Prevalence and cow-level risk factors for mild and severe subclinical mastitis in intensive and extensive dairy systems in Bangladesh

Prevalence and cow-level risk factors for mild and severe subclinical mastitis in intensive and extensive dairy systems in Bangladesh | 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 Short Report Prevalence and cow-level risk factors for mild and severe subclinical mastitis in intensive and extensive dairy systems in Bangladesh Muhammad Aktaruzzaman, Md. Ariful Islam, Solama Akter Shanta, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9371264/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Subclinical mastitis (SCM) substantially limits dairy productivity in Bangladesh, yet evidence from high-density herds remains limited. This study estimated the prevalence of mild and severe SCM and identified cow-level risk factors. A cross-sectional survey was conducted from January 2023 to December 2024 across 76 herds using a two-stage sampling design, enrolling all lactating cows ( \(\:n\:=\:\text{3,173}\) ). Composite milk samples were tested using EKOMILK SCAN® to measure somatic cell count (SCC) and classify SCM status. Cow-level data were collected via structured questionnaires. Descriptive statistics summarized herd characteristics, while a multivariable Bayesian hierarchical multinomial regression model evaluated predictors. Model adequacy was assessed through trace plots, R-hat values, and posterior predictive checks. SCM prevalence did not differ significantly between extensive (64.5%) and intensive (63.3%) systems. Late lactation significantly increased the risk of (relative risk ratio [RRR] 2.03; 95% credible interval [CrI] 1.55–2.69) and severe SCM (RRR 1.92; 95% CrI 1.36–2.72). A previous clinical mastitis episode strongly elevated severe SCM risk (RRR 4.26; 95% CrI 2.20–8.54). Lower-parity and higher-producing cows showed reduced SCM risk. SCM risk in high-density herds is largely influenced by lactation stage, parity, milk yield, and mastitis history. Targeting late-lactation cows and animals with prior mastitis may improve surveillance efficiency and reduce the overall SCM burden more effectively than uniform herd-level approaches. Multinomial Bayesian Relative Risk Ratio Lactation stage Parity Milk yield Figures Figure 1 Introduction Mastitis, an inflammation of the mammary gland, remains one of the most economically important infectious diseases in dairy cattle worldwide (Sharun et al. 2021 ). Subclinical mastitis (SCM) is particularly problematic because affected cows show no visible clinical signs, yet exhibit elevated somatic cell counts (SCC), reduced milk yield, and compromised milk quality. Consequently, SCM leads to substantial economic losses and may pose public-health risks through contamination of the milk supply (Sajib et al. 2024 ). SCC is the most widely used indicator of udder health. Traditionally, SCM has been defined using a single cutoff, commonly > 200,000 cells/mL (Martins et al. 2020 ). However, growing evidence indicates that graded SCC thresholds improve disease characterization. Cows with SCC between 100,000 and 200,000 cells/mL show early inflammatory responses and microbial shifts compared with cows below 100,000 cells/mL (Zhang et al. 2022 ). In addition, optimal SCC thresholds vary by sampling strategy, with lower cutoffs recommended for composite samples than for quarter-level diagnosis (Petzer et al. 2017 ). These findings support categorizing SCM into multiple severity levels for improved detection and management. SCM is highly prevalent in Bangladesh, with reported cow-level prevalence ranging from approximately 28% to 67.9% across regions (Bari et al. 2022 ; Sayeed et al. 2020 ). Risk factors include crossbred status, older age, higher parity, early lactation stage, and high milk yield, along with management factors such as poor hygiene and milking practices (Meher et al. 2018 ; Sajib et al. 2024 ). Variations in prevalence and determinants are also observed across production systems, emphasizing the need for context-specific analyses (Kitila et al. 2021 ). Despite extensive research, most studies treat SCM as a binary outcome, overlooking heterogeneity between infection severities. Few studies apply multinomial approaches to distinguish risk factors for mild versus severe SCM (Fuenzalida et al. 2015 ) , limiting targeted intervention strategies. Therefore, this study (i) classifies cows into healthy, mild SCM, and severe SCM using graded SCC thresholds; (ii) compares prevalence across production systems in Sirajganj District, Bangladesh; and (iii) applies multinomial regression to identify risk factors associated with each SCM severity category. Material and Methods Study Area, design, population and management systems A cross-sectional study was conducted from January 2023 to December 2024 in Sirajganj District, Bangladesh. The study population comprised predominantly high-yielding dairy cattle, mainly Holstein Friesian crosses, with smaller numbers of Jersey and Sahiwal crosses. Cattle in this region are managed under a seasonal production system alternating between extensive (bathan grazing) and intensive/semi-intensive housing. In the intensive system (monsoon season), cattle are stall-fed with limited movement, receiving rice straw supplemented with concentrates. Herd sizes typically range from 10 to 80 animals. In the extensive system (December–June), cattle graze freely on cultivated bathan fields (e.g., Jumbo grass, black gram), with temporary shelters used for housing and milking. Herd sizes in this system range from 50 to 300 animals. Sampling strategy and data collection A two-stage sampling approach was applied. First, a sampling frame of active dairy herds was obtained from Milk Vita, from which 76 herds were randomly selected. Second, all lactating cows within selected herds were included, yielding a final sample of 3,173 cows. Sample size was calculated using the standard formula: $$\:n=\frac{{Z}^{2}pq}{{d}^{2}}$$ Assuming an expected prevalence of 44% (Bari et al. 2022 ), 95% confidence level (Z = 1.96), and 1.8% precision, the minimum required sample size was 2,921 cows. Cow-level data were collected using a pre-tested questionnaire, including age, breed, parity, lactation stage, milk yield, pregnancy status, and history of clinical mastitis (Supplementary file 1). A 40 mL composite milk sample was collected aseptically from each cow (10 mL per quarter) into sterile tubes and transported on ice to the laboratory. Somatic cell count and outcome definition Somatic cell count (SCC) was measured using an EKOMILK SCAN® analyzer. Based on the SCC distribution, cows were categorized into three levels: Healthy (SCC ≤ 147,292 cells/mL), Mild SCM (147,292 799,051 cells/mL). Statistical analysis Continuous variables (age, milk yield) were standardized (mean = 0, SD = 1), while parity and lactation stage were categorized into quintiles. Categorical predictors were re-leveled using the lowest-prevalence category as the reference. Univariable Bayesian multinomial models were initially fitted to screen predictors for inclusion in the multivariable analysis. Associations between predictors and SCM were evaluated using a Bayesian hierarchical multinomial logistic regression model, with “Healthy” as the reference category and a random intercept for herd to account for clustering. Multicollinearity was assessed using Cramér’s V (> 0.5) and variance inflation factors (> 5). The final model included predictors selected based on biological relevance and univariable evidence, estimating log-odds for Mild and Severe SCM relative to Healthy. Weakly informative priors were specified as Student-t (3, 0, 2.5) for fixed effects and Half-Student-t (3, 0, 2.5) for random-effect standard deviations. Models were implemented in R (version 4.5.0) using the brms package with Stan. Three chains of 33,000 iterations (5,000 warm-up) were run. Convergence was assessed using trace plots and \(\:\widehat{R}\) statistics. Results are reported as Relative Risk Ratios (RRR) with 95% credible intervals (CrI). The R code used for all analyses is provided in Supplementary file 2. Results Descriptive statistics and SCM prevalence The mean age of the 3,173 cows was 5.4 years, mean parity 3.0, and average milk yield 9.6 L/day. The overall mean SCC was 436,863 ± 466,611 cells/mL. SCM prevalence was high: 35.9% were healthy, 44.7% had mild SCM, and 19.4% had severe SCM. Healthy cows tended to be younger with higher milk yield. SCC increased markedly with severity. Prevalence was comparable between intensive (63.3%) and extensive (64.5%) systems (Table 1 ). Table 1 Descriptive statistics of continuous predictors and prevalence of subclinical mastitis (SCM) by severity and management system in Sirajganj, Bangladesh ( \(\:n=3173\) ). Variable Healthy (n = 1,138) Mild SCM (n = 1,419) Severe SCM (n = 616) Continuous Variables (Mean ± SD) Age (years) 5.1 ± 2.1 5.5 ± 2.1 5.9 ± 2.3 Parity 3.0 ± 2.0 3.0 ± 2.0 4.0 ± 2.0 Milk yield (L/day) 10.1 ± 3.9 9.4 ± 4.1 8.9 ± 4.2 Lactation stage (days) 5.5 ± 3.5 6.7 ± 4.2 6.8 ± 4.1 Somatic Cell Count (×10³ cells/mL) 102.0 ± 17.6 322.7 ± 145.2 1318.3 ± 241.1 Prevalence, n (%) Overall 1,138 (35.9%) 1,419 (44.7%) 616 (19.4%) Management System Extensive (n = 2,208) 784 (35.5%) 1,011 (45.8%) 413 (18.7%) Intensive (n = 965) 354 (36.7%) 408 (42.3%) 203 (21.0%) The association between overall subclinical mastitis and the continuous predictors—age (Supplementary Fig. 1), milk yield (Supplementary Fig. 2), lactation stage (Supplementary Fig. 3), and parity (Supplementary Fig. 4)—was examined by illustrating how SCM prevalence changed across the range of each variable. Risk factors for mild and severe SCM Results of the univariable multinomial models used for predictor screening are provided in Supplementary file 3. Multivariable analysis identified lactation stage, parity, milk yield, and prior mastitis history as independent predictors of SCM severity (Fig. 1 ). Late lactation (Q5) was strongly associated with increased risk of both mild (RRR = 2.03; 95% CrI: 1.55–2.69) and severe SCM (RRR = 1.92; 95% CrI: 1.36–2.72), relative to mid-lactation (Q4). Lower parity cows (Q1) had reduced risk of SCM, particularly for severe disease (RRR = 0.46; 95% CrI: 0.33–0.65). In contrast, moderate parity (Q3) showed a modest increase in mild SCM risk. Milk yield was inversely associated with severe SCM (RRR = 0.79; 95% CrI: 0.69–0.89), indicating lower risk among higher-producing cows. History of clinical mastitis was the strongest predictor. Compared with cows with $ \ge $ 3 prior episodes, those with one previous episode had substantially higher risk of severe SCM (RRR = 4.26; 95% CrI: 2.20–8.54). Cows with no prior mastitis showed lower risk of severe SCM but a modest increase in mild SCM. Discussion This study quantified the prevalence and cow-level determinants of subclinical mastitis (SCM) in high-density dairy herds in Sirajganj, Bangladesh, using graded somatic cell count (SCC) thresholds to distinguish mild and severe infections. SCM was highly prevalent, affecting nearly two-thirds of cows, with comparable overall prevalence across management systems. These findings highlight that individual cow characteristics and past clinical history are crucial considerations for designing effective, localized SCM interventions in high-density settings. The absence of meaningful differences between extensive and intensive systems suggests that management classification alone is insufficient to explain SCM risk. Instead, this aligns with evidence that parity, milk yield, and hygiene-related factors play a more decisive role than system-level distinctions (Kitila et al. 2021 ). This emphasizes the importance of targeted, cow-level approaches over generalized system-based interventions. Milk yield showed an inverse association with severe SCM, indicating reduced risk among higher-producing cows. Similar findings have been reported elsewhere (Schunig et al. 2024 ). However, contrasting evidence from Bangladesh suggests increased SCM risk in high-producing animals due to metabolic stress and immune compromise (Sajib et al. 2024 ). This discrepancy likely reflects context-specific interactions between productivity, nutrition, and management. In the present setting, higher-yielding cows may receive better nutritional and managerial support, contributing to improved udder health. Lactation stage emerged as a strong determinant, with late-lactation cows at significantly higher risk of both mild and severe SCM. This is consistent with increased SCC associated with prolonged pathogen exposure and mammary involution (Nabi et al. 2024 ). Although early lactation is often linked to elevated susceptibility due to metabolic stress (Meher et al. 2018 ), no clear association was observed here, suggesting that risk varies dynamically across the production cycle. Parity and prior mastitis history were also important predictors. Lower parity cows had reduced risk, whereas older animals showed increased susceptibility, consistent with cumulative physiological stress (Sharma et al. 2023 ). A history of clinical mastitis was the strongest predictor of severe SCM, supporting evidence of heightened risk of recurrence (Sordillo 2018 ). Overall, these findings support a risk-based control strategy focusing on late-lactation cows and those with prior mastitis. Such targeted interventions may improve surveillance efficiency and reduce economic losses more effectively than uniform herd-level approaches. However, the cross-sectional design and reliance on single time-point SCC measurements limit causal inference. Longitudinal studies are needed to better understand temporal dynamics and optimize intervention strategies. Conclusion Subclinical mastitis remains highly prevalent in dairy herds in Sirajganj, with its burden driven primarily by cow-level factors rather than management system type. Late lactation, higher parity, lower milk yield, and prior clinical mastitis were the strongest predictors of both mild and severe SCM. These findings support a targeted control strategy focused on high-risk cows, which may improve surveillance efficiency and reduce unnecessary interventions. Future studies should integrate microbiome-based approaches to better characterize pathogen dynamics across SCM severity and refine early detection and cow-specific management strategies. Declarations Ethics Statement The study was conducted in accordance with the Declaration of Helsinki. The animal study protocol was approved by the Animal Welfare and Experimentation Ethical Committee (AWEEC) of Bangladesh Agricultural University (AWEEC/BAU/2022/ approved on 20 December 2022). Milk samples were collected by trained animal handlers or caretakers in a manner that caused no pain, distress, or harm to the animals. Funding This study was supported by Bangladesh Agricultural University Research System Funded project: 2021/1399/BAU. Data Availability Statement The data supporting the findings of this study are included within the manuscript and its supplementary materials. Acknowledgments The authors are grateful to the dairy farmers for participating in this study and for providing milk samples and data. Conflicts of Interest The authors declare no conflicts of interest. Authors contributions Muhammad Aktaruzzaman and M. Ariful Islam contributed equally to the study design, data collection, and manuscript drafting. Solama Akter Shanta and Mst. Tahomina Akter contributed to data analysis, interpretation, and critical revision of the manuscript. Muhammad Tofazzal Hossain provided guidance on methodology, study design, and overall supervision. Md. Nazmul Islam and Md. Shaffiul Alam assisted with fieldwork, data curation, and laboratory analysis. A K M Anisur Rahman supervised the study, contributed to software and data visualization, and finalized the manuscript. All authors read and approved the final version of the manuscript. References Arefin K, Nizami T (2025) Prevalence and risk factors of mastitis in dairy cows in Chattogram, Bangladesh. 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Animals 12:1694. https://doi.org/10.3390/ani12131694 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfilecaptions.docx Supplementaryfile1SCCdata.csv Supplementaryfile2Rcode.rtf Supplementaryfile3.docx Supplementaryfile4.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9371264","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":629245938,"identity":"97273359-b58f-456c-b15e-86a5f7cd1ae4","order_by":0,"name":"Muhammad Aktaruzzaman","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Aktaruzzaman","suffix":""},{"id":629245940,"identity":"3fdecb98-b64a-4c25-904c-1e84a78ea6ce","order_by":1,"name":"Md. 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Tahomina Akter","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mst.","middleName":"Tahomina","lastName":"Akter","suffix":""},{"id":629245943,"identity":"b65b885d-38f5-4552-b016-77909b3be3d2","order_by":4,"name":"Muhammad Tofazzal Hossain","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Tofazzal","lastName":"Hossain","suffix":""},{"id":629245944,"identity":"5a6f3df8-b74d-4f6d-934c-bdcfedffdc98","order_by":5,"name":"Md. Nazmul Islam","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Nazmul","lastName":"Islam","suffix":""},{"id":629245945,"identity":"a07340bc-6c0b-4e86-9279-721acefe840e","order_by":6,"name":"Md. Shaffiul Alam","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Shaffiul","lastName":"Alam","suffix":""},{"id":629245946,"identity":"92483191-6929-4693-b9b1-de2667fb94c6","order_by":7,"name":"A K M Anisur Rahman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYHACNhjD8DGETmBgRgji12JsTLIWM2mitPDPPvzs0Y2aOwy67c3bqgtqDjPws+cYMBeU4dYicS7N3Djn2DMGszPHym7POHaYQbLnjQHzjHN4nHUG6J4ctsMMZjdyzG7zABkGN4C28Lbh1iF/hv2bdM4/oJb7b8yKeYAMe0JaDM7wmEnntoFs4TEDqgTaIkFAi+EZnjLp3D6gljNpxdIz+9J5JM48KziMzy9yZ9i3Sed8A2o5fnjj54Jv1nL87ckbH+MLMRiob4AyeEDEAcIaRsEoGAWjYBTgAwAbAE4DjK/RlgAAAABJRU5ErkJggg==","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"A","middleName":"K M Anisur","lastName":"Rahman","suffix":""}],"badges":[],"createdAt":"2026-04-09 17:09:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9371264/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9371264/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107965672,"identity":"f63831b3-3e2e-497b-8c85-52e722c5c8dc","added_by":"auto","created_at":"2026-04-28 05:41:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4695336,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated posterior relative risk ratios (95% credible intervals) for factors associated with mild and severe subclinical mastitis in dairy cows, based on the Bayesian multinomial mixed-effects regression model\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9371264/v1/739d2c269f598b8f02171142.png"},{"id":108006452,"identity":"fef8cb2c-e827-4563-a322-a5e935fd264e","added_by":"auto","created_at":"2026-04-28 12:55:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4896792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9371264/v1/65a2b2ac-5bfd-40d7-9d21-71ab9c0f9666.pdf"},{"id":107965813,"identity":"efc2618b-4504-448a-a876-d0ccf4ec966d","added_by":"auto","created_at":"2026-04-28 05:41:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16264,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfilecaptions.docx","url":"https://assets-eu.researchsquare.com/files/rs-9371264/v1/df5c598a3a5cdef681a121b1.docx"},{"id":107965668,"identity":"b2214efd-4d7a-4fe2-a2f7-3b003f0f88dc","added_by":"auto","created_at":"2026-04-28 05:41:02","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":237800,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1SCCdata.csv","url":"https://assets-eu.researchsquare.com/files/rs-9371264/v1/913e409e1b8fff62b6030e93.csv"},{"id":107965670,"identity":"7723e2ef-4c60-4af4-8de5-2db5e5a465ae","added_by":"auto","created_at":"2026-04-28 05:41:02","extension":"rtf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9984,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2Rcode.rtf","url":"https://assets-eu.researchsquare.com/files/rs-9371264/v1/7083e861d2f599295ce9cfc9.rtf"},{"id":107965745,"identity":"95e3ec59-762b-4f21-825c-0c9091506cae","added_by":"auto","created_at":"2026-04-28 05:41:17","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26601,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9371264/v1/5b220325a6af0026df1c16fd.docx"},{"id":107965744,"identity":"6fc8ec6b-70a2-4adc-a94d-2c63421a4d42","added_by":"auto","created_at":"2026-04-28 05:41:17","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1067138,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-9371264/v1/a95af2bc465ee7649a11e13e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and cow-level risk factors for mild and severe subclinical mastitis in intensive and extensive dairy systems in Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMastitis, an inflammation of the mammary gland, remains one of the most economically important infectious diseases in dairy cattle worldwide (Sharun et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Subclinical mastitis (SCM) is particularly problematic because affected cows show no visible clinical signs, yet exhibit elevated somatic cell counts (SCC), reduced milk yield, and compromised milk quality. Consequently, SCM leads to substantial economic losses and may pose public-health risks through contamination of the milk supply (Sajib et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSCC is the most widely used indicator of udder health. Traditionally, SCM has been defined using a single cutoff, commonly\u0026thinsp;\u0026gt;\u0026thinsp;200,000 cells/mL (Martins et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, growing evidence indicates that graded SCC thresholds improve disease characterization. Cows with SCC between 100,000 and 200,000 cells/mL show early inflammatory responses and microbial shifts compared with cows below 100,000 cells/mL (Zhang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, optimal SCC thresholds vary by sampling strategy, with lower cutoffs recommended for composite samples than for quarter-level diagnosis (Petzer et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These findings support categorizing SCM into multiple severity levels for improved detection and management.\u003c/p\u003e \u003cp\u003eSCM is highly prevalent in Bangladesh, with reported cow-level prevalence ranging from approximately 28% to 67.9% across regions (Bari et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sayeed et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Risk factors include crossbred status, older age, higher parity, early lactation stage, and high milk yield, along with management factors such as poor hygiene and milking practices (Meher et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sajib et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Variations in prevalence and determinants are also observed across production systems, emphasizing the need for context-specific analyses (Kitila et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite extensive research, most studies treat SCM as a binary outcome, overlooking heterogeneity between infection severities. Few studies apply multinomial approaches to distinguish risk factors for mild versus severe SCM (Fuenzalida et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) , limiting targeted intervention strategies.\u003c/p\u003e \u003cp\u003eTherefore, this study (i) classifies cows into healthy, mild SCM, and severe SCM using graded SCC thresholds; (ii) compares prevalence across production systems in Sirajganj District, Bangladesh; and (iii) applies multinomial regression to identify risk factors associated with each SCM severity category.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area, design, population and management systems\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted from January 2023 to December 2024 in Sirajganj District, Bangladesh. The study population comprised predominantly high-yielding dairy cattle, mainly Holstein Friesian crosses, with smaller numbers of Jersey and Sahiwal crosses. Cattle in this region are managed under a seasonal production system alternating between extensive (bathan grazing) and intensive/semi-intensive housing. In the intensive system (monsoon season), cattle are stall-fed with limited movement, receiving rice straw supplemented with concentrates. Herd sizes typically range from 10 to 80 animals. In the extensive system (December\u0026ndash;June), cattle graze freely on cultivated bathan fields (e.g., Jumbo grass, black gram), with temporary shelters used for housing and milking. Herd sizes in this system range from 50 to 300 animals.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling strategy and data collection\u003c/h3\u003e\n\u003cp\u003eA two-stage sampling approach was applied. First, a sampling frame of active dairy herds was obtained from Milk Vita, from which 76 herds were randomly selected. Second, all lactating cows within selected herds were included, yielding a final sample of 3,173 cows.\u003c/p\u003e \u003cp\u003eSample size was calculated using the standard formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n=\\frac{{Z}^{2}pq}{{d}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAssuming an expected prevalence of 44% (Bari et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), 95% confidence level (Z\u0026thinsp;=\u0026thinsp;1.96), and 1.8% precision, the minimum required sample size was 2,921 cows.\u003c/p\u003e \u003cp\u003eCow-level data were collected using a pre-tested questionnaire, including age, breed, parity, lactation stage, milk yield, pregnancy status, and history of clinical mastitis (Supplementary file 1). A 40 mL composite milk sample was collected aseptically from each cow (10 mL per quarter) into sterile tubes and transported on ice to the laboratory.\u003c/p\u003e\n\u003ch3\u003eSomatic cell count and outcome definition\u003c/h3\u003e\n\u003cp\u003eSomatic cell count (SCC) was measured using an EKOMILK SCAN\u0026reg; analyzer. Based on the SCC distribution, cows were categorized into three levels: Healthy (SCC\u0026thinsp;\u0026le;\u0026thinsp;147,292 cells/mL), Mild SCM (147,292\u0026thinsp;\u0026lt;\u0026thinsp;SCC\u0026thinsp;\u0026le;\u0026thinsp;799,051 cells/mL), and Severe SCM (SCC\u0026thinsp;\u0026gt;\u0026thinsp;799,051 cells/mL).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables (age, milk yield) were standardized (mean\u0026thinsp;=\u0026thinsp;0, SD\u0026thinsp;=\u0026thinsp;1), while parity and lactation stage were categorized into quintiles. Categorical predictors were re-leveled using the lowest-prevalence category as the reference. Univariable Bayesian multinomial models were initially fitted to screen predictors for inclusion in the multivariable analysis. Associations between predictors and SCM were evaluated using a Bayesian hierarchical multinomial logistic regression model, with \u0026ldquo;Healthy\u0026rdquo; as the reference category and a random intercept for herd to account for clustering. Multicollinearity was assessed using Cram\u0026eacute;r\u0026rsquo;s V (\u0026gt;\u0026thinsp;0.5) and variance inflation factors (\u0026gt;\u0026thinsp;5). The final model included predictors selected based on biological relevance and univariable evidence, estimating log-odds for Mild and Severe SCM relative to Healthy. Weakly informative priors were specified as Student-t (3, 0, 2.5) for fixed effects and Half-Student-t (3, 0, 2.5) for random-effect standard deviations. Models were implemented in R (version 4.5.0) using the \u003cem\u003ebrms\u003c/em\u003e package with Stan. Three chains of 33,000 iterations (5,000 warm-up) were run. Convergence was assessed using trace plots and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{R}\\)\u003c/span\u003e\u003c/span\u003e statistics. Results are reported as Relative Risk Ratios (RRR) with 95% credible intervals (CrI). The R code used for all analyses is provided in Supplementary file 2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics and SCM prevalence\u003c/h2\u003e \u003cp\u003eThe mean age of the 3,173 cows was 5.4 years, mean parity 3.0, and average milk yield 9.6 L/day. The overall mean SCC was 436,863\u0026thinsp;\u0026plusmn;\u0026thinsp;466,611 cells/mL. SCM prevalence was high: 35.9% were healthy, 44.7% had mild SCM, and 19.4% had severe SCM. Healthy cows tended to be younger with higher milk yield. SCC increased markedly with severity. Prevalence was comparable between intensive (63.3%) and extensive (64.5%) systems (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of continuous predictors and prevalence of subclinical mastitis (SCM) by severity and management system in Sirajganj, Bangladesh (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=3173\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,138)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild SCM\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,419)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere SCM\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;616)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous Variables (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk yield (L/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactation stage (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomatic Cell Count (\u0026times;10\u0026sup3; cells/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322.7\u0026thinsp;\u0026plusmn;\u0026thinsp;145.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1318.3\u0026thinsp;\u0026plusmn;\u0026thinsp;241.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevalence, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,138 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,419 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e616 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eManagement System\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtensive (n\u0026thinsp;=\u0026thinsp;2,208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e784 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,011 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e413 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive (n\u0026thinsp;=\u0026thinsp;965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e354 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e408 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe association between overall subclinical mastitis and the continuous predictors\u0026mdash;age (Supplementary Fig.\u0026nbsp;1), milk yield (Supplementary Fig.\u0026nbsp;2), lactation stage (Supplementary Fig.\u0026nbsp;3), and parity (Supplementary Fig.\u0026nbsp;4)\u0026mdash;was examined by illustrating how SCM prevalence changed across the range of each variable.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk factors for mild and severe SCM\u003c/h3\u003e\n\u003cp\u003eResults of the univariable multinomial models used for predictor screening are provided in Supplementary file 3. Multivariable analysis identified lactation stage, parity, milk yield, and prior mastitis history as independent predictors of SCM severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Late lactation (Q5) was strongly associated with increased risk of both mild (RRR\u0026thinsp;=\u0026thinsp;2.03; 95% CrI: 1.55\u0026ndash;2.69) and severe SCM (RRR\u0026thinsp;=\u0026thinsp;1.92; 95% CrI: 1.36\u0026ndash;2.72), relative to mid-lactation (Q4). Lower parity cows (Q1) had reduced risk of SCM, particularly for severe disease (RRR\u0026thinsp;=\u0026thinsp;0.46; 95% CrI: 0.33\u0026ndash;0.65). In contrast, moderate parity (Q3) showed a modest increase in mild SCM risk. Milk yield was inversely associated with severe SCM (RRR\u0026thinsp;=\u0026thinsp;0.79; 95% CrI: 0.69\u0026ndash;0.89), indicating lower risk among higher-producing cows. History of clinical mastitis was the strongest predictor. Compared with cows with \u003cspan\u003e$\u003c/span\u003e\\ge\u003cspan\u003e$\u003c/span\u003e3 prior episodes, those with one previous episode had substantially higher risk of severe SCM (RRR\u0026thinsp;=\u0026thinsp;4.26; 95% CrI: 2.20\u0026ndash;8.54). Cows with no prior mastitis showed lower risk of severe SCM but a modest increase in mild SCM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study quantified the prevalence and cow-level determinants of subclinical mastitis (SCM) in high-density dairy herds in Sirajganj, Bangladesh, using graded somatic cell count (SCC) thresholds to distinguish mild and severe infections. SCM was highly prevalent, affecting nearly two-thirds of cows, with comparable overall prevalence across management systems. These findings highlight that individual cow characteristics and past clinical history are crucial considerations for designing effective, localized SCM interventions in high-density settings.\u003c/p\u003e \u003cp\u003eThe absence of meaningful differences between extensive and intensive systems suggests that management classification alone is insufficient to explain SCM risk. Instead, this aligns with evidence that parity, milk yield, and hygiene-related factors play a more decisive role than system-level distinctions (Kitila et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This emphasizes the importance of targeted, cow-level approaches over generalized system-based interventions.\u003c/p\u003e \u003cp\u003eMilk yield showed an inverse association with severe SCM, indicating reduced risk among higher-producing cows. Similar findings have been reported elsewhere (Schunig et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, contrasting evidence from Bangladesh suggests increased SCM risk in high-producing animals due to metabolic stress and immune compromise (Sajib et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This discrepancy likely reflects context-specific interactions between productivity, nutrition, and management. In the present setting, higher-yielding cows may receive better nutritional and managerial support, contributing to improved udder health.\u003c/p\u003e \u003cp\u003eLactation stage emerged as a strong determinant, with late-lactation cows at significantly higher risk of both mild and severe SCM. This is consistent with increased SCC associated with prolonged pathogen exposure and mammary involution (Nabi et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although early lactation is often linked to elevated susceptibility due to metabolic stress (Meher et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), no clear association was observed here, suggesting that risk varies dynamically across the production cycle.\u003c/p\u003e \u003cp\u003eParity and prior mastitis history were also important predictors. Lower parity cows had reduced risk, whereas older animals showed increased susceptibility, consistent with cumulative physiological stress (Sharma et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A history of clinical mastitis was the strongest predictor of severe SCM, supporting evidence of heightened risk of recurrence (Sordillo \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, these findings support a risk-based control strategy focusing on late-lactation cows and those with prior mastitis. Such targeted interventions may improve surveillance efficiency and reduce economic losses more effectively than uniform herd-level approaches. However, the cross-sectional design and reliance on single time-point SCC measurements limit causal inference. Longitudinal studies are needed to better understand temporal dynamics and optimize intervention strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSubclinical mastitis remains highly prevalent in dairy herds in Sirajganj, with its burden driven primarily by cow-level factors rather than management system type. Late lactation, higher parity, lower milk yield, and prior clinical mastitis were the strongest predictors of both mild and severe SCM. These findings support a targeted control strategy focused on high-risk cows, which may improve surveillance efficiency and reduce unnecessary interventions. Future studies should integrate microbiome-based approaches to better characterize pathogen dynamics across SCM severity and refine early detection and cow-specific management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Statement\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. The animal study protocol was approved by the Animal Welfare and Experimentation Ethical Committee (AWEEC) of Bangladesh Agricultural University (AWEEC/BAU/2022/ approved on 20 December 2022). Milk samples were collected by trained animal handlers or caretakers in a manner that caused no pain, distress, or harm to the animals.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by Bangladesh Agricultural University Research System Funded project: 2021/1399/BAU.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The data supporting the findings of this study are included within the manuscript and its supplementary materials.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors are grateful to the dairy farmers for participating in this study and for providing milk samples and data.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eAuthors contributions\u003c/p\u003e\n\u003cp\u003eMuhammad Aktaruzzaman and M. Ariful Islam contributed equally to the study design, data collection, and manuscript drafting. Solama Akter Shanta and Mst. Tahomina Akter contributed to data analysis, interpretation, and critical revision of the manuscript. Muhammad Tofazzal Hossain provided guidance on methodology, study design, and overall supervision. Md. Nazmul Islam and Md. Shaffiul Alam assisted with fieldwork, data curation, and laboratory analysis. A K M Anisur Rahman supervised the study, contributed to software and data visualization, and finalized the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArefin K, Nizami T (2025) Prevalence and risk factors of mastitis in dairy cows in Chattogram, Bangladesh. 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Animals 12:1694. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ani12131694\u003c/span\u003e\u003cspan address=\"10.3390/ani12131694\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"veterinary-research-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"verc","sideBox":"Learn more about [Veterinary Research Communications](https://www.springer.com/journal/11259)","snPcode":"11259","submissionUrl":"https://submission.nature.com/new-submission/11259/3","title":"Veterinary Research Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multinomial, Bayesian, Relative Risk Ratio, Lactation stage, Parity, Milk yield","lastPublishedDoi":"10.21203/rs.3.rs-9371264/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9371264/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSubclinical mastitis (SCM) substantially limits dairy productivity in Bangladesh, yet evidence from high-density herds remains limited. This study estimated the prevalence of mild and severe SCM and identified cow-level risk factors. A cross-sectional survey was conducted from January 2023 to December 2024 across 76 herds using a two-stage sampling design, enrolling all lactating cows (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\:=\\:\\text{3,173}\\)\u003c/span\u003e\u003c/span\u003e). Composite milk samples were tested using EKOMILK SCAN\u0026reg; to measure somatic cell count (SCC) and classify SCM status. Cow-level data were collected via structured questionnaires. Descriptive statistics summarized herd characteristics, while a multivariable Bayesian hierarchical multinomial regression model evaluated predictors. Model adequacy was assessed through trace plots, R-hat values, and posterior predictive checks. SCM prevalence did not differ significantly between extensive (64.5%) and intensive (63.3%) systems. Late lactation significantly increased the risk of (relative risk ratio [RRR] 2.03; 95% credible interval [CrI] 1.55\u0026ndash;2.69) and severe SCM (RRR 1.92; 95% CrI 1.36\u0026ndash;2.72). A previous clinical mastitis episode strongly elevated severe SCM risk (RRR 4.26; 95% CrI 2.20\u0026ndash;8.54). Lower-parity and higher-producing cows showed reduced SCM risk. SCM risk in high-density herds is largely influenced by lactation stage, parity, milk yield, and mastitis history. Targeting late-lactation cows and animals with prior mastitis may improve surveillance efficiency and reduce the overall SCM burden more effectively than uniform herd-level approaches.\u003c/p\u003e","manuscriptTitle":"Prevalence and cow-level risk factors for mild and severe subclinical mastitis in intensive and extensive dairy systems in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 05:39:57","doi":"10.21203/rs.3.rs-9371264/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"119297523076387230821024215426148150877","date":"2026-05-18T06:06:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2337586207686285171794026374604070967","date":"2026-04-24T23:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-19T22:27:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T17:35:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T17:35:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Veterinary Research Communications","date":"2026-04-09T17:00:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"veterinary-research-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"verc","sideBox":"Learn more about [Veterinary Research Communications](https://www.springer.com/journal/11259)","snPcode":"11259","submissionUrl":"https://submission.nature.com/new-submission/11259/3","title":"Veterinary Research Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9f1e66c1-9dac-4ad3-90ba-803dcdba7e3b","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"119297523076387230821024215426148150877","date":"2026-05-18T06:06:57+00:00","index":120,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T05:39:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 05:39:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9371264","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9371264","identity":"rs-9371264","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}