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,1,
* Department of Production Animal Clinical Sciences, Norwegian School of Veterinary Science, Oslo, Norway
Department of Norwegian Cattle Health Services, TINE Norwegian Dairies, Ås, Norway
TINE Norwegian Dairies Mastitis Laboratory, Molde, Norway
1 Corresponding author: anne.c.whist{at}veths.no
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Key Words: Streptococcus dysgalactiae • cow • clinical mastitis • culling
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Streptococcus dysgalactiae behaves contagiously in some dairy herds and environmentally in other herds and is a significant pathogen associated with bovine mastitis in lactating and nonlactating dairy cows (International Dairy Federation, 1999). Streptococcus dysgalactiae is related to summer mastitis in 37% of cases (Madsen et al., 1990). In spite of this relatively high prevalence, little is known about bacterial and host factors that contribute to the establishment and persistence of IMI caused by Strep. dysgalactiae and the natural reservoir of the bacteria. Controlling Strep. dysgalactiae by treatment strategy, at drying-off or during the lactation, may be a solution. The cure rate is expected to be close to 100% for Strep. dysgalactiae during the dry period (Østerås et al., 1991; St. Rose et al., 2003), and treatment of Strep. dysgalactiae mastitis during the lactation has shown some promising results (Oliver et al., 2004). The question of treatment, no treatment, or culling is always about cost and benefit. The cost of mastitis consists of quality cost, production loss, therapy cost, discarded milk, or replacement costs as well as extra labor cost (International Dairy Federation, 2005). Little research, if any, has been conducted on the impact of subclinical Strep. dysgalactiae mastitis and the lactation curve for SCC and the economic losses this may cause. De Haas et al. (2002) studied the effect of pathogen-specific CM on the lactation curve for SCC. They showed a continuous increase in SCC until the case of Strep. dysgalactiae mastitis occurred, and afterwards SCC stayed at a higher level.
The objective of the present study was to investigate the association between Strep. dysgalactiae IMI in early lactation and milk yield, SCC, CM, and culling in the same lactation.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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The exclusion criteria were as follows: 1) the farmer did not follow the protocol; 2) the local veterinarian suspected that the protocol was not being followed or if 50% or more of the quarter milk samples were forgotten; or 3) herds were withdrawn if co-ownerships were dissolved during the study period.
Randomization of the combined selective dry-cow therapy and PMTD regimen was conducted as a computerized systematic random assignment. The trial lasted for 2 yr, from October 2002 until June 2005, and was based on a PMTD protocol and the Norwegian selective dry-cow therapy regimen described by Østerås and Sølverød (2005). Three annual herd samples with 365-d intervals were taken during the study period. Bacteriological sampling, dry-cow treatment, PMTD, and measurement of the herds started at the first annual sample and ended at the last annual sample. During this study period, every cow was also sampled at 6 DIM. This sample was used to classify the cows as Strep. dysgalactiae-positive cows or culture-negative cows. A Strep. dysgalactiae-positive cow was defined as a cow having a positive isolate of Strep. dysgalactiae in one or more quarters, and a culture-negative cow was defined as one that had no bacteria isolated in any of the quarters.
Bacteriological Examination of Quarter Milk Samples
All quarter milk samples from all the samples taken 6 DIM into lactation were submitted for bacteriological examination at TINE Norwegian Dairies Mastitis Laboratory, Molde, Norway. The samples were analyzed for bacterial growth on blood agar plates (Blood Agar Base, Oxoid Ltd., Hampshire, UK) mixed with 5% washed bovine erythrocytes. The examination of bacterial growth and diagnostics followed the official Norwegian procedure (National Veterinary Institute, 1993) and was in agreement with the recommendations of the International Dairy Federation (1981). The procedure and examination of bacterial growth were done as described by Østerås et al. (2006). Streptococcus dysgalactiae was identified on Difco heart infusion agar (Becton, Dickinson and Co, Sparks, MD) with 5% bovine erythrocytes and 5% esculin by typical colony morphology, hemolysis, and negative esculin reaction. Colonies not showing typical colony morphology were grouped according to Lancefield’s grouping system with Prolex TM Streptococcus Grouping Latex Kit (Pro-Lab Diagnostics, Toronto, Ontario, Canada).
Selective Dry-Cow Therapy and PMTD Protocol
The dry-cow therapies were as follows: 1) combined lactation formula (Boehringer Ingelheim Vetmedica AS, Asker, Norway), a short-acting antibiotic consisting of 300 mg of penethamate hydriodide benzyl penicillin (300,000 IU of benzylpenicillin) and 300 mg of dihydrostreptomycin sulfate. If 1 or 2 quarters were infected with Staph. aureus or Strep. dysgalactiae, only these quarters were treated; if 3 or 4 quarters had infection, all 4 were treated. The treatment was repeated 4 times at 24-h intervals. 2) Dry-cow formula (Boehringer Ingelheim Vetmedica), a long acting antibiotic consisting of 0.17 g of penicillinbenzatin (200,000 IU) and 0.4 g of dihydrostreptomycin sulfate. All 4 quarters were treated once, independent of the number of infected quarters. 3) Penicillin lactation formula (VetPharma AS, Snarøya, Norway), a short-acting antibiotic consisting of 300 mg of penicillin (300,000 IU of benzyl penicillin procain). If 1 or 2 quarters were infected with Staph. aureus or Strep. dysgalactiae, only these quarters were treated; if 3 or 4 quarters had infection, all 4 were treated. The treatment was repeated 4 times at 24-h intervals.
The PMTD groups were as follows: 1) group A, the negative control group: no PMTD was applied in the herd. If any PMTD or external teat sealant had been used previously, this practice was stopped before the herd entered the study period; 2) group B: iodine PMTD using Proactive plus (DeLaval AS, Tumba, Sweden), a teat dip containing 0.15% (1,500 ppm) iodine (equivalent to 6 to 8 ppm of free iodine). All teats in all lactating cows were dipped routinely after milking during the whole lactation. At drying-off, cows maintained in tie-stalls were dipped 2 to 3 d postmilking, and cows maintained in free-stalls were dipped up to the last day of milking. The whole teat was dipped in the iodine suspension and the remaining suspension left in the dip cup was emptied. The dip cup was washed daily after each milking; 3) group C: external teat sealant (DryFlex, DeLaval AS). All teats of all lactating cows were dipped with teat sealant at drying-off, 10 d before expected calving (including heifers), and again if the teat sealant had fallen off within 3 d of being applied. The teats were washed carefully and disinfected with a 70% alcohol pad after the last milking. The teats were dried properly before application of the teat sealant. A DryFlex application cup was used for each cow, and the whole teat was dipped in the suspension.
Data Retrieval
Cow Production, Milk Quality, and Health Data.
Complete lactations were followed during the trial. A complete lactation was defined from 15 d before a calving to 15 d before the next calving or to the culling date. Calving date, corresponding parity, culling date, monthly test days, and corresponding daily milk yield (in kg) were extracted from the NDHRS database during the complete research period. Milk laboratory test results for milk quality variables such as milk fat, protein, and lactose percentages as well as free fatty acids and urea (in mmol/mL) analyzed from milk samples were also extracted from the NDHRS database. For each sample, DIM was estimated from available calving date and test day. Milk fat, protein, lactose, free fatty acids, and urea contents were determined using Milko-Scan (Foss Electric, Hillerød, Denmark) The SCC was carried out using Fossomatic 5000 cell counter (Foss Electric) according to recommendations from the International Dairy Federation (1981).
All treatments of all dairy cows were recorded with a specific health code and the date of event on the Norwegian Cow Health Card by the veterinarian or by the farmer and were reported to the NDHRS central database, as is done routinely in Norway. This system has been operating nationally since 1975 (Solbu, 1983). In Norway, only veterinarians are allowed to start a treatment of dairy cattle with antibiotics. All treatments of mastitis cases are defined as severe or moderate (code 303), mild (code 304), or subclinical (code 305). The definitions of diagnoses were according to recommendations from the International Dairy Federation (1999). Codes 303 and 304 were included in the CM cases in this study.
Statistical Analysis
The data were imported into SAS and all calculations were performed using SAS, version 9.1 (SAS Institute, Inc., Cary, NC). Five different regression models were made to evaluate any associations between Strep. dysgalactiae-positive cows and their lactation performances. The culture-negative cows were used as a control group. The models are described separately, one by one. However, the independent fixed variables included in the formula of ß were included in the primary full model for all 5 models:
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In all 5 models, the independent variables, both those included in ßik and others, were excluded one by one from the full model using a backward elimination procedure until all remaining variables were assessed as being significant or P 
0.10. Before all models were fitted, the variable A1 was split in 2 classes: A1a included cows with isolates of Strep. dysgalactiae only, and A1b included cows with isolates of both Strep. dysgalactiae and Staph. aureus in the same or another quarter but from the same cow. If there was no significant difference associated with these 2 subclasses of A1a and A1b, they were merged as one variable A1. The model fits were evaluated by assessing –2 Log Likelihood.
Model 1: SCC.
Before fitting the SCC model, all lactations with a CM before the bacteriological sample taken at 6 DIM were deleted. The SCC test-day results were used as dependent variable and fitted in model 1 according to the principle of Wood’s lactation curve (Wood, 1967). The Wood model is defined as:
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where Yyield(t) is milk yield on SCC test day t; a, b, and c are different constants; and e equals the natural base e 
2.71828.
This model was fitted in the log linear form:
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where t = DIM; logt = lnDIM; logYSCC(t) = lnSCC; and loga = the intercept denoted a.
The full model was a multivariable model constructed using PROC MIXED procedures with repeated observation at lactation level. First-order autoregressive [AR(1)] covariance matrix was used in the repeated statement for SCC within lactation and the random statement for SCC at herd level using independent covariance matrix structure:
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where t corresponds to observation on test day t; i to observation at ith lactation; and k to observation at kth herd; Z
ik represents the repeated variation for ith individual at kth herd; Zk the random variation at kth herd; and e the random effect. ß, A1, and A3 are defined in equation 1. A6 represents the SCC test-day transformed to a sine and cosine function to account for any seasonal effect as described by Schukken et al. (1992) and Østerås et al. (2006).
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In addition to the dependent variables in equation 4, A7 was also included, denoting if the cow was diseased (noted by the farmer) on the test day (yes = 1, no = 0). Furthermore, a variable A8 was included, denoting if the cow was treated for CM during the lactation and at what time the CM treatment occurred according to the test day. The time (DIM) from the CM episode to the test day (A8) was divided into different class intervals and included in the model (>90 d before, 61 to 90 d before, 31 to 60 d before, 0 to 30 d before, 1 to 30 d after, 31 to 60 d after, 61 to 90 d after, 91 to 120 d after, and >120 d after).
Model 2: Milk Test-Day Variables: Milk Yield, Milk Fat, Lactose, Protein, Free Fatty Acids, and Urea.
This full model was fitted exactly as model 1, represented by equation 4. Six different models (M2milk yield, M2milk fat, M2lactose, M2protein, M2free fatty acids, M2urea) were made in which milk yield, milk fat, lactose, protein, free fatty acids, and urea values were used instead of Ln SCCtik in equation 4. These values were not log-transformed but used as real values. The equation will thus be a combined exponential and linear model proposed by Wilmink (Macciotta et al., 2005). The equation is thus:
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where Y represents milk yield, milk fat, protein, lactose, free fatty acids, or urea values for DIM t for the ith lactation and the kth herd for each separate model. For M2milk yield, separate models were constructed for primiparous and multiparous cows. The independent variable parity was omitted in the model for primiparous cows.
Model 3: Clinical Mastitis.
The hazard ratio (HR) for a cow to achieve CM was estimated using Cox regression analyses (Cox, 1972) using the general equation:
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where ß in this particular study is defined with the fixed covariates of equation 1.
When fitting the models, PROC PHREG (SAS Institute Inc.) including the positive stable frailty models in the SAS macro available from Shu and Klein (1999, 2005) were used. All frailty effect was at herd level. The full model before reducing the model to include only variables with a P-value < 0.10 was as follows:
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In equation 9, t is time from start of observation to mastitis therapy and Wk is the positive frailty effect at herd level; A10 in this model represents the calving date so that the seasonal association was linked to season of calving instead of season of test day as was used in models 1 and 2. The A11 indicates if the lactation represented a cow from a herd with free-stall (1) or not (0). Each observation entered the dataset at the time of the bacteriological sample (6 DIM) where the cows were designated as either a Strep. dysgalactiae-positive cow or a culture-negative cow. The cows were censored at culling date, 15 d before the next calving date, or at a maximum observation time of 420 d. The limit for censoring at 420 DIM covered the full length of 95% of all lactations in the dataset. The HR for CM was calculated using time from bacteriological sampling (6 DIM) until the first CM event date as dependent variable. Hazard ratios with 95% confidence intervals were obtained for all covariates.
The fit of the model was evaluated by plotting the deviance residuals against the covariates to see if the models fitted the data adequately (Allison, 2000). To check for proportional hazard assumption, the log of the negative log of survival was plotted against time with the most important independent variable as strata. These assessments showed no evident violations of the proportional hazard assumption, extreme deviance residuals, or patterns in the models. The significance of the frailty effect was assessed by likelihood ratio of independence model by H0: 
= 1. Frailty effect was judged significant when P < 0.05.
Model 4: Culling.
The HR for a cow to be culled was estimated using Cox regression analyses (Cox, 1972) following the same principle as for CM in model 3 except that the dependent variable was the time from the bacteriological sample (6 DIM) where the cows were either a Strep. dysgalactiae-positive cow or a culture-negative cow until they were culled. The dataset was censored at 15 d before the next calving date or at a maximum observation time of 420 d. The model was evaluated and reduced using a backward elimination procedure including the frailty effect for the final model exactly as for model 3. The full model before backward elimination was as follows:
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In equation 10, t is time from start of observation to culling, and A12 denotes if the cow was treated for CM during the lactation and after the bacteriological sample at 6 DIM.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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At calving there were 4,626 culture-negative and 562 Strep. dysgalactiae-positive lactations; 449 of the positive lactations had isolated Strep. dysgalactiae from 1 quarter, 80 from 2, 27 from 3, and 6 from all 4 quarters. Of the 562 cows with Strep. dysgalactiae isolates, 61 (10.9%) also had Staph. aureus isolated from the same quarter, and 105 (18.7%) also had Staph. aureus isolated from another quarter within the same cow. There were no significant differences associated in the outcome for CM, SCC, or daily milk yield among cows with only Strep. dysgalactiae or combined Strep. dysgalactiae and Staph. aureus isolates. The 2 variables A1a and A1b were therefore merged to a single variable A1. For culling as outcome, the 2 variables A1a and A1b were used separately. The crude distribution of CM, culling, and daily milk yield for Strep. dysgalactiae-positive and culture-negative cows in early lactation is presented in Table 2.
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together with the number of observations at lactation level. The interaction term, DIM x Strep. dysgalactiae, was significant (P < 0.05) in model 1, but was removed because the fit of that model did not improve. The shapes of the SCC curves were different between primiparous and multiparous cows (Figure 1), and Strep. dysgalactiae-positive primiparous cows had significantly lower SCC compared to Strep. dysgalactiae-positive multiparous cows (Table 3). The Strep. dysgalactiae-positive primiparous or multiparous cows had a significantly higher SCC throughout the remaining lactation compared with culture-negative cows. For primiparous cows, the differences in the geometric mean SCC between Strep. dysgalactiae-positive and culture-negative cows was 197,000 cells/mL at the beginning of the lactation, 24,000 cells/mL in mid lactation, and 39,000 cells/mL at the end of the first lactation. For multiparous cows (>3 parities), the differences were 280,000 cells/mL at the beginning, 46,000 cells/mL in mid lactation, and 111,000 cells/mL at the end of the lactation. There was a significant negative association between iodine PMTD and SCC, but a positive effect of the interaction between iodine PMTD and lnDIM. The positive effect of the interaction indicates a reduction of this difference as lactation proceeds. The SCC was also significantly associated with season, with the highest geometric mean of 4.21 (67,000 cells/mL) in the summer months of July and August, and the lowest geometric mean of 3.93 (51,000 cells/mL) in the winter months of November to January.
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). For multiparous cows, the loss was estimated at 0.82 (SD = 0.23) kg per test day, corresponding to 246 kg during a 305-d lactation. The estimates for the final model are presented in Table 4, which also illustrates that cows that acquired CM had higher milk yield 2 mo before acquired the mastitis compared to cows not treated.
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Models 3 and 4: Clinical Mastitis and Culling.
Included in the survival analysis for CM and culling were 5,188 lactations from 178 herds. Of the 510 registered CM treatments, 99 Strep. dysgalactiae-positive cows and 411 culture-negative cows were treated in the remaining lactations. Compared with culture-negative cows, the overall HR for CM in Strep. dysgalactiae-positive cows was 2.32 (1.85 to 2.91; Table 5).
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). However, cows with a combined isolate had an HR of 2.48 (1.93 to 3.18) for being culled. |
DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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to 5; Figures 1 and 2). Thus, a Strep. dysgalactiae IMI in early lactation would be associated with an extra cost compared to culture-negative cows. The question whether this cost could be reduced by treating these cows as early as possible could not be answered in this study. However, our results for SCC and milk yield could be influenced by treatments of CM during the lactation. We therefore divided the time from CM treatment to the SCC test days into monthly intervals. In general, cows treated for a case of CM during the lactation had significantly higher SCC before treatment and a few months after treatment (Table 3). For milk yield there was no association with the treatment in first parity, but multiparous cows milked more before treatment and less 1 mo after treatment of a CM; later in lactation there was no difference (Table 4). This was true for Strep. dysgalactiae-positive cows as well as for cows that were culture-negative at 6 DIM, because there was no significant interaction between a CM treatment and a positive-or negative-culture cow at 6 DIM. Our results are in agreement with Harmon (1994) and De Haas et al. (2002) who documented the association between specific mastitis pathogens and SCC. De Haas et al. (2002) found a continuous increase in SCC until Strep. dysgalactiae mastitis occurred, and afterwards SCC remained elevated. The Strep. dysgalactiae-positive cows at calving were not supposed to be treated for the subclinical mastitis. Accordingly, treatment of CM should mainly be due to clinical symptoms that need therapy and not be based on a decision from the bacteriological results. The farmers were advised to milk these cows last and separate them (in separate pens if possible) from the remaining herd. Thus, our results of HR on CM should be unbiased or moderately overestimated compared with the situation in which bacteriology was unknown. If the SCC remained high, these cows were assigned for culling, and thus the HR estimate for culling could be an overestimation compared to a non-research situation where the bacteriology is unknown. Because the treated Strep. dysgalactiae-positive cows responded well to treatment, and Strep. dysgalactiae could cause more new infections, the lack of therapy might be a reason why we did not manage to reduce the prevalence of Strep. dysgalactiae during the trial (Whist et al., 2006a). Dry-cow therapy and previous SCC could be related to the outcome in this study. Dry-cow therapy and SCC only exist for >1 parity cows, and separate models should be made. Only 9% of second-parity and 15% of greater than second parity cows were treated with dry-cow therapy in this study and the therapy groups would be too small to detect any statistical significant differences if used in the model. Therefore, dry-cow therapy is omitted from the models. The problem with previous SCC is that there is a correlation between SCC and parity, and parity has to stay in the model because it is a well-known confounder to the occurrence of Strep. dysgalactiae (Østerås et al., 2006). Our data set was specially constructed to analyze the association between Strep. dysgalactiae-positive cows in early lactation, not the effect of dry-cow therapy and SCC in the previous lactation.
Several authors have looked at the cure rate for Strep. dysgalactiae during lactation. Oliver et al. (2004) focused on treatment of cows with subclinical mastitis during lactation. The cure rate for 8 d of extended ceftiofur treatment regimen was 80% for Strep. dysgalactiae. St. Rose et al. (2003) conducted a randomized, controlled field trial in the Netherlands to determine the therapeutic efficacy of parenteral penethamate hydriodide (Leocillin) against naturally occurring, chronic, streptococcal mastitis during lactation. Bacteriological cure occurred in 59% of the 29 treated quarters. They also concluded that treatment resulted in a significant decrease in SCC at cow and quarter level in comparison with untreated controls. Shephard et al. (2000) concluded that treatment during lactation had a significant effect upon lowering the SCC for cows infected with Strep. dysgalactiae. There is also a probability that some Strep. dysgalactiae IMI will spontaneously cure during the lactation period. This needs further research.
Our results also demonstrate an association between Strep. dysgalactiae-positive cows at calving and reduced milk yield in the remaining lactation. The crude mean milk yield results (Table 2) are similar between the 2 groups, but there is an obvious selection bias in the crude mean because Strep. dysgalactiae-positive cows have a higher HR for being culled and are relatively more represented in early lactation compared with culture-negative cows. Milk production loss is not obvious to the producer because the milk has never been produced. It is a hidden cost or lost income opportunity (International Dairy Federation, 2005). Milk yield is related to SCC; as SCC increases, milk yield drops. Our results are not in complete agreement with Hortet et al. (1999) who illustrated that the drop is higher in late lactation than in early lactation. The reason why we got different results is most likely that our study focused on lactations that already had an IMI in early lactation. Another reason could be that a large proportion of Strep. dysgalactiae-positive cows are cured at the end of their lactation.
Whist et al. (2006b) concluded that PMTD reduced the incidence of CM in tie-stalls. This effect was not seen for the prevalence of Strep. dysgalactiae-positive cows at the herd level or at calving (Whist et al. 2006a). Our data set was probably too small, meaning too few Strep. dysgalactiae-positive cows, to detect any association between iodine PMTD in Strep. dysgalactiae-positive cows at calving and later occurrence of CM.
The seasonal effect contributed significantly in some of the models of SCC and milk yield in primiparous and multiparous cows. The association between SCC and season is in agreement with Whist and Østerås (2006) and De Vliegher et al. (2004). The latter authors found that primiparous cows calving from April to June had the highest SCC. The association between milk yield and season is in agreement with Barash et al. (2001) who found that cows calving in December produced the highest milk and milk protein yields, and those that calved in June produced the lowest. This research was conducted in Israel with a climate different from that of Norway, but heat stress is known to reduce the amount of milk produced (Armstrong, 1994).
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Received for publication July 13, 2006. Accepted for publication October 4, 2006.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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