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. 2012 Nov 23;53(3):315–320. doi: 10.1007/s12088-012-0336-1

Incidence of Subclinical Mastitis and Prevalence of Major Mastitis Pathogens in Organized Farms and Unorganized Sectors

Raveendra Hegde 1,4, Shrikrishna Isloor 1, K Nithin Prabhu 1, B R Shome 2, D Rathnamma 1, V V S Suryanarayana 3, S Yatiraj 1, C Renuka Prasad 4, N Krishnaveni 1, S Sundareshan 1, D S Akhila 1, A R Gomes 4, Nagendra R Hegde 5,✉
PMCID: PMC3689408  PMID: 24426129

Abstract

Subclinical mastitis (SCM) represents a major proportion of the burden of mastitis. Determining somatic cell count (SCC) and electrical conductivity (EC) of milk are useful approaches to detect SCM. In order to correlate grades of SCM with the load of five major mastitis pathogens, 246 milk samples from a handful of organized and unorganized sectors were screened. SCC (>5 × 105/mL) and EC (>6.5 mS/cm) identified 110 (45 %) and 153 (62 %) samples, respectively, to be from SCM cases. Randomly selected SCM-negative samples as well as 186 samples positive by either SCC or EC were then evaluated for isolation of five major mastitis-associated bacteria. Of the 323 isolates obtained, 95 each were S. aureus and coagulase-negative staphylococci (CoNS), 48 were E. coli and 85 were streptococci. There was no association between the distribution of organisms and (a) the different groups of SCC, or (b) organised farms and unorganised sectors. By contrast, there was a significant difference in the distribution of CoNS, and not other species, between organized farms and unorganized sectors. In summary, bacteria were isolated irrespective of the density of somatic cells or the type of farm setting, and the frequency of isolation of CoNS was higher with organized farms. These results suggest the requirement for fine tuning SCC and EC limits and the higher probability for CoNS to be associated with SCM in organized diary sectors, and have implications for the identification, management and control of mastitis in India.

Keywords: Subclinical mastitis, Somatic cell count, Electrical conductivity, Major bacterial pathogens causing mastitis

Mastitis is the major lactation-associated disease that causes serious losses to the dairy industry globally, including India. Clinical mastitis is easy to detect and hence amenable for immediate treatment. Subclinical mastitis (SCM), on the other hand, is an invisible malady, and routine surveillance and monitoring is necessary for its detection. Unfortunately, SCM represents a significant proportion (20–25 %) of the burden of mastitis in modern dairy management [1]. The cost of SCM is very difficult to quantify, but is definitely more than that due to clinical mastitis. Approximately 70 % of the cost is associated with reduction in milk production [2]. A survey conducted about 20 years ago estimated that in India, the average decrease in milk yield due to clinical and subclinical mastitis was 50 and 17.5 %, respectively [3]. The same survey estimated the economic loss to be Rs. 6,038.7 and 4,831 millions due to subclinical mastitis and Rs. 2,856.4 and Rs. 2,345.9 millions due to clinical mastitis in cattle and buffaloes, respectively. The total loss due to both forms of mastitis has increased almost five-fold from Rs. 16,072 millions [3] to Rs. 71,655 millions [4] in a span of just 15 years.

While the cross-breeding programme was a major reason for thrusting India to be the highest milk producer in the world, the associated decrease in threshold for susceptibility to diseases has possibly increased the incidence of mastitis. The dairy owner needs to be cognizant of this fact and needs to perform routine screening of milk samples to devise appropriate management practices. Tests such as somatic cell count (SCC) and electrical conductivity (EC) can be used as reliable tools for the detection of SCM in individual herds [5–7]. However, it is not clear whether the cut-off values used for these tests to declare positivity in other countries are correlative for local breeds, crossbreds and non-descript cattle and buffaloes in different husbandry settings in India. Based on the impact on cow health, milk quality and productivity, five pathogens (Staphylococcus aureus, Streptococcus agalactiae, Strep. dysgalactiae, Strep. uberis and Escherichia coli) are considered to be the major mastitogens in most countries, and similar observations have been made with SCM cases in India [8–12]. However, a comprehensive study investigating the correlation between the grade of SCM and the type and density of organisms involved either alone or in combination is lacking anywhere in the world, let alone in India.

The present study was carried out to examine the association between the extent of SCM, different groups of SCC, distribution of the major mastitis pathogens in bovine milk samples and the kind of dairy sector viz., organized or unorganized. This approach is of significance in detection and effective management of SCM in India.

Materials and Methods

Following strict aseptic measures, 246 cattle milk samples were collected over a period of 13 months (September 2009 to October 2010) from four organized farms and three unorganized sectors from various geographic locations in and around the metropolitan setting of Bengaluru (see Table 1). The milk samples were immediately transported to the laboratory in cold chain and screened for SCM by SCC and EC, using Nucleocounter (ChemoMetec A/S, Denmark) and Milk Checker (Oriental Instruments Ltd., Japan), respectively.

Table 1.

Number of samples under various SCC and EC values

Setting No. of milk samples SCC values EC values
0–1 × 105/mL 1–2 × 105/mL 2–5 × 105/mL >5 × 105/mL <6.5 mS/cm >6.5 mS/cm
Organised farm A 10 Nil Nil 1 9 1 9
Organised farm B 17 1 1 1 14 9 8
Organised farm C 61 9 3 11 38 7 54
Organised farm D 27 10 4 4 9 4 23
Unorganised sector A 75 38 7 9 21 43 28
Unorganised sector B 30 11 1 8 10 12 18
Unorganised sector C 26 9 3 4 10 16 10
Total 246 78 19 38 111 92 150
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All bacteriological media (Colloids Impex Pvt. Ltd., Bengaluru), and reagents for biochemical tests (HiMedia Laboratories, Mumbai) were obtained from commercial sources.

For isolation of staphylococci, milk samples were initially enriched in brain heart infusion (BHI) broth for 6 h at 37 °C and then streaked onto mannitol salt agar and incubated at 37 °C for an additional 24 h. After recording the colony morphology, the colonies were re-streaked onto BHI agar for further identification procedures.

Isolation of streptococci was carried out by enrichment in streptococcus selection broth for 6 h followed by streaking onto blood agar plates and incubating in 5 % CO2. After recording the pattern of haemolysis and the colony morphology, the colonies were re-streaked onto blood agar plates and incubated further at 37 °C for 48 h in 5 % CO2 to obtain pure cultures. The pure cultures were then streaked onto BHI agar for further identification procedures.

For isolation of E. coli, initial enrichment was carried out in tryptone phosphate broth for 18 h at 37 °C, followed by streaking onto MacConkey agar and incubation at 37 °C for 24 h. The lactose fermenting colonies were further streaked onto Eosin Methylene Blue agar and incubated at 37 °C for 24 h. The metallic sheen colonies were streaked onto BHI agar for further identification procedures.

Pure cultures were subjected for various biochemical tests as per standard procedures [13]. For identification of streptococci, catalase, Voges Proskauer (VP), pyrrolidonyl arylamidase, hippurate hydrolysis, esculin hydrolysis and sugar fermentation tests were employed. For staphylococci, catalase, coagulase, thermonuclease, urease, and VP tests were employed and for E. coli, nitrate, indole, methyl red, VP, citrate and urease production tests were employed.

The distribution of isolates was subjected to two-way analysis of variance with Bonferroni post tests at P < 0.05, P < 0.01 and P < 0.001 against various groups of SCC using GraphPad Prism software version 5 (GraphPad Software Inc., USA).

Results and Discussion

Since direct microbiological investigation is not feasible, indirect tests are necessary to identify intra-mammary infections (IMI). The gold standard is to measure inflammation through cytological investigation [6], i.e., counting somatic cells. In addition, “cow side” tests such as the California Mastitis Test and measuring the EC of milk can also be used. In order to test for correlations between SCC and/or EC results and SCM of specific aetiology, or for the ability of the results to predict the incidence of SCM in organized or unorganized sectors, clinical diagnostic (SCC and EC) and microbiological analyses were performed on 246 milk samples in this study.

The International Dairy Federation recommends that the diagnosis of mastitis be based on the SCC and microbiological status. The acceptability break point for SCC is 5 × 105 cells/mL [14], but different cut-off values are adopted by different countries. In the European Union, Australia and New Zealand, the penalty limit for saleable milk is 4 × 105 cells/mL, whereas Canada, the US and Sweden use 5 × 105, 7.5 × 105 and 2 × 105 cells/mL limit, respectively [15]. Since no standards are adopted in India, the cut-off of 5 × 105 cells/mL was applied in this study, when 45 % of the 246 milk samples were positive for SCM. Further, 78 (31.7 %), 19 (7.7 %), 38 (15.4 %) and 111 (45 %) samples grouped under 0–1 × 105, 1–2 × 105, 2–5 × 105 and >5 × 105cells/mL, respectively (Table 1). With the same samples, 92 (38 %) were negative and 150 (62 %) were positive for SCM by EC, considering >6.5 mS/cm as the cut-off (Table 1). When either of the two tests was applied, 186 samples were positive for SCM. These results show that in the sampled location, SCM was prevalent anywhere between 45 % (by SCC alone) and 75.6 % (when both methods were combined). Further analyses revealed that on an average 61 % of the samples from organized farms showed SCM, although the level varied from 33 to 90 % between individual farms.

All the samples positive for SCM by either of the methods, as well as random negative samples were subjected to microbiological evaluation. Of the total 323 bacterial isolates recovered from the 186 milk samples (Table 2), staphylococci were the most frequent (95 isolates each of S. aureus and CoNS) followed by streptococci (85 isolates) and E. coli (48 isolates). In general, milk samples with SCC >5 × 105/mL, irrespective of their source from organised or unorganised sector, showed high prevalence of bacterial pathogens (Table 2, Fig. 1). The number of isolates at 5 × 105 SCC was statistically significant from all the other SCC categories for streptococci (p < 0.05 for all the pair-wise comparisons), S. aureus (P < 0.05 for >5 × 105 with 0–1 × 105 as well as 2–5 × 105; p < 0.01 for 5 × 105 with 1–2 × 105), and CoNS (p < 0.05 for >5 × 105 with 02–5 × 105 and p < 0.01 for 5 × 105 with 0–1 × 105 as well as 1–2 × 105). No significant difference was observed for E. coli (Fig. 1). In organized farms, the only significant difference observed was with CoNS when the >5 × 105 SCC category was compared to 0–1 × 105 (p < 0.01), 1–2 × 105 or 2–5 × 105 (p < 0.05) categories (Table 3; Fig. 2a). In the unorganized sector, the only significant difference observed was with S. aureus when the >5 × 105 SCC category was compared with 1–2 × 105 (p < 0.05) category (Table 3, Fig. 2b).

Table 2.

Distribution of organisms in different settings

Farm/Sector code No. of milk samples Streptococci S. aureus CoNS E. coli
Organised farm A 10 14 4 8 8
Organised farm B 17 2 3 11 1
Organised farm C 61 16 19 40 6
Organised farm D 27 10 5 14 5
Unorganised sector A 75 8 24 14 7
Unorganised sector B 30 19 22 5 14
Unorganised sector C 26 16 18 3 7
Total 246 85 95 95 48
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Fig. 1.

Fig. 1

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Distribution of organisms in different groups of SCC. Milk samples were subjected to SCC, which was then categorized arbitrarily into four different groups as shown by the different bars. Milk samples were subjected to microbiological evaluation and distribution of the four major bacterial species/groups in each SCC group is shown. a compared with 0–1 × 105 cells, b compared with 1 × 105–2 × 105 cells, c compared with 2 × 105–5 × 105 cells. *P < 0.05, **P < 0.01, ***P < 0.001

Table 3.

Distribution of isolates according to SCC

Farm somatic cell count (SCC)
0–1 × 105/mL 1–2 × 105/mL 2–5 × 105/mL >5 × 105/mL
Strepto Sau EC CoNS Strepto Sau EC CoNS Strepto Sau EC CoNS Strepto Sau EC CoNS
Organised farm A 0 0 0 0 0 0 0 0 1 0 0 0 13 4 8 11
Organised farm B 0 0 0 0 0 0 0 0 0 0 0 0 2 6 1 11
Organised farm C 1 0 0 0 0 1 1 1 5 1 0 7 10 8 5 29
Organised farm D 3 3 1 2 1 0 1 4 1 0 3 2 5 2 0 6
Total for organised farms 4 3 1 2 1 1 2 5 6 1 3 9 30 20 14 57
Unorganised A 1 5 1 3 2 2 0 5 0 5 1 3 5 15 5 3
Unorganised B 5 4 7 3 1 1 0 0 5 7 3 2 8 10 4 0
Unorganised C 2 3 3 0 3 3 2 0 3 2 1 2 8 10 1 1
Total for unorganised sector 8 12 11 6 6 6 2 5 8 14 5 7 21 35 10 4
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Strepto streptococci, SauStaph. aureus, ECE. coli,CoNS Coagulase negative staphylococci

Fig. 2.

Fig. 2

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Distribution of organisms in organized and unorganized sectors according to groups of SCC. Milk samples obtained from organized farms (a) and unorganized sector (b) were subjected to microbiological evaluation and the number of isolates of each of the major bacterial species/groups are shown under four different arbitrary SCC groups. a compared with 0-1 × 105 cells, b compared with 1 × 105–2 × 105 cells, c compared with 2 × 105–5 × 105 cells. *P < 0.05, **P < 0.01, ***P < 0.001

Surprisingly, pathogens could be isolated even with very low SCC of 10,000–25,000/mL. Out of 17 such milk samples, five isolates of streptococci, ten isolates of staphylococci, five isolates of CoNS and eight isolates of E. coli were obtained. Similar observations were made with EC, which increases variably from the normal range of 5.5–6.5 mS/cm during SCM [16–19]. Out of 58 samples with EC values < 6.5 mS/cm, nine isolates of streptococci, 32 isolates of staphylococci, 21 isolates of CoNS and six isolates of E. coli were obtained. Thus, lower SCC or EC could be misleading in accurately reflecting the bacteriological status or udder health, although it is noteworthy that we did not assess the potential of these isolates to be pathogenic. However, the analyses are limited to a few hundred samples from a small region in South India. Moreover, no effort was made to restrict the sampling to species (cattle, buffalo), breed, age, parity or the time of lactation. Since India has different agro-climatic conditions under various kinds of husbandry practices, it would be difficult to extrapolate these data to other parts of the country.

The distribution of S. aureus, CoNS, streptococci and E. coli observed in this study is in accordance with earlier reports from India [8–10, 12] and elsewhere [8, 20, 21]. The presence of S. aureus in almost a third of the samples buttressed its importance in SCM. Indeed a recent metagenomic study of SCM revealed the preponderance of E. coli in the two indigenous breeds and S. aureus in cross-bred cattle in India [22]. Streptococci could not be identified to species level despite conducting a thorough biochemical investigation. Similar confounding observations have been reported by others, especially with the hydrolysis of esculin and the Christie Atkins Munch Peterson (CAMP) test [23–27]. Furthermore, since several atypical streptococci also test positive in biochemical tests, the identification of Streptococcus at species level is difficult.

Prior to the 1970, CoNS were regarded as contaminants in clinical specimens [28]. Of the more than 40 staphylococcal species described [29], CoNS are often considered as minor pathogens, with insignificant or little impact on udder health [28]. However, recent reports suggest that CoNS have become the predominant pathogens isolated from SCM and may cause substantial herd problems in many countries [1, 30–32]. In Finland, CoNS were isolated from 17 % of all the samples and from 50 % of the bacteriologically positive quarters [33]. In two dairy research herds in Ontario, Canada, CoNS were the most common (51 %) bacteria causing IMI at drying off [29]. In the present study, 29.4 % of the isolates were CoNS, a finding which is in agreement with other reports [31, 34]. In organized farms, a significant difference (p < 0.01 or p < 0.05) was observed in the distribution of CoNS between the different SCC groups, whereas distribution of other organisms was similar. Interestingly, in unorganized sector, there was a random distribution of the organisms, except that more number of S. aureus isolates was recovered, the only significant difference being that between 5 × 105 and 1–2 × 105 SCC categories (p < 0.05). If extended and confirmed, the higher frequency of CoNS in organized farms and that of S. aureus in unorganized sectors could form one of the criteria for management of SCM in organized versus unorganized sectors in India.

In summary, although generally accepted cut-off values for SCC and EC may be applied, baseline parameters need to be established and/or fine-tuned for each breed and/or agro-climatic region in India. Simultaneous quantitative estimation, and not merely the qualitative detection, of the major pathogens as well as determining their virulence potential may be required. This would enable a reasonably accurate and realistic determination of the status of SCM and in turn enable implementation of mastitis control programmes to ensure quality milk production.

Acknowledgments

This work was funded by the Indian Council of Agricultural Research under the National Agriculture Innovation Projects Scheme. We thank Avinash Bhat, Dept. of Pharmacology, Veterinary College, Bengaluru, for assistance with statistical analysis.

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