Prevalence of clinical and subclinical bovine mastitis in Kashmir, India: Evaluating diagnostic methods and implications for udder health

Introduction

Bovine mastitis is a common and costly disease affecting dairy cattle worldwide. In Kashmir, where dairy farming is a vital part of the agricultural economy, understanding the prevalence of mastitis is crucial for developing effective control measures. Mastitis is an inflammation of the mammary gland characterized by pathological changes in the udder and abnormal compositional or visible changes in the milk of dairy animals. The pooled prevalence of subclinical mastitis and clinical mastitis in dairy animals is reported to be 42% and 15% worldwide, respectively, and 45% and 18% in India, respectively (Krishnamoorthy et al., 2021) [9] . The disease can occur in either clinical or subclinical form. Clinical mastitis presents with overt inflammatory signs such as heat, pain, redness, and swelling in the udder, along with visible abnormalities in the milk, while subclinical mastitis, the more common form, shows no visible signs but leads to reduced milk yield and alterations in milk composition. The disease causes an estimated annual economic loss of 100 trillion US dollars worldwide (Lal et al., 2022) [11] . The average case of clinical mastitis results in a total loss of 435 US dollars (Rollin et al., 2015) [16] . In India, a study over a decade ago reported an annual economic loss of Rs 7165.5 crores due to mastitis (Bansal and Gupta, 2009) [3] , which now likely reflects even greater economic damage. These losses are primarily due to decreased milk production, the need to discard contaminated milk, rising medication costs, increased management costs, and the premature culling and replacement of affected animals. Mastitis primarily results from intramammary infections, mainly caused by bacteria but occasionally by viruses, fungi, and algae (Krishnamoorthy et al., 2013, Cobirka et al., 2020; Ksouri et al., 2015,) [9,5,10] . It can also be induced by various factors, including injuries (Physical and chemical), toxic substances (e.g., Dicomural toxicity), allergies, and neoplasms (Knife Kibebew, 2017; Constable et al; 2017; Peek and Divers., 2018) [8,6,12] . Although various mastitis vaccines are commercially available, none of them provide sufficient protection and are not cost-effective at the same time (Sharun et al., 2021; Rainard et al., 2022, Tomanic et al., 2023) [17,15,20,15,20] . The short comings of vaccines have been attributed to low intensity of antigenic stimulus in subclinical cases, virulence and immune evasion tactics of pathogens, Dilution and quenching of defence factors and oxygen radicals by milk respectively, diversity of pathogenic isolates etc. (Reinard et al., 2022) [15] . Antibiotic therapy is the only solution for the disease but current Antibiotic based therapeutic interventions seem costly, risky and at times ineffective due to widespread resistance. The newer antibacterial drugs available against mastitis belong to third generation cephalosporins group considered highest priority critically important antimicrobials (HPCIA) for humans and discouraged for use in Farm animals (WHO 6 th revision, 2018) [23] . Hence, early diagnosis of mastitis cases is of paramount importance owing to the current shortcomings of mastitis vaccines and therapeutics globally and the problem of antibiotic resistance. This study provides a comprehensive assessment of mastitis prevalence in dairy cattle in Kashmir, India, and highlights the importance of using multiple diagnostic methods to achieve accurate detection. The findings emphasize the need for regular screening and early intervention strategies to improve udder health and ensure the sustainability of the region's dairy sector

Materials and Methods

Ethical approval

Thermal Imaging and milk sampling were performed as per the guidelines of the National Dairy Research Institute (NDRI) Animal Ethical Committee for care and use of experimental animals.

Study Design and Sample Selection:

This cross-sectional study was conducted in several districts of Kashmir, India, to investigate the prevalence of clinical and subclinical bovine mastitis. A total of 394 dairy cattle from both organised and unorganised farms across six districts of Kashmir were selected for examination. The sample size was calculated using prevalence rates from existing literature, with a 95% confidence level, using Fischer formula to ensure statistical significance, Random sampling and representativeness of the dairy cattle population in the region.

Sample Size Formula

The formula for calculating the sample size for estimating a population proportion in a large population is:

Where n is the sample size, Z is the Z-value for the desired confidence level (1.96 for 95% confidence), P is the expected prevalence and E is the margin of error (5%). Calculated Sample size for the study on the prevalence of mastitis across the various districts and blocks/tehsils in Kashmir were subjected to proportional allocation. Samples are distributed proportionally, based on the cattle population in each block or district, to ensure representation reflective of the actual population.

Inclusion and Exclusion Criteria

The study's inclusion criteria consisted of cross bred cows aged 2 years and above, of various dairy breeds such as Holstein and Jersey, and those in different stages of lactation. Conversely, the exclusion criteria excluded cows with concurrent diseases other than mastitis, as well as those currently undergoing treatment for mastitis.

Selection of Dairy Farms

Dairy units were randomly selected from each district and block to ensure a representative sample (Figure1).

Fig 1:

The image visualizes the geographic sampling strategy for the prevalence study, showcasing various districts of Kashmir and their respective blocks or tehsils where milk samples were collected

Thermal Imaging

Thermographic images were captured using a Teledyne FLIR E8XT WiFi infrared camera (Wilsonville, Oregon, USA). This camera features an auto-focus lens and can measure temperatures ranging from -20 °C to +550 °C. It boasts a 320 x 240 pixel resolution for both the detector and display, with a temperature accuracy of ±2% or ±2 °C, and a three-inch display screen. Images were taken at the udder level, maintaining a minimum distance of 0.5 meters from each udder side to ensure a clear view for analysis. All thermographic images were collected right before milking commenced.

Milk Sampling and Analysis

Following the thermal imaging, milk samples were collected from cows in the specified districts and blocks/tehsils of Kashmir, JK (Figure 1) involving the following steps:

1. Sample Collection: Milk samples were collected aseptically from all four quarters of the udder of each cow. The udder was cleaned and disinfected before sampling to avoid contamination. 2. Sample Handling: Sterile containers were used for milk collection, and each sample was labelled with the farm, district, and block details.

Transport and Storage:

The samples were transported to the laboratory on ice to prevent bacterial growth and preserve sample integrity.

Diagnostic tests

Approximately 10 mL of collected composite milk was used for diagnostic tests. These samples were immediately tested for the California Mastitis Test (CMT) score and somatic cell count using a De Laval automatic SCC Counter (Tumba, Botkyrka, Sweden. Composite milk samples with an SCC range from 200,000 to 1,200,000 cells per mililiter in crossbred cows were identified as positive for subclinical mastitis. Composite milk with a CMT score of 2 or higher and an SCC exceeding 1,200,000 cells per milliliter were classified as clinical cases (Lal et al., 2022) [11] . The samples were also subjected to Bacterial culture using standard laboratory techniques and selective media (Quinn et al.,

2002) [14] . Aseptically collected milk samples were cultured on selective media for mastitis-causing pathogens (Figure 3). Samples were incubated for 24-48 hours, and bacterial growth was identified and quantified according to standard microbiological procedures. Prevalence was calculated as the number of positive cases divided by the total number of animals examined, expressed as a percentage. The efficacy of each diagnostic method was evaluated by comparing the number of cases detected to the total number of confirmed cases (based on SCC and clinical examination).

Results

Analysis of Mastitis Detection Methods in Kashmir, India

This prevalence study compared the performance of four diagnostic methods for detecting mastitis in 394 dairy cattle in Kashmir, India. The results revealed that each method has its strengths and weaknesses, highlighting the importance of a multi-faceted approach to mastitis diagnosis. The study employed four diagnostic methods to detect mastitis in dairy cows, each with its advantages and limitations. Somatic Cell Count (SCC) method used as standard test detected 141 cases, including 37 clinical and 104 subclinical (Figure 1), and is the most effective method for detecting subclinical mastitis, although it also requires specialized equipment and expertise. The California Mastitis Test (CMT) detected 85 cases, including 33 clinical and 52 subclinical, and is relatively simple and cost-effective, although its accuracy may be compromised for subclinical mastitis (Table 1). The prevalence of mastitis was found to be 35.80 percent in Kashmir (26.4% subclinical mastitis and 9.40% clinical mastitis) based on SCC (Figure 2). Bacterial culture and Pathogen Identification Out of 141 samples detected positive by SCC method, Bacterial pathogens were isolated successfully from 98 (68%) samples on selective media (Figure 3). The most common pathogens isolated from mastitis cases were mainly Staphylococcus aureus (73%) and Escherichia coli (24%).

Other pathogens detected included Klebsiella species (3%) (Figure 4).

Infrared thermography

It examines the udder skin surface temperature (USST) in crossbred cows, focusing on the differences between healthy and infected quarters under varying ambient temperatures while maintaining a constant core body temperature. The study findings reveal distinct temperature patterns in udder quarters based on health status. For healthy udder quarters, the average Udder Surface Temperature (USST) at an ambient temperature of 30 °C was 37.10 °C, with a standard deviation of ±0.2 °C, indicating a slight increase that correlates with environmental conditions and reflects the body's thermoregulatory response. In contrast, infected udder quarters exhibited higher temperatures: subclinical cases had an average USST of 37.90 °C (±0.4 °C) (Figure 5), significantly elevated due to the inflammatory response, while clinical cases showed an even greater average USST of 38.20 °C (±0.2 °C), suggesting a more severe inflammatory reaction associated with the progression of the infection. Infrared Thermography detected 118 cases, including 36 clinical and 82 subclinical, and is effective at detecting subclinical mastitis, but requires specialized equipment and expertise. The sensitivity, specificity, true positives (TP), false negatives (FN), true negatives (TN), and false positives (FP) for each test was calculated as shown in table 2.

Accuracy: With the total actual Mastitis cases being 141 as detected by somatic cell count, the accuracies for other detection method are as follows: The California Mastitis Test (CMT) has an accuracy of 60.28%, Infrared Thermography reaches 83.69%, and Bacterial Culture shows an accuracy of 68.09%. These percentages indicate the effectiveness of each method in detecting cases of mastitis relative to the total known cases. This table provides a clear comparison of the effectiveness of each method in detecting mastitis, showcasing both the sensitivity and specificity of each testing approach (Figure 6 &7)

Risk Factors

Analysis of risk factors revealed that poor udder hygiene, inadequate milking practices, and lack of proper nutrition were significantly associated with higher incidence and prevalence of mastitis. Farms with better management practices showed lower rates of infection.

Statistical Analysis

The SCC (Somatic cell count method) is considered the gold standard for detection, with an accuracy of 100%. Infrared Thermography emerges as the most accurate among the other methods, with an accuracy of 83.69%, Bacterial culture method has 68% sensitivity while CMT test has the lowest accuracy at 60.28%, indicating it is the least reliable method overall. The Chi-square test for independence reveals a significant difference in the detection rates among the four methods. The null hypothesis, which states that there is no significant difference in the detection rates, is rejected, as the Chi-square statistic of 23.76 and the p-value of less than 0.0001 suggest a statistically significant difference in the detection rates among the four methods. Additionally, among 96 bacterial isolates, the pathogen distribution analysis shows a predominance of Staphylococcus aureus (70 isolates), followed by Escherichia coli (23 isolates) and Klebsiella species (3 isolates). The chi-square goodness-of-fit test (χ² = 91.94, Degree of freedom:2, p< 0.0001) indicates this distribution is significantly different from what would be expected by chance, highlighting the importance of targeting these specific pathogens in mastitis prevention and treatment strategies.

Discussion

Mastitis remains a significant challenge in dairy farming globally, impacting both the health of dairy cows and the profitability of dairy operations. Early diagnosis is important to curtail the infection. For the effectiveness of different mastitis detection methods, it's crucial to consider the sensitivity and specificity of each technique, as revealed by the data collected from testing 394 milk samples. The Somatic Cell Count (SCC) method demonstrated the highest effectiveness, achieving 100% in both sensitivity and specificity. This aligns with research that highlights SCC as a highly reliable indicator of mastitis, particularly in identifying subclinical infections that might not be detectable through physical symptoms alone (Smith et al., 2018) [19] . The complete absence of false negatives and false positives in this method underscores its precision and reliability in field conditions. Infrared Thermography also showed high sensitivity at 83.69%, indicating its strong capability in identifying mastitis cases, particularly in the early stages when physical symptoms are not yet prominent. This method's high sensitivity makes it an excellent tool for early detection, which is crucial for effective management and treatment, reducing potential losses in dairy production. These findings underscore the utility of measuring USST as a diagnostic tool for detecting mastitis in dairy cows. The elevated temperatures in infected quarters, regardless of the stage of infection, reflect the physiological changes associated with inflammation. This method provides a noninvasive way to screen for udder health, enabling early detection and management of mastitis, which is crucial for [21] reported an overall prevalence of clinical mastitis of 8.08% in Kashmir. Dar et al. (2014) [7] found the prevalence of subclinical mastitis in cows to be 51.61% on a quarter basis in J&K. [4] observed the incidence of clinical mastitis in bovines of the Jammu region to be 11.5% on an animal basis and 5.76% on a quarter basis. Qadri et al.

(2017) [13] reported the prevalence of subclinical mastitis as 45% in crossbred Jersey cows at the Mountain Livestock Research Institute (MLRI) in Mansbal. Ashraf et al. (2018) [2] found the prevalence of subclinical mastitis in cattle from the Kashmir Valley during the winter season to be 47.58% on an animal basis and 26.67% on a quarter basis. Ali et al.

(2021) [1] reported an overall prevalence of subclinical mastitis in crossbred Holstein-Friesian cows to be 81.48% in the Ganderbal district. The high prevalence of bovine mastitis in Kashmir necessitates improved management strategies. With over one-third of dairy cattle affected, targeted antibiotic therapy for Staphylococcus aureus and E. coli, along with enhanced hygiene practices, are crucial. Infrared Thermography is the most effective method for detecting subclinical mastitis, and a multi-method approach is recommended for accurate diagnosis. Regular screening and early intervention are vital to improve udder health and reduce economic impact.

Comprehensive control programs, including vaccination and farmer education, are essential for sustainable dairy production.

Further research is needed to investigate the costeffectiveness of different diagnostic methods, evaluate the impact of mastitis control programs, and explore novel diagnostic tools and technologies for mastitis detection. This study provides valuable insights into the prevalence and diagnosis of mastitis in Kashmir and underscores the need for tailored control strategies to ensure sustainable dairy production.

Conclusion

This study of mastitis prevalence in Kashmir, India, reveals significant variations in the effectiveness of different diagnostic techniques. The Somatic Cell Count (SCC) method proved most accurate with perfect sensitivity and specificity, particularly for subclinical cases. Infrared Thermography showed high sensitivity, while Bacterial Culture was valuable for pathogen identification, highlighting the prevalence of Staphylococcus aureus. The California Mastitis Test, though simple, was the least reliable. These findings highlight the importance of a multifaceted approach to mastitis detection, balancing accuracy with practical and economic considerations. This research provides a foundation for improving animal health, milk quality, and economic outcomes in the dairy industry, and points to future areas for investigation, including costbenefit analyses and novel diagnostic technologies.

Acknowledgement

AcknowledgmentsThe authors would like to acknowledge the support provided by Disease Investigation Officers, Incharge District Diagnostic Laboratories, of the Animal Husbandry Department Kashmir for the research work.

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