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Prospects for predictive modeling of transition cow diseases

Part of: Big Data

Published online by Cambridge University Press:  16 September 2019

Lauren Wisnieski Open the ORCID record for Lauren Wisnieski [Opens in a new window] ,
Bo Norby ,
Steven J. Pierce ,
Tyler Becker  and
Lorraine M. Sordillo
Lauren Wisnieski*
Affiliation:
Center for Research Outcomes and Epidemiology, Kansas State University, 310 Coles Hall, Manhattan, KS66506, USA
Bo Norby
Affiliation:
Department of Large Animal Clinical Sciences, Michigan State University, 736 Wilson Rd, Room A-201, East Lansing, MI48824, USA
Steven J. Pierce
Affiliation:
Center for Statistical Training and Consulting, Michigan State University, 293 Farm Lane, Room 100A, East Lansing, MI48824, USA
Tyler Becker
Affiliation:
Department of Food Science and Human Nutrition, Michigan State University, 469 Wilson Rd, Room 125, East Lansing, MI48824, USA
Lorraine M. Sordillo
Affiliation:
Department of Large Animal Clinical Sciences, Michigan State University, 736 Wilson Rd, Room A-201, East Lansing, MI48824, USA
*
Author for correspondence: Lauren Wisnieski, E-mail: [email protected]
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Abstract

Transition cow diseases can negatively impact animal welfare and reduce dairy herd profitability. Transition cow disease incidence has remained relatively stable over time despite monitoring and management efforts aimed to reduce the risk of developing diseases. Dairy cattle disease risk is monitored by assessing multiple factors, including certain biomarker test results, health records, feed intake, body condition score, and milk production. However, these factors, which are used to make herd management decisions, are often reviewed separately without considering the correlation between them. In addition, the biomarkers that are currently used for monitoring may not be representative of the complex physiological changes that occur during the transition period. Predictive modeling, which uses data to predict future or current outcomes, is a method that can be used to combine the most predictive variables and their interactions efficiently. The use of an effective predictive model with relevant predictors for transition cow diseases will result in better targeted interventions, and therefore lower disease incidence. This review will discuss predictive modeling methods and candidate variables in the context of transition cow diseases. The next step is to investigate novel biomarkers and statistical methods that are best suited for the prediction of transition cow diseases.

Keywords

Disease predictionpredictive modelingstatistical methodstransition dairy cattle
Type
Review Article
Information
Animal Health Research Reviews , Volume 20 , Issue 1 , June 2019 , pp. 19 - 30
Copyright
Copyright © Cambridge University Press 2019

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References

Abebe, R, Hatiya, H, Abera, M, Megersa, B and Asmare, K (2016) Bovine mastitis: prevalence, risk factors and isolation of Staphylococcus aureus in dairy herds at Hawassa milk shed, South Ethiopia. BioMed Central Veterinary Research 12, 270.Google ScholarPubMed
Abuelo, A, Hernandez, J, Benedito, JL and Castillo, C (2013) Oxidative stress index (OSi) as a new tool to assess redox status in dairy cattle during the transition period. Animal: An International Journal of Animal Bioscience 7, 1374–1378.CrossRefGoogle ScholarPubMed
Autier, P, Boniol, M, Pizot, C and Mullie, P (2014) Vitamin D status and ill health: a systematic review. Lancet Diabetes Endocrinology 2, 76–89.CrossRefGoogle ScholarPubMed
Badolato, R, Wang, JM, Murphy, WJ, Lloyd, AR, Michiel, DF, Bausserman, LL, Kelvin, DJ and Oppenheim, JJ (1994) Serum amyloid A is a chemoattractant: induction of migration, adhesion, and tissue infiltration of monocytes and polymorphonuclear leukocytes. The Journal of Experimental Medicine 180, 203–209.CrossRefGoogle ScholarPubMed
Baldi, A (2005) Vitamin E in dairy cows. Livestock Production Science 98, 117–122.CrossRefGoogle Scholar
Barkema, HW, Schukken, YH, Lam, TJGM, Beiboer, ML, Benedictus, G and Brand, A (1998) Management practices associated with low, medium, and high somatic cell counts in bulk milk. Journal of Dairy Science 81, 1917–1927.CrossRefGoogle ScholarPubMed
Benzaquen, ME, Risco, CA, Archbald, LF, Melendez, P, Thatcher, MJ and Thatcher, WW (2007) Rectal temperature, calving-related factors, and the incidence of puerperal metritis in postpartum dairy cows. Journal of Dairy Science 90, 2804–2814.CrossRefGoogle ScholarPubMed
Betteridge, DJ (2000) What is oxidative stress? Metabolism 49, 3–8.CrossRefGoogle ScholarPubMed
Bewley, JM and Schutz, MM (2008) Review: an interdisciplinary review of body condition scoring for dairy cattle. The Professional Animal Scientist 24, 507–529.CrossRefGoogle Scholar
Boronat, M, Saavedra, P, Varillas, VF and Novoa, FJ (2009) Use of confirmatory factor analysis for the identification of new components of the metabolic syndrome: the role of plasminogen activator inhibitor-1 and haemoglobin A1c. Nutrition, Metabolism & Cardiovascular Diseases 19, 271–276.CrossRefGoogle ScholarPubMed
Bradford, BJ, Yuan, K, Farney, JK, Mamedova, LK and Carpenter, AJ (2015) Invited review: inflammation during the transition to lactation: new adventures with an old flame. Journal of Dairy Science 98, 6631–6650.CrossRefGoogle ScholarPubMed
Brown, TA (2015) Confirmatory Factor Analysis for Applied Research, 2nd Edn. New York, NY: The Guilford Press.Google Scholar
Burnham, KP and Anderson, DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods & Methods 33, 261–304.CrossRefGoogle Scholar
Ceciliani, F, Ceron, JJ, Eckersall, PD and Sauerwein, H (2012) Acute phase proteins in ruminants. Journal of Proteomics 75, 4207–4231.CrossRefGoogle ScholarPubMed
Celi, P (2010). Biomarkers of oxidative stress in ruminant medicine. Immunopharmacology and Immunotoxicology 33, 233–240.CrossRefGoogle ScholarPubMed
Chapinal, N, Carson, M, Duffield, TF, Capel, M, Godden, S, Overton, M and Santos, JEP (2011) The association of serum metabolites with clinical disease during the transition period. Journal of Dairy Science 94, 4897–4903.CrossRefGoogle ScholarPubMed
Collier, RJ, Dahl, GE and VanBaale, MJ (2006) Major advances associated with environmental effects on dairy cattle. Journal of Dairy Science 89: 1244–1253.CrossRefGoogle ScholarPubMed
Contreras, GA and Sordillo, LM (2011) Lipid mobilization and inflammatory responses during the transition period of dairy cows. Comparative Immunology, Microbiology and Infectious Diseases 34, 281–289.CrossRefGoogle ScholarPubMed
Cook, NB (2003) Prevalence of lameness among dairy cattle in Wisconsin as a function of housing type and stall surface. Journal of the American Veterinary Medical Association 9, 1324–1328.CrossRefGoogle Scholar
Cook, NB and Nordlund, KV (2009) The influence of the environment on dairy cow behavior, claw health and herd lameness dynamics. The Veterinary Journal 179, 360–369.CrossRefGoogle ScholarPubMed
Corica, F, Corsonella, A, Apolone, G, Mannuncci, E, Lucchetti, M, Bonfiglio, C, Melchoinda, N, Marchesini, G and the QUOVADIS Study Group (2008) Metabolic syndrome, psychological status and quality of life in obesity: the QUOVADIS study. International Journal of Obesity 32, 185–191.CrossRefGoogle ScholarPubMed
Das, R, Sailo, L, Verma, N, Bharti, P, Saikia, J, Kumar, I and Kumar, R (2016) Impact of heat stress on health and performance of dairy animals: a review. Veterinary World 9, 260–268.CrossRefGoogle ScholarPubMed
DeGaris, PJ and Lean, IJ (2008) Milk fever in dairy cows: a review of pathophysiology and control principles. The Veterinary Journal 58, 69.Google Scholar
Dervishi, E, Zhang, G, Hailemariam, D, Dunn, SM and Ametaj, BN (2015) Innate immunity and carbohydrate metabolism alterations precede occurrence of subclinical mastitis in transition dairy cows. Journal of Animal Science and Technology 57, 46.CrossRefGoogle ScholarPubMed
Dervishi, E, Zhang, G, Hailemariam, D, Dunn, SM and Ametaj, BN (2016) Occurrence of retained placenta is preceded by an inflammatory state and alterations of energy metabolism in transition dairy cows. Journal of Animal Science and Biotechnology 7, 8.CrossRefGoogle ScholarPubMed
DeVries, TJ, von Keyserlingk, MAG and Weary, DM (2004) Effect of Feeding Space on the Inter-Cow Disease, Aggression, and Feeding Behavior of Free-Stall Housed Lactation Dairy Cows. Journal of Dairy Science 87, 1432–1438.CrossRefGoogle Scholar
de Vries, M, Bookers, EAM, van Reenen, CG, Engel, B, van Schaik, G, Dijkstra, T and de Boer, IJM (2015) Housing and management factors associated with indicators of dairy cattle welfare. Preventive Veterinary Medicine 118, 80–92.CrossRefGoogle ScholarPubMed
DiStefano, C, Zhu, M and Mindrila, D (2009) Understanding and using factor scores: considerations for the applied researcher. Practical Assessment, Research & Evaluation 14, 1–11.Google Scholar
Dohoo, I, Martin, W and Stryhn, H (2003) Veterinary Epidemiologic Research. Charlottetown, Prince Edward Island, Canada: AVC Inc.Google Scholar
Drackley, JK (1999) Biology of dairy cows during the transition period: the final frontier? Journal of Dairy Science 82, 2259–2273.CrossRefGoogle ScholarPubMed
Dubuc, J, Duffield, TF, Leslie, KE, Walton, JS and LeBlanc, SJ (2010) Risk factors for postpartum uterine disease in dairy cows. Journal of Dairy Science 93, 5764–5771.CrossRefGoogle ScholarPubMed
Duncan, C, Jones, K and Moon, G (1998) Context, composition, and heterogeneity: using multilevel models in health research. Social Science & Medicine 46, 97–117.CrossRefGoogle ScholarPubMed
Esposito, G, Irons, PC, Webb, EC and Chapwanya, A (2014) Interactions between negative energy balance, metabolic diseases, uterine health and immune response in transition dairy cows. Animal Reproduction Science 144, 60–71.CrossRefGoogle ScholarPubMed
Fenlon, A, O'Grady, L, Mee, JF, Butler, ST, Doherty, ML and Dunnion, L (2017) A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows. Journal of Dairy Science 100, 1–13.CrossRefGoogle ScholarPubMed
Fleischer, P, Metzner, M, Beyerbach, M, Hoedemaker, M and Klee, W (2001) The relationship between milk yield and the incidence of some diseases in dairy cows. Journal of Dairy Science 84, 2025–2035.CrossRefGoogle ScholarPubMed
Fragoso, TM, Bertoli, W and Louzada, F (2018) Bayesian model averaging: a systematic review and conceptual classification. International Statistical Review 86, 1–28.CrossRefGoogle Scholar
Galbraith, S, Daniel, JA and Vissel, B (2010) A study of clustered data and approaches. The Journal of Neuroscience 30, 10601–10608.CrossRefGoogle ScholarPubMed
Galvão, KN, Flaminio, MJBF, Brittin, SB, Sper, R, Fraga, M, Caixeta, L, Ricci, A, Guard, CL, Butler, WR and Gilbert, RO (2010) Association between uterine disease and indicators of neutrophil and systemic energy status in lactation Holstein cows. Journal of Dairy Science 93, 2926–2937.CrossRefGoogle Scholar
Gelman, A, Carlin, JB, Stern, HS, Dunson, DB, Vehtari, A and Rubin, DB (2014) Bayesian Data Analysis, 3rd Edn.Boca Raton, FL: Taylor & Francis Group, LLC.Google Scholar
Ghosh, D and Yuan, Z (2009) An improved model averaging scheme for logistic regression. Journal of Multivariate Analysis 100, 1670–1681.CrossRefGoogle ScholarPubMed
Goff, JP (2008a) The monitoring, prevention, and treatment of milk fever and subclinical hypocalcemia in dairy cows. Veterinary Journal 176, 50–57.CrossRefGoogle Scholar
Goff, JP (2008b). Transition cow immune function and interaction with metabolic diseases. Tri-State Dairy Nutrition Conference, April 22 and 23, 2008. pp. 45–57.Google Scholar
Goff, JP and Horst, RL (1997) Physiological changes at parturition and their relationship to metabolic disorders. Physiology and Management 80, 1260–1268.Google ScholarPubMed
Gombart, G (2012) Vitamin D: Oxidative Stress, Immunity, and Aging. Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
Gordis, L (2014). Epidemiology. Philadelphia, PA: Elsevier Saunders, pp. 209–210.Google Scholar
Grandjean, P (1995) Biomarkers in epidemiology. Clinical Chemistry 41, 1800–1803.CrossRefGoogle ScholarPubMed
Green, MJ, Bradley, AJ, Medley, GF and Brown, WJ (2007) Cow, farm, and management factors during the dry period determine the rate of clinical mastitis after calving. Journal of Dairy Science 90, 3764–3776.CrossRefGoogle ScholarPubMed
Grice, JW (2001) Computing and evaluating factor scores. Psychological Methods 6, 430–450.CrossRefGoogle ScholarPubMed
Grohn, YT, Eicker, SW and Hertl, JA (1995) The association between previous 305-day milk yield and disease in New York state dairy cows. Journal of Dairy Science 78, 1693–1702.CrossRefGoogle ScholarPubMed
Gurka, MJ, Lilly, CL, Oliver, MN and DeBoer, MD (2014) An examination of sex and racial/ethnic differences in the metabolic syndrome among adults: a confirmatory factor analysis and a resulting continuous severity score. Metabolism 63, 218–225.CrossRefGoogle Scholar
Hammon, DS, Evjen, IM, Dhiman, TR, Goff, JP and Walters, JL (2006) Neutrophil function and energy status in Holstein cows with uterine health disorders. Veterinary Immunology and Immunopathology 113, 21–29.CrossRefGoogle ScholarPubMed
Hardin, JW and Hilbe, JM (2007) Generalized Linear Models and Extensions, 2nd Edn.College Station, Texas: Stata Press.Google Scholar
Harrell, FE (2015) Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, 2nd Edn.New York: Springer.CrossRefGoogle Scholar
Heinrichs, AJ, Costello, SS and Jones, CM (2009) Control of heifer mastitis by nutrition. Veterinary Microbiology 134, 172–176.CrossRefGoogle Scholar
Hilbe, JM (2014) Modeling Count Data. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Horst, R, Goff, JP and Reinhardt, TA (1994) Symposium: calcium metabolism and utilization. Journal of Dairy Science 77: 1936–1951.CrossRefGoogle Scholar
Hosmer, DW, Lemeshow, S and Sturdivant, RX (2013) Applied Logistic Regression, 3rd Edn.Hoboken, New Jersey: John Wily & Sons, Inc.CrossRefGoogle Scholar
Huang, PL (2009) A comprehensive definition for metabolic syndrome. Disease Models and Mechanisms 2, 231–237.Google Scholar
Hutjens, MF and Aalseth, EP (2005) Caring for Transition Cows. Fort Atkinson, Wisconsin: W.D. Hoards & Sons.Google Scholar
Huzzey, JM, Veira, DM, Weary, DM and von Keyserlingk, MAG (2007) Prepartum behavior and dry matter intake identify dairy cows at risk for metritis. Journal of Dairy Science 90, 3220–3233.CrossRefGoogle ScholarPubMed
Huzzey, JM, Nydam, DV, Grant, RJ and Overton, TR (2011) Associations of prepartum plasma cortisol, haptoglobin, fecal cortisol metabolites, and nonesterified fatty acids with postpartum health status in Holstein dairy cows. Journal of Dairy Science 94, 5878–5889.CrossRefGoogle ScholarPubMed
Ingvartsen, KL (2006) Feeding- and management-related diseases in the transition cow: physiological adaptations around calving and strategies to reduce feeding-related diseases. Animal Feed Science and Technology 126, 175–213.CrossRefGoogle Scholar
Ingvartsen, KL, Dewhurst, RJ and Friggens, NC (2003) On the relationship between lactational performance and health: is it yield or metabolic imbalance that cause production diseases in dairy cattle? A position paper. Livestock Production Science 83, 277–308.CrossRefGoogle Scholar
International Diabetes Federation (IDF) (2006) The IDF Consensus Worldwide Definition of the Metabolic Syndrome. Brussels, Belgium: IDF.Google Scholar
Ivanescu, AE, Li, P, George, B, Brown, AW, Keith, SW, Raju, D and Allison, DB (2016) The importance of prediction model validation and assessment in obesity and nutrition research. International Journal of Obesity 40, 887–894.CrossRefGoogle ScholarPubMed
Kerr, KF and Pepe, MS (2011) Joint modeling, covariate adjustment, and interaction contrasting notions in risk prediction models and risk prediction performance. Epidemiology 22, 805–812.CrossRefGoogle ScholarPubMed
Kim, IH and Suh, GH (2003) Effect of the amount of body condition loss from the dry of near calving periods on the subsequent body condition change, occurrence of postpartum diseases, metabolic parameters and reproductive performance in Holstein dairy cows. Theriogenology 60, 1445–1456.CrossRefGoogle ScholarPubMed
Klecka, WR (1980) Discriminant Analysis. Series: Quantitative Applications in the Social Science. Newbery Park, London, New Delhi: Sage Publications, Inc.CrossRefGoogle Scholar
Kulberg, S, Storset, AK, Heringstad, B and Larsen, HJS (2002) Reduced levels of total leukocytes and neutrophils in Norwegian cattle selected for decreased mastitis incidence. Journal of Dairy Science 85, 3470–3475.CrossRefGoogle ScholarPubMed
LeBlanc, SJ (2008) Postpartum uterine disease and dairy herd reproductive performance: a review. Veterinary Journal 176, 102–114.CrossRefGoogle ScholarPubMed
LeBlanc, SJ (2010) Monitoring metabolic health of dairy cattle in the transition period. Journal of Reproductive Development 56 (Suppl), S29–S35.CrossRefGoogle ScholarPubMed
LeBlanc, SJ, Duffield, TF, Leslie, KE, Batement, KG, TenHag, J, Walton, JS and Johnson, WH (2002) The effect of prepartum injection of vitamin E on health in transition dairy cows. Journal of Dairy Science 85, 1416–1426.CrossRefGoogle ScholarPubMed
LeBlanc, SJ, Herdt, TH, Seymour, WM, Duffield, TF and Leslie, KE (2004) Peripartum serum vitamin E, retinol, and beta-carotene in dairy cattle and their associations with disease. Journal of Dairy Science 87, 609–619.CrossRefGoogle ScholarPubMed
LeBlanc, SJ, Leslie, KE and Duffield, TF (2005) Metabolic predictors of displaced abomasum in dairy cattle. Journal of Dairy Science 88, 159–170.CrossRefGoogle ScholarPubMed
LeBlanc, SJ, Lissemore, KD, Kelton, DF, Duffield, TF and Leslie, KE (2006) Major advances in disease prevention in dairy cattle. Journal of Dairy Science 89, 1267–1279.CrossRefGoogle ScholarPubMed
Leite, MLC, Nicolosi, A, Firmo, JOA and Lima-Costa, MF (2007) Features of metabolic syndrome in non-diabetic Italians and Brazilians: a discriminant analysis. International Journal of Clinical Practice 61, 32–38.CrossRefGoogle ScholarPubMed
Lemeshow, S and Hosmer, DW (1982) A review of goodness of fit statistics for use in the development of logistic regression models. American Journal of Epidemiology 115, 92–106.CrossRefGoogle ScholarPubMed
Li, RH and Belford, GG (2002) Instability of decision tree classification algorithms. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, Edmonton. pp. 570–575.Google Scholar
Li, C and Ford, ES (2007) Is there a single underlying factor for the metabolic syndrome in adolescents? Diabetes Care 30, 1556–1561.CrossRefGoogle Scholar
Lima, FS, Filho, MF, Greco, LF and Santos, JEF (2012) Effects of feeding rumen-protected choline on incidence of diseases and reproduction of dairy cows. The Veterinary Journal 193, 140–145.CrossRefGoogle ScholarPubMed
Loh, WY (2011) Classification and regression trees. WIREs Data Mining and Knowledge Discovery 1, 14–23.CrossRefGoogle Scholar
Long, SJ and Freese, J (2006) Regression Models for Categorical Dependent Variables Using Stata, 2nd Edn.College Station, Texas: Stata Press.Google Scholar
Ma, X (2018) Using Classification and Regression Trees: A Practical Primer. Charlotte, NC: Information Age Publishing Inc.Google Scholar
Malarkey, LM and McMorrow, ME (2011) Saunders Nursing Guide to Laboratory and Diagnostic Tests, 2nd Edn.New York: Elsevier.Google Scholar
Mazerolle, MJ (2006) Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses. Amphibia-Reptilia 27, 169–180.CrossRefGoogle Scholar
McArt, JAA, Nydam, DV and Oetzel, GR (2012) Epidemiology of subclinical ketosis in early lactation dairy cattle. Journal of Dairy Science 95, 5056–5066.CrossRefGoogle ScholarPubMed
McArt, JAA, Nydam, DV and Oetzel, GR (2013) Dry period and parturient predictors of early lactation hyperketonemia in dairy cattle. Journal of Dairy Science 96, 198–209.CrossRefGoogle ScholarPubMed
McCrum-Gardner, E (2008) Which is the correct statistical test to use? British Journal of Oral and Maxillofacial Surgery 46, 38–41.CrossRefGoogle ScholarPubMed
McDowell, LR, Wilkinson, N, Madison, R and Felix, T (2007) Vitamins and minerals functioning as antioxidants with supplementation considerations. Florida Ruminant Nutrition Symposium, Gainesville, Florida.Google Scholar
McLachlan, G (2004) Discriminant Analysis and Statistical Pattern Recognition. Hoboken, New Jersey: John Wiley & Sons, Inc.Google Scholar
Melendez, P, Barolome, J, Archbald, LF and Donovan, A (2003) The association between lameness, ovarian cysts, and fertility in lactation dairy cows. Theriogenology 59, 927–937.CrossRefGoogle Scholar
National Animal Health Monitoring System (NAHMS) (1996) Part 1: Reference of 1996 Dairy Management Practices. Fort Collins, Colorado: United States Department of Agriculture Animal and Plant Health Inspection Service: Veterinary Sciences: Centers for Epidemiology and Animal Health (USDA: APHIS: VS: CEAH).Google Scholar
National Animal Health Monitoring System (NAHMS) (2018) Health and Management Practices on U.S. Dairy Operations, 2014. Fort Collins, Colorado: United States Department of Agriculture-Animal and Plant Health Inspection Service: Veterinary Sciences: Centers for Epidemiology and Animal Health (USDA: APHIS: VS: CEAH).Google Scholar
Neave, HW, Lomb, J, von Keyserlingk, MAG, Behnam-Shabahang, A and Weary, DM (2017) Parity differences in the behavior of transition dairy cows. Journal of Dairy Science 100, 548–561.CrossRefGoogle ScholarPubMed
Nezlek, JB (2008) An introduction to multilevel modeling for social and personality psychology. Social and Personality Psychology Compass 2, 842–860.CrossRefGoogle Scholar
Ngo, THD (2016) Generalized linear models for non-normal data. Proceedings from SAS Global Forum 2016, Las Vegas, NV, April 18–21, 2016.Google Scholar
Nordlund, K, Cook, N and Oetzel, G (2006). Commingling dairy cows: pen moves, stocking density, and health. 39th Proceedings American Association Bovine Practitioners, St. Paul, MN, September 20–24, 2006. pp. 36–42.Google Scholar
Oetzel, GR (2004) Monitoring and testing dairy herds for metabolic disease. Veterinary Clinics Food Animal Practice 20, 651–674.CrossRefGoogle ScholarPubMed
Ospina, PA, Nydam, DV, Stokol, T and Overton, TR (2010) Associations of elevated nonesterified fatty acids and beta-hydroxybutyrate concentrations with early lactation reproductive performance and milk production in transition dairy cattle in the northeastern United States. Journal of Dairy Science 93, 1596–1603.CrossRefGoogle ScholarPubMed
Ospina, PA, McArt, JA, Overton, TR, Stokol, T and Nydam, DV (2013) Using nonesterified fatty acids and beta-hydroxybutyrate concentrations during the transition period for herd level monitoring of increased risk of disease and decreased reproductive and milking performance. Veterinary Clinics of North America – Food Animal Practice 29, 387–412.CrossRefGoogle ScholarPubMed
Overton, TR and Waldron, MR (2004) Nutritional management of transition dairy cows: strategies to optimize metabolic health. Journal of Dairy Science 87(E. Suppl.), E105–E119.CrossRefGoogle Scholar
Pavlou, M, Ambler, G, Seaman, SR, Guttmann, O, Elliot, P, King, M and Omar, RZ (2015) How to develop a more accurate risk prediction model when there are few events. British Medical Journal 351, h3868.CrossRefGoogle ScholarPubMed
Peng, CJ, Lee, KL and Ingersoll, GM (2002) An introduction to logistic regression analysis and reporting. The Journal of Educational Research 96, 3–14.CrossRefGoogle Scholar
Pepys, MB (2012) Acute Phase Proteins in the Acute Phase Response. London: Spring-Verlag London Limited.Google Scholar
Petrovski, KR, Trajcev, M and Buneski, G (2006) A review of the factors affecting the costs of bovine mastitis. Journal of the South African Veterinary Association 77, 52–60.CrossRefGoogle ScholarPubMed
Pladevall, M, Singal, B, Williams, LK, Brotons, C, Guyer, H, Sadurni, J, Falces, C, Serrano Rios, M, Gabriel, R, Shaw, JE, Zimmet, PZ and Haffner, SH (2006) A single factor underlies the metabolic syndrome. Diabetes Care 29, 113–122.CrossRefGoogle ScholarPubMed
Potter, TJ, Guitian, J, Fishwish, J, Gordon, PJ and Sheldon, IM (2010) Risk factors for clinical endometritis in postpartum dairy cattle. Theriogenology 74, 127–134.CrossRefGoogle ScholarPubMed
Press, SJ and Wilson, S (1978) Choosing between logistic regression and discriminant analysis. Journal of the American Statistical Association 73, 699–705.CrossRefGoogle Scholar
Putman, AK, Brown, JL, Gandy, JC, Wisnieski, L and Sordillo, LM (2018) Changes in biomarkers of nutrient metabolism, inflammation, and oxidative stress in dairy cows during the transition into the early dry period. Journal of Dairy Science 101, 9350–9359.CrossRefGoogle ScholarPubMed
Rabe-Hesketh, S and Skrondal, A (2008) Multilevel and Longitudinal Model Using Stata, 2nd Edn.College Station, Texas: Stata Press.Google Scholar
Richert, RM, Cicconi, KM, Gamrock, MJ, Schukken, YN, Stiglbauer, KE and Ruegg, PL (2013) Risk factors for clinical mastitis, ketosis, and pneumonia in dairy cattle on organic and small conventional farms in the United States. Journal of Dairy Science 96, 1–17.Google ScholarPubMed
Roche, JR, Friggens, NC, Kay, JK, Fisher, MW, Stafford, KJ and Berry, DP (2009) Invited review: body condition score and its association with dairy cow productivity, health, and welfare. Journal of Dairy Science 92, 5769–5801.CrossRefGoogle ScholarPubMed
Sainani, KL (2014) Explanatory versus predictive modeling. Physical Medicine and Rehabilitation 6, 841–844.Google ScholarPubMed
Santos, JEP, Bisinotto, RS, Ribeiro, ES, Lima, FS and Thatcher, WW (2012) Impacts of metabolism and nutrition during the transition period on fertility of dairy cows. 2012 High Plains Dairy Conference, Amarillo, Texas. pp. 97–112.Google Scholar
Saun, V (2004) Metabolic profiling and health risk in transition cows. Proceedings 37th Annual American Association of Bovine Practitioners Convention, Ft. Worth, Texas, September 23–25, 2004. pp. 212–213.Google Scholar
Schmidt, CO and Kohlmann, T (2008) When to use the odds ratio or the relative risk? International Journal of Public Health 53, 165–167.CrossRefGoogle ScholarPubMed
Seifi, HA, LeBlanc, SJ, Leslie, KE and Duffield, TF (2011) Metabolic predictors of post-partum disease and culling risk in dairy cattle. The Veterinary Journal 188, 216–220.CrossRefGoogle ScholarPubMed
Shmueli, G (2010) To explain or to predict? Statistical Science 25, 289–310.CrossRefGoogle Scholar
Shtatland, ES, Cain, E and Barton, MB (2001) The perils of stepwise logistic regression and how to escape them using information criteria and the output delivery system. Proceedings from the 26th Annual SAS Users Group International Conference. pp. 222–226.Google Scholar
Shtatland, ES, Kleinman, K and Cain, EM (2004) A new strategy of model building in PROC LOGISTIC with automatic variable selection, validation, shrinkage, and model averaging. Proceedings from SUGI'29, Cary, NC. pp. 121–129.Google Scholar
Simensen, E, Ostera, O, Boe, KE, Kielland, C, Rudd, LE and Naess, G (2010) Housing system and herd size interactions in Norwegian dairy herds; association with performance and disease incidence. Acta Veterinaria Scandinavica 52, 14.CrossRefGoogle Scholar
Singal, AG, Mukherjee, A, Elmunzer, BJ, Higgins, PDR, Lok, AS, Zhu, J, Marrero, JA and Walijee, AK (2013) Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma. American Journal of Gastroenterology 108, 1723–1730.CrossRefGoogle ScholarPubMed
Sordillo, LM and Aitken, SL (2009) Impact of oxidative stress on the health and immune function of dairy cattle. Veterinary Immunology and Immunopathology 128, 104–109.CrossRefGoogle ScholarPubMed
Sordillo, L and Erskine, R (2010) Bovine leukosis virus update II: impact on immunity and disease resistance. Michigan Dairy Review 15, 4–5.Google Scholar
Sordillo, LM and Mavangira, V (2014) The nexus between nutrient metabolism, oxidative stress and inflammation in transition cows. Animal Production Sciences 54, 1204–1214.CrossRefGoogle Scholar
Sordillo, LM and Raphael, W (2013) Significance of metabolic stress, lipid mobilization, and inflammation on transition cow disorders. Veterinary Clinics of North America: Food Animal Practice 29, 267–278.Google ScholarPubMed
Spears, JW and Weiss, WP (2008) Role of antioxidants and trace elements in health and immunity of transition dairy cows. Veterinary Journal 176, 70–76.CrossRefGoogle ScholarPubMed
Steensels, M, Antler, A, Bahr, C, Berckmans, D, Maltz, E and Halachmi, I (2016) A decision tree model to detect post-calving disease based on rumination, activity, milk yield, BW, and voluntary visits to the milking robots. Animal: An International Journal of Animal Bioscience 10, 1493–1500.CrossRefGoogle Scholar
Steyerberg, EW, Eijkemans, MJC and Habbema, JDF (2001 a) Application of shrinkage techniques in logistic regression analysis: a case study. Statistica Neerlandica 55, 76–88.CrossRefGoogle Scholar
Steyerberg, EW, Harrell, FE Jr, Borsboom, GJJM, Eijkemans, MJCR, Vergouwe, Y and Habbema, DF (2001b) Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. Journal of Clinical Epidemiology 54, 774–781.CrossRefGoogle Scholar
Suhr, DD (2006) Exploratory or confirmatory factor analysis. SAS Users Group International Conference. Cary: SAS Institute, Inc., pp. 1–17.Google Scholar
Turner, H (2008) Introduction to generalized linear models. ESRC National Centre for Research Methods, UK and Department of Statistics, University of Warwick. Available at http://statmath.wu.ac.at/courses/heather_turner/glmCourse_001.pdf (Accessed 23 June 2018).Google Scholar
van Breukelen, GJP and Candel, MJJM (2012) Calculating sample sizes for cluster randomized trials: we can keep it simple and efficient! Journal of Clinical Epidemiology 65, 1212–1218.CrossRefGoogle ScholarPubMed
Van der Ark, LA, Croon, MA and Sijtsma, K (eds.) (2012) New Developments in Categorical Data Analysis for the Social and Behavioral Sciences. New York, NY and Hove, East Sussex: Psychology Press.Google Scholar
Vanholder, T, Papen, J, Bemers, R, Vertenten, G and Berge, ACB (2015) Risk factors for subclinical and clinical ketosis and association with production parameters in dairy cows in the Netherlands. Journal of Dairy Science 98, 880–888.CrossRefGoogle ScholarPubMed
Van Saun, RJ and Sniffen, CJ (2014) Transition cow nutrition and feeding management for disease prevention. Veterinary Clinics of North America: Food Animal Practice 30, 689–719.Google ScholarPubMed
Vergara, CF, Dopfer, D, Cook, NB, Nordlund, KV, McArt, A, Nydram, DV and Oetzel, GR (2014) Risk factors for postpartum problems in dairy cows: explanatory and predictive modeling. Journal of Dairy Science 97, 4127–4140.CrossRefGoogle ScholarPubMed
Wathes, DC, Cheng, Z, Bourne, N, Taylor, VJ, Coffey, MP and Brotherstone, S (2007) Differences between primiparous and multiparous dairy cows in the inter-relationships between metabolic traits, milk yield and body condition score in the periparturient period. Domestic Animal Endocrinology 33, 203–225.CrossRefGoogle ScholarPubMed
Weiss, WP (1998) Requirements of fat-soluble vitamins for dairy cows: a review. Journal of Dairy Science 81, 2493–2501.CrossRefGoogle ScholarPubMed
Wisnieski, L, Norby, B, Pierce, SJ, Becker, T, Gandy, JC and Sordillo, S (2019a) Predictive models for early lactation diseases in transition dairy cattle at dry-off. Preventive Veterinary Medicine 163, 68–78.CrossRefGoogle Scholar
Wisnieski, L, Norby, B, Pierce, SJ, Becker, T, Gandy, JC and Sordillo, S (2019b) Cohort-level disease prediction by extrapolation of individual-level predictions in transition dairy cattle. Preventive Veterinary Medicine 169, 104692.CrossRefGoogle Scholar
Wisnieski, L, Norby, B, Pierce, SJ, Becker, T, Gandy, JC and Sordillo, S (2019c) Cohort-level disease prediction using aggregate biomarker data measured at dry-off in transition dairy cattle: a proof-of-concept study. Preventive Veterinary Medicine 169, 104692.CrossRefGoogle Scholar
Yang, FL and Li, XS (2015) Role of antioxidant vitamins and trace elements in mastitis in dairy cows. Journal of Advanced Veterinary and Animal Research 2, 1–9.CrossRefGoogle Scholar
Yang, RZ, Lee, MJ, Hu, H, Pollin, TI, Ryan, AS, Nicklas, BJ, Snitker, S, Horenstein, RB, Hull, K, Goldberg, NH, Goldberg, AP, Shuldiner, AR, Fried, SK and Gong, DW (2006) Acute-phase serum amloid A: an inflammatory adipokine and potential link between obesity and its metabolic complications. PLoS Medicine 3, e287.CrossRefGoogle Scholar
Yin, K and Agrawal, DK (2014) Vitamin D and inflammatory diseases. Journal of Inflammation Research 7, 69–87.Google ScholarPubMed
Zaborski, D, Grzesiak, W and Pilarczyk, R (2016) Detection of difficult calvings in the polish Holstein black-and-white heifers. Journal of Applied Animal Research 44, 42–53.CrossRefGoogle Scholar
Zhang, G, Hailemariam, D, Dervishi, E, Deng, Q, Goldansaz, SA, Dunn, SM and Ametaj, BN (2015) Alterations of innate immunity reactants in transition dairy cows before clinical signs of lameness. Animals 5, 717–747.CrossRefGoogle ScholarPubMed
Zorn, C (2005) A solution to separation in binary response models. Political Analysis 13, 157–170.CrossRefGoogle Scholar
Zurbrigg, K, Kelton, D, Anderson, N and Millman, S (2005) Tie-stall design and its relationship to lameness, injury, and cleanliness on 317 Ontario Dairy Farms. Journal of Dairy Science 88, 3201–3210.CrossRefGoogle ScholarPubMed