{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T05:07:43Z","timestamp":1784092063853,"version":"3.55.0"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Milk somatic cell count (SCC) and somatic cell score (SCS) are good indicators of the occurrence of subclinical mastitis (SCM) in dairy cows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, multi-factor ANOVA model was used to analyze the fixed effects of 6 factors, including farm size, management, parity, calving season, lactation stage, and previous SCS (PSCS), on the current month SCS in milk of dairy cows in a training dataset containing 858,546 dairy herd improvement (DHI) records from 12 dairy farms in Jiangsu Province of China. The Logistic regression models of SCS or SCM were established for each factor and and then valiated with a separate dataset containing 27,575 DHI records from 6 other dairy farms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The results showed that the six factors had significant effects on SCS and SCM incidence in dairy cows (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.01). Among them, milk SCS was significantly higher in the farms with fewer than 2,000 cows and fermented bed breeding, and in the cows having more than 5 parities, calving in summer, in late lactation, and when PSCS was 9. The odds ratios (OR) at different levels of each factor were consistent with the actual incidence of SCM. Among them, SCM incidence and OR of cows were lowest when farms had more than 10,000 cows, free stalls, when cows were in first parity, and during the autumn and mid lactation, and when the PSCS was 0. Prediction accuracy and balanced accuracy of risk of SCM during validation were 87.5 and 56.0%. The multivariable logistic regression model demonstrated fair discriminative ability (AUC\u202f=\u202f0.771). However, due to its low sensitivity at the default threshold, the model should not be recommended as a standalone farm-level early warning tool without threshold calibration and external validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The model may be useful for risk stratification (e.g., identifying high-risk subgroups), but prospective validation is needed before field application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fvets.2026.1863556","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T04:42:42Z","timestamp":1784090562000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Risk assessment of subclinical mastitis in Holstein cows in Jiangsu Province of China"],"prefix":"10.3389","volume":"13","author":[{"given":"Yan","family":"Liang","sequence":"first","affiliation":[{"name":"College of Animal Science, Anhui Science and Technology University","place":["Chuzhou, China"]},{"name":"College of Animal Science and Technology, Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaozheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Animal Science, Anhui Science and Technology University","place":["Chuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Animal Science, Anhui Science and Technology University","place":["Chuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niel A.","family":"Karrow","sequence":"additional","affiliation":[{"name":"Department of Animal Biosciences, Center for Genetic Improvement of Livestock, University of Guelph","place":["Guelph, ON, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinling","family":"Hua","sequence":"additional","affiliation":[{"name":"College of Animal Science, Anhui Science and Technology University","place":["Chuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjiang","family":"Mao","sequence":"additional","affiliation":[{"name":"College of Animal Science and Technology, Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.3390\/molecules27061829","article-title":"The influence of bacteria causing subclinical mastitis on the structure of the cow's milk microbiome","volume":"27","author":"Kaczorowski","year":"2022","journal-title":"Molecules"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"4354","DOI":"10.3168\/jds.2021-20703","article-title":"Effects of bovine casein hydrolysate as a dry cow therapy on prevention and cure of bovine intramammary infection, milk production, and somatic cell count in the subsequent lactation","volume":"105","author":"Shoshani","year":"2022","journal-title":"J Dairy Sci"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"3588","DOI":"10.3168\/jds.2017-13840","article-title":"Estimating milk yield and value losses from increased somatic cell count on US dairy farms","volume":"101","author":"Hadrich","year":"2018","journal-title":"J Dairy Sci"},{"key":"ref4","volume-title":"Isolation, Identification and Drug Resistance Analysis of Pathogenic Bacteria of Dairy cow Mastitis in Shanghai Area","author":"Zhu","year":"2020"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"690377","DOI":"10.3389\/fcimb.2021.690377","article-title":"Bacteriophage cocktails protect dairy cows against mastitis caused by drug resistant Escherichia coli infection","volume":"11","author":"Guo","year":"2021","journal-title":"Front Cell Infect Microbiol"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.rvsc.2019.11.009","article-title":"Evaluation of a bovine cathelicidin ELISA for detecting mastitis in the dairy buffalo: comparison with milk somatic cell count and bacteriological culture","volume":"128","author":"Puggioni","year":"2020","journal-title":"Res Vet Sci"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.prevetmed.2009.08.015","article-title":"Explaining mastitis incidence in Dutch dairy farming: the influence of farmers\u2019 attitudes and behaviour","volume":"92","author":"Jansen","year":"2009","journal-title":"Prev Vet Med"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"137","DOI":"10.15933\/j.cnki.1004-3268.2019.05.020","article-title":"Analysis of factors affecting the somatic cell score difference in Holstein","volume":"48","author":"Liang","year":"2019","journal-title":"J Henan Agric Sci"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"992","DOI":"10.3390\/ani10060992","article-title":"Factors affecting the patterns of total amount and proportions of leukocytes in bovine milk","volume":"10","author":"Zecconi","year":"2020","journal-title":"Animals"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"224","DOI":"10.3390\/pathogens10020224","article-title":"Dry period or early lactation-time of onset and associated risk factors for intramammary infections in dairy cows","volume":"10","author":"Nitz","year":"2021","journal-title":"Pathogens"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"955","DOI":"10.3760\/cma.j.issn.0253-9624.2019.09.018","article-title":"Overview of logistic regression model analysis and application","volume":"53","author":"Wang","year":"2019","journal-title":"Chin J Integr Med"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1001\/jama.2016.20441","article-title":"Logistic regression diagnostics: understanding how well a model predicts outcomes","volume":"317","author":"Meurer","year":"2017","journal-title":"J Am Med Assoc"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e001290","DOI":"10.1136\/fmch-2021-001290","article-title":"Primer on binary logistic regression","volume":"9","author":"Harris","year":"2021","journal-title":"Family Med Commun Health"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/7531371","article-title":"Establishment and verification of logistic regression model for qualitative diagnosis of ovarian cancer based on MRI and ultrasound signs","volume":"2022","author":"Guo","year":"2022","journal-title":"Comput Math Methods Med"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"2212624","DOI":"10.1155\/2022\/2212624","article-title":"Prevalence and triggering factors of childhood anemia: an application of ordinal logistic regression model","author":"Islam","year":"2022","journal-title":"Int J Clin Pract"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1177\/1460458219898568","article-title":"PredictMed: a logistic regression-based model to predict health conditions in cerebral palsy","volume":"26","author":"Bertoncelli","year":"2020","journal-title":"Health Informatics J"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"2666","DOI":"10.3168\/jds.S0022-0302(87)80337-5","article-title":"A lactation measure of somatic cell count","volume":"70","author":"Wiggans","year":"1987","journal-title":"J Dairy Sci"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.ajhg.2018.07.015","article-title":"A one-penny imputed genome from next generation reference panels","volume":"103","author":"Browning","year":"2018","journal-title":"Am J Hum Genet"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.rvsc.2015.09.021","article-title":"Polymorphisms in the promoter region of the bovine lactoferrin gene influence milk somatic cell score and milk production traits in Chinese Holstein cows","volume":"103","author":"Mao","year":"2015","journal-title":"Res Vet Sci"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"e857","DOI":"10.4088\/jcp.15f10150","article-title":"Understanding relative risk, odds ratio, and related terms: as simple as it can get","volume":"76","author":"Andrade","year":"2015","journal-title":"J Clin Psychiatry"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"503","DOI":"10.3390\/medicina57050503","article-title":"Foundational statistical principles in medical research: sensitivity, specificity, positive predictive value, and negative predictive value","volume":"57","author":"Monaghan","year":"2021","journal-title":"Medicina"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"2282","DOI":"10.3390\/ani10122282","article-title":"Polymorphisms of the ACSL1 gene influence Milk production traits and somatic cell score in Chinese Holstein cows","volume":"10","author":"Liang","year":"2020","journal-title":"Animals"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"3363","DOI":"10.3390\/ani11123363","article-title":"Detection of the Core Bacteria in colostrum and their association with the rectal microbiota and with Milk composition in two dairy cow farms","volume":"11","author":"Chen","year":"2021","journal-title":"Animals"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"13","DOI":"10.5713\/ab.20.0720","article-title":"Somatic cell score: gene polymorphisms and other effects in Holstein and Simmental cows","volume":"35","author":"\u010c\u00edtek","year":"2022","journal-title":"Anim Biosci"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"314","DOI":"10.5455\/javar.2020.g423","article-title":"Dynamics of somatic cell count and intramammary infection in lactating dairy cows","volume":"7","author":"Sumon","year":"2020","journal-title":"J Adv Vet Anim Res"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"5466","DOI":"10.3168\/jds.2017-13324","article-title":"Dairy farmers with larger herd sizes adopt more precision dairy technologies","volume":"101","author":"Gargiulo","year":"2018","journal-title":"J Dairy Sci"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"88","DOI":"10.13320\/j.cnki.jauh.2014.0070","article-title":"Milk production, nitrogen balance and utilization efficiency of lactating cows in dairy operations with different sizes","volume":"37","author":"Ma","year":"2014","journal-title":"J Hebei Agric Univ"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"78","DOI":"10.15933\/j.cnki.1004-3268.2019.05.020","article-title":"Serological epidemiological investigation of bovine infection with BPIv-3 and Brucella under different feeding modes","volume":"35","author":"Ma","year":"2019","journal-title":"J Baotou Med Coll"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1007\/s00203-020-02145-x","article-title":"Effect of fermentation bed on bacterial growth in the fermentation mattress material and cecum of ducks","volume":"203","author":"Wang","year":"2021","journal-title":"Arch Microbiol"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1017\/s0022029921000807","article-title":"Effects of milking, over-milking and vacuum levels on front and rear quarter teats in dairy cows","volume":"88","author":"Vierbauch","year":"2021","journal-title":"J Dairy Res"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.3168\/jds.2006-567","article-title":"The effect of season on somatic cell count and the incidence of clinical mastitis","volume":"90","author":"Riekerink","year":"2007","journal-title":"J Dairy Sci"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1080\/1745039X.2014.950451","article-title":"Impact of mild heat stress on dry matter intake, milk yield and milk composition in mid-lactation Holstein dairy cows in a temperate climate","volume":"68","author":"Gorniak","year":"2014","journal-title":"Arch Anim Nutr"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"1855","DOI":"10.2527\/jas.2011-4675","article-title":"Ruminant nutrition symposium: ruminant production and metabolic responses to heat stress","volume":"90","author":"Baumgard","year":"2012","journal-title":"J Anim Sci"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"644","DOI":"10.3168\/jds.2009-2295","article-title":"Effects of heat stress on energetic metabolism in lactating Holstein cows","volume":"93","author":"Wheelock","year":"2010","journal-title":"J Dairy Sci"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"61","DOI":"10.3168\/jds.2024-25357","article-title":"Factors influencing somatic cell count and leukocyte composition in cow milk: a field study","volume":"11","author":"Mondini","year":"2024","journal-title":"Dairy Sci"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"200","DOI":"10.12074\/201812.00842V1","article-title":"Multiple regression analysis on serum nutritional status indexes and antioxidant indexes in different lactation stages of dairy cows","volume":"30","author":"Qin","year":"2008","journal-title":"Chinese J Anim Nutr"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.3168\/jds.s0022-0302(05)72878-2","article-title":"Influence of body condition score on relationships between metabolic status and oxidative stress in periparturient dairy cows","volume":"88","author":"Bernabucci","year":"2005","journal-title":"J Dairy Sci"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1007\/s11250-012-0319-6","article-title":"Amelioration of altered oxidant\/antioxidant balance of Indian water buffaloes with subclinical mastitis by vitamins a, D3, E, and H supplementation","volume":"45","author":"Dimri","year":"2013","journal-title":"Trop Anim Health Prod"},{"key":"ref39","first-page":"3672","article-title":"Impact of early lactation somatic cell count in heifers on somatic cell counts over the first lactation","volume-title":"J Dairy Sci","author":"De Vliegher","year":"2004"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"13642","DOI":"10.1038\/s41598-021-93056-4","article-title":"Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows","volume":"11","author":"Bobbo","year":"2021","journal-title":"Sci Rep"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"4978","DOI":"10.3168\/jds.2022-22803","article-title":"Early detection of subclinical mastitis in lactating dairy cows using cow-level features","volume":"106","author":"Pakrashi","year":"2023","journal-title":"J Dairy Sci"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"45","DOI":"10.4103\/0301-4738.37595","article-title":"Understanding and using sensitivity, specificity and predictive values","volume":"56","author":"Parikh","year":"2008","journal-title":"Indian J Ophthalmol"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1258\/phleb.2012.012j06","article-title":"Diagnostic tests (2) - positive and negative predictive values","volume":"27","author":"Smith","year":"2012","journal-title":"Phlebology"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"1396799","DOI":"10.3389\/fvets.2025.1396799","article-title":"TBESO-BP: an improved regression model for predicting subclinical mastitis","volume":"12","author":"Han","year":"2025","journal-title":"Front Vet Sci"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"3959","DOI":"10.3168\/jds.2023-24243","article-title":"Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: bagging, boosting, stacking, and super-learner ensembles versus single machine learning models","volume":"107","author":"Satola","year":"2024","journal-title":"J Dairy Sci"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"126","DOI":"10.19556\/j.0258-7033.20240614-06","article-title":"Application effect analysis of cow mastitis risk assessment model in medium and small farms","volume":"61","author":"Zhang","year":"2025","journal-title":"Chin J Anim Sci"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"3427","DOI":"10.3390\/agriengineering6030195","article-title":"Influence of preprocessing methods of automated milking systems data on prediction of mastitis with machine learning models","volume":"6","author":"Kashongwe","year":"2024","journal-title":"Agri Eng"}],"container-title":["Frontiers in Veterinary Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fvets.2026.1863556\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T04:42:58Z","timestamp":1784090578000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fvets.2026.1863556\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"references-count":47,"alternative-id":["10.3389\/fvets.2026.1863556"],"URL":"https:\/\/doi.org\/10.3389\/fvets.2026.1863556","relation":{},"ISSN":["2297-1769"],"issn-type":[{"value":"2297-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]},"article-number":"1863556"}}