted an exploratory analysis of herd management related variables for each herd using contingency tables to better differentiate between systematic and random errors. Thirdly, we examined the diagnostic documentation for each herd across years and months, comparing different diagnostic results with overall patterns to understand and interpret the trends in health documentation and anticipate documentation variability. On some of the farms, more detailed diagnostic data is available with subordinate terms, such as the subdivision of mastitis into forms of inflammation or pathogen etiology. It was agreed upon the utilization of the generic term mastitis as the least common denominator of diagnostic health documentation across all herds. After excluding inconsistent time periods (e. g. initial phase/ first year of health documentation in a herd, allowing time for familiarization), diagnostic health documentation of mastitis was assessed reliable for analysis. Findings of overall exploratory analysis suggest that the variables \u003cem\u003eherd\u003c/em\u003e and \u003cem\u003ecalving year\u003c/em\u003e inherit high influence on \u003cem\u003emastitis\u003c/em\u003e and other variables, consequently these were included as random effects in the subsequent linear mixed-effects regression. Additional to mastitis, we incorporated further robust and objective variables, which are less susceptible to variations in documentation. Originating from milk testing, \u003cem\u003eSCC\u003c/em\u003e and milk yield parameters are hardly affected by humans. Thus, the authors regard such variables as more robust than \u003cem\u003emastitis\u003c/em\u003e. Given that milk recordings occur at monthly intervals on the respective farms, the variability in DIM among data points raises concerns about the comparability of the groups. To address this issue, we investigated the average milk performance in relation to the average DIM on the day of milk testing. Our findings suggest that the observed increase in average milk performance does not exceed the contrast estimates obtained through univariable analysis. As a result, we can reasonably conclude that the differing timing of data collection did not significantly impact the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePrevious studies have suggested that vaccination in cows may lead to NSEs. However, this study does not provide significant evidence to support this assumption regarding NSEs of prepartal maternal vaccination against NCD in mammary health and milk yield parameters. Instead, findings underline the importance of herd management-related factors. This work provides insights from a large database in the field, particularly for the German dairy industry. The focus is on study design and statistical methodology. Further research is needed to explore the potential impact of vaccination on other infectious diseases, as well as any correlations with other organ systems and production metrics. It remains to be further elaborated whether vaccination ingredients, such as live or attenuated vaccines, as well as adjuvants, play a role in NSEs.\u003c/p\u003e\n"},{"header":"Methods","content":"\u003cp\u003e\u003cu\u003eStudy Population and Farm Selection\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eData was obtained from test herds of the RinderAllianz (RA), a breeding organization that supervises numerous dairy farms in Mecklenburg-Vorpommern, Saxony-Anhalt, and Brandenburg. The service includes sperm sales, mating, insemination, cattle marketing, milk control and analyses. In cooperation with the RA, comprehensive data from 20 test farms, were obtained in the context of this work. At the timepoint of data collection in June 2021, prepartal vaccination was performed on ten of the participating farms. Milk yield was leveled, so that each group of farms contained five high yielding (mean ECM 305 above 11,000kg) and five low yielding farms. Informed consent was obtained from all participating farms. All of these farms share a common history as former Agricultural Production Cooperatives (LPG) in the German Democratic Republic until 1989. Altogether, data of these 20 farms comprises herd records, reproduction data, milk recordings, health documentation and holding registers of 73,378 dairy cows of 22 herds located across all three operating regions of the RA between January 2007 and September 2020.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eOn-farm data collection\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eEach farm was visited on-site to collect contextual information and conduct an in-depth survey. The survey comprised information on vaccination history, dry-off management, housing system, health management, monitoring during birth, milking and colostrum management, hygiene and feeding management. On-site visits took place between October 2021 and August 2022 with the herd managers. Depending on the size, infrastructure of the farms and distribution of tasks between workers, responsible persons were consulted, if necessary. The questionnaire was always filled out by the same surveyor in order to ensure conformity of documentation. One farm was surveyed online, due to pandemic precaution-measures, all others were visited on-site, allowing a deeper understanding of the conditions of the location. Participating farmers were very cooperative and, with no exception, showed the surveyors around all relevant areas of the facility, housing of dairy cows in all stages of the production cycle – calves, heifers, dry cows, fresh and late-lactating cows -, milking installation and especially calving area was inspected carefully. Apart from data collection, the purpose of the visits ensured that at least no obvious problems in management and hygiene are apparent on the respective farms. Furthermore, attendant veterinarians were consulted to better estimate herd management. Depending on time availability, in some farm visits attendant veterinarians were participating in person, in other cases, they were consulted by phone and in all cases confirmed reliability of health management.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData cleaning and wrangling\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eStep-by-step data was joined and cleaned according to the needs of the study, using the software R version 4.3.1 and R Studio. Herd record data were inspected for inconsistencies before converted into a format, where each observation corresponds to one lactation, whereby chronologic adaptions were made. Events during the dry period are assigned to the subsequent lactation, in order to tailor the database to the TP of the cow. Reproduction data of 148,268 lactations, milk yield and components from 1,561,273 recordings, 1,298,703 diagnoses from health documentation was added. Moreover, on-site survey data were manually transferred from paper-based forms filled out on-site into Excel\u003csup\u003eTM\u003c/sup\u003e-sheets and further integrated into the final database. Observations were excluded when on-site data or health documentation were not available. Information on vaccination management on each farm were assigned to the vaccination status on an individual cow basis and TP. All observations with other vaccinations than prepartal vaccination against neonatal calf diarrhea were excluded to reduce interaction with other vaccines. When a farm changed its vaccination management or didn’t vaccinate for a distinct time period, a buffer of one month (15 days prior to and 15 days after the date of change) was created, thus risk of false entries is minimized. Here, farm-specific time of vaccination was paid attention to, in order to correctly assign vaccination periods and calving dates. The original database, containing 73,378 cows on ten vaccinating farms and ten non-vaccinating farms, was finally adapted to 120,394 TP of 53,370 cows on 19 farms and distributed across non-vaccinated, continuously vaccinated and alternately vaccinated herds, resulting in overall 57,166 NON VACC and 63,228 VACC in D1 (table 1). Despite retrospectively adapting the groups according to the specification of vaccination periods through on-farm data collection, NON VACC and VACC are comparable in mammary health and milk yield parameters (table 2). The second and smaller dataset (D2) represents a subset of D1, containing 1,002 TP from 1,002 primiparous, utmost healthy cows on four alternately vaccinated herds. D2 allows analyses on rather ideal conditions, while presuming, that health and immunological status of primiparous cows differ from multiparous cows\u0026nbsp;(29, 31). Furthermore, we intended to reduce the influences of pathologic processes by excluding diseased cows. Regarding mammary health, those cows diagnosed with mastitis, were excluded, taking into account, that mastitis can lead to lower milk yields\u0026nbsp;(25). As SCC below 100,000 cells is considered physiological\u0026nbsp;(32, 33)\u0026nbsp;lactations were only included, when SCC of the first test day of milk recordings were below this threshold. Risk of ketosis and diagnosis of retained placenta and metritis could also be eliminated from D2. In alternately vaccinated herds a timeframe of twelve months before and twelve months after change of vaccination management (from vaccination to non-vaccination or vice versa) was subset. By this means, the comparison of VACC and NONVACC is less influenced by calving year.\u003c/p\u003e\n\u003cp\u003e[\u003cstrong\u003eFigure 3\u003c/strong\u003e]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eElaboration of vaccination related parameters\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAn additional table provides an overview of response and independent variables, definitions, composition, and values (see Additional file 2). Three vaccines were used:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eBovilis® Rotavec® Corona (Intervet) (RC): n=27,769, containing inactivated bovine rotavirus (serotype G6 P5), inactivated bovine coronavirus (strain Mebus), E. coli (K99 Antigen), mineral oil and aluminium hydroxide.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eScourguard® 3 (Zoetis) (SG): n=8,352, containing live attenuated bovine rotavirus (strain Lincoln), live attenuated bovine coronavirus (strain Hansen), inactivated E.coli (K99 Antigen), Alhydrogel.\u003c/li\u003e\n \u003cli\u003eBovigen® Scour (Forte Healthcare) (BS): n=8,004, containing inactivated bovine rotavirus (serotype G6 P1), inactivated bovine coronavirus (strain C-197), E. coli (K99 Antigen), Montanide ISA 206 VG.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRC and BS are applicated once and SG is applicated twice, whereby the date of the first application was considered as time of vaccination. In 18,453 cases the vaccine product could not be associated (not specified). On the basis of the used vaccine products, the variable \u003cem\u003eadjuvant\u003c/em\u003e was designed grouping animals in those vaccinated with alum (RC, SG) or montanide-containing (BS) vaccines.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eElaboration of mammary health and milk yield parameters\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eEach two response variables were elaborated to represent milk performance and mammary health: \u003cem\u003eECM 305\u003c/em\u003e defines the energy corrected milk yield in 305 days of lactation and \u003cem\u003eECM FTD\u003c/em\u003e on the first test day of milk recording. For this, the milk recording parameters were consulted to calculate ECM as follows: milk yield in kg × (0.38 x fat content in % + 0.21 x protein content in % + 1.05) ÷ 3.28\u0026nbsp;(34, 35). To approach an adequate representation of mammary health status of the cows, we consulted parameters of different origin. Farm health documentation was transformed into the binary variable \u003cem\u003emastitis\u003c/em\u003e indicating the prevalence of mastitis diagnoses of each cow, when the diagnosis mastitis was registered at least once within the time period of 10 days p.p. We complemented the analysis with SCC as a further more stringent variable, less influential by personnel or documentation. It is defined as the somatic cell count on the first day of milk recording and was logarithmically transformed to stabilize variance. SCC is seen as a good parameter to identify intramammary infections\u0026nbsp;(36), and is considered physiological below 100,000\u0026nbsp;(32, 33). The farms conducted milk recordings once a month. Thus, the first test day of milk recordings and therefore Days in Milk (DIM) at the time of data collection of the variables ECM FTD and SCC differs across the observations, resulting in limitations for these variables, as they represent only one single point of time during the first weeks of lactation, where the milk yield and cell count vary. The mean DIM was considered between the groups investigated, ascertaining comparability.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEligible influencing variables were defined, firstly the above-mentioned vaccination related variables, and further, farm management and cow related variables. Management related variables encompass the following variables: The \u003cem\u003eherd\u003c/em\u003e itself representing the general farm management. \u003cem\u003eCalving year\u003c/em\u003e denotes the year when cows calved, potentially impacting health due to environmental or management changes. The variables \u003cem\u003eherd\u003c/em\u003e and \u003cem\u003ecalving year\u003c/em\u003e might not be understood in isolation, but rather in combination. Although e. g. climate or pathogen diversity might vary between the years, the fluctuations of the years are probably more subject to farm management than the year itself. For this reason, they were consequently combined as random effects in the statistical models. \u003cem\u003eHerd Size\u003c/em\u003e indicates the annual total number of cows in the herd, potentially influencing disease spread and herd dynamics. The variable was transformed from numeric to character type by segmenting into the categories small, medium and large with the binning-function of the \u003cem\u003edlookr\u003c/em\u003e-package. The quantile method was employed to determine break points, ensuring an equitable distribution of farm sizes across the derived categories. \u003cem\u003eHerd replacement rate\u003c/em\u003e of the previous year\u003cem\u003e,\u0026nbsp;\u003c/em\u003eaffecting overall herd health and productivity in the respective year, was calculated as percentage of number of primiparous calvings, compared to the number of secondi- or multiparous calvings. \u003cem\u003eAccess to pasture\u003c/em\u003e reflects the availability and quality of grazing areas during the dry period, impacting especially the exercise level of the cow during calving and lactation. \u003cem\u003eFlooring\u003c/em\u003e refers to the type of flooring within the barn during TP, impacting cow comfort and hoof health with the two categories deeplitter or slatted floor. \u003cem\u003eScore of hygiene\u003c/em\u003e indicates the cleanliness level of the cows and density of possible pathogens. A score between 1 and 4 was documented on the date of on-site survey\u0026nbsp;(37). \u003cem\u003eCalving box\u003c/em\u003e describes the two options of calving in a group or in an individual box, influencing behavioral dynamics around calving. When calving in individual boxes, pen change was conducted during TP, which can affect the cows’ metabolism in combination with prepartal vaccination\u0026nbsp;(38). \u003cem\u003eMilking Frequency\u003c/em\u003e defines how often cows are milked daily, affecting mammary health. Here robot-milked cows could not be traced for this variable. Moreover, \u003cem\u003eType of dry-off\u003c/em\u003e describes the method used when cows are dried off from milking, impacting mammary gland health, by using or not using antibiotic dry-cow treatment. Additionally, supplements such as \u003cem\u003eEnergy Supplements\u003c/em\u003e, \u003cem\u003eCalcium Supplements\u003c/em\u003e, \u003cem\u003eVitamin D3 Supplements\u003c/em\u003e, further \u003cem\u003eVitamin and Trace Element Supplements\u003c/em\u003e and \u003cem\u003eMonensin\u003c/em\u003e, given shortly before, during or shortly after calving were provided, possibly impacting nutritional and immune status. Following aspects were assigned to cow related variables: Cows were categorized as primi-, secondi- or multiparous in the variable \u003cem\u003eparity\u003c/em\u003e. Length of the \u003cem\u003edry period\u003c/em\u003e was provided partly in weeks during the on-site survey and calculated in days and can therefore deviate slightly. The first lactation age was calculated as the difference between calving date of first calving and the cows’ birthdate, assigned to categories: low first lactation age therefore is less than or equal to 700 days, medium between 701-750 days and high more than 750 days, taking into consideration that milk yield and ingredients can differ between these groups\u0026nbsp;(39). The four \u003cem\u003ecalving seasons\u003c/em\u003e enable us to investigate potential seasonal variations in dairy cow health, while spring is defined as calving date between March and May, summer between June and August, autumn between September and November and winter between December and February. This variable not only refers to the calving itself, but also allows us to explore whether specific health events were more prevalent during certain times of the year. Finally, \u003cem\u003erisk of ketosis\u003c/em\u003e allows a conclusion on the ketotic metabolic status, determined by the milk components on the first day of milk recording. Ketotic risk was assumed, if the fat-protein-ratio exceeds 1.4 and lower limits of protein content are undercut or upper limits of fat content are passed. Limits were calculated according to Glatz-Hoppe et al.\u0026nbsp;(40). By assessing the risk of ketosis in this critical period, we aimed to predict and mitigate potential metabolic disruptions affecting the cows’ health.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStatistical Analyses\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe software R version 4.3.1 and R Studio were used for statistical analyses\u0026nbsp;(41). Descriptive analysis of D1 for the categories NON VACC and VACC was undertaken to get an overview of the data structure. All eligible variables were examined for any possible influence on the response variables \u003cem\u003emastitis, SCC, ECM FTD\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;ECM 305\u0026nbsp;\u003c/em\u003ein univariable analysis\u003cem\u003e.\u003c/em\u003e The variables in question were further checked for missing value patterns. (Generalized) linear mixed-effects models were executed using the lme4- package. Here, we considered the variables \u003cem\u003eherd\u003c/em\u003e and \u003cem\u003ecalving\u003c/em\u003e \u003cem\u003eyear\u003c/em\u003e as nested random effects. Given the nested structure of the data involving herds and calving years, we assessed different combinations of random effects in mixed-effects models to determine the most suitable formulation based on Akaike’s Information Criterion. P-value threshold was adjusted in accordance with Goods’ recommendations for large number of observations\u0026nbsp;(30). By applying the function\u0026nbsp;\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\"\u003e, p-value of each model outcome could be evaluated, resulting in significance thresholds between 0.0158 (D1) and 0.0014 (D2). All variables, that were significant in univariable (generalized) linear mixed-effects regression of the response variables \u003cem\u003emastitis\u003c/em\u003e and \u003cem\u003eSCC\u003c/em\u003e, according to Goods’ p-value threshold, were further included in the subsequent multivariable analysis. When preparing multivariable analysis for the response variables ECM FTD and ECM 305, it soon became clear, that other than vaccination-related parameters overlap results. Furthermore, missing value patterns foreclosed multivariable analysis. Therefore, D2, a subset of D1 was elaborated, reducing influencing variables. The available disease-associated variables, \u003cem\u003emastitis\u003c/em\u003e, \u003cem\u003eSCC\u003c/em\u003e above 100,000, \u003cem\u003eretained placenta\u003c/em\u003e, \u003cem\u003emetritis\u003c/em\u003e, and \u003cem\u003eketotic risk\u003c/em\u003e were used to exclude diseased cows, as these are known influencing factors on milk yield\u0026nbsp;(42, 43). Parity, another known influencing variable on milk yield and the periparturient cow’s immune system\u0026nbsp;(29, 44, 45)\u0026nbsp;proved to be significant in univariable analysis of the present study. Thus, parity was reduced to primiparous cows. Furthermore, pairing of VACC and NON VACC on four alternately vaccinating farms was conducted to further control confounding effects. In addition, only data in the period of twelve months before and after change of vaccination management was selected to minimize the influence of \u003cem\u003ecalving year\u003c/em\u003e. With D2, containing primiparous, utmost healthy cows from alternately vaccinated herds, univariable and multivariable analysis was carried out. Quantile regression was conducted to model the relationship between predictor variables and median of the response variable. Additional broadening of the analysis with the random forest-algorithm\u0026nbsp;(46)\u0026nbsp;was conducted in order to obtain a ranking of importance of influencing variables for \u003cem\u003emastitis\u003c/em\u003e and \u003cem\u003eSCC\u003c/em\u003e, including \u003cem\u003eherd\u003c/em\u003e and \u003cem\u003ecalving year\u003c/em\u003e, previous inserted as random effects.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eBCG:\u003c/strong\u003e Bacille Calmette Guerin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBS\u003c/strong\u003e: Bovigen\u0026reg; Scour\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD1:\u003c/strong\u003e dataset 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD2:\u003c/strong\u003e dataset 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDIM\u003c/strong\u003e: days in milk\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECM 305:\u003c/strong\u003e energy corrected milk yield in 305 days of lactation (in kg)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECM FTD:\u003c/strong\u003e energy corrected milk yield on the first test day of milk recording (in kg)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECM\u003c/strong\u003e: energy corrected milk\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLPG\u003c/strong\u003e: Agricultural Production Cooperatives\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNCD\u003c/strong\u003e: neonatal calf diarrhea\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNON VACC:\u0026nbsp;\u003c/strong\u003eno vaccination during the dry period\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNSEs:\u003c/strong\u003e non-specific effects\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ep.p.:\u003c/strong\u003e post-partum\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRA:\u0026nbsp;\u003c/strong\u003eRinderAllianz\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRC\u003c/strong\u003e: Bovilis\u0026reg; Rotavec\u0026reg; Corona\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSG\u003c/strong\u003e: Scourguard\u0026reg; 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTP:\u0026nbsp;\u003c/strong\u003etransition period\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVACC\u003c/strong\u003e: vaccination during dry period\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are within the paper and its supporting additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Intervet Deutschland GmbH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.K. wrote the main manuscript text. Formal analysis and software were performed by C.K. and Y.Z., while Y.Z. and A.R. adapted the methodology and validated the analysis. C.K. and Y.Z. prepared figures and tables and H.Z., H-J.S., Y.Z. and A.R. edited the text. The study was supervised by Y.Z., H.Z., H-J.S. and A.R. Project administration and funding acquisition was ensured by H.Z., M.R. and A.S and detailed conceptualization was obtained by H.Z. H-J.S., Y.Z. and C.K. Data was obtained by D.K. and C.W. and curated by C.K., Y.Z., A.R. All authors discussed and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eCorresponding author: Caroline Kuhn ([email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all farmers who participated in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eViidu D-A, M\u0026otilde;tus K. Implementation of a pre-calving vaccination programme against rotavirus, coronavirus and enterotoxigenic Escherichia coli (F5) and association with dairy calf survival. BMC Veterinary Research. 2022;18(1):59.\u003c/li\u003e\n\u003cli\u003eGonzalez R, Elvira L, Carbonell C, Vertenten G, Fraile L. The Specific Immune Response after Vaccination against Neonatal Calf Diarrhoea Differs between Apparent Similar Vaccines in a Case Study. Animals : an open access journal from MDPI. 2021;11(5).\u003c/li\u003e\n\u003cli\u003eDurel L, Rose C, Bainbridge T, Roubert J, Dressel K-U, Bennemann J, et al. Immune response of mature cows subjected to annual booster vaccination against neonatal calf diarrhoea with two different commercial vaccines: A non-inferiority study. Livestock Science. 2017;204:52-8.\u003c/li\u003e\n\u003cli\u003eSmith BI, Rieger RH, Dickens CM, Schultz RD, Aceto H. Anti-bovine herpesvirus and anti-bovine viral diarrhea virus antibody responses in pregnant Holstein dairy cattle following administration of a multivalent killed virus vaccine. American Journal of Veterinary Research. 2015;76(10):913-20.\u003c/li\u003e\n\u003cli\u003eVidlund J, Gelalcha BD, Gillespie BE, Agga GE, Schneider L, Swanson SM, et al. Efficacy of novel staphylococcal surface associated protein vaccines against Staphylococcus aureus and non-aureus staphylococcal mastitis in dairy cows. Vaccine. 2024;42(6):1247-58.\u003c/li\u003e\n\u003cli\u003eLeik NKO, Ahmedy F, Guad RM, Baharuddin DMP. Covid-19 vaccine and its consequences in pregnancy: Brief review. Annals of Medicine and Surgery. 2021;72:103103.\u003c/li\u003e\n\u003cli\u003eWeikard R, Demasius W, Hadlich F, K\u0026uuml;hn C. Different Blood Cell-Derived Transcriptome Signatures in Cows Exposed to Vaccination Pre- or Postpartum. PloS one. 2015;10(8):e0136927.\u003c/li\u003e\n\u003cli\u003eDemasius W, Weikard R, Hadlich F, M\u0026uuml;ller KE, K\u0026uuml;hn C. Monitoring the immune response to vaccination with an inactivated vaccine associated to bovine neonatal pancytopenia by deep sequencing transcriptome analysis in cattle. Veterinary research. 2013;44(1):93.\u003c/li\u003e\n\u003cli\u003eAmir M, Zeng MY. Immune imprinting in utero. Science. 2021;373(6558):967-8.\u003c/li\u003e\n\u003cli\u003eGeckin B, Konstantin F\u0026ouml;hse F, Dom\u0026iacute;nguez-Andr\u0026eacute;s J, Netea MG. Trained immunity: implications for vaccination. Current Opinion in Immunology. 2022;77:102190.\u003c/li\u003e\n\u003cli\u003eKleinnijenhuis J, Quintin J, Preijers F, Joosten LAB, Ifrim DC, Saeed S, et al. Bacille Calmette-Guerin induces NOD2-dependent nonspecific protection from reinfection via epigenetic reprogramming of monocytes. Proceedings of the National Academy of Sciences of the United States of America. 2012;109(43):17537-42.\u003c/li\u003e\n\u003cli\u003eZiogas A, Netea MG. Trained immunity-related vaccines: innate immune memory and heterologous protection against infections. Trends in molecular medicine. 2022;28(6):497-512.\u003c/li\u003e\n\u003cli\u003eGuerra-Maupome M, Vang DX, McGill JL. Aerosol vaccination with Bacille Calmette-Guerin induces a trained innate immune phenotype in calves. PloS one. 2019;14(2):e0212751.\u003c/li\u003e\n\u003cli\u003eBenn CS, Fisker AB, Rieckmann A, S\u0026oslash;rup S, Aaby P. Vaccinology: time to change the paradigm? The Lancet Infectious Diseases. 2020;20(10):e274-e83.\u003c/li\u003e\n\u003cli\u003eArega SM, Knobel DL, Toka FN, Conan A. Non-specific effects of veterinary vaccines: a systematic review. Vaccine. 2022;40(11):1655-64.\u003c/li\u003e\n\u003cli\u003eScott HM, Sargeant JM, Ireland MJ, Lissemore KD, Leslie KE, Kelton DF, et al. Effects of a core antigen vaccine against gram-negative bacteria on physiologic and yield parameters of dairy cows during late lactation and the dry period. Journal of Dairy Science. 1998;81(7):1928-35.\u003c/li\u003e\n\u003cli\u003eSchulze LSC, Borchardt S, Ouellet V, Heuwieser W. Effect of a phase I Coxiella burnetii inactivated vaccine on body temperature and milk yield in dairy cows. Journal of Dairy Science. 2016;99(1):541-50.\u003c/li\u003e\n\u003cli\u003eRetamal P, \u0026Aacute;balos P, Alegr\u0026iacute;a-Mor\u0026aacute;n R, Valdivieso N, Vordermeier M, Jones G, et al. Vaccination of Holstein heifers with Mycobacterium bovis BCG strain induces protection against bovine tuberculosis and higher milk production yields in a natural transmission setting. Transboundary and Emerging Diseases. 2022;69(3):1419-25.\u003c/li\u003e\n\u003cli\u003eGrummer RR. Impact of changes in organic nutrient metabolism on feeding the transition dairy cow. Journal of animal science. 1995;73(9):2820-33.\u003c/li\u003e\n\u003cli\u003eDrackley JK. Biology of dairy cows during the transition period: the final frontier? Journal of Dairy Science. 1999;82(11):2259-73.\u003c/li\u003e\n\u003cli\u003eIngvartsen KL, Dewhurst RJ, Friggens NC. On the relationship between lactational performance and health: is it yield or metabolic imbalance that cause production diseases in dairy cattle? A position paper. Livestock Production Science. 2003;83(2-3):277-308.\u003c/li\u003e\n\u003cli\u003eLeBlanc SJ. Postpartum uterine disease and dairy herd reproductive performance: a review. The Veterinary Journal. 2008;176(1):102-14.\u003c/li\u003e\n\u003cli\u003eMulligan FJ, Doherty ML. Production diseases of the transition cow. The Veterinary Journal. 2008;176(1):3-9.\u003c/li\u003e\n\u003cli\u003ePy\u0026ouml;r\u0026auml;l\u0026auml; S. Mastitis in post-partum dairy cows. Reproduction in domestic animals. 2008;43:252-9.\u003c/li\u003e\n\u003cli\u003eHagnestam C, Emanuelson U, Berglund B. Yield losses associated with clinical mastitis occurring in different weeks of lactation. Journal of Dairy Science. 2007;90(5):2260-70.\u003c/li\u003e\n\u003cli\u003eTrevisi E, Minuti A. Assessment of the innate immune response in the periparturient cow. Research in Veterinary Science. 2018;116:47-54.\u003c/li\u003e\n\u003cli\u003eSordillo LM, Mavangira V. The nexus between nutrient metabolism, oxidative stress and inflammation in transition cows. Animal Production Science. 2014;54(9):1204.\u003c/li\u003e\n\u003cli\u003eCortese VS, Pinedo PJ, Manriquez D, Velasquez-Munoz A, Solano G, Short TH, et al. Effect of periparturient intranasal vaccination on post parturient health parameters in holstein cows. Concepts of Dainry and Veterinary Sciences. 2020;3(4):318-27.\u003c/li\u003e\n\u003cli\u003eWisnieski L, Norby B, Pierce SJ, Becker T, Sordillo LM. Prospects for predictive modeling of transition cow diseases. Anim Health Res Rev. 2019;20(1):19-30.\u003c/li\u003e\n\u003cli\u003eGood IJ. The Interface Between Statistics and Philosophy of Science. Statistical Science. 1988;3(4):386-97.\u003c/li\u003e\n\u003cli\u003eGilbert RO, Gr\u0026ouml;hn YT, Miller PM, Hoffman DJ. Effect of parity on periparturient neutrophil function in dairy cows. Vet Immunol Immunopathol. 1993;36(1):75-82.\u003c/li\u003e\n\u003cli\u003eDoggweiler R, Hess E. Zellgehalt in der Milch ungesch\u0026auml;digter Euter. Milchwissenschaft. 1983;38:5-8.\u003c/li\u003e\n\u003cli\u003eHamann J, Fehlings K. Leitlinien zur Bek\u0026auml;mpfung der Mastitis des Rindes als Bestandsproblem. 4 ed. Gie\u0026szlig;en: Deutsche Veterin\u0026auml;rmedizinische Gesellschaft e.V. (DVG), Fachgruppe \u0026quot;Milchhygiene\u0026quot;; 2002.\u003c/li\u003e\n\u003cli\u003eErn\u0026auml;hrung BfLu. Definitionen und Begriffe: Milch und Milcherzeugnisse 2021 [Available from: https://www.ble.de/DE/BZL/Daten-Berichte/Milch-Milcherzeugnisse/milch-milcherzeugnisse_node.html.\u003c/li\u003e\n\u003cli\u003eSpiekers H, Potthast V. Erfolgreiche Milchviehf\u0026uuml;tterung. 4 ed. Frankfurt am Main: DLG-Verl.; 2004.\u003c/li\u003e\n\u003cli\u003eNyman AK, Emanuelson U, Waller KP. Diagnostic test performance of somatic cell count, lactate dehydrogenase, and N-acetyl-\u0026beta;-D-glucosaminidase for detecting dairy cows with intramammary infection. Journal of Dairy Science. 2016;99(2):1440-8.\u003c/li\u003e\n\u003cli\u003eCook N, Reinemann D. A Tool Box for Assessing Cow, Udder and Teat Hygiene. NMC annual Meeting Paper. 2007.\u003c/li\u003e\n\u003cli\u003eMenichetti BT, Garcia-Guerra A, Lakritz J, Weiss WP, Velez JS, Bothe H, et al. Effect of timing of prepartum vaccination relative to pen change with an acidogenic diet on lying time and metabolic profile in Holstein dairy cows. Journal of Dairy Science. 2021;104(10):11059-71.\u003c/li\u003e\n\u003cli\u003eEttema JF, Santos JEP. Impact of Age at Calving on Lactation, Reproduction, Health, and Income in First-Parity Holsteins on Commercial Farms. Journal of Dairy Science. 2004;87(8):2730-42.\u003c/li\u003e\n\u003cli\u003eGlatz-Hoppe J, Losand B, Kampf D, Onken F, Spiekers H. Nutzung von Milchkontrolldaten zur F\u0026uuml;tterungs- und Gesundheitskontrolle bei Milchk\u0026uuml;hen 2022 [2:[Available from: https://www.dlg.org/de/landwirtschaft/themen/tierhaltung/futter-und-fuetterung/dlg-merkblatt-451.\u003c/li\u003e\n\u003cli\u003eTeam RC. R: A language and environment for statistical computing. Vienna, Austria2023.\u003c/li\u003e\n\u003cli\u003eHagnestam-Nielsen C, Emanuelson U, Berglund B, Strandberg E. Relationship between somatic cell count and milk yield in different stages of lactation. Journal of Dairy Science. 2009;92(7):3124-33.\u003c/li\u003e\n\u003cli\u003eHeikkil\u0026auml; AM, Liski E, Py\u0026ouml;r\u0026auml;l\u0026auml; S, Taponen S. Pathogen-specific production losses in bovine mastitis. Journal of Dairy Science. 2018;101(10):9493-504.\u003c/li\u003e\n\u003cli\u003eLee J-Y, Kim I-H. Advancing parity is associated with high milk production at the cost of body condition and increased periparturient disorders in dairy herds. J Vet Sci. 2006;7(2):161-6.\u003c/li\u003e\n\u003cli\u003eOhtsuka H, Terasawa S, Watanabe C, Kohiruimaki M, Mukai M, Ando T, et al. Effect of parity on lymphocytes in peripheral blood and colostrum of healthy Holstein dairy cows. Can J Vet Res. 2010;74(2):130-5.\u003c/li\u003e\n\u003cli\u003eBreiman L. Random Forests. Machine Learning. 2001;45(1):5-32.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transition period, Maternal vaccination, Trained immunity, innate immune memory, Dairy cow, Peripartum","lastPublishedDoi":"10.21203/rs.3.rs-4259469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4259469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePrepartal vaccinations against newborn calf diarrhea pathogens are performed in the last weeks of pregnancy, with the intention to induce a maternal adaptive humoral immune response and to protect calves in the first weeks of life via colostral transferred pathogen-specific antibodies. There is evidence that vaccination-related innate immune responses can also affect the mother's immune system response to stressors. Whether such non-specific vaccine effects alter the disease susceptibility of dairy cows in the very sensitive transition period has not been addressed so far. In a retrospective cross-sectional study, we investigated the influence of prepartal maternal vaccination on mammary health and milk yield of the periparturient dairy cow. Herd record data from 73,378 dairy cows from 20 farms located in Eastern Germany, together with on-site-collected survey data, were analyzed using linear mixed-effects regression, quantile regression and random forest machine-learning algorithms. A total of 57,166 transition periods without prior vaccination, distributed along 16 herds, and 63,228 transition periods on 13 herds with prior vaccination are included. Additionally healthy primiparous cows from alternately vaccinated herds were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eHerd management-related factors, such as herd in general and calving year, as well as parity proved to be most influential for mammary health and milk yield, while prepartal vaccination occurred as least influential. Vaccinated cows did not show significant differences in mastitis prevalence and somatic cell count as compared with non-vaccinated cows. Also, energy corrected milk yield on first test day of milk recordings, as well as in 305-days of lactation, of healthy primiparous cows with and without prior vaccination showed no significant differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study presents evidence that prepartal vaccination against newborn calf diarrhea does not have significant non-specific effects on mammary health and milk yield parameters. Instead, the findings highlight the importance of herd management factors. This work emphasizes the significance of multivariable analysis based on a large database with high statistical power. It also recommends further research to explore potential non-specific vaccination effects on other organ systems, infectious diseases, and production metrics in cows.\u003c/p\u003e","manuscriptTitle":"Prepartal vaccination has no influence on mammary health and milk yield of dairy cows: a retrospective study addressing non-specific effects of vaccination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 22:26:49","doi":"10.21203/rs.3.rs-4259469/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3418a7e-6c70-46b8-9fe7-9e47dca9cbbd","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-17T16:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-24 22:26:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4259469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4259469","identity":"rs-4259469","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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