Prevalence of Bovine Mastitis and Isolation of Staphylococcus aureus from Cow Milk in Dessie Town, Northeast Ethiopia

Prevalence of Bovine Mastitis and Isolation of Staphylococcus aureus from Cow Milk in Dessie Town, Northeast Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prevalence of Bovine Mastitis and Isolation of Staphylococcus aureus from Cow Milk in Dessie Town, Northeast Ethiopia Sossina Ashenafi, Habtamu Tassew, Halo Yohannes, Tigist Berie, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6568137/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Bovine mastitis remains one of the most prevalent and economically significant diseases affecting dairy cattle worldwide. In Ethiopia, limited data exist on its prevalence, associated risk factors, and causative pathogens in emerging dairy farms such as Dessie Town. This study aimed to determine the prevalence of bovine mastitis, identify associated risk factors, and isolate Staphylococcus aureus from mastitic milk samples in Dessie Town, Northeast Ethiopia. Methods: A cross-sectional study was conducted on 304 lactating dairy cows across smallholder farms and semi-intensive farms. Clinical examination and the California mastitis test (CMT) were used to detect clinical and subclinical mastitis, respectively. Risk factors were assessed via structured questionnaires and analysed through univariable and multivariable logistic regression. A bacteriological analysis of 322 mastitic milk samples was performed for the isolation of Staphylococcus aureus via standard microbiological techniques. Results: The cow-level prevalence of mastitis was 61.18%, with the prevalence of subclinical cases (48.68%) far exceeding that of clinical cases (12.17%). At the quarter level, 30.75% of the teats were affected. The right rear quarter showed the highest incidence (34.21%), whereas the left front quarter was least affected (27.63%). Multivariate logistic regression revealed that breed (p=0.001), parity (p=0.001), milking hygiene (p=0.017), and teat end morphology (p=0.003) were significant predictors of mastitis. Staphylococcus aureus was isolated from 35.71% of mastitis-positive milk samples, including 37.39% from clinical samples and 35.06% from subclinical samples. Conclusion: This study revealed a high burden of both clinical and subclinical mastitis in dairy farms around Dessie Town, with S. aureus being a leading pathogen. The strong association between mastitis and risk factors such as hygiene and teat anatomy highlights the need for integrated control measures, farmer training, and routine screening to improve udder health and milk quality. Biological sciences/Microbiology Health sciences/Diseases Bovine mastitis Ethiopia Dairy cattle Risk factors Staphylococcus aureus Subclinical mastitis Prevalence Background Ethiopia has the largest livestock population in Africa, with cattle playing a central role in the livelihoods and food security of rural communities. Among the national cattle herd, approximately 55.90% are female, with approximately 20.7% being lactating cows, which are primarily composed of indigenous breeds, and a smaller proportion of crossbreeds and exotics introduced to improve dairy productivity[ 1 ]. While the dairy sector is gradually transitioning from traditional extensive systems to semi-intensive and intensive production in peri-urban areas, infectious diseases remain a major constraint on productivity, milk quality, and animal health [ 2 , 3 ]. Bovine mastitis is one of the most prevalent and economically important diseases affecting dairy cows globally, with significant impacts on milk yield, quality, animal welfare, and public health [ 4 ]. It is characterized by inflammation of the mammary gland parenchyma and exists in clinical and subclinical forms [ 5 , 6 ]. Clinical mastitis is easily detectable due to visible symptoms such as swelling, redness, pain, and abnormalities in milk, including clotting or discolouration[ 7 ]. In contrast, subclinical mastitis presents no visible signs on the udder or in the milk, making it more challenging to diagnose. Despite the absence of obvious symptoms, subclinical mastitis can significantly impact milk production and quality, often requiring laboratory tests for detection. Depending on the mode of transmission, mastitis can be classified as environmental—linked to pathogens present in the cow’s surroundings—or contagious, where the infection spreads primarily during milking from infected udders [ 8 ]. Among the numerous pathogens associated with contagious mastitis, Staphylococcus aureus is one of the most frequently isolated bacteria. It is a gram-positive, coagulase-positive, opportunistic pathogen that persists in the mammary gland, evades host immune responses, and leads to chronic and recurrent infections[ 9 , 10 ]. Its ability to form biofilms and develop resistance to antimicrobials poses a dual threat to animal health and public health. Infected milk, especially in regions where raw milk consumption is common, serves as a reservoir for zoonotic transmission and foodborne illness[ 11 , 12 ]. In Ethiopia, several studies have reported S. aureus as a major etiological agent in bovine mastitis, which is often isolated in both clinical and subclinical cases [ 13 – 16 ]. The economic consequences of S. aureus -associated mastitis are considerable. These include milk losses, treatment costs, premature culling of affected animals, reduced milk shelf-life, and drug residues in milk [ 4 , 17 ]. Furthermore, the spread of antimicrobial-resistant strains of S. aureus exacerbates concerns regarding food safety and effective treatment options, aligning with the broader challenges addressed through alternatives such as herbal medicine[ 18 , 19 ]. Despite increasing recognition of this issue, region-specific data are lacking in many emerging dairy hubs in Ethiopia. Although some studies have examined mastitis prevalence and etiology in various parts of Ethiopia, limited recent evidence exists from Dessie Town and surrounding areas in the Amhara Region. Local data on the prevalence of both clinical and subclinical mastitis, risk factors, and the role of S. aureus in disease occurrence are essential for designing effective control strategies tailored to regional conditions. Therefore, the aim of this study was to determine the prevalence of bovine mastitis, identify associated risk factors, and isolate Staphylococcus aureus from mastitic milk in lactating dairy cows in Dessie Town, Northeast Ethiopia. Methods Description of the study area The study was conducted in Dessie town, which is located in the South Wollo Zone of the Amhara Region, Northeast Ethiopia. Dessie lies approximately 401 km north of Addis Ababa at an elevation ranging from 2,470 to 2,550 m above sea level. The town is situated at 11°8′N latitude and 39°38′E longitude, with an average annual temperature of 9°C[20]. Study population and husbandry practices A total of 304 lactating dairy cows, comprising both local and crossbred (Holstein Friesian × local zebu) animals, were selected from 28 dairy farms employing either intensive or semi-intensive management systems. Cows in intensive systems were housed indoors and provided with concentrated feed, hay, green forage, and crop residues. Semi-intensively managed cows were allowed to graze during the day and were supplemented with feed during morning and evening milking sessions. Cows were categorized on the basis of age (young: 8 years), parity (few: ≤3 calves, moderate: 4–6 calves, many: ≥7 calves), lactation stage (early: ≤121 days, middle: 121–240 days, late: >240 days), and milk yield (low: 9 L/day). Teat end morphology and body condition scores were also recorded via standard classification systems reported in prior studies[21,22]. Source of Animals Milk samples were collected from dairy cows maintained in intensive and semi-intensive dairy farms located in Dessie Town, Northeastern Ethiopia. The source of dairy cows was not purchased rather used from farm owners rearing their own dairy farms in the study area. All the animals used were under normal farm management with no experimental treatment or intervention. Ethical Consideration All the experimental procedures using animals were reviewed and cleared by the Research and Ethics Committee of the College of Agriculture and Environmental Sciences, Bahir Dar University (Ref No: 1/Vetsc/342/1-1-3, dated 15 October 2024). All the procedures were conducted following applicable institutional, national, and international guidelines and regulations, including the OIE Manual for Serum Sample Collection[23] and ARRIVE guidelines 2.0 [24]. Human Ethics Statement All experimental protocols were approved by College of Agriculture and Environmental Sciences, Bahir Dar University Research and Ethics Committee. Informed consent was obtained from all subjects and/or their legal guardian(s). Study Design and Sampling Techniques A cross-sectional study was conducted from December 2023 to July 2024 to estimate the prevalence of bovine mastitis, identify associated risk factors, and isolate Staphylococcus aureus from mastitic milk. The sample size was calculated via the formula described by Thrusfield [25]. A total of 28 dairy farms were selected via convenience sampling, and 304 lactating cows were then randomly chosen via the lottery method. Physical Examination and Milk Sample Collection Each udder was visually inspected and palpated to detect clinical signs such as swelling, heat, fibrosis, or atrophy. Milk from each quarter was examined for abnormalities, including blood, clots, or flakes. For subclinical mastitis, the California mastitis test (CMT) was used. Before sampling, the teats were cleaned with water and dried with disposable towels. Each teat was disinfected with 70% alcohol, starting with healthy quarters and ending with suspected infected quarters to prevent cross-contamination. The first 3–4 streams of milk were discarded, and approximately 10 mL of milk was aseptically collected into sterile universal bottles, which were held at an angle to prevent contamination. The samples were stored in iceboxes and transported to the Veterinary Microbiology Laboratory at Wollo University. The clinical samples were cultured immediately, while the other samples were stored at 4 °C for a maximum of 24 hours prior to processing. California mastitis test (CMT) Subclinical mastitis was detected via the CMT as described by Moawad et al. [26]. Equal volumes of milk and CMT reagent were mixed in the CMT paddle, and the degree of gel formation was scored as 0 (negative), trace, + (weak), ++ (distinct), or +++ (strong). Quarters scoring + or higher were considered positive. Cows were classified as subclinically infected if at least one quarter tested positive[26]. Questionnaire Survey A semistructured questionnaire was administered to 65 respondents across the 28 farms to gather information on herd-level and animal-level factors. These included farm hygiene, milking practices, floor type, breed, age, parity, body condition score, lactation stage, teat morphology, milk yield, history of mastitis, concurrent illnesses, dry cow therapy, and udder/milk abnormalities. Isolation and identification of Staphylococcus aureus Isolation of S. aureus was performed following the procedures described by Zang et al. [27]. Ten microliters (0.01 mL) of milk were streaked onto 7% sheep blood agar and incubated aerobically at 37 °C for 24 hours. Colonies were evaluated for morphology and hemolysis and then subcultured on nutrient agar for further purification. Gram-positive cocci showing catalase-positive reactions were tested via slide or tube coagulase tests. Coagulase-positive colonies were further confirmed by growth on mannitol salt agar. Data analysis All the data were entered into Microsoft Excel and analysed via STATA version 14 (StataCorp, College Station, TX, USA). Descriptive statistics were used to calculate the prevalence. Pearson’s chi-square test was applied to examine associations between potential risk factors and mastitis occurrence. Variables with p < 0.05 in the univariable analysis were entered into the multivariable logistic regression to identify independent predictors. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported, and significance was considered at p < 0.05. Results Prevalence of Bovine Mastitis Among the 304 lactating dairy cows examined, 186 were positive for mastitis, resulting in an overall cow-level prevalence of 61.18%. Among the affected cows, 12.17% (n = 37) had clinical mastitis, whereas 48.68% (n = 148) were diagnosed with subclinical mastitis. Quarter-level analysis of 1,216 teats revealed that 374 quarters (30.75%) were affected by mastitis, with 11.75% (n = 143) showing clinical signs and 18.99% (n = 231) showing subclinical infections. These findings are summarized in Table 1. Table 1. Prevalence of bovine mastitis Type of Mastitis Cow level prevalence % (n) Quarter Level prevalence % (n) Clinical Mastitis 12.17 (37) 11.75 (143) Subclinical Mastitis 48.95 (148) 18.99 (231) Total 61.18 (186) 30.75 (374) Distribution of Mastitis among Teat Quarters Assessment of mastitis distribution among the four quarters revealed variation in susceptibility. The right rear (RR) quarter showed the highest prevalence at 34.21% (n = 104), followed by the left rear (LR) at 32.23% (n = 98), the right front (RF) at 28.94% (n = 88), and the left front (LF), with the lowest prevalence at 27.63% (n = 84). Blind teats were also observed, most frequently in the LR (5.59%) and LF (4.93%) quarters. The detailed quarter-level data are presented in Table 2. Table 2 : Prevalence of subclinical and clinical mastitis at the quarter level Quarter No of teats examined Overall Mastitis % (n) Subclinical mastitis % (n) Clinical mastitis % (n) Blind teat % (n) RF 304 28.94 (88) 21.71(66) 7.2(22) 2.6 (8) RR 304 34.21 (104) 19.40(59) 14.8(45) 3.95 (12) LR 304 32.23 (98) 19.07(58) 13.15(40) 5.59 (17) LF 304 27.63 (84) 15.78(48) 11.84(36) 4.93 (15) Total 1216 30.75 (374) 18.99(231) 11.75(143) 4.27 (52) Potential factors associated with bovine mastitis Descriptive analysis of potential demographic and management-related risk factors revealed varying mastitis prevalence across categories. Older cows (>8 years), crossbreeds, poor body condition scores, high parity (>6 calves), late-stage lactation (>240 days), poor milking and farm hygiene, and soil flooring were associated with markedly high mastitis prevalence. Cows with short and narrow teat ends, no dry cow therapy, and a history of previous mastitis also demonstrated elevated mastitis occurrence. The descriptive distribution of mastitis across these variables is presented in Table 3. Table 3: Potential factors associated with the occurrence of bovine mastitis Variables Category No. Examined Prevalence of mastitis % (n) Age Young (8yrs) 126 77.77 (98) Breed Local 133 16.54 (22) Cross breed 171 71.92 (123) Body condition score Good 133 36.84 (49) Medium 146 52.05 (76) Bad 25 84.0 (21) Parity Few (6 calves) 108 96.29 (104) Lactation stage Early (240 days) 112 91.96 (103) Farm hygiene Good 213 42.72 (91) Bad 91 58.24 (53) Milking hygiene Good 136 9.55 (13) Poor 168 77.97 (131) Milk yield Low (9 Lt) 11 18.18 (2) Flour type Concrete 200 26.5 (53) Soil 104 82.69 (86) Teat end morphology Short and narrow 171 11.11 (19) Long and wide 133 90.22 (120) Dry cow therapy Yes 129 37.98 (49) No 125 60.8 (76) Concurrent diseases Yes 180 20.55 (37) No 124 85.48 (106) Previous mastitis history Yes 143 72.72 (104) No 201 24.87 (50) Univariable logistic regression analysis Univariable logistic regression identified several factors significantly associated with mastitis (p 8 years) were more likely to have mastitis than younger cows were (OR = 15.68; 95% CI: 8.24–29.81). Similarly, crossbred cows had significantly greater odds of mastitis than local breeds did (OR = 68.57; 95% CI: 35.35–132.98). The results of the univariable analysis are shown in Table 4. Table 4: Univariate logistic regression analysis of potential risk factors associated with mastitis Variables Category Prevalence (%) OR 95%CI (p value) Age Young 17.33 Ref Ref Ref Adult 32.02 2.18984 1.15 - 4.16 0.016 Old 77.77 15.67647 8.24 - 29.81 0.000 Breed Local 16.54 Ref Ref Cross breed 71.92 68.57 35.35- 132.98 0.000 Body condition score Good 36.42 Ref Ref Ref Medium 52.84 1.924603 1.26 - 2.93 0.002 Bad 84.0 9.603175 3.16- 29.12 0.000 Parity Few 76.92 Ref Ref Ref Moderate 34.78 6.368421 3.01- 13.48 0.000 Many 96.26 319.44 106.17- 961.08 0.000 Lactation stage Early 3.36 Ref Ref Middle 49.3 22.97872 9.22- 57.27 0.000 Late 91.96 285.8181 102.81- 794.59 0.000 Farm hygiene Good 42.72 Ref Ref Bad 58.24 26.94013 15.33-47.35 0.000 Milking hygiene Good 9.55 Ref Ref Poor 77.97 35.18478 19.13- 64.72 0.000 Milk yield <4 liter 20.26 Ref Ref 0.000 4-9 liter 62.1 1.242518 0.26- 5.75 Above 9 liters 18.18 Flour type Yes 26.5 Ref Ref 0.000 No 82.69 11.3584 6.72- 19.21 Teat end morphology Long and wide 11.11 Ref Ref 0.000 Short and narrow 90.22 126.4 58.69- 272.22 Dry cow therapy Yes 37.98 Ref Ref Ref No 60.8 2.52063 1.66 - 3.83 0.000 Concurrent disease Yes 20.55 Ref Ref Ref No 85.48 23.50097 13.5- 40.87 0.000 Previous mastitis history Yes 72.72 Ref Ref Ref No 24.87 0.124682 0.079- 0.19 0.000 OR stands for odds ratio, *= P< 0.05 (significant) Multivariate logistic regression analysis In the multivariable model, four variables remained significantly associated with mastitis. Compared with local breeds, crossbred cows were 10.15 times more likely to develop mastitis (95% CI: 2.52–40.94; p = 0.001). Higher parity and poor milking hygiene also strongly correlated, with cows with >6 calves being 20.05 times more likely to have mastitis than cows with fewer calves (95% CI: 3.32–147.24; p = 0.001). Teat end morphology was a significant predictor, with cows having short and narrow teats showing significantly increased odds (OR = 9.03; 95% CI: 2.15–37.98; p = 0.003). The full results are presented in Table 5. Table 5: Multivariable logistic regression analysis of potential risk factors associated with mastitis Variables Category Prevalence (%) OR 95%CI (p value) Breed Local 16.56 Ref Ref Cross Breed 71.62 10.15 2.52 - 40.94 0.001 Parity Few 7.63 Ref Ref Moderate 34.5 4.77 1.81 - 12.53 0.002 Many 96.35 20.05 3.32-147.24 0.001 Milking hygiene Good 9.30 Ref Ref Poor 78.3 5.64 1.35- 23.52 0.017 Teat end morphology Long and wide 11.11 Ref Ref 0.003 Short and narrow 90.47 9.03 2.15 - 37.98 OR= odds ratio, P< 0.05 (significant) Occurrence of Staphylococcus aureus in Mastitic Milk Among the 322 milk samples analysed from mastitic cows, Staphylococcus aureus was isolated from 115 samples (35.71%). Among these isolates, 34 (37.39%) were recovered from clinical mastitis cases, whereas 81 (35.06%) were from subclinical cases. These findings indicate the widespread occurrence of S. aureus across both clinical and subclinical mastitis patients. The isolation frequencies are summarized in Table 6. Table 6 : Staphylococcus aureus isolates Mastitis type Milk samples No. of positive Frequency (%) Clinical mastitis 91 34 37.36 Subclinical mastitis 231 81 35.06 Total 322 115 35.71 Discussion The present study revealed a considerable cow-level prevalence of bovine mastitis at 61.18% (n = 186), with subclinical mastitis (48.95%) being the dominant form, occurring nearly four times more frequently than clinical mastitis (12.17%). These findings align with previous reports from Ethiopia, such as those by Lakew et al. [ 5 ] in Haramaya (63.02%) and Abebe et al. [ 28 ] in Hawassa (62.6%), suggesting a consistent burden across different regions. The comparable prevalence rates may be attributed to shared risk factors, including inadequate milking hygiene, suboptimal housing conditions, and the absence of routine mastitis screening programs. However, the current overall prevalence is lower than the 80% prevalence reported by Mbindyo et al. [ 29 ] in Kenya, which may be due to differences in dairy management systems, breed susceptibility, and disease control strategies. Conversely, the prevalence reported in this study exceeds that reported by Hailay et al. [ 30 ] in Adwa (35.9%), Dabele et al. [ 31 ] in the West Shewa Zone (30.5%), and Belay et al. [ 32 ] in the Gamo Zone (17.1%). These variations underscore the influence of diverse environmental conditions, breed differences, and management practices on the epidemiology of mastitis. The prevalence of cow-level clinical mastitis (12.17%) recorded in this study is consistent with the 12.5% prevalence reported by Zeryehun and Abera [ 33 ] in East Hararghe. However, these findings are lower than those of Fesseha et al. [ 34 ] in Modjo Town (28.9%) and Mekibib et al. [ 35 ] in Holeta (22.4%), where clinical cases were more frequently observed. Conversely, the present findings surpass those of Delelesse [ 36 ] in the Holeta area (10.3%) and Hailay et al. [ 30 ] in Adwa (3.85%), potentially due to differences in mastitis detection methods, farmer awareness, and veterinary intervention strategies. The relatively high clinical mastitis burden in this study could be linked to the absence of dry cow therapy, delayed treatment of subclinical infections, and the presence of concurrent diseases that compromise udder immunity. Subclinical mastitis remains a more significant challenge, with a prevalence of 48.95%, which is in agreement with the findings of Mekibib et al. [ 35 ] in Holeta (48.6%). However, it is lower than the findings of Shiferaw and Telila [ 37 ] (61.19%) in Wolayta Sodo, Zeryehun and Abera [ 33 ] (51.8%) in East Hararghe, and Fesseha et al. [ 34 ] (71.02%) in Modjo Town. The observed variations could stem from differences in sampling periods, diagnostic approaches, and levels of farmer awareness regarding subclinical mastitis. Historically, the detection of subclinical mastitis has been challenging, as its asymptomatic nature leads to underestimation by farmers and delays in intervention. Over time, increased awareness and advancements in diagnostic methods, such as the California mastitis test (CMT) and somatic cell count (SCC) techniques, have facilitated better detection and reporting. In contrast, the current prevalence of subclinical mastitis is higher than that reported by Kebebew and Jorga [ 38 ] in Ambo (31.6%) and Demissie et al. [ 39 ] in Wukro Tigray (26%). This discrepancy may be explained by variations in climatic conditions, parity, lactation stage, breed susceptibility, and farm management practices. Notably, Erskine [ 40 ] emphasized that subclinical mastitis consistently surpasses clinical mastitis because the udder's protective immune responses limit severe clinical manifestations. The higher prevalence of subclinical mastitis in this study highlights the urgent need for routine screening programs, farmer education, and the implementation of stringent mastitis control measures to mitigate its impact on dairy productivity and milk quality. The overall quarter-level prevalence of mastitis in this study was 30.75%, which is higher than the 26.9% reported by Tezera and Ali [ 41 ] in Assosa town and the 21.94% reported by Shiferaw and Telila [ 37 ] in Wolaita. Similarly, lower prevalence rates were reported by Belay et al. [ 32 ] (7.94%) and Demissie et al. [ 39 ] (15.19%) elsewhere in Ethiopia. The disparity in prevalence rates may be attributed to variations in farm management, udder hygiene, and environmental sanitation. Poor hygiene practices, such as inadequate cleaning and drying of cow housing and udder surfaces, could have contributed to the higher prevalence observed in the present study. Conversely, our findings were lower than the 36.9% reported by Fesseha et al. [ 34 ], which could be explained by differences in climatic conditions and farm biosecurity measures. In this study, the quarter-level prevalence of clinical mastitis was 11.78%, which closely aligns with the 11.9% reported by Tezera and Ali [ 41 ] in Assosa and the 13.3% documented by Demissie et al. [ 39 ] in Wukro, Tigray. However, this prevalence was higher than the 5.2% reported by Zeryehun et al. [ 42 ] and the 3.4% recorded by Musse et al. [ 43 ] in Addis Ababa, which may reflect differences in dairy management systems, climates, and milking hygiene. Extreme environmental conditions—either excessively hot or cold—can also influence the survival and proliferation of mastitis-causing pathogens, potentially impacting the variation in prevalence across different regions. Subclinical mastitis at the quarter level was identified in 18.99% of the examined teats, which is comparable to the 17.9% reported by Almaw et al. [ 44 ]. However, this prevalence was lower than those reported by Fesseha et al. [ 34 ] (34.9%) and Zeryehun and Abera [ 33 ] (46.4%) but higher than the 2.6% reported in Wukro, Tigray[ 39 ]. Differences in detection methods, study durations, sample sizes, and farm management practices may explain these inconsistencies. Subclinical mastitis remains a major concern, as it often goes undetected, leading to chronic infections, reduced milk production, and economic losses. Among the four quarters, the right rear teat presented the highest infection rate (34.11%), followed by the left rear (32.29%), which aligns with several studies [ 45 , 46 ]. The higher susceptibility of the rear quarters to infection may be attributed to their greater milk production capacity and increased exposure to environmental contaminants, particularly fecal matter [ 7 ]. Additionally, this study revealed that 4.3% of the teats were blind, which is consistent with previous reports by Fesseha et al. [ 34 ] (3.4%) and Kebebew & Jorga [ 38 ] (5.5%) but lower than the prevalence reported by Biffa et al. [ 47 ]. The presence of blind quarters may indicate chronic mastitis infections, inadequate treatment of clinical cases, and the absence of effective culling practices. The failure to identify and treat subclinical mastitis early may contribute to the progression of infections, leading to irreversible damage to the mammary gland. This, in turn, reduces milk yield, impacts dairy productivity, and poses food security concerns in the region. This study investigated the associations between various demographic and management-related risk factors and the prevalence of bovine mastitis. The findings revealed significant associations between mastitis occurrence and factors such as breed, parity, milking hygiene, teat end morphology, and previous mastitis history, which aligns with the literature on the subject. A statistically significant correlation was observed between breed type and mastitis prevalence, with crossbred cows being more susceptible than local breeds. Compared with local breeds, crossbred breeds had a 10.15-fold greater likelihood of developing mastitis, which is consistent with previous reports[ 21 , 22 ]. The increased susceptibility of crossbred cows may be attributed to their greater milk production capacity, larger udder size, and reduced genetic resistance to infections. Local breeds, on the other hand, have developed a natural resilience to mastitis due to their adaptation to the environment and lower milk yield, which reduces stress on the udder and decreases the risk of microbial invasion. This study revealed a strong statistical relationship between parity and mastitis occurrence. Cows with three to five calvings were 4.77 times more likely to develop mastitis than those with fewer than three calvings were, whereas cows with more than five calvings presented a similar increase in risk. This pattern supports findings from a previous study[ 32 ], which indicated that repeated lactation cycles contribute to increased susceptibility. The heightened risk in multiparous cows could be due to anatomical and physiological changes in the udder, cumulative tissue stress, and prolonged exposure to environmental and infectious agents. Milking hygiene practices were significantly associated with mastitis prevalence, with cows subjected to poor milking hygiene being 5.64 times more likely to develop mastitis. These findings are consistent with those of previous studies [ 35 , 42 ]. Poor hygiene practices, such as inadequate udder washing, the use of a common cloth for multiple cows, and milking of subclinical mastitic cows first, have been reported as major risk factors for contagious mastitis. Proper hygiene practices, including individual udder cloths and pre- and postmilking teat disinfection, are critical control measures to mitigate the spread of mastitis-causing pathogens. Teat end morphology was another significant risk factor associated with mastitis incidence. Cows with long and wide teat ends were 9.03 times more likely to develop mastitis than those with short and narrow teat ends. This result is in agreement with findings from [ 28 , 48 ], who reported an increased risk of mastitis in cows with wide or flat teat-end shapes. The greater susceptibility may be due to the wider streak canals in such teats, which facilitate pathogen entry and increase bacterial colonization, ultimately leading to mastitis. These findings point to the need for practical training of dairy farmers on proper milking hygiene and selective breeding for anatomical traits that are less prone to mastitis. The microbiological analysis conducted in this study revealed Staphylococcus aureus in 35.63% (115/322) of the mastitic milk samples. This finding is consistent with previous reports, such as those by Fesseha et al. [ 34 ] (40.3%) and Belay et al. [ 32 ] (42.6%). However, the prevalence reported by Asmelash et al. [ 49 ] (73.3%) in Kombolcha and Abebe et al. [ 28 ] (73.2%) was considerably higher than that reported in the present study. A lower percentage (13.3%) was reported in Gondar by Tegegne and Tesfay [ 50 ]. This variation may be attributed to differences in climatic conditions, which can influence bacterial survival and transmission. S. aureus has been widely reported as a primary etiological agent of bovine mastitis in multiple studies across Africa and Asia [ 48 ]. In the present study, the prevalence of S. aureus was 10.55% in clinical mastitis cases and 25.55% in subclinical mastitis cases, which is lower than the rates reported by Abera et al. [ 51 ] (33.3% and 44.5%, respectively). The higher prevalence of S. aureus in subclinical mastitis than in clinical cases is consistent with the findings of previous studies, highlighting its role as a persistent and often undetected pathogen in dairy farms. The significant occurrence of S. aureus in this study may be associated with several management factors, including the absence of postmilking teat disinfection, the failure to cull chronically infected cows, the lack of dry cow therapy, and the common practice of hand milking. S. aureus is a highly contagious pathogen that colonizes the udder and teat surfaces of infected cows, serving as a key vector for transmission between infected and uninfected quarters, particularly during milking. Notably, although all the observed herds followed hand-washing practices before milking, this was only performed before milking the first cow, which may have contributed to cross-contamination during subsequent milkings. Limitations of the Study This study has several limitations that should be acknowledged. First, the sample size was limited to a specific geographic area, which may restrict the generalizability of the findings to other regions with different management practices and climatic conditions. Additionally, the study relied primarily on conventional microbiological methods for the isolation of S. aureus without molecular confirmation, which could affect the accuracy of species identification. Furthermore, factors such as antibiotic resistance patterns and the genetic diversity of S. aureus strains were not assessed, limiting a comprehensive understanding of their epidemiology. Conclusion This study revealed a high prevalence of bovine mastitis among lactating cows in and around Dessie Town, with subclinical cases predominating over clinical cases, highlighting the silent yet substantial burden of the disease. The distribution of mastitis across quarters, particularly the higher incidence in rear teats, and the significant associations with various animal- and management-related risk factors underscore the multifactorial nature of the disease. Multivariable analysis further identified breed, parity, milking hygiene, and teat end morphology as critical predictors of mastitis occurrence. The isolation of S. aureus as the predominant pathogen in both clinical and subclinical cases confirms its central role in the epidemiology of mastitis in the study area. These findings emphasize the urgent need for comprehensive mastitis control strategies focused on improved hygiene practices, early detection of subclinical infections, and farmer education. Implementing targeted interventions that consider herd demographics and management practices is vital to reduce mastitis prevalenceincidence, limit economic losses, and safeguard milk quality and udder health in Ethiopia’s growing dairy sector. Abbreviations CMT California Mastitis Test S .aureus Staphylococcus aureus CI Confidence interval OR Odds ratio RR Right Rear LR Left Rear RF Right Front LF Left Front Declarations All the samples that included animals and humans were collected in accordance with the relevant ethical principles and guidelines. The animal study was conducted in accordance with ethical principles and guidelines for the use of animals for scientific purposes. Consent for publication Not applicable Data Availability Statement Upon a reasonable request, the datasets and materials used throughout the study are accessible from the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This research work was part of a DVM thesis and was financially supported by Bahir Dar University, Ethiopia. Authors' contributions SA drafted the study design and performed the laboratory. HT supervised the research work. TB performed the data collection and analysis. HA and ST participated in the data analysis and manuscript writing. SA, HA, ST and HT critically reviewed the manuscript. All the authors approved the final manuscript. Acknowledgements The authors acknowledge Bahir Dar University for providing financial support. The authors sincerely acknowledge dairy farmers in Dessie town for allowing sample collection and participation in the questionnaire survey. We are grateful to staff at the Veterinary Microbiology Laboratory, Wollo University, for their support during laboratory work. References Asfaw Y, Begna R, Masho W. Evaluation of breeding objectives, breeding practices and reproductive performance of indigenous dairy cows in selected districts of Kaffa Zone, South West Ethiopia. Vet Med Sci [Internet]. 2023 [cited 2025 Apr 1];9:2820–34. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650342/ Banu MG, Geberemedhin EZ. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6568137","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":492518626,"identity":"397574be-5d6f-4b69-894a-8e79a778a8ca","order_by":0,"name":"Sossina Ashenafi","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Sossina","middleName":"","lastName":"Ashenafi","suffix":""},{"id":492518627,"identity":"f93a7eed-fd59-4250-beb2-8087bdc0fbb4","order_by":1,"name":"Habtamu Tassew","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Habtamu","middleName":"","lastName":"Tassew","suffix":""},{"id":492518628,"identity":"a8601efc-7019-472e-bcce-2afaa6ff8646","order_by":2,"name":"Halo Yohannes","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Halo","middleName":"","lastName":"Yohannes","suffix":""},{"id":492518629,"identity":"3dc1d2ac-1603-4f76-ba51-8a8087c2f4fa","order_by":3,"name":"Tigist Berie","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Tigist","middleName":"","lastName":"Berie","suffix":""},{"id":492518630,"identity":"bb276bf1-44f8-4a36-81ad-d6f1b8ea4736","order_by":4,"name":"Solomon Tibebu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFAC5sYDCSBaAog/ADEbO0EtjA0gLRIgLYwzQFqYidHCANXCzAO2loAG+fbGhgMP22zq+Gc3H/ts82ubPB8zA+OHjzm4tRicOdhwILEtTULizrHk2bl9tw3bmBmYJWduw6NFIhHolzOHJRhu5Bgz5/bcZgRqYWPmxaNFfgZYy38JeZAWy57b9gS1MNwAaak4IGEA0sLw43YiQS1gvyRUJEtuBPqFsbfhdnIbM2MzXr/ItzcffPjDwI5f7nbzYYYff27bzgeKfPiIz2EogLENTDYQqx4E/pCieBSMglEwCkYKAACguFVNKl1vPwAAAABJRU5ErkJggg==","orcid":"","institution":"Bahir Dar University","correspondingAuthor":true,"prefix":"","firstName":"Solomon","middleName":"","lastName":"Tibebu","suffix":""}],"badges":[],"createdAt":"2025-04-30 22:53:08","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6568137/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6568137/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88212976,"identity":"2828d52c-88b6-4003-a9da-75675b67c09e","added_by":"auto","created_at":"2025-08-04 05:54:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1155399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6568137/v1/3a8aae85-8d82-42b8-9e19-f4ab1b170441.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence of Bovine Mastitis and Isolation of Staphylococcus aureus from Cow Milk in Dessie Town, Northeast Ethiopia","fulltext":[{"header":"Background","content":"\u003cp\u003eEthiopia has the largest livestock population in Africa, with cattle playing a central role in the livelihoods and food security of rural communities. Among the national cattle herd, approximately 55.90% are female, with approximately 20.7% being lactating cows, which are primarily composed of indigenous breeds, and a smaller proportion of crossbreeds and exotics introduced to improve dairy productivity[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While the dairy sector is gradually transitioning from traditional extensive systems to semi-intensive and intensive production in peri-urban areas, infectious diseases remain a major constraint on productivity, milk quality, and animal health [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBovine mastitis is one of the most prevalent and economically important diseases affecting dairy cows globally, with significant impacts on milk yield, quality, animal welfare, and public health [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is characterized by inflammation of the mammary gland parenchyma and exists in clinical and subclinical forms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Clinical mastitis is easily detectable due to visible symptoms such as swelling, redness, pain, and abnormalities in milk, including clotting or discolouration[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In contrast, subclinical mastitis presents no visible signs on the udder or in the milk, making it more challenging to diagnose. Despite the absence of obvious symptoms, subclinical mastitis can significantly impact milk production and quality, often requiring laboratory tests for detection. Depending on the mode of transmission, mastitis can be classified as environmental\u0026mdash;linked to pathogens present in the cow\u0026rsquo;s surroundings\u0026mdash;or contagious, where the infection spreads primarily during milking from infected udders [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the numerous pathogens associated with contagious mastitis, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e is one of the most frequently isolated bacteria. It is a gram-positive, coagulase-positive, opportunistic pathogen that persists in the mammary gland, evades host immune responses, and leads to chronic and recurrent infections[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Its ability to form biofilms and develop resistance to antimicrobials poses a dual threat to animal health and public health. Infected milk, especially in regions where raw milk consumption is common, serves as a reservoir for zoonotic transmission and foodborne illness[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In Ethiopia, several studies have reported \u003cem\u003eS. aureus\u003c/em\u003e as a major etiological agent in bovine mastitis, which is often isolated in both clinical and subclinical cases [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe economic consequences of \u003cem\u003eS. aureus\u003c/em\u003e-associated mastitis are considerable. These include milk losses, treatment costs, premature culling of affected animals, reduced milk shelf-life, and drug residues in milk [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, the spread of antimicrobial-resistant strains of \u003cem\u003eS. aureus\u003c/em\u003e exacerbates concerns regarding food safety and effective treatment options, aligning with the broader challenges addressed through alternatives such as herbal medicine[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite increasing recognition of this issue, region-specific data are lacking in many emerging dairy hubs in Ethiopia.\u003c/p\u003e \u003cp\u003eAlthough some studies have examined mastitis prevalence and etiology in various parts of Ethiopia, limited recent evidence exists from Dessie Town and surrounding areas in the Amhara Region. Local data on the prevalence of both clinical and subclinical mastitis, risk factors, and the role of \u003cem\u003eS. aureus\u003c/em\u003e in disease occurrence are essential for designing effective control strategies tailored to regional conditions.\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study was to determine the prevalence of bovine mastitis, identify associated risk factors, and isolate \u003cem\u003eStaphylococcus aureus\u003c/em\u003e from mastitic milk in lactating dairy cows in Dessie Town, Northeast Ethiopia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eDescription of the study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in Dessie town, which is located in the South Wollo Zone of the Amhara Region, Northeast Ethiopia. Dessie lies approximately 401 km north of Addis Ababa at an elevation ranging from 2,470 to 2,550 m above sea level. The town is situated at 11\u0026deg;8\u0026prime;N latitude and 39\u0026deg;38\u0026prime;E longitude, with an average annual temperature of 9\u0026deg;C[20].\u003c/p\u003e\n\u003cp id=\"_Toc178432057\"\u003e\u003cstrong\u003eStudy\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003epopulation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ehusbandry practices\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 304 lactating dairy cows, comprising both local and crossbred (Holstein Friesian \u0026times; local zebu) animals, were selected from 28 dairy farms employing either intensive or semi-intensive management systems. Cows in intensive systems were housed indoors and provided with concentrated feed, hay, green forage, and crop residues. Semi-intensively managed cows were allowed to graze during the day and were supplemented with feed during morning and evening milking sessions.\u003c/p\u003e\n\u003cp\u003eCows were categorized on the basis of age (young: \u0026lt;4 years, adult: 4\u0026ndash;8 years, old: \u0026gt;8 years), parity (few: \u0026le;3 calves, moderate: 4\u0026ndash;6 calves, many: \u0026ge;7 calves), lactation stage (early: \u0026le;121 days, middle: 121\u0026ndash;240 days, late: \u0026gt;240 days), and milk yield (low: \u0026lt;4 L/day, medium: 4\u0026ndash;9 L/day, high: \u0026gt;9 L/day). Teat end morphology and body condition scores were also recorded via standard classification systems reported in prior studies[21,22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource of Animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMilk samples were collected from dairy cows maintained in intensive and semi-intensive dairy farms located in Dessie Town, Northeastern Ethiopia. The source of dairy cows was not purchased rather used from farm owners rearing their own dairy farms in the study area. All the animals used were under normal farm management with no experimental treatment or intervention. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Consideration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the experimental procedures using animals were reviewed and cleared by the Research and Ethics Committee of the College of Agriculture and Environmental Sciences, Bahir Dar University (Ref No: 1/Vetsc/342/1-1-3, dated 15 October 2024). All the procedures were conducted following applicable institutional, national, and international guidelines and regulations, including the OIE Manual for Serum Sample Collection[23] and ARRIVE guidelines 2.0 [24].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental protocols were approved by College of Agriculture and Environmental Sciences, Bahir Dar University Research and Ethics Committee. Informed consent was obtained from all subjects and/or their legal guardian(s). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Design and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSampling Techniques\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional study was conducted from December 2023 to July 2024 to estimate the prevalence of bovine mastitis, identify associated risk factors, and isolate \u003cem\u003eStaphylococcus aureus\u003c/em\u003e from mastitic milk. The sample size was calculated via the formula described by Thrusfield [25]. A total of 28 dairy farms were selected via convenience sampling, and 304 lactating cows were then randomly chosen via the lottery method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Examination and Milk Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach udder was visually inspected and palpated to detect clinical signs such as swelling, heat, fibrosis, or atrophy. Milk from each quarter was examined for abnormalities, including blood, clots, or flakes. For subclinical mastitis, the California mastitis test (CMT) was used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBefore sampling, the teats were cleaned with water and dried with disposable towels. Each teat was disinfected with 70% alcohol, starting with healthy quarters and ending with suspected infected quarters to prevent cross-contamination. The first 3\u0026ndash;4 streams of milk were discarded, and approximately 10 mL of milk was aseptically collected into sterile universal bottles, which were held at an angle to prevent contamination. The samples were stored in iceboxes and transported to the Veterinary Microbiology Laboratory at Wollo University. The clinical samples were cultured immediately, while the other samples were stored at 4 \u0026deg;C for a maximum of 24 hours prior to processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalifornia mastitis test (CMT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubclinical mastitis was detected via the CMT as described by Moawad et al. [26]. Equal volumes of milk and CMT reagent were mixed in the CMT paddle, and the degree of gel formation was scored as 0 (negative), trace, + (weak), ++ (distinct), or +++ (strong). Quarters scoring + or higher were considered positive. Cows were classified as subclinically infected if at least one quarter tested positive[26].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestionnaire Survey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA semistructured questionnaire was administered to 65 respondents across the 28 farms to gather information on herd-level and animal-level factors. These included farm hygiene, milking practices, floor type, breed, age, parity, body condition score, lactation stage, teat morphology, milk yield, history of mastitis, concurrent illnesses, dry cow therapy, and udder/milk abnormalities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolation and identification of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIsolation of \u003cem\u003eS. aureus\u003c/em\u003e was performed following the procedures described by Zang et al. [27]. Ten microliters (0.01 mL) of milk were streaked onto 7% sheep blood agar and incubated aerobically at 37 \u0026deg;C for 24 hours. Colonies were evaluated for morphology and hemolysis and then subcultured on nutrient agar for further purification. Gram-positive cocci showing catalase-positive reactions were tested via slide or tube coagulase tests. Coagulase-positive colonies were further confirmed by growth on mannitol salt agar.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data were entered into Microsoft Excel and analysed via STATA version 14 (StataCorp, College Station, TX, USA). Descriptive statistics were used to calculate the prevalence. Pearson\u0026rsquo;s chi-square test was applied to examine associations between potential risk factors and mastitis occurrence. Variables with p \u0026lt; 0.05 in the univariable analysis were entered into the multivariable logistic regression to identify independent predictors. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported, and significance was considered at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePrevalence of Bovine Mastitis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 304 lactating dairy cows examined, 186 were positive for mastitis, resulting in an overall cow-level prevalence of 61.18%. Among the affected cows, 12.17% (n = 37) had clinical mastitis, whereas 48.68% (n = 148) were diagnosed with subclinical mastitis. Quarter-level analysis of 1,216 teats revealed that 374 quarters (30.75%) were affected by mastitis, with 11.75% (n = 143) showing clinical signs and 18.99% (n = 231) showing subclinical infections. These findings are summarized in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Prevalence of bovine mastitis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Mastitis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCow level prevalence % (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuarter Level prevalence % (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClinical Mastitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.17 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.75 (143)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubclinical Mastitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.95 (148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.99 (231)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e61.18 (186)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.75 (374)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of Mastitis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eamong\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Teat Quarters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssessment of mastitis distribution among the four quarters revealed variation in susceptibility. The right rear (RR) quarter showed the highest prevalence at 34.21% (n = 104), followed by the left rear (LR) at 32.23% (n = 98), the right front (RF) at 28.94% (n = 88), and the left front (LF), with the lowest prevalence at 27.63% (n = 84). Blind teats were also observed, most frequently in the LR (5.59%) and LF (4.93%) quarters. The detailed quarter-level data are presented in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc178352355\"\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Prevalence of subclinical and clinical mastitis at the quarter level\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuarter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of teats examined\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Mastitis % (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Subclinical mastitis % (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical mastitis % (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlind teat % (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.94 (88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.71(66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2(22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.21 (104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.40(59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.8(45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.95 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.23 (98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.07(58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.15(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.59 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e304 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.63 (84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.78(48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.84(36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.93 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1216\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.75 (374)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e18.99(231)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.75(143)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.27 (52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePotential\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efactors associated with bovine mastitis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive analysis of potential demographic and management-related risk factors revealed varying mastitis prevalence across categories. Older cows (\u0026gt;8 years), crossbreeds, poor body condition scores, high parity (\u0026gt;6 calves), late-stage lactation (\u0026gt;240 days), poor milking and farm hygiene, and soil flooring were associated with markedly high mastitis prevalence. Cows with short and narrow teat ends, no dry cow therapy, and a history of previous mastitis also demonstrated elevated mastitis occurrence. The descriptive distribution of mastitis across these variables is presented in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Potential factors associated with the occurrence of bovine mastitis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. \u0026nbsp; \u0026nbsp; Examined\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence of mastitis % (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYoung (\u0026lt;4yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.33 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdult (4-8yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.02 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOld (\u0026gt;8yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.77 (98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBreed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.54 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.92 (123)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBody condition score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.84 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.05 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.0 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFew (\u0026lt;3 calves)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.69 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate (3-6 calves)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.78 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMany (\u0026gt;6 calves)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96.29 (104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLactation stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEarly (\u0026lt;121 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.36 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMiddle (121-240 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.3 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLate (\u0026gt;240 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.96 (103)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFarm hygiene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.72 (91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.24 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMilking hygiene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.55 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.97 (131)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMilk yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow (\u0026lt;4 Lt)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.26 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedium (4-9 Lt)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.1 (87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh (\u0026gt;9 Lt)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.18 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFlour type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConcrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.5 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82.69 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTeat end morphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShort and narrow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.11 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLong and wide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.22 (120)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDry cow therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.98 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.8 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eConcurrent diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.55 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.48 (106)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePrevious mastitis history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.72 (104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.87 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp id=\"_Toc178352359\"\u003e\u003cstrong\u003eUnivariable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elogistic regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable logistic regression identified several factors significantly associated with mastitis (p \u0026lt; 0.05). These included age, breed, body condition score, parity, lactation stage, teat end morphology, and previous mastitis history. For example, older cows (\u0026gt;8 years) were more likely to have mastitis than younger cows were (OR = 15.68; 95% CI: 8.24\u0026ndash;29.81). Similarly, crossbred cows had significantly greater odds of mastitis than local breeds did (OR = 68.57; 95% CI: 35.35\u0026ndash;132.98). The results of the univariable analysis are shown in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4: Univariate logistic regression analysis of potential risk factors associated with mastitis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(p value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYoung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.18984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15 - 4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOld\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.67647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.24 - 29.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBreed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.35- 132.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBody condition score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Ref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.924603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.26 - 2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.603175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.16- 29.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Ref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.368421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.01- 13.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e319.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106.17- 961.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLactation stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEarly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.97872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.22- 57.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e285.8181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e102.81- 794.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFarm hygiene\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.94013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.33-47.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMilking hygiene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.18478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.13- 64.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMilk yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;4 liter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4-9 liter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.242518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26- 5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbove 9 liters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFlour type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.3584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.72- 19.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTeat end morphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLong and wide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShort and narrow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e126.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.69- 272.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDry cow therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.52063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66 - 3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eConcurrent disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.50097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.5- 40.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePrevious mastitis history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.124682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.079- 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR stands for odds ratio, *= P\u0026lt; 0.05 (significant)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate logistic regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the multivariable model, four variables remained significantly associated with mastitis. Compared with local breeds, crossbred cows were 10.15 times more likely to develop mastitis (95% CI: 2.52\u0026ndash;40.94; p = 0.001). Higher parity and poor milking hygiene also strongly correlated, with cows with \u0026gt;6 calves being 20.05 times more likely to have mastitis than cows with fewer calves (95% CI: 3.32\u0026ndash;147.24; p = 0.001). Teat end morphology was a significant predictor, with cows having short and narrow teats showing significantly increased odds (OR = 9.03; 95% CI: 2.15\u0026ndash;37.98; p = 0.003). The full results are presented in Table 5.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003eMultivariable logistic regression analysis of potential risk factors associated with mastitis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrevalence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;(p value)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBreed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross Breed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.52 - 40.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.81 - 12.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.32-147.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMilking hygiene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.35- 23.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTeat end morphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLong and wide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShort and narrow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.15 - 37.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR= odds ratio, P\u0026lt; 0.05 (significant)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOccurrence of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in Mastitic Milk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 322 milk samples analysed from mastitic cows, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e was isolated from 115 samples (35.71%). Among these isolates, 34 (37.39%) were recovered from clinical mastitis cases, whereas 81 (35.06%) were from subclinical cases. These findings indicate the widespread occurrence of \u003cem\u003eS. aureus\u003c/em\u003e across both clinical and subclinical mastitis patients. The isolation frequencies are summarized in Table 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e: \u003cem\u003eStaphylococcus aureus\u003c/em\u003e isolates\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMastitis type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMilk samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of positive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClinical mastitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubclinical mastitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study revealed a considerable cow-level prevalence of bovine mastitis at 61.18% (n\u0026thinsp;=\u0026thinsp;186), with subclinical mastitis (48.95%) being the dominant form, occurring nearly four times more frequently than clinical mastitis (12.17%). These findings align with previous reports from Ethiopia, such as those by Lakew et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] in Haramaya (63.02%) and Abebe et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] in Hawassa (62.6%), suggesting a consistent burden across different regions. The comparable prevalence rates may be attributed to shared risk factors, including inadequate milking hygiene, suboptimal housing conditions, and the absence of routine mastitis screening programs. However, the current overall prevalence is lower than the 80% prevalence reported by Mbindyo et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] in Kenya, which may be due to differences in dairy management systems, breed susceptibility, and disease control strategies. Conversely, the prevalence reported in this study exceeds that reported by Hailay et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] in Adwa (35.9%), Dabele et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] in the West Shewa Zone (30.5%), and Belay et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] in the Gamo Zone (17.1%). These variations underscore the influence of diverse environmental conditions, breed differences, and management practices on the epidemiology of mastitis.\u003c/p\u003e \u003cp\u003eThe prevalence of cow-level clinical mastitis (12.17%) recorded in this study is consistent with the 12.5% prevalence reported by Zeryehun and Abera [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] in East Hararghe. However, these findings are lower than those of Fesseha et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] in Modjo Town (28.9%) and Mekibib et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] in Holeta (22.4%), where clinical cases were more frequently observed. Conversely, the present findings surpass those of Delelesse [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] in the Holeta area (10.3%) and Hailay et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] in Adwa (3.85%), potentially due to differences in mastitis detection methods, farmer awareness, and veterinary intervention strategies. The relatively high clinical mastitis burden in this study could be linked to the absence of dry cow therapy, delayed treatment of subclinical infections, and the presence of concurrent diseases that compromise udder immunity.\u003c/p\u003e \u003cp\u003eSubclinical mastitis remains a more significant challenge, with a prevalence of 48.95%, which is in agreement with the findings of Mekibib et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] in Holeta (48.6%). However, it is lower than the findings of Shiferaw and Telila [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] (61.19%) in Wolayta Sodo, Zeryehun and Abera [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (51.8%) in East Hararghe, and Fesseha et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (71.02%) in Modjo Town. The observed variations could stem from differences in sampling periods, diagnostic approaches, and levels of farmer awareness regarding subclinical mastitis. Historically, the detection of subclinical mastitis has been challenging, as its asymptomatic nature leads to underestimation by farmers and delays in intervention. Over time, increased awareness and advancements in diagnostic methods, such as the California mastitis test (CMT) and somatic cell count (SCC) techniques, have facilitated better detection and reporting. In contrast, the current prevalence of subclinical mastitis is higher than that reported by Kebebew and Jorga [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] in Ambo (31.6%) and Demissie et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] in Wukro Tigray (26%). This discrepancy may be explained by variations in climatic conditions, parity, lactation stage, breed susceptibility, and farm management practices. Notably, Erskine [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] emphasized that subclinical mastitis consistently surpasses clinical mastitis because the udder's protective immune responses limit severe clinical manifestations. The higher prevalence of subclinical mastitis in this study highlights the urgent need for routine screening programs, farmer education, and the implementation of stringent mastitis control measures to mitigate its impact on dairy productivity and milk quality.\u003c/p\u003e \u003cp\u003eThe overall quarter-level prevalence of mastitis in this study was 30.75%, which is higher than the 26.9% reported by Tezera and Ali [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] in Assosa town and the 21.94% reported by Shiferaw and Telila [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] in Wolaita. Similarly, lower prevalence rates were reported by Belay et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] (7.94%) and Demissie et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (15.19%) elsewhere in Ethiopia. The disparity in prevalence rates may be attributed to variations in farm management, udder hygiene, and environmental sanitation. Poor hygiene practices, such as inadequate cleaning and drying of cow housing and udder surfaces, could have contributed to the higher prevalence observed in the present study. Conversely, our findings were lower than the 36.9% reported by Fesseha et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which could be explained by differences in climatic conditions and farm biosecurity measures.\u003c/p\u003e \u003cp\u003eIn this study, the quarter-level prevalence of clinical mastitis was 11.78%, which closely aligns with the 11.9% reported by Tezera and Ali [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] in Assosa and the 13.3% documented by Demissie et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] in Wukro, Tigray. However, this prevalence was higher than the 5.2% reported by Zeryehun et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and the 3.4% recorded by Musse et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] in Addis Ababa, which may reflect differences in dairy management systems, climates, and milking hygiene. Extreme environmental conditions\u0026mdash;either excessively hot or cold\u0026mdash;can also influence the survival and proliferation of mastitis-causing pathogens, potentially impacting the variation in prevalence across different regions.\u003c/p\u003e \u003cp\u003eSubclinical mastitis at the quarter level was identified in 18.99% of the examined teats, which is comparable to the 17.9% reported by Almaw et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, this prevalence was lower than those reported by Fesseha et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (34.9%) and Zeryehun and Abera [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (46.4%) but higher than the 2.6% reported in Wukro, Tigray[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Differences in detection methods, study durations, sample sizes, and farm management practices may explain these inconsistencies. Subclinical mastitis remains a major concern, as it often goes undetected, leading to chronic infections, reduced milk production, and economic losses.\u003c/p\u003e \u003cp\u003eAmong the four quarters, the right rear teat presented the highest infection rate (34.11%), followed by the left rear (32.29%), which aligns with several studies [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The higher susceptibility of the rear quarters to infection may be attributed to their greater milk production capacity and increased exposure to environmental contaminants, particularly fecal matter [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, this study revealed that 4.3% of the teats were blind, which is consistent with previous reports by Fesseha et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (3.4%) and Kebebew \u0026amp; Jorga [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (5.5%) but lower than the prevalence reported by Biffa et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The presence of blind quarters may indicate chronic mastitis infections, inadequate treatment of clinical cases, and the absence of effective culling practices. The failure to identify and treat subclinical mastitis early may contribute to the progression of infections, leading to irreversible damage to the mammary gland. This, in turn, reduces milk yield, impacts dairy productivity, and poses food security concerns in the region.\u003c/p\u003e \u003cp\u003eThis study investigated the associations between various demographic and management-related risk factors and the prevalence of bovine mastitis. The findings revealed significant associations between mastitis occurrence and factors such as breed, parity, milking hygiene, teat end morphology, and previous mastitis history, which aligns with the literature on the subject.\u003c/p\u003e \u003cp\u003eA statistically significant correlation was observed between breed type and mastitis prevalence, with crossbred cows being more susceptible than local breeds. Compared with local breeds, crossbred breeds had a 10.15-fold greater likelihood of developing mastitis, which is consistent with previous reports[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The increased susceptibility of crossbred cows may be attributed to their greater milk production capacity, larger udder size, and reduced genetic resistance to infections. Local breeds, on the other hand, have developed a natural resilience to mastitis due to their adaptation to the environment and lower milk yield, which reduces stress on the udder and decreases the risk of microbial invasion.\u003c/p\u003e \u003cp\u003eThis study revealed a strong statistical relationship between parity and mastitis occurrence. Cows with three to five calvings were 4.77 times more likely to develop mastitis than those with fewer than three calvings were, whereas cows with more than five calvings presented a similar increase in risk. This pattern supports findings from a previous study[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which indicated that repeated lactation cycles contribute to increased susceptibility. The heightened risk in multiparous cows could be due to anatomical and physiological changes in the udder, cumulative tissue stress, and prolonged exposure to environmental and infectious agents.\u003c/p\u003e \u003cp\u003eMilking hygiene practices were significantly associated with mastitis prevalence, with cows subjected to poor milking hygiene being 5.64 times more likely to develop mastitis. These findings are consistent with those of previous studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Poor hygiene practices, such as inadequate udder washing, the use of a common cloth for multiple cows, and milking of subclinical mastitic cows first, have been reported as major risk factors for contagious mastitis. Proper hygiene practices, including individual udder cloths and pre- and postmilking teat disinfection, are critical control measures to mitigate the spread of mastitis-causing pathogens.\u003c/p\u003e \u003cp\u003eTeat end morphology was another significant risk factor associated with mastitis incidence. Cows with long and wide teat ends were 9.03 times more likely to develop mastitis than those with short and narrow teat ends. This result is in agreement with findings from [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], who reported an increased risk of mastitis in cows with wide or flat teat-end shapes. The greater susceptibility may be due to the wider streak canals in such teats, which facilitate pathogen entry and increase bacterial colonization, ultimately leading to mastitis. These findings point to the need for practical training of dairy farmers on proper milking hygiene and selective breeding for anatomical traits that are less prone to mastitis.\u003c/p\u003e \u003cp\u003eThe microbiological analysis conducted in this study revealed \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in 35.63% (115/322) of the mastitic milk samples. This finding is consistent with previous reports, such as those by Fesseha et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (40.3%) and Belay et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] (42.6%). However, the prevalence reported by Asmelash et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] (73.3%) in Kombolcha and Abebe et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] (73.2%) was considerably higher than that reported in the present study. A lower percentage (13.3%) was reported in Gondar by Tegegne and Tesfay [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This variation may be attributed to differences in climatic conditions, which can influence bacterial survival and transmission. \u003cem\u003eS. aureus\u003c/em\u003e has been widely reported as a primary etiological agent of bovine mastitis in multiple studies across Africa and Asia [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, the prevalence of \u003cem\u003eS. aureus\u003c/em\u003e was 10.55% in clinical mastitis cases and 25.55% in subclinical mastitis cases, which is lower than the rates reported by Abera et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] (33.3% and 44.5%, respectively). The higher prevalence of \u003cem\u003eS. aureus\u003c/em\u003e in subclinical mastitis than in clinical cases is consistent with the findings of previous studies, highlighting its role as a persistent and often undetected pathogen in dairy farms. The significant occurrence of \u003cem\u003eS. aureus\u003c/em\u003e in this study may be associated with several management factors, including the absence of postmilking teat disinfection, the failure to cull chronically infected cows, the lack of dry cow therapy, and the common practice of hand milking. \u003cem\u003eS. aureus\u003c/em\u003e is a highly contagious pathogen that colonizes the udder and teat surfaces of infected cows, serving as a key vector for transmission between infected and uninfected quarters, particularly during milking. Notably, although all the observed herds followed hand-washing practices before milking, this was only performed before milking the first cow, which may have contributed to cross-contamination during subsequent milkings.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of the Study\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be acknowledged. First, the sample size was limited to a specific geographic area, which may restrict the generalizability of the findings to other regions with different management practices and climatic conditions. Additionally, the study relied primarily on conventional microbiological methods for the isolation of \u003cem\u003eS. aureus\u003c/em\u003e without molecular confirmation, which could affect the accuracy of species identification. Furthermore, factors such as antibiotic resistance patterns and the genetic diversity of \u003cem\u003eS. aureus\u003c/em\u003e strains were not assessed, limiting a comprehensive understanding of their epidemiology.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed a high prevalence of bovine mastitis among lactating cows in and around Dessie Town, with subclinical cases predominating over clinical cases, highlighting the silent yet substantial burden of the disease. The distribution of mastitis across quarters, particularly the higher incidence in rear teats, and the significant associations with various animal- and management-related risk factors underscore the multifactorial nature of the disease. Multivariable analysis further identified breed, parity, milking hygiene, and teat end morphology as critical predictors of mastitis occurrence. The isolation of \u003cem\u003eS. aureus\u003c/em\u003e as the predominant pathogen in both clinical and subclinical cases confirms its central role in the epidemiology of mastitis in the study area. These findings emphasize the urgent need for comprehensive mastitis control strategies focused on improved hygiene practices, early detection of subclinical infections, and farmer education. Implementing targeted interventions that consider herd demographics and management practices is vital to reduce mastitis prevalenceincidence, limit economic losses, and safeguard milk quality and udder health in Ethiopia\u0026rsquo;s growing dairy sector.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eCMT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;California Mastitis Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cem\u003eS .aureus \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Staphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Odds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eRR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Right Rear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Left Rear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eRF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Right Front\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eLF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Left Front\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll the samples that included animals and humans were collected in accordance with the relevant ethical principles and guidelines. The animal study was conducted in accordance with ethical principles and guidelines for the use of animals for scientific purposes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon a reasonable request, the datasets and materials used throughout the study are accessible from the corresponding author. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research work was part of a DVM thesis and was financially supported by Bahir Dar University, Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSA drafted the study design and performed the laboratory. HT supervised the research work. TB performed the data collection and analysis. HA and ST participated in the data analysis and manuscript writing. SA, HA, ST and HT critically reviewed the manuscript. All the authors approved the final manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge Bahir Dar University for providing financial support. The authors sincerely acknowledge dairy farmers in Dessie town for allowing sample collection and participation in the questionnaire survey. We are grateful to staff at the Veterinary Microbiology Laboratory, Wollo University, for their support during laboratory work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsfaw Y, Begna R, Masho W. 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Journal of Veterinary Medicine and Animal Health. 2010;2:29\u0026ndash;34. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bovine mastitis, Ethiopia, Dairy cattle, Risk factors, Staphylococcus aureus, Subclinical mastitis, Prevalence","lastPublishedDoi":"10.21203/rs.3.rs-6568137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6568137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Bovine mastitis remains one of the most prevalent and economically significant diseases affecting dairy cattle worldwide. In Ethiopia, limited data exist on its prevalence, associated risk factors, and causative pathogens in emerging dairy farms such as Dessie Town. This study aimed to determine the prevalence of bovine mastitis, identify associated risk factors, and isolate \u003cem\u003eStaphylococcus aureus\u003c/em\u003e from mastitic milk samples in Dessie Town, Northeast Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional study was conducted on 304 lactating dairy cows across smallholder farms and semi-intensive farms. Clinical examination and the California mastitis test (CMT) were used to detect clinical and subclinical mastitis, respectively. Risk factors were assessed via structured questionnaires and analysed through univariable and multivariable logistic regression. A bacteriological analysis of 322 mastitic milk samples was performed for the isolation of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e via standard microbiological techniques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The cow-level prevalence of mastitis was 61.18%, with the prevalence of subclinical cases (48.68%) far exceeding that of clinical cases (12.17%). At the quarter level, 30.75% of the teats were affected. The right rear quarter showed the highest incidence (34.21%), whereas the left front quarter was least affected (27.63%). Multivariate logistic regression revealed that breed (p=0.001), parity (p=0.001), milking hygiene (p=0.017), and teat end morphology (p=0.003) were significant predictors of mastitis. \u003cem\u003eStaphylococcus aureus\u003c/em\u003e was isolated from 35.71% of mastitis-positive milk samples, including 37.39% from clinical samples and 35.06% from subclinical samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study revealed a high burden of both clinical and subclinical mastitis in dairy farms around Dessie Town, with \u003cem\u003eS. aureus\u003c/em\u003e being a leading pathogen. The strong association between mastitis and risk factors such as hygiene and teat anatomy highlights the need for integrated control measures, farmer training, and routine screening to improve udder health and milk quality.\u003c/p\u003e","manuscriptTitle":"Prevalence of Bovine Mastitis and Isolation of Staphylococcus aureus from Cow Milk in Dessie Town, Northeast Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 05:46:23","doi":"10.21203/rs.3.rs-6568137/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e6d0cd0-7c1c-418c-b914-680f661af759","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52295130,"name":"Biological sciences/Microbiology"},{"id":52295131,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2025-08-04T05:46:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 05:46:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6568137","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6568137","identity":"rs-6568137","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}