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Relationship of early first lactation somatic cell count with risk
of subsequent first clinical mastitis
*
R. Rupp , D. Boichard
´ ´ ´
Institut National de la Recherche Agronomique, Station de Genetique Quantitative et Appliquee, 78352 Jouy-en-Josas, France
Received 26 October 1998; received in revised form 5 March 1999; accepted 6 April 1999
Abstract
The relationship between initial Somatic Cell Count (SCC) and time to first clinical mastitis was estimated from data including 20,422 Holstein heifers, without clinical signs of mastitis during the first month of lactation and with a first test day SCC lower than 400,000 cells / ml. Number of days from 35 days after first calving to first clinical mastitis event in first or second lactation was studied using survival analysis methodology, allowing for censored data. The model included the effects of SCC and milk yield at first test, herd-year, calving month, and lactation stage. Separate analyses were also performed for subsets of herds with low or high mastitis frequency and SCC. The risk of first clinical mastitis was highest around the second calving, in lactations starting in summer, and for high-yielding cows. The probability of clinical mastitis occurring increased continuously as initial SCC increased. The same pattern was observed in herds with low or high SCC level. In herds with the lowest mastitis frequencies, relationship between initial SCC and mastitis occurrence was weakest. But in all situations, results indicated that cows with the lowest initial SCC had the lowest risk for first clinical mastitis, without any intermediate optimum. 2000 Elsevier Science B.V. All rights reserved.
´ ´ Resume
`
La relation entre comptage de cellules somatiques initial (SCC) et le risque de premiere occurrence de mammite clinique
´ ´
est analysee dans un echantillon de 20,422 vaches primipares de race Holstein, sans mammite clinique au cours du premier
´ ` `
mois de lactation et avec un premier comptage cellulaire inferieur a 400,000 cellules par ml. L’intervalle de temps jusqu’a la
` ` ´
premiere mammite clinique survenant en premiere ou seconde lactation, exprime en nombre de jours entre 35 jours suivant le
ˆ ´
premier velage et la date du premier cas clinique, fait l’objet d’une analyse de survie. Cette methodologie permet d’inclure
´ ´ `
les donnees censurees. Le modele prend en compte les effets de la concentration cellulaire et du niveau de production au
ˆ ´ ´
premier controle, de la combinaison troupeau-annee, du mois de mise bas et du stade de lactation. Les analyses sont menees
´ ` ´
sur l’ensemble de l’echantillon ainsi que sur des groupes de troupeaux a haute ou basse frequence de mammite clinique, ou
ˆ ´ ˆ
de niveau cellulaire haut ou bas. Le risque de mammite clinique appara ı t superieur autour du second velage, pour les
´ ´ ´ ´
lactations debutant en ete, ainsi que pour les fortes productrices. La probabilite de mammite clinique augmente de fac¸on
ˆ ´ ´
monotone avec la concentration cellulaire au premier controle dans l’echantillon global et dans les groupes definis sur la base ´
du niveau cellulaire. Dans les troupeaux avec une frequence faible de mammite clinique, la relation entre SCC initial et ´
occurrence de mammite est plus faible. Mais dans tous les cas, les resultats indiquent que les vaches avec le niveau cellulaire
*Corresponding author. Tel.:133-134-652-214; fax:133-1-652-210.
E-mail address: [email protected] (R. Rupp)
´ ´ initial le plus bas presentent le risque le plus faible de mammite clinique, et qu’il n’y a pas d’optimum intermediaire.
2000 Elsevier Science B.V. All rights reserved.
Keywords: Clinical mastitis; Dairy cow; Somatic cell count; Survival analysis
1. Introduction 1995; Mrode and Swanson, 1996; Boichard and
Rupp, 1997).
However, the relationship between SCC and mas-Because of its high incidence (Seegers H. et al.,
titis is far from being clear. Some authors (Coffey et 1997a; Seegers J. et al., 1997b) and biological effects
al., 1986a; Kehrli and Shuster, 1994; Schukken et al., (Schukken et al., 1997), mastitis is the most costly
1994) were concerned by the recommendation of disease in dairy cattle (Shook and Schutz, 1994). For
continuously decreasing SCC by selection, and ar-farmers, the economic consequences of mastitis
gued that such a trend could impair the cow’s include losses of milk production (Lescourret and
capacity for leukocyte recruitment and, therefore, her Coulon, 1994) or milk sale, increased culling rates,
ability to respond to intramammary infection. Ac-cost for veterinary treatments, and higher Somatic
cording to Kehrli and Shuster (1994), cows with Cell Counts (SCC) in milk. Although management is
very low SCC would be more susceptible to mastitis. the most effective way to prevent intramammary
These arguments were based on early studies report-infection, selection for mastitis resistance is an
ing that moderate cell counts in milk play a protec-alternative to be considered, at least to prevent any
tive role in the defense of the mammary gland detrimental effect of selection for milk yield on (Schalm et al., 1964a,b). These authors found that udder health. quarters with moderate to high initial SCC Direct selection against clinical mastitis is dif- (400,000–600,000 cells / ml) had lower risk of being ficult, because in most countries clinical mastitis infected after experimental challenge with mastitis events are not widely recorded, and because the pathogens, and concluded that a cell count of corresponding heritability trait is very low, close to 500,000 cells / ml afforded protection against in-0.02 (Emanuelson et al., 1988; Weller et al., 1992; tramammary infection. Following these results,
in-¨ ¨
Lund et al., 1994; Poso and Mantysaari, 1996). trammamary polyethylene devices were developed to Conversely, several arguments promote the interest artificially increase SCC and potentially afford of SCC in the selection for mastitis resistance. As protection to bacterial infection (Schultze and Paape, SCC are routinely recorded in most milk recording 1984; Timms, 1990). Use of such devices was shown systems, they are available on a large scale at a to be effective against severe coliform infections but moderate cost. Although detecting short clinical unfortunately led to undesirable milk losses (Timms, events on the basis of monthly test day SCC is 1990). More recently, a study based on an ex-usually not possible, SCC efficiently account for perimental infection with Staphylococcus aureus on subclinical and chronic infections. As reviewed by 113 cows (Schukken et al., 1994) and using bac-Mrode and Swanson (1996), the heritability of SCC, teriological tests to define establishment of infection, close to 0.15, is much greater than for clinical supported that theory and reported that animals mastitis. In addition, the genetic correlation between resisting infection had higher SCC just prior to both traits is positive and moderate to high (around infection than animals becoming infected (282,000 0.7), suggesting that some genes reduce both cell and 91,000 cells / ml, respectively). Whereas selec-counts and clinical infection rates. Consequently, it is tion against cows with high SCC is supposed to believed that selection for decreased SCC would reduce mastitis incidence, the question is now raised reduce susceptibility to clinical and subclinical mas- whether SCC should be decreased to the lowest titis (Colleau and Le Bihan-Duval, 1995). Indeed, possible value or should not be lower than a critical SCC has already been included in the breeding goal threshold.
traits. McDaniel (1993) found that regressions of SCC information and to occur from 5 to 35 days absence or presence of mastitis cases in first lactation after calving. Mastitis events were declared by cows on PTA for sire somatic cell score were farmers and collected every month by Milk Record-positive: one unit change in PTA corresponded to an ing technicians until March 1, 1997. Reliability and increase of 36% in mastitis incidence. Rogers et al. completeness of disease recording was assessed by (1996) found similar results for US bulls used in an additional survey conducted by the Milk Record-Denmark and Sweden. In two Swedish breeds, ing agents. Herds (36%) for which clinical cases Philipsson et al. (1995) reported a linear relationship recording was considered to be incomplete by the between Relative Breeding Values (RBV) for clinical technicians were excluded from the study. Herds mastitis and SCC, with a 0.35 increase in RBV for without any clinical mastitis event recorded were mastitis per unit of RBV in SCC. All these studies also discarded. The information collected was the suggested that SCC could and should be decreased to date of the monthly test following the event. If the lowest possible value. mastitis occurred around calving (28 to 18 days), Other authors investigated the relationship be- this was additionally stated. Consequently, the confi-tween SCC at a given time, e.g., the onset of dence interval of the exact date of clinical mastitis lactation, and occurrence of intramammary infection was 35 days during the lactation (average interval or clinical mastitis later in lactation (Coffey et al., between two consecutive test day) or 16 days around 1986b; Schukken et al., 1994; Beaudeau et al., calving. Accordingly, for processing this informa-1998). These studies, however, were carried out with tion, a mastitis during lactation was arbitrarily sup-a limited sup-amount of dsup-atsup-a or did not sup-account for the posed to occur 16 days before test day and a mastitis time period up to mastitis. around calving was supposed to occur the day of Alternatively, in addition to the presence or ab- calving. The whole data set included 25,833 cows, sence of mastitis events, survival analysis can also out of them 5156 (20%) had at least one clinical account for the length of the time period up to the mastitis in first lactation. To avoid the possible effect
¨
event (Grohn et al., 1997), i.e., the number of days of a previous clinical mastitis event on the first SCC, up to first mastitis. Survival analysis is based on the 1940 cows with a clinical mastitis recorded before 35 concept of hazard rate, defined as the probability of days after first calving were discarded. Finally, to occurrence of some event at time t, given that it did study the relationship between early SCC and clini-not happen just before t. This methodology provides cal mastitis in a range of low to moderate cell estimates of relative risks of an event for groups of counts, only cows with a first SCC lower than cows defined according to given characteristics. The 400,000 cells / ml were considered. Accordingly, objective of this study was to determine if low SCC 2779 cows were discarded. These edited cows were cows are at greater risk to first clinical mastitis than found to have high mastitis frequency, as 476 (17%) cows with somewhat higher SCS. Relationship be- of them had at least one clinical mastitis in first tween SCC at initial test in first lactation and time to lactation. Finally, cows culled before 35 days and first mastitis later in first and second lactation was herds with less than three selected cows (692) were assessed by survival analysis. discarded. After edits, the final data set consisted of 20,422 cows in 2611 herds, with 13% of these cows having at least one clinical mastitis. Distribution of
2. Material and methods clinical events is shown in Fig. 1.
The variable analyzed was the interval to mastitis 2.1. Data event. Because of editing rules, it was defined as the number of days from 35 days after first calving to The data consisted of Holstein cows from Mor- first mastitis occurring in first or second lactation. If
`
Fig. 1. Distribution of time to first clinical mastitis (CM) (j) and relationship between total number of clinical events and time to first clinical mastitis (s).
whether she had begun a new lactation or not, her estimate constant relative risks (RR) associated with record was also censored at March 1, 1997. a factor w. The risk of effect w1 relative to w2 is computed as the ratio of the hazard function of the corresponding groups of animals:
2.2. Model
RR5l(t; w1) /l(t; w2) The survival model used was the proportional
5l0(t) exp(w19u) /l0(t) exp(w29u) hazard model (Cox, 1972), which is based on the
5exp[(w12w2)9u]5constant concept of a hazard functionl(t), wherel(t) was the
limiting probability for a cow to have her first
The baseline functionl0(t) was assumed to follow a mastitis at time t (expressed in days), given that she
Weibull hazard distribution: was still unaffected just prior to t. The hazard rate
r 21
was defined as the product of a baseline hazard l0(t)5lr(lt)
function l0(t), which acts as an average hazard
with parametersl andr. This distribution is flexible function, and of a function w of explanatory
vari-and has been shown to usually adequately fit bio-ables, including the initial SCC:
logical data (Ducrocq et al., 1988a). In the Weibull regression model, the hazard function can be
sim-l(t; w)5l0(t) exp(w9u)
plified as: where u is a vector representing the effects of the r 21
l(t; w)5rt exp(rlog(l)1w9u) explanatory covariables.
baseline hazard function andr log(l) appears as an these levels included 8% to 25% of cows. The intercept on the logarithmic scale. The effects in- variable IMY was also categorized into six groups: cluded in the model were the random herd-year (1) less than 21.5 kg; (2) 21.5–23.5 kg; (3) 23.5– effect and the fixed effects of lactation stage, month 25.5 kg; (4) 25.5–27.5 kg; (5) 27.5–29.5 kg; and (6) of first calving, initial SCC, and initial milk yield. more than 29.5 kg. Each of the six IMY classes The herd-year effect, with 5222 levels, was as- included approximately the same number of records. sumed to be time-dependent, with changes at Sep- In a preliminary analysis, the effect of days in milk tember 1, 1996, to allow for a modification in at first test day was found not to be significant and mastitis hazard in a herd from one year to the next, was removed from the final model.
accounting for differences in udder health
manage-ment, bacteriological pressure, and other environ- 2.3. Method mental changes. It was assumed to be random and to
follow a log–gamma distribution. The latter assump- Effect estimates were obtained by a maximum tion allowed to algebraically integrate the herd-year likelihood technique, using the ‘Survival Kit’, a set effect out of the joint posterior density, decreasing of FORTRAN programs written by Ducrocq and dramatically the number of parameters to estimate Solkner (1994). The random herd-year effect was¨ (Ducrocq et al., 1988b). assumed to follow a log–gamma distribution with Stage of lactation was a fixed time-dependent parameter g (Ducrocq et al., 1988a). The marginal effect with five classes starting at day 35, 91, 181 posterior distribution was obtained, after algebrai-after first calving, 10 days before and 30 days algebrai-after cally integrating the herd-year effect out of the joint second calving, respectively. Such a definition al- posterior distribution. Finally, fixed (possibly time-lowed for possible hazard changes during the life of dependent) effects, the herd-year parameter g and the cow, and particularly for a decrease in hazard the Weibull parameters r and r log(l) were esti-during lactation and an increase around calving. A mated by maximization of the resulting logarithm of slight drawback for the use of time-dependent the marginal posterior density (Ducrocq et al., covariates is that comparison of hazard of cows at 1988a; Ducrocq, 1993). Standard errors of estimates different points in time must be done by combining (r,rlog(l), and fixed effects) were computed as the the estimates of all time-dependent covariates with square root of the diagonal elements of the inverse of the values of the baseline hazard function at these the Hessian matrix. The moments of the distribution time points. Similarly, estimates of time-dependent of the herd-year effect were:
effects must be interpreted jointly with the values of
( 1 )
ˆ ˆ
¨
the baseline hazard function (Grohn et al., 1997). E5C(gˆ)2log(gˆ) and Var5C (gˆ), Month of first calving was treated as a fixed
( 1 )
ˆ ˆ
discrete variable with 12 levels, from September whereC(g) andC (g) are the digamma and the
ˆ
1995 to August 1996. trigamma function evaluated at g, respectively Because somatic cell count and milk yield at first (Kalbfleisch and Prentice, 1980). Likelihood ratio test day (ISCC and IMY, respectively) were highly tests of explanatory variables were obtained by dependent on days in milk, they were pre-adjusted comparing the full model with reduced models for days in milk by regression with linear and excluding one variable at a time.
scores (SCS) were defined in a classical way through computed using Kaplan–Meier’s formula (8). This a logarithmic-transformation of test day SCC: SCS5 plot displayed a straight line, which validated the log (SCC / 100,000)2 13 and a lactation mean of all assumption of Weibull proportional hazard model. test day SCS was computed for each cow. As for Likelihood ratio tests are shown in Table 1 for the herd mastitis frequency, herd SCS mean was calcu- complete data set. All factors were found to be lated from data of all parities and all cows in each highly significant.
herd. Separate analyses allowed to account for
possible differences in the baseline hazard function 3.1. Estimates of the Weibull parameters and of and changes in effects of covariates. the explanatory covariates
Table 2 presents the estimates of Weibull
parame-3. Results ters r and r log(l). For all analyses, r estimates
were similar and close to 1. In general, values were In the complete original data set, 20% of lactations slightly lower than one, reflecting a monotonous were affected by at least one clinical mastitis event. slight decrease in hazard, i.e., in risk of occurrence However, out of the 20,422 selected cows and over of first clinical mastitis, with time. However, this is the 19 months under study, only 2649 (13%) had true only within defined period of lactation stage (5 clinical mastitis recorded, because of the editing levels). Indeed, as the lactation stage effect depends strategy. Out of the 2649 first clinical mastitis cases on time, it interacts with the general shape of the recorded, 64% (1693 events) occurred in first lacta- baseline hazard function and only this interaction tion and 36% (956 events) occurred in the beginning should be interpreted. This is illustrated in Fig. 2, of the second lactation. Fig. 1 displays the dis- which displays the hazard rate of a cow calving in tribution of failure times (i.e. time to first clinical September in an average herd, and with ISCC mastitis). Mean failure time was 246 days. Failure between 50,000 and 75,000 cells / ml, IMY between time was quite high because clinical mastitis cases 25.5 and 27.5 kg and 360 days calving interval. The occurring before day 35 were not included in the hazard slightly decreased from 35 days to the end of analysis, and because cases occurring in the begin- first lactation and strongly increased around second ning of the second lactation were considered. Mean calving. When compared with first lactation, the censoring time of unaffected cows (87%) reached relative increase around second calving was more 362 days. Mean SCC at first test day was 101,500 pronounced in herds with low clinical mastitis and 89,500 c / ml, for cows with and without clinical frequency than in herds with high clinical mastitis mastitis, respectively, and 91,000 c / ml in the whole frequency (Fig. 2), but no difference was observed selected population. between herds with low or high SCS level (not The adequacy of the Weibull distribution to the shown). However, it should be noted that the hazard density of the failure times observed in the data was function drawn is left truncated in first lactation, as
ˆ
checked by plotting the log[2SKM(t)] against log(t), selection of data required edit of cows censored ˆ
where SKM(t) is the empirical survivor function before 35 days, i.e. cows with a clinical mastitis
Table 1
Results of the likelihood ratio test: comparison of the full model with models excluding one effect at a time
Models tested Change in df P value
22 log-likelihood
a
Full Model excluding ISCC 104.0 5 #0.0001
Full Model excluding IMY 53.3 5 #0.0001
Full Model excluding MC 51.1 11 #0.0001
Full Model excluding ST 1431.0 4 #0.0001
a
Table 2
Estimates ofr,r log(l) (standard errors) andgparameters for global (all herds) and separated (subgroups of herds) analyses
Subgroups Number of cows Number of herds r r log(l) g
All herds 20,317 2611 0.91 (0.02) 27.95 (0.17) 2.21
a
CM1 11,781 1468 1.04 (0.05) 29.16 (0.33) 4.99
b
CM2 8536 1143 0.87 (0.03) 27.22 (0.20) 4.81
c
SCS1 10,183 1270 0.93 (0.05) 28.17 (0.26) 2.67
d
SCS2 10,134 1341 0.90 (0.05) 27.69 (0.23) 2.08
a
CM15group of herds with low mastitis frequency. b
CM25group of herds with high mastitis frequency. c
SCS15group of herds with low somatic cell score average. d
SCS25group of herds with high somatic cell score average.
Fig. 2. Hazard rate of cow over time, estimated in global (solid line) and separate analyses according to mastitis frequency: subgroups of herds with low (dot line) or high (bold dot line) frequency.
]] Œ
event or a SCC higher than 400,000 cells / ml during exp(12 0.55)54.43. As expected, g estimates the first 35 days of lactation. For these discarded increased to 4.99 and 4.81 (i.e., variances decreased cows (4719), clinical mastitis frequency was high to 0.22 and 0.23, respectively) when strata were (51.2%) and 90% of these clinical events occurred defined according to mastitis frequency, i.e., in before 100 days after first calving. Therefore, be- subgroups with less and more than 20% of clinical cause of selection of data, the hazard was likely to be cases within herds, respectively (Table 2).
underestimated from 35 days to the end of first Table 3 displays estimates of relative first mastitis lactation. hazard for month of first calving. A large increase in The estimate of the g parameter for herd year risk was observed for first calving occurring in effect distribution is in Table 2. Its value was equal summer and, to a smaller extent, in spring. This to 2.21 in the overall analysis. This rather low value month effect was the same in both herd groups based indicated a high variability of mastitis frequency on SCS level, whereas relative increase in risk was among herds. On the log-scale, the corresponding more (less) pronounced in herds with low (high) herd-year expected mean wasC(2.21)2log(2.21)5 mastitis frequency.
( 1 )
2 0.24 and the herd-year variance wasC (2.21)5 Table 3 and Fig. 3 show the influence of initial 0.55. Thus, relative mastitis rates for different herd- milk yield on mastitis frequency. Increased milk
]] Œ
Table 3
Relative hazard ratio for effects of month of first calving, initial SCC, and initial milk yield Effects and levels Relative hazard ratio
All herds Subgroups
a b c d
CM1 CM2 SCS1 SCS2
Month of first calving
September 95 1.00 1.00 1.00 1.00 1.00
October 95 1.19** 1.40** 1.08 1.27** 1.09
November 95 1.35** 1.35** 1.36** 1.36** 1.31**
December 95 1.13 1.23 1.08 1.21 1.04
January 96 1.16* 1.46** 1.05 1.11 1.16
February 96 1.13 1.41** 1.01 0.99 1.21
March 96 1.01 1.28 0.92 0.92 1.03
April 96 1.15 1.90** 0.90 1.42** 0.93
May 96 1.09 1.44** 0.93 0.91 1.17
June 96 1.52** 2.47** 1.17 1.48** 1.47**
July 96 1.56** 1.78* 1.47** 2.12** 1.11
August 96 1.79** 2.48** 1.58** 1.73** 1.79**
Initial SCC(in 1000 cells /ml)
Less than 35 1.00 1.00 1.00 1.00 1.00
35–50 1.17** 1.03 1.27** 1.13 1.14
Less than 21.5 1.00 1.00 1.00 1.00 1.00
21.5–23.5 1.14* 1.11 1.12 1.29** 1.04
23.5–25.5 1.14* 1.12 1.12 1.18 1.12
25.5–27.5 1.32** 1.12 1.40** 1.36** 1.32**
27.5–29.5 1.24** 1.09 1.25** 1.23* 1.27**
More than 29.5 1.57** 1.46** 1.50** 1.78** 1.42**
a
CM15group of herds with low mastitis frequency. b
CM25group of herds with high mastitis frequency. c
SCS15group of herds with low somatic cell score average. d
SCS25group of herds with high somatic cell score average. *P#0.10 different from 1.00.
**P#0.05 different from 1.00.
with increased risk of first mastitis. However, this lower SCS level than in herds with greatest SCS effect was not uniform. It was especially large for level. On the other hand, first milk yield had less greatest yields: cows producing more than 29.5 kg of impact in the group of herds with lower mastitis milk were 1.5 times more likely to be affected than frequency than in the overall analysis.
Fig. 3. Relative hazard ratio for initial milk yield effect (IMY) estimated in global (s) and separate analyses according to mastitis frequency: subgroups of herds with low (j) or high (m) mastitis frequency.
Fig. 4. Relative hazard ratio for initial SCC effect (ISCC) in global (s) and separate analyses according to mastitis frequency: subgroups of
herds with low (j) or high (m) mastitis frequency.
ISCC in the highest class, were 0.75 times as likely latter study are similar to the estimates of risk of first to have first mastitis as cows with ISCC from 75,000 case obtained in this paper. These results, obtained in to 215,000 cells / ml. natural conditions, do not support earlier observa-tions of increased risk of low SCC cows for infection after experimental challenge (Schalm et al., 1964a;
4. Discussion and conclusion Schukken et al., 1994). Hence, the latter authors
indicated possible confounding between protective The methodology used required selecting cows effect of high SCC and subclinical infection prior to without clinical mastitis in early productive life. challenge (Schukken et al., 1994). The discrepancy Therefore it is not suited to analyze clinical events could also result from different biological charac-around first calving and conclusion drawn cannot be teristics. In natural conditions, low SCC may reflect fully generalized. Similarly, this method cannot be an efficient anatomical barrier which minimizes the extended to analyze repeated clinical mastitis events. risk of subsequent mastitis. However, after an ex-However, Fig. 1 shows the relationship between time perimental infection, anatomical characteristics of of first clinical mastitis and the total number of the cow are no longer involved in mastitis resistance, clinical events in first lactation, and suggests that the and then, presence of SCC, minor pathogens, or earlier the first clinical mastitis, the greater the total both, may provide some protection against major number of cases in first lactation. The SCC in- pathogens.
formation should be as early as possible in order to The relative risk of first clinical mastitis associated be available before the first clinical mastitis event for with initial SCC is similar in herds with low or high any cow. Therefore, analysis was restricted to the SCS level. But differences are observed for groups first test day, although this SCC measure is known to of herds with low or high mastitis frequency. In be more variable and not fully representative of the ‘good’ herds, the relationship between initial cell rest of the lactation. However, in spite of this count and clinical mastitis was weaker than in ‘bad’ drawback, the results of this study clearly indicate herds. This is in agreement with previous observa-that cows with the lowest SCC (less than 35,000 tions (McDermott et al., 1982) of variation in ability cells / ml) at initial test in first lactation are at the of SCC to predict intrammamary infection; the lowest probability to be affected by a clinical ability of SCC to predict mastitis is much lower in mastitis later in first lactation or at the beginning of herds with low infection incidence than in highly the second lactation. Because early SCC records and infected herds. In herds with few clinical cases, first clinical mastitis may be quite distant, risk ratio significant increase in risk of first case is observed of first event associated to the initial SCC classes only for ISCC above 75,000 cells / ml with some does not measure a direct biological effect; this unexpected results for the highest class of ISCC. statistical association more likely reflects a number Cows with initial SCC between 215,000 and 400,000 of general and more constant characteristics of the cells / ml appeared to be less likely to have a first cow, as udder and teat morphology for instance. mastitis than cows with somewhat lower ISCC. This Risk of first clinical mastitis gradually increases result, if confirmed, may indicate some protective for initial SCC in a range of 35,000 to 400,000 effect of subclinical infection. Protective effect of cells / ml. These results are in agreement with Coffey natural or induced pre-infection with minor patho-et al. (1986b), who observed that the rate of infection gens against challenge with major pathogens has in first and subsequent lactations could be related to been reported by Poutrel and Lerondelle (1980). the initial SCC. Recently, Beaudeau et al. (1998) However, in herds with high or low mastitis fre-pointed out that the relative risk of occurrence of quency, cows with the lowest initial SCC are always clinical mastitis increases with cell count prior to at the lowest risk of first mastitis.
a disease on culling: an illustration of the use of time
decreased SCC may be effective to reduce clinical
dependent covariates in survival analysis. J. Dairy Sci. 80,
mastitis incidence and that the breeding goal should
1755–1766.
favor cows with the lowest observed SCC. Kalbfleisch, J.D., Prentice, R.L., 1980. The Statistical Analysis of
Failure Time Data, Wiley, New York, NY.
Kehrli, Jr. M.E., Shuster, D.E., 1994. Factors affecting milk somatic cells and their role in health of the bovine mammary
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Lescourret, F., Coulon, J.B., 1994. Modeling the impact of
The authors are grateful to the Milk Recording mastitis on milk production by dairy cows. J. Dairy Sci. 77, agencies for providing clinical mastitis data. The 2289–2301.
Lund, T., Miglior, F., Dekkers, J.C.M., Burnside, E.B., 1994.
authors also thank V. Ducrocq for the computer
Genetic relationships between clinical mastitis, somatic cell
program and useful discussion and J.J Colleau, H.
count, and udder conformation in Danish Holsteins. Livest.
Seegers, E. Strandberg and P. Rainard for reading
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on sire evaluations for somatic cell score. J. Dairy Sci. 76(Suppl. 1), 238 (Abstr.).
McDermott, M.P., Erb, H.N., Natzke, R.P., 1982. Predictability by
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