Relationship of early first lactation somatic cell count with risk of subsequent first clinical mastitis

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1 Livestock Production Science 62 (2000) locate/ livprodsci 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, 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 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 premier velage ˆ 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 premier controle, ˆ 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 de niveau cellulaire haut ou bas. Le risque de mammite clinique apparaıt ˆ superieur autour du second velage, ˆ pour les lactations debutant en ete, ainsi que pour les fortes productrices. La probabilite de mammite clinique augmente de façon monotone avec la concentration cellulaire au premier controle ˆ 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.: ; fax: address: rupp@dga.jouy.inra.fr (R. Rupp) / 00/ $ see front matter 2000 Elsevier Science B.V. All rights reserved. PII: S (99)

2 170 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) initial le plus bas presentent le risque le plus faible de mammite clinique, et qu il n y a pas d optimum intermediaire 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). Because of its high incidence (Seegers H. et al., However, the relationship between SCC and mas- 1997a; Seegers J. et al., 1997b) and biological effects titis is far from being clear. Some authors (Coffey et (Schukken et al., 1997), mastitis is the most costly al., 1986a; Kehrli and Shuster, 1994; Schukken et al., disease in dairy cattle (Shook and Schutz, 1994). For 1994) were concerned by the recommendation of farmers, the economic consequences of mastitis continuously decreasing SCC by selection, and ar- include losses of milk production (Lescourret and gued that such a trend could impair the cow s Coulon, 1994) or milk sale, increased culling rates, capacity for leukocyte recruitment and, therefore, her cost for veterinary treatments, and higher Somatic ability to respond to intramammary infection. Ac- Cell Counts (SCC) in milk. Although management is cording to Kehrli and Shuster (1994), cows with very low SCC would be more susceptible to mastitis. the most effective way to prevent intramammary These arguments were based on early studies reportinfection, selection for mastitis resistance is an ing that moderate cell counts in milk play a protecalternative 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 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 (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 exusually 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, ), suggesting that some genes reduce both cell and 91,000 cells/ ml, respectively). Whereas seleccounts 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. and genetic evaluations are routinely published in To answer that question, some authors investiseveral countries (Schutz, 1994; Reents and Dekkers, gated the linearity of the relationship between both

3 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) 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 Recordpositive: one unit change in PTA corresponded to an ing technicians until March 1, 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 to1 8 days), Other authors investigated the relationship be- this was additionally stated. Consequently, the confitween 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 supa limited amount of data or did not 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 clininot 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 bihan and Finistere ` regions in Western France, with no mastitis occurred during the first lactation and if first calving between September 1, 1995 and August the cow did not calve a second time before March 1, 31, Cow calving dates, parity, test day milk 1997, her record was censored the day of her last test yield and SCC were extracted from the national data day. If a cow was still present in the herd and still base. The first test day record was required to have unaffected at the end of the period under study,

4 172 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) 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 record was also censored at March 1, Model estimate constant relative risks (RR) associated with 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: RR 5 l(t;w1)/l(t;w2) 5 l 0(t) exp(w19u )/l 0(t) exp(w29u ) 5 exp[(w1 2 w2)9 u ] 5 constant The baseline function l 0(t) was assumed to follow a Weibull hazard distribution: l (t) 5 lr( lt) The survival model used was the proportional hazard model (Cox, 1972), which is based on the concept of a hazard function l(t), where l(t) was the limiting probability for a cow to have her first mastitis at time t (expressed in days), given that she was still unaffected just prior to t. The hazard rate was defined as the product of a baseline hazard function l 0(t), which acts as an average hazard function, and of a function w of explanatory variables, including the initial SCC: with parameters l and r. This distribution is flexible and has been shown to usually adequately fit bio- logical data (Ducrocq et al., 1988a). In the Weibull l(t; w) 5 l (t) exp(w9u ) regression model, the hazard function can be sim- 0 plified as: where u is a vector representing the effects of the r 21 l(t; w) 5 rt exp(r log(l) 1 w9u ) explanatory covariables. Use of proportional model makes it possible to Therefore only the parameter r describes the 0 r 21

5 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) baseline hazard function and r 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) kg; (3) 23.5 effect and the fixed effects of lactation stage, month 25.5 kg; (4) kg; (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 management, bacteriological pressure, and other environmental changes. It was assumed to be random and to 2.3. Method 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 algebraiafter first calving, 10 days before and 30 days after cally integrating the herd-year effect out of the joint second calving, respectively. Such a definition al- posterior distribution. Finally, fixed (possibly timelowed 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 estiduring 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, r log(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 the baseline hazard function (Grohn et al., 1997). ˆ ˆ (1) E 5C(g ˆ) 2 log(g ˆ) and Var 5C (g ˆ), Month of first calving was treated as a fixed discrete variable with 12 levels, from September (1) where C(g ˆ) and C (g ˆ) are the digamma and the 1995 to August trigamma function evaluated at g, ˆ 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. quadratic terms. Regression coefficients were previ- The analysis was carried out on the complete data ously estimated from the data set. Then ISCC and set as well as on different herd subgroups. First, two IMY were treated as fixed categorical variables to subgroups were defined according to herd mastitis account for any nonlinear association with mastitis frequency (less or more than 20% affected lactaoccurrence. Six levels were defined for ISCC: (1) tions), which was calculated from data of all parities less than 35,000 cells/ ml; (2) 35,000 to 50,000 and all cows in each herd, even those not selected for cells/ ml; (3) 50,000 to 75,000 cells/ ml; (4) 75,000 the survival analysis. Secondly, two subgroups were to 150,000 cells/ ml; (5) 150,000 to 215,000 cells/ also defined according to herd somatic cell score ml; and (6) 215,000 to 400,000 cells/ml. Each of mean (below or above 3.4). Test day somatic cell

6 174 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) scores (SCS) were defined in a classical way through a logarithmic-transformation of test day SCC: SCS5 log 2(SCC/ 100,000)13 and a lactation mean of all test day SCS was computed for each cow. As for herd mastitis frequency, herd SCS mean was calculated from data of all parities and all cows in each herd. Separate analyses allowed to account for possible differences in the baseline hazard function and changes in effects of covariates. computed using Kaplan Meier s formula (8). This plot displayed a straight line, which validated the assumption of Weibull proportional hazard model. Likelihood ratio tests are shown in Table 1 for the complete data set. All factors were found to be highly significant Estimates of the Weibull parameters and of 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 checked by plotting the log[2s ˆ KM(t)] against log(t), selection of data required edit of cows censored where S ˆ (t) is the empirical survivor function before 35 days, i.e. cows with a clinical mastitis KM 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 # Full Model excluding IMY # Full Model excluding MC # Full Model excluding ST # a Full model includes effects of Herd-Year (HY), Month of First Calving (MC), Stage of Lactation (ST), Initial Somatic Cell Count (ISCC), and Initial Milk Yield (IMY).

7 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) Table 2 Estimates of r, r log(l) (standard errors) and g parameters 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, (0.02) (0.17) 2.21 a CM1 11, (0.05) (0.33) 4.99 b CM (0.03) (0.20) 4.81 c SCS1 10, (0.05) (0.26) 2.67 d SCS2 10, (0.05) (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. event or a SCC higher than 400,000 cells/ ml during exp( 12 Π0.55) ]] 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 was C(2.21)2log(2.21)5 mastitis frequency. (1) and the herd-year variance was C (2.21)5 Table 3 and Fig. 3 show the influence of initial Thus, relative mastitis rates for different herd- milk yield on mastitis frequency. Increased milk years varied roughly from exp(22 Π]] 0.55)50.23 to production at first test day was generally associated

8 176 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) 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 October ** 1.40** ** 1.09 November ** 1.35** 1.36** 1.36** 1.31** December January * 1.46** February ** March April ** ** 0.93 May ** June ** 2.47** ** 1.47** July ** 1.78* 1.47** 2.12** 1.11 August ** 2.48** 1.58** 1.73** 1.79** Initial SCC (in 1000 cells/ml) Less than ** ** ** ** 1.26** ** 1.31** 1.41** 1.48** 1.24** ** 2.00** 1.73** 1.79** 1.75** 215, , ** 1.51** 2.06** 1.80** 1.76** Initial milk yield (kg) Less than * ** * ** ** 1.36** 1.32** ** ** 1.23* 1.27** More than ** 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 **P#0.05 different from 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. the lowest producing cows (less than 21.5 kg). Table 3 and Fig. 4 present the relationship be- However, cows above the average for milk pro- tween initial somatic cell count and risk of first duction, but not in the highest category, appeared to mastitis. In the overall analysis, the relative hazard be less affected than average cows. The unfavorable gradually increased with ISCC. Cows with the effect of the highest yield was larger in herds with lowest ISCC, i.e., less than 35,000 cells/ml, were at

9 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) 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. lowest risk. When compared with cows with the with fewer clinical cases. In the latter herds, cows lowest ISCC, the relative hazard ratio reached 1.75 with the lowest ISCC remained at the lowest risk, for cows with the highest ISCC, i.e., between but no significant increase in risk was observed until 215,000 and 400,000 c/ ml. Estimates of the ISCC up to 75,000 cells/ ml. Relative hazard was sigeffect were similar for groups of herds with low or nificantly higher for ISCC from 75,000 to 215,000 high SCS level. In contrast, estimates of ISCC effect cells/ ml, and seemed to decrease for the highest revealed somewhat different patterns for herds with class, i.e., from 215,000 to 400,000 cells/ ml. This high mastitis frequency when compared with herds decrease was significant (P#0.05), i.e., cows with

10 178 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) 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 observations 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 characaround 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 observathat 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 pathoet 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 frepointed 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. mastitis. This relative risk reaches 2.4 for a SCC In this study, no increase in susceptibility to around 300,000 cells/ml compared with a SCC of clinical mastitis has been shown for very low SCC. 50,000 cells/ ml. The relative risks reported in the On the contrary, results suggest that selection for

11 R. Rupp, D. Boichard / Livestock Production Science 62 (2000) decreased SCC may be effective to reduce clinical a disease on culling: an illustration of the use of time mastitis incidence and that the breeding goal should dependent covariates in survival analysis. J. Dairy Sci. 80, favor cows with the lowest observed SCC. Kalbfleisch, J.D., Prentice, R.L., The Statistical Analysis of Failure Time Data, Wiley, New York, NY. Kehrli, Jr. M.E., Shuster, D.E., Factors affecting milk somatic cells and their role in health of the bovine mammary Acknowledgements gland. J. Dairy Sci. 77, Lescourret, F., Coulon, J.B., 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 authors also thank V. Ducrocq for the computer Lund, T., Miglior, F., Dekkers, J.C.M., Burnside, E.B., program and useful discussion and J.J Colleau, H. 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