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Effect of Repeated Episodes of Generic Clinical Mastitis on Milk Yield in Dairy Cows

D. Bar^*,1, Y. T. Gröhn^*, G. Bennett, R. N. González, J. A. Hertl^*, H. F. Schulte, L. W. Tauer, F. L. Welcome and Y. H. Schukken

^* Section of Epidemiology,
Quality Milk Production Services, Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, and
Department of Applied Economics and Management, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853

¹ Corresponding author: db324{at}cornell.edu

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Our objective was to estimate the milk losses associated with multiple occurrences of generic bovine clinical mastitis (CM) within and across lactations. We studied 10,380 lactations from 5 large, high-producing dairy herds that used automatic recording of daily milk yields. Mixed models, with a random herd effect and an autoregressive covariance structure to account for repeated measurements, were used to quantify the effect of CM and other control variables (parity, week of lactation, other diseases) on milk yield. Many cows that developed CM were higher producers than their non-mastitic herdmates before CM occurred. Milk yield began to drop after diagnosis; the greatest loss occurred in the first weeks (up to 126 kg) and then gradually tapered to a constant value approximately 2 mo after CM. Mastitic cows often never recovered their potential yield. First-lactation cows lost 164 kg of milk for the first episode and 198 kg for the second in the 2 mo after CM diagnosis, compared with their potential yield. Among older cows, this estimate was 253 kg for the first, 238 kg for the second, and 216 kg for the third CM case. A cow that had 1 or more CM episodes in her previous lactation produced 1.2 kg/d less milk over the whole current lactation (95% confidence interval: 0.6, 1.7) than a cow without CM in her previous lactation. These findings provide dairy producers with information on the average milk loss associated with CM cases without considering the causative agent, and can be used for economic analysis.

Key Words: clinical mastitis • recurrent • dairy cow • mixed model

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Mastitis is a common disease in dairy herds in many countries (Barkema et al., 1998; Rajala-Schultz et al., 1999; Sviland and Waage, 2002). It can be very detrimental for dairy farm profitability because of lost production and treatment costs (Houben et al., 1993; Seegers et al., 2003; Wilson et al., 2004). A dairy producer may decide it is more economical to cull a mastitic cow than to treat her, if her expected future revenue is less than that from a replacement heifer. Clinical mastitis (CM) can be caused by different pathogens, differing in their effects (Gröhn et al., 2004) and treatment potential. Still, the current situation is that the farmer has to weigh options regarding treatment, culling, and preventive measures, because CM milk is not cultured without knowledge about the CM-causing agent.

We applied the technique of mixed linear models to study the effect of CM without specific pathogen identification on milk yield in both Finnish (Rajala-Schultz et al., 1999) and 2 New York State (Wilson et al., 2004) dairy herds. In the Finnish study, milk losses in the first 2 wk after diagnosis ranged from 1.0 to 2.5 kg/d, and the total loss over the entire lactation ranged from 110 to 552 kg. In the New York study, milk losses caused by CM in first-parity cows were 5 to 7 kg/d in the first 2 wk after diagnosis, and 690 kg over the entire lactation. Among older cows, milk losses caused by CM in the first 2 wk following diagnosis ranged from 6 to 9 kg/d, and 570 kg over the entire lactation. Yet among these older cows, many mastitic cows were higher producers before disease onset than their nonmastitic herdmates, having a potential daily advantage of 2.6 kg. Therefore, the total lactational loss among cows in parity 2+ (Wilson et al., 2004) was more accurately estimated as 1,155 kg. Thus, when studying the effect of a disease on milk yield, it is important to look at repeated measures of milk yield (daily, weekly, monthly), rather than a single summary measure for the 305-d lactational milk yield (Gröhn et al., 1999), because milk yield may be higher among mastitic than nonmastitic cows before the CM episode(s).

Mastitis is often a recurrent event (Houben et al., 1993; Döpfer et al., 1999; Zadoks et al., 2001). In our previous studies, because of the limited size of the data set and the length of the observation period, only the first case of CM could be modeled, masking the possible effect of repeated CM cases. Repeated CM cases might have different effects on milk losses than the first case. In addition, repeated cases can cause an additive effect (i.e., if 2 cases are closer in time, the resultant milk loss is higher than if they are far apart).

In this study, the objective was to estimate the effects of multiple occurrences of CM on milk production in dairy farms with high milk production and with a low incidence of contagious mastitis-causing pathogens.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Herd Descriptions
The data were from 5 dairy farms located in New York State. These farms milked an average of 1,200, 1,100, 750, 650, and 600 Holstein milking cows and were followed for approximately 18 mo. The rolling herd average was close to 11,000 kg per cow/yr on a 305-d basis (range 10,700 to 11,500); monthly SCC was 225,000 cells/mL (range 180,000 to 355,000) and varied little among the 5 farms. Cows were housed in covered barns with concrete floors and free stalls and were classified by lactation, production, and reproductive status into milking groups. All groups of cows were fed a balanced TMR via feed alleys with headlocks that allowed restraint of cows for examination and treatments. Cows were milked 3 times daily. Each milking unit had milk meters capable of automatically recording milk production and (on 2 farms) milk conductivity. Most cases of CM were identified by milkers (warm, swollen udder or changes in the milk consistency), whereas others were detected by the herdsperson examining cows whose milk electrical conductivity had increased 30% above their last 10-d mean (Afimilk, SAE Afikim, Israel) or had a concurrent drop in milk production (on most farms, this was set to 30% below their average milk yield). Sick cows were treated according to well-defined protocols that were similar, but not completely the same, on all farms and throughout the study (one farm also treated gram-negative CM cases with antibiotics in the first months of the study). Farm personnel used DairyComp305 herd management software (Valley Agricultural Software, Tulare, CA) to record lactation, reproductive, and medical data for each cow. Information on parity, diseases, drying off, calving, and culling was readily available.

Case Definition
All lactating cows in the 5 study herds were eligible for inclusion as cases of CM. Training and standardization concerning CM detection was provided at the beginning of the study. Although we were specifically interested in the milk loss associated with CM without knowledge of the causative agent, farm personnel sampled milk for microbiological culture from quarters with signs of CM. The samples were collected daily and were cultured by the Quality Milk Production Services laboratories. The bacteriological culture procedures are described in detail in Gröhn et al. (2004).

Some cows had 2 clinical episodes in the same quarter within several days of each other. Any such episode that occurred within 5 d, or that occurred within 14 d with the same etiologic agent isolated from both occurrences, was considered the same case of mastitis. Any episode that occurred more than 14 d after the previous episode was considered a new CM case.

Other Diseases
While focusing on CM, we chose 6 other diseases for inclusion in the models as potential confounders. These diseases are among the most common clinical conditions that are universally a problem in dairy cows, and reliable information about their occurrence was present in the data set for all farms. The rationale for choosing them is that they may cause milk loss, in addition to the effects of CM.

The additional 6 recorded diseases were milk fever, retained placenta, metritis, ketosis, displaced abomasum (DA), and pneumonia. They were defined as follows (Wilson et al., 2004): 1) milk fever occurred if a cow was unable to rise or had cool extremities and sluggish rumen motility near the time of calving, but was treated successfully with calcium; 2) retained placenta was retention of fetal membranes for at least 24 h postcalving; 3) metritis involved a febrile state accompanying a purulent or fetid vaginal discharge, or a diagnosis of an enlarged uterus by veterinary palpation; 4) ketosis was diagnosed by detection of ketones in milk or urine, and response to treatment; 5) DA occurred when the abomasum was enlarged with fluid, gas, or both, and was mechanically trapped in either the left or right side of the abdominal cavity (nearly every DA was confirmed by surgery); 6) pneumonia was diagnosed by the presence of pathological breathing sounds (using a stethoscope). Every effort was taken to ensure that disease definition and diagnostic criteria were the same in all herds. Written disease definitions were provided to the dairy producers and veterinarians involved.

Statistical Methods
The SAS PROC MIXED (SAS Institute, 2006) was used to study the effects of CM and the control variables [herd, parity, week in milk (WIM), and other diseases] on weekly averaged milk yield in 10,380 lactations. Because we were not interested in individual farms, but rather the farms in general with these characteristics (i.e., large, high-milk-producing dairy farms with a low incidence of contagious mastitis), herd was modeled as a random (intercept) effect. The other covariates were modeled as fixed effects.

The outcome variable, weekly averaged milk yield, was calculated by adding the milk weights of the 3 daily milkings. Within each week of lactation, the 7 daily values were then summed and divided by 7 to give the mean daily milk yield for the particular week in lactation. Randomly occurring zero-values for a particular milking were filled by using the weekly average value for the particular milking. We used weekly average milk yields over daily measurements because the latter made the size of the data set unsolvable by using available hardware and did not deliver statistical efficacy because daily milk yields had a larger variance.

The data set contained repeated measurements of milk yield within a cow over a lactation, and these were correlated with one another. This correlation was corrected for in the regression model by specifying a correlation structure among the repeated measurements (R matrix). In previous work, the first-order autoregressive correlation structure was found optimal for this purpose (Wilson et al., 2004); therefore, it was used in the current analysis.

Parity was divided into 2 groups, which were analyzed separately: first, and second and higher. Within the older group, parity was further subdivided into parities 2, 3, 4, 5, 6, and 7. Older cows (>parity 7) were not analyzed because of the low number of observations. Milk yields were modeled for the first 50 wk of lactation.

The first 3 episodes of CM during the current lactation and a carryover effect of CM in the previous lactation were studied; the other diseases controlled for in the models were retained placenta, milk fever, metritis, DA, ketosis, and pneumonia.

An index variable for each CM episode was created to classify the milk weights according to when they were measured in relation to disease occurrence. This enabled precise determination of when a disease had an effect on milk yield. After the initial data analysis, these indices were collapsed as follows: before diagnosis; same week as disease diagnosis; and 1, 2, 3, 4, 5, 6, 7, and 8 wk after diagnosis. The same index scheme was used for the other 6 diseases.

Several carryover effects across lactations were studied. In the final model the simple definition of having any CM occurrence in the previous lactation was preferred over more complex definitions.

Herd was modeled as a random effect. It was chosen over other possible random effects after the initial data analysis, based on Akaike’s information criterion (AIC) as a measure of goodness of fit (Wilson et al., 2004).

For parity 2+ cows, the following linear mixed model was used:

[1]

where Y is the mean milk yield per day in a particular week of lactation, the independent variables are as defined above, and e is a complex error term representing the within-cow correlation of milk weights and residual error. For parity 1 cows, the same model was used, except that the terms for parity, CM in the previous lactation, and milk fever were omitted because they were not applicable. The reference cow was always a cow free of that disease at the time of milk measurement. This parameterization (3 sets of covariates: 1 for each CM occurrence) assumes an additive carryover effect of the previous CM case on the current CM milk loss. For example, if a cow had her second CM case 4 wk after the first case, in that week, her milk yield was assumed to be the sum of the effect the first case caused (i.e., 4 wk after first CM) and the milk loss associated with the same week of the second CM case.

Primipara and multipara were analyzed separately because of the greatly differing shapes of their lactation curves and a possibly different CM effect. After restricting the lactation follow-up period to the first 50 WIM in the mixed model analysis, there were 3,681 parity 1 cows and 6,699 parity 2+ cows. In the analysis of parity 1 cows, 112,475 weekly milk weights were used. In the analysis of parity 2+ cows, 192,691 weekly observations were used.

To represent a CM effect, regardless of the time of occurrence of the previous CM, the parameterization of the CM was changed so that only one index (M) was used to represent the time of the milk measurement in relation to the time of CM occurrence. For parity 2+ cows, the following linear mixed model was used:

[2]

where all terms are identical to the previous model except for the representation of the current lactation CM effects. This parameterization (1 covariate with 28 index variables expressing when the milk weights were measured in relation to the first 3 CM cases) assumed that the losses observed were associated with this particular CM occurrence without adjusting the losses potentially due to the previous CM. For example, if a cow had her second CM case 4 wk after the first case, in that week, her milk loss was modeled as the milk loss associated with the second CM case without separately modeling her previous CM history. Table 1 illustrates the different coding schemes used for model [1] (M1, M2, and M3) vs. model [2] (M). The reference cow was always a cow free of that disease at the time of milk measurement.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Descriptive Findings
Table 2 gives the number of occurrences, LIR, and median WIM for the diseases present. The LIR of the first CM episode in multipara was twice that in primipara (P < 0.0001). Lactational incidence risks for subsequent CM cases were higher in multipara compared with primipara (P < 0.0001). The median WIM (13 WIM) for the first CM episode was the same in both age groups. Nevertheless, a second CM case occurred sooner for multipara (20 WIM) and later for primipara (26 WIM; P < 0.0001). The same trend was observed for a third CM case.

Although we did not model the mastitis-causing pathogens separately, the bacteriological results of the samples taken at the time of CM are presented in Table 3. The vast majority of CM cases were environmental bacteria (P < 0.0001), with Escherichia coli, Streptococcus spp., "no growth" (i.e., fewer than 2 colonies per plate), and Klebsiella being the most common findings.

The estimated lactation curves for the first 305 DIM are graphically presented for primiparous cows in Figure 1 and for multiparous cows in Figure 2. The estimated effect of parity was 1.2, 0.3, 0.0, –2.1, and –3.4 kg of milk for parity 3, 4, 5, 6, and 7, respectively, compared with a cow in her second lactation. Standard errors for these estimates were 0.15, 0.18, 0.24, 0.37, and 0.57 kg of milk, respectively.

View larger version (11K): [in this window] [in a new window]   Figure 1. Effect of clinical mastitis (CM) on the lactation curve of primiparous cows (LSM from 3,681 lactations). The solid line with filled circles represents a cow with 2 CM occurrences (generic); the dashed line with open squares represents a cow without CM (healthy). The arrows indicate median weeks in milk of CM. The dotted line portrays the estimated lactation curve of the CM-diseased cow if she had remained CM free (potential). Standard errors for these estimates (first 43 dummy variables) were in the range of 0.16 to 0.17 kg of milk in primipara.

View larger version (12K): [in this window] [in a new window]   Figure 2. Effect of clinical mastitis (CM) on the lactation curve of multiparous cows (LSM from 6,699 lactations). The solid line with filled circles represents a cow in the second lactation with 3 CM occurrences (generic); the dashed line with open squares represents a cow in the second lactation without CM (healthy). The arrows indicate median weeks in milk of CM. The dotted line portrays the estimated lactation curve of the CM-diseased cow if she had remained CM free (potential). Standard errors for these estimates (first 43 dummy variables) were in the range of 0.18 to 0.19 kg of milk in multipara.

Figure 1 graphically displays the information obtained by the statistical model (model [2]). Although mastitic primipara had a slight production advantage before CM diagnosis (0.7 kg/d, 95% CI: 0.3, 1.0), this soon vanished upon diagnosis. The milk yield of mastitic cows remained well below (P < 0.0001) that of their non-CM herdmates, and dropped further with a subsequent episode. Figure 1 displays the potential lactation curve of the mastitic cows if they had not contracted CM. Compared with CM-free cows, the first CM episode was associated with a milk loss of 126 kg in the first 2 mo, and the second episode with 160 kg. Considering the potential milk yield of CM-diseased cows, these cows lost 164 and 198 kg of milk in the 2 mo after the first and second CM episodes, respectively.

In multiparous cows (Table 7) before CM, cows that would go on to develop this disease were producing 1.7 kg/d more milk than their nonmastitic herdmates. Once CM had occurred, the picture changed markedly. For the first episode, milk loss (P < 0.0001) occurred for 6 wk after diagnosis. For the second and third episodes, milk loss (P < 0.0001) continued for 5 and 4 wk, respectively. Within each episode, milk loss was greatest immediately after diagnosis, and then tapered off in subsequent weeks until production returned nearer to levels of non-CM cows. Nonetheless, as seen in Figure 2 (results from model [2]), even by the end of lactation, production of CM cows remained well below (P < 0.0001) that of their potential. After their first CM case, multiparous cows were producing substantially less milk (P < 0.0001) than their non-CM herdmates, even after more than 8 wk after CM diagnosis. Compared with CM-free cows, the first CM episode was associated with a milk loss of 156 kg in the first 2 mo, the second episode with 141 kg, and the third with 119 kg. Considering the higher potential milk yield of CM-diseased cows (1.7 kg/d), these cows lost 253, 238, and 216 kg of milk for the above-mentioned period and cases, respectively.

If a cow experienced CM in her previous lactation, she produced less milk in her subsequent lactation. We estimated this effect as 1.2 kg/d over the whole lactation (95% CI: 0.6, 1.7). There were no substantial differences (P > 0.05) either by number of occurrences of CM or by the time when CM occurred in the previous lactation (data not shown).

Estimates of Milk Loss Associated with Other Diseases
In addition to CM, retained placenta, metritis, ketosis, DA, and pneumonia all reduced milk yield (P < 0.05) in primipara (Table 8). All of these diseases, except for retained placenta, which can only occur right after calving, were associated with lower production (P < 0.05) even before they were diagnosed. Retained placenta continued to have a negative effect (P < 0.0001) on production throughout the lactation period evaluated. Metritic and DA-diseased primipara produced less milk (P < 0.0001) than their herdmates for 5 wk after diagnosis (and treatment). Pneumonia had a negative effect (P < 0.0001) on production until 7 to 8 wk after diagnosis. Milk losses associated with ketosis continued for about a month after diagnosis, at which time ketotic cows began to outproduce their nonketotic herdmates (P < 0.001).

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

We purposely chose to study large, high-producing dairy herds with low incidences of contagious mastitis pathogens, because these are the farms that produce most of the milk in industrialized countries. The incidence of CM in our sample herds, and the estimated milk loss associated with this disease, demonstrate that despite the success of control programs against contagious mastitis pathogens, CM remains a serious, economically limiting disease in the dairy industry. Clinical mastitis affects cows that generally have high milk production potential. This finding is in agreement with previous studies (Seegers et al., 2003; Wilson et al., 2004). It also has a long-term effect on future milk production. In the current study, even one CM episode in the lactation was associated with a loss of approximately 400 kg of milk in the next lactation. Houben et al. (1993) found this carryover effect only after 3 or more CM episodes, but their study involved cows producing only 7,000 kg of milk per lactation and only approximately 500 CM cases (vs. more than 3,000 cases).

Several parameterization schemes for estimating repeated CM cases were possible. We presented 2 and prefer the CM coding scheme in which the carryover effect of previous CM case(s) was included in the current CM (model [2]), because it results in a better model fit and is simpler to implement in economic models.

Taking into account the higher milk production of cows before the first CM episode and the long-term milk loss of the first case, the effect of subsequent CM episodes on milk production was less severe than that caused by the first case. Several explanations for this effect are feasible. Bradley and Green (2001) postulated that agents with less pathogenicity are more likely involved in repeated cases. These more "udder-adaptive" agents, after a CM episode has occurred, are not completely cleared from the udder. They may exist for some time at undetectable levels. The levels of these pathogens may then rise above the detection threshold and even cause symptoms of CM, thus resulting in a repeated CM case. Such an effect could be a result of acquired immunity after a first CM case (Paape et al., 2002), leading to a less severe response (at least in milk yield) in subsequent episodes. Another possible explanation for this decreasing effect is that the milk loss is relative and not absolute. This theory can explain why our estimates are higher than those found in lower producing cows (Rajala-Schultz et al., 1999). Transforming our estimates into percentages of actual milk yield, the estimates from both studies, and the effect of repeated cases, make our results more similar.

A third explanation is that observational studies made in commercial herds inevitably suffer from various selection and misclassification biases (Kleinbaum et al., 1982). A selection bias will be present because cows suffering from high milk losses after CM are culled as a result of low production, so that the resulting CM milk loss is actually biased toward the null. This is especially so for the repeated CM cases. A misclassification bias is present if a CM cow is not diagnosed as such. In this case we attributed the effect of undetected CM cases to the long-term milk loss of the previous case. The exceptionally large data set needed to estimate the effects of repeated CM episodes makes the wish to do such estimations in research herds (with a forced "no cull" policy) a prohibitively costly endeavor.

The milk losses obtained in this study are slightly less for the first case of CM than those found in 2 other New York State dairy herds with comparable production levels (Wilson et al., 2004). One reason for the discrepancy is that in the previous study, the effect of repeated cases was attributed to the first CM case. Repeated cases occurred in approximately 30% of cows with a first CM case; hence, this more correct production loss accounting is quite substantial.

Although we included other production diseases only as potential confounders in our models, the fact that we had daily milk yield records, the size of the study data set, and the statistical procedure used make the estimated milk losses associated with these diseases a valuable contribution in evaluating their economic importance.

Because estimations of the effects of repeated CM have rarely been addressed in previous studies, the current study on the milk losses associated with repeated CM episodes is an important step forward in helping dairies assess the profitability of individual cows as they progress through lactation and overall herd life.

	ACKNOWLEDGEMENTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
The USDA (Cooperative State Research, Education, and Extension Service) Award No. 2005-35204-15714 provided the funding for this study. The authors want to thank the owners and personnel of the 5 dairies, and the personnel of the Ithaca and Canton Regional Laboratories, Quality Milk Production Services, for their valuable cooperation during the study.

Received for publication February 24, 2007. Accepted for publication June 21, 2007.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 


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